Liu Huan (1983-), Master of Science (First Class Honours), The University of Auckland.

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 楼主| 发表于 2020-12-20 17:08:59 | 显示全部楼层
Biophysics and Enzymology / 生物物理与酶谱生物化学


Article 1: Biophysical Simulation of Bio-signals and The Biochemical Dynamics of Enzymology /生物物理对生物信号的模拟及相应的酶谱生化反应动力机制
Author: Liu Huan, MSc (First Class Honours), The University of Auckland.
Published after graduation on 11/01/2016

Methods (All the tables and figures are in PDF version):


The same strain of microbes is divided into two samples for the bio-signal simulation:
1.There are two kinds of cultivation conditions simulated in Lab for microbe reproduction process: one is the ‘comfortable’ condition (Sample 1); the other is under UV-B radiation for cultivation (Sample 2). The microbe samples are collected after sufficient reproduction process (Ten generations).
2.After sufficient reproduction process, the UV-B radiation simulation stops. Then both sample 1 and sample 2 are separately transferred into moisture simulation process: different moisture conditions of microbial cultivation are simulated in Lab, and labeled as T1, T2, ..., Tn.
3.Metabolomics tests are conducted (listed by the appendix 2 in Chapter 1 of this book) after moisture simulation of T1, T2, ..., Tn respectively, resulting in different zymograms as: M1, M2, ..., Mn.
4.Each isozyme family is labeled as 1, 2, 3..., and E; It is hypothesized that the bands at the same line across different isozyme families are the enzyme species at the same locus, named as enzyme ‘species i’ (i = 1, 2, ..., I), and each isozyme family has the same amount (I) of enzyme species (Please note: this is different from the identification of real enzyme species in the appendix 2 of chapter 1). Then there is a 3-dimension (I× E × N) matrix presented in this research. I is the total amount of enzyme species within a isozyme family; E is the total amount of isozyme families; N is the total amount of zymograms among different simulated moisture conditions:


X= │Xien │( i = 1, 2, ....I; e = 1, 2, .... E; n= 1, 2, ... N)
Xien is the occurrence of enzyme ‘species i’ in the isozyme ‘family e’ during simulated moisture condition Tn. The value of Xien is one or zero.
X111 X211        X112 ... X11n X212 ... X21n        X121 X221        X122 ...... X12n ......
X222 ......X22n .......        X1i1 X2i1        X1i2 X2i2        ......
.......        X1in X2in
X =        .....        .......        ......        .......        .......        ...........        .......        ......        ......        ......        .........
        Xi11        Xi12 ...        Xi1n        Xi21        Xi22        Xi2n        ........        Xie1        Xie2        ......        Xien
        .......        .......        .......        ......        ......        ......        ......        .......        .......        ......        ........


Matrix Se = Xe × (Xe)T Xe = │Xin│( i = 1, 2, ....I; n= 1, 2, ... N); (Xe)T is the transpose of the matrix Xe:

X11                X12 ... X1n X21        X22 ... X2n
Xe = ..... ....... ......

Xi1        Xi2 ...        Xin
....        .....        ......
The Principal Components Analysis (PCA) method of matrix X is specified [1]. PCA is firstly conducted on the basis of matrix Se, revealing the biochemical dynamics of a isozyme ‘family e’ among different simulated moisture conditions. In matrix Se, it is hypothesized that the variable in PCA represents the biochemistry dynamics of each enzyme ‘species i’.

S = ΣSe (e = 1, 2, E)

PCA is further conducted on the basis of matrix S, revealing the biochemical dynamics among different isozyme families over the whole simulated moisture conditions. In matrix S, it is hypothesized that the variable in PCA represents the biochemistry dynamics of each enzyme ‘species i’ across all the isozyme families.

However, for the comparison between sample 1 and sample 2, this book need to present more procedures for subsequent analysis: in matrix Se, the biochemistry

dynamics of the first three enzyme species, which reveal the most differences in the total variation by PCA in an isozyme family, are selected for comparison between sample 1 and sample 2; in matrix S, the biochemistry dynamics of the first three enzyme species, which reveal the most differences in the total variation by PCA  across all the isozyme families, are selected for comparison between sample 1 and sample 2; the sum dynamics of the first three enzyme species in a isozyme family (= the sum Variance Contribution Ratio (VCR) of the first three enzyme species  in matrix Se), represents the total variation of a isozyme family over the whole simulated moisture conditions; the sum dynamics of the first three enzyme species across all the isozyme families (= the sum Variance Contribution Ratio (VCR) of the first three enzyme species in matrix S), represents the variation of the total zymograms over the whole simulated moisture conditions.

Hypotheses:
1.The higher variation in biochemical dynamics of enzyme expression, the better environmental adaptiveness or immunology (the reason of this hypothesis is presented in chapter 7 of this book). It is deduced that the biochemistry dynamics of the first three isozyme families, which show the highest variation by PCA, determines the conclusion of this comparison;

2.Sample 2 leads to higher variation in biochemical dynamics of enzyme expression, which is also revealed by the higher adaptiveness during drought stress or higher immunology.

Discussion:
The findings of this chapter further support the theory, ‘memory’ of gene expression, proposed by the appendix 2 of chapter 1 in this book; As discussed by the chapter 8 of this book, the memory of cells can be ‘trained’ by the biophysical simulation in site, indicated by the zymograms in metabolomics test. Consequently, the memory of cells, in terms of identifying the bio-signals of an environmental factor (can be biotic or abiotic) triggering the gene expression for environmental adaptiveness or immunology, can be trained by the biophysical simulation of other environmental factors. The appendix of this chapter (biophysical simulation for blood cell division) further supports above theories (please note: the theory, ‘memory’ of gene expression, is also applicable on cell division in an individual) by assessment of resistance or immunology in host cells.




References:
[1] 陶玲,任裙 (2004)。进化生态学的数量研究方法。第一章,第六节,第 49 页。 中国林业出版社。 ISBN:7-5038-3735-7.


Appendix 1. The Experiment Procedure for Blood Cell Cultivation in Biophysical Simulation/生物物理实验中血细胞培养方法
The blood samples of a rat is abstracted and divided into two samples for the bio-signal simulation:
1.There are two kinds of cultivation conditions simulated in Lab for cell division: one is the ‘comfortable’ condition (Sample 1); the other is under electromagnetism simulation for cell cultivation (Sample 2); the cell samples are collected after sufficient cell division (Ten generations).
2.After sufficient cell division process, the electromagnetism simulation stops. Then both sample 1 and sample 2 are separately transferred into the simulation process of physiological saline: cells are cultivated individually in different concentrations of physiological saline in Lab, and different cell environment (salinity stress of cell environment or ‘thirsty’ simulation) are labeled as T1, T2, ..., Tn.
3.Metabolomics tests are conducted (listed by the appendix 2 in Chapter 1 of this book) in cell samples after simulation process of physiological saline, T1, T2, ..., Tn, respectively, resulting in different zymograms as: M1, M2, ..., Mn.
The other procedures are the same as described above.
However, for the comprehensive assessment of immunology in host cells, the simulation process of physiological saline is replaced by the invasion simulation caused by different families of bacteria (or virus):
The blood samples of a rat is abstracted and divided into two samples for the bio-signal simulation:
1.There are two kinds of cultivation conditions simulated in Lab for cell division: one is the ‘comfortable’ condition (Sample 1); the other is under electromagnetism simulation for cell cultivation (Sample 2); the cell samples are collected after sufficient cell division (Ten generations).
2.After sufficient cell division process, the electromagnetism simulation stops. Then both sample 1 and sample 2 are separately transferred into the simulation process of bacteria (or virus) invasion: cells are cultivated individually and independently during the simulation of different families of bacteria (or virus) in Lab, and the invasion simulation process of different bacteria (or virus) families are labeled as T1, T2, ..., Tn.
3.Metabolomics tests are conducted (listed by the appendix 2 in Chapter 1 of this book) in cell samples after simulation process  of bacteria  (or  virus) invasion, T1,  T2, ..., Tn, respectively, resulting in different zymograms as: M1, M2, ..., Mn.
The other procedures are the same as described above. This comprehensive assessment of immunology is closer to the real situation of disease caused by multiple species of bacteria, as described by the chapter 8 of this book. Even if the pathology  of host cells (such as cancerous blood cells of rat) is not caused by multiple species of invasive virus or bacteria (and by one species only), the invasive virus or bacteria of the same genetic strain also evolves into various phenotypes in host body, which reflects the significance of comprehensive assessment of immunology.

Please note: if all the blood cells have been ‘eaten’ up (or no cell division rate) by a strain of bacteria during invasion simulation, then the value of this zymogram can be counted as zero for subsequent matrix calculation.
For the comprehensive assessment of immunology in host cells caused by the invasive virus or bacteria of the same genetic strain with different phenotypes:
The blood samples of a rat is abstracted and divided into two samples for the bio-signal simulation:
1.There are two kinds of cultivation conditions simulated in Lab for cell division: one is the ‘comfortable’ condition (Sample 1); the other is under electromagnetism simulation for cell cultivation (Sample 2); the cell samples are collected after sufficient cell division (Ten generations).
2.After sufficient cell division process, the electromagnetism simulation stops. Then both sample 1 and sample 2 are separately transferred into the simulation process of bacteria (or virus) invasion of the same genetic strain with different phenotypes: cells are cultivated individually and independently during the invasive simulation by different phenotypes of the same genetic bacteria (or virus) in Lab, and the invasion simulation process by different phenotypes of the same genetic bacteria (or virus) are labeled as T1, T2, ..., Tn.
3.Metabolomics tests are conducted (listed by the appendix 2 in Chapter 1 of this book) in cell samples after simulation process  of bacteria  (or  virus) invasion, T1,  T2, ..., Tn, respectively, resulting in different zymograms as: M1, M2, ..., Mn.
The other procedures are the same as described above. This electromagnetism simulation can be either constant electromagnetism fields or time-varying electromagnetic waves, which are further discussed later.
Conclusion:
The comprehensive assessment of immunology in host cells also provides indicators of training host cells by adjusting the parameters of biophysical simulation, once the specific zymograms, indicating the immunology against the specific invasive bacteria or virus (or the specific phenotype of an invasive pathogen), are identified by the methods presented in the appendix of chapter 8. However, the higher dynamics, the better immunology against various pathogen species (or various phenotypes of a pathogen genotype).


Appendix 2. The Determination Method of Bio-signal Range for Biophysical Simulation /生物物理模拟试验中生物信号范围的确定方法
Step 1. The host cells of the same genetic strain (such as the blood cells of rat) are abstracted, which are divided  into  several  cell  samples,  and  labeled  as  S1,  S2,  S3 ,Sn;
Step 2. The simulation of a specific virus (or bacteria) invasion targeting the host cells is conducted in Lab, immediately after host cells are abstracted from host body;
Step 3. The samples of host cells with apparent antibiotics are identified, as described by the appendix of chapter 8; and the samples of host cells without apparent antibiotics are also continuously observed until they are ‘eaten up’ by the specific invasive pathogen;
Step 4. The separation of virus from each sample of host cell without apparent antibiotics are conducted independently in Lab, and the metabolomics test is conducted in each virus sample;
Objective:
The different phenotypes of an invasive virus (or bacteria) strain are identified, and  the biochemistry dynamics of this invasive virus strain is calculated, as discussed in this chapter. The result of biochemistry dynamics calculation helps to determine the range of bio-physical training parameters to enhance the comprehensive immunology of host cells, as described above.
Please note: the simulation of a specific virus (or bacteria) invasion targeting the host cells should be conducted immediately after host cells are abstracted from host body, otherwise the uniform cell cultivation in Lab lead to the homogeneity of host cells, so that different phenotypes of an invasive pathogen can be hardly detected.
Because the virus sample for invasion simulation is cultivated in Lab, which is the uniform phenotype, the samples of host cells with apparent antibiotics usually show specific zymograms correspondingly to the specific invasive virus. However, if virus samples, which are separated from host cell without apparent antibiotics after step 3, re-invade the host cells with apparent antibiotics identified in step 3, virus infection would occur, due to the evolution of new virus phenotypes.


Appendix 3. Bio-magnetic field of Cell and Its Application on Separation of Blood Cell Communities along Environmental
Gradient/细胞的生物磁场及血细胞群落在环境梯度上的分离

Step 1. The host cells (such as blood cells of rat) are abstracted from host body. Step 2. Electrophoresis of blood cells is conducted in moderate electromagnetism;
Step 3. Different blood cell communities are separated along the environmental gradient of electromagnetism signal, leading to cell samples with different immunology.
Discussion
The bio-magnetic field of blood cells varies even within the same genetic strain, so that different cell communities can be separated according to the gradual variation in electromagnetism signals (environmental gradient of electromagnetism) in this electrophoresis, leading to cell samples with different immunology. The cell samples, abstracted from different electric potential (j1, j2...jn), are labeled on the basis of electric potential.
Step 4. The specificity of host-invasion interaction is examined on each cell sample, according to the appendix of chapter 8 in this book. It is expected that the specific electric potential corresponds to the host cells with apparent antibiotics against the specific invasive virus (or bacteria), which also becomes the key parameter of biophysical training for the host cells with immunology against the specific invasive virus (or bacteria). Nevertheless, for the mobilizable blood cells, it is expected that the 'ecological niche' of cells vary in their life cycle along this environmental gradient of electromagnetism signal, because of the variation in bio-magnetic field over cell's life cycle, moving from a specific electric potential to another electric potential.
It is expected that the time-varying electromagnetic field of biophysical training is better than constant electromagnetic field, due to the phenotype evolution of invasive virus (bacteria).
Please note: the intensity of electromagnetism is preliminarily set to be 1.6 H (1H = 1 A/m) in this research, three times than earth magnetism fields. If the intensity of electromagnetism is more than 5 times than earth magnetism fields, blood cell  division rate of rats starts to decline apparently, ‘looking nervous,’ which is closer to the situation of ‘hemorrhage.’ They are unlike microbes who can survive long-termly in sunshine intensity.


Appendix 4. Bio-signal Simulation of Electromagnetic Wave and Its Specificity on the Isozyme Expression/电磁波的生物信号模拟及同工酶表达的专一性
In appendix 3, the specificity of electric potential to the host cells with apparent antibiotics against the specific invasive virus (or bacteria) is determined. However, this method is relatively broader, so that the accuracy of this biophysical training is not sufficient for the synthesis of antibiotics in cells against the specific phenotype of an invasive virus (or bacteria).
Consequently, this section presents a novel methods to train the specific isozyme families catalyzing the synthesis of antibiotics in cells against the specific phenotype of an invasive virus (or bacteria):
Step 1. Host cells (such as blood cells) are cultivated during simulation of electromagnetic wave conditions;
Step 2. Different frequency of electromagnetic wave (or different wavelength) are simulated, and labeled as F1, F2, ..., Fn;
Step 3. Metabolomics test is conducted individually after cultivation in F1, F2,...Fn, respectively.
Step 4. Under each simulated frequency of electromagnetic wave, different electromagnetic wave intensity are simulated, and labeled as I1, I2, ..., and In.
Step 5. Metabolomics test is conducted individually after cultivation in I1, I2,...In, respectively. The amount of N×N metabolomics tests are conducted in total.
Objectives:
The specific frequency of electromagnetic wave simulates the bio-signal regulating gene expression as a specific isozyme family, and the specific electromagnetic wave intensity (AND amplitude) corresponds to the bio-signal regulating gene expression as a specific enzyme species within an isozyme family, which can be determined by metabolomics tests. Consequently, the immunology against the specific phenotype of an invasive virus (or bacteria) can be trained according to the zymograms, described  in the appendix of chapter 8. Please note: the intensity is adjusted and controlled by the amplitude instructed in appendix 5.

This experiment is similar to chapter 4 (UV-B is one of electromagnetic waves). Let’s re-discuss the chapter 4 on the basis of plant cell data (the blood cell data of rat is not clear to this date 18/02/2016): As discussed in chapter 4, UV-B significantly (P<0.001) affected the net photosynthesis (A) (Table 1). Nevertheless, for Tienshan clover and Caucasian clover, there was no significant UV-B induced difference in the total aerial biomass yield, under well-water conditions, and there was no significant effect of UV-B on the relative chlorophyll content, whereas enhanced UV-B apparently decreased the biomass of Kopu II. Further more, the water deficit did not influence   the relative chlorophyll content as comparison to the well-water condition (Table 1).

There are two reasons to explain this science discovery: firstly, the Light Use Efficiency (LUE) already exceeded the saturation point of LUE under well water condition without enhanced UB treatment (as discussed in chapter 2), so that the reduction of net photosynthesis under enhanced UB treatment did not influence the total aerial biomass yield; Secondly, enhanced UV-B treatment effectively triggered the gene expression of enzyme species within the isozyme families involving in the chlorophyll synthesis in plant cells, which revealed that the isozyme families involving in the chlorophyll synthesis could express effectively under a broader range of UV-B intensity especially for Caucasian clover, but the relevant gene of Kopu II was not effectively expressed as enzyme species within the isozyme families involving in the chlorophyll synthesis under enhanced UV-B. Please note: within the isozyme families involving in the chlorophyll synthesis in plant cells, the enzyme species under enhanced UV-B is different from the one without enhanced UV-B. However, drought condition did not influence the synthesis of chlorophyll, which showed different metabolic pathway in response to the environmental stress. The treatment without UV-B in this experiment was not without any UV-B radiation, and was just lower intensity of UV-B treatment. Although chapter 4 explains that ‘these results indicated that these clovers might have adequately photo-protective mechanism, such as enhancing the synthesis of UV-B screening  secondary metabolites (Hofmann et al., 2003a),’ this explanation is consistent with the above explanation in this section, because the synthesis of UV-B screening secondary metabolites as photo-protective mechanism is also the phenomenon utilizing the light energy effectively, adjusting the photo-metabolic pathways in response to the change of UV-B intensity (UV-B is also the utilizable light energy in photosynthesis rather than visible light only, which can be proven the result that Caucasian clover showed increased biomass during enhanced UV-B of well water treatment as compared to the well water condition without UV-B, although the main utilizable energy is from the visible light --- without visible light, photosynthesis can not only rely on UV-B to happen --- this is the conclusion of this book). As discussed in appendix 5, the receptors (or cells) of electromagnetic wave can NOT identify more than three different frequencies of electromagnetic wave concurrently, it is hypothesized that plant cells themselves select three frequencies of light waves with the highest  intensity for photosynthesis, and Caucasian clover selects UV-B frequency for photosynthesis whereas Kopu II can not, this is definitely the environmental adaptiveness evolved from its origin.

Please note: for the identification of specific zymograms of host cells with specific immunology against invasive gene mutation virus in chapter 8, then invasive simulation of gene mutation virus is added during the whole process of biophysics simulation for identifying the specificity of host-invasion interaction (in which frequency and intensity of cultivation condition, the host cells show effective immunology against the gene mutation virus).
Nevertheless, for the virus (or bacteria) with dormant characters (such as HIV), it is expected that long-term observation is required for this specificity examination after

biophysics simulation stops, because this virus would become dormant in host cells after puncturing cell membrane during biophysics simulation, so that the host cells with effective immunology against the dormant virus are NOT specifically identified during biophysical simulation. In this case, the host cells with really effective immunology against the dormant virus kill the invasive virus during biophysical simulation, whereas the host cells with dormant virus would be re-infected after biophysical simulation stops. After long-termly observing if dormant virus re-starts pathogenetic metabolism in host cells, the identified host cells with really effective immunology against the dormant virus would be screened and become more specific. Finally the range of biophysics parameters in appendix 5 should be based on all the host cell samples which have been identified as effective immunology against the dormant virus during biophysics simulation. The more specific, the more punctual to kill the invasive virus.
Please note: the intensity of electromagnetic waves is preliminarily set to be 1.6 H  (1H = 1 A/m) for blood cells in this research, three times than earth magnetism fields. If the intensity of electromagnetism is more than 5 times than earth magnetism fields, blood cell division rate of rats starts to decline apparently, ‘looking nervous.’ They are unlike microbes who can survive long-termly in sunshine intensity. However, the frequency of electromagnetic waves is preliminarily set to be around UV-B frequency, the sunshine one. Actually, blood cells still function (such as oxygen-carrying capacity) effectively under exposure to sunshine radiation, but the blood cell division only occurs when sunshine radiation is shielded. This is why hematopoietic function of blood cells mainly occurs in marrow! and blood cells division rate actively increases during evening as well!


Appendix 5. The Parameterization of Time-varying Electromagnetic Field for Biophysics Simulation/生物物理模拟实验中时变电磁场参数的确定方法
Method:
This section presents a novel method to determine the parameters of time-varying electromagnetic field, on the basis of ‘Skin Effect’ equations in combination with ‘Maxwell’ equations:
1.Skin effect equations:
I (t)= √2 I sin (wt); w = 2πf ;
2.Maxwell's equations:
I (t)= j H (t)
S = I (t) * H (t)
I is the effective intensity of electric field, t is the varying time, w is the angular frequency (rad/s), f is the frequency, H is the intensity of magnetic field, j is the conductivity, and S is the energy of wave (or the electromagnetic wave intensity) [1]. The determination of biophysical training method is presented for parameter f and S, in appendix 4, and the range of S is determined by appendix 2 and 3 of this chapter.
In this situation, the rhythm of electromagnetic wave in terms of intensity and frequency fluctuates around 3 times earth electromagnetic field and sunshine frequency respectively. Obviously, the intensity of I also determines the amplitude of waves. The intensity of electromagnetic waves is determined by both parameter I and
j. This is important for cells to recognize the bio-signals.

Discussion:
As discussed in this chapter, it is deduced that the biochemistry dynamics of the first three isozyme families, which show the highest variation by PCA, determines the conclusion of the whole biochemistry dynamics in this research. Consequently, three different frequencies of electromagnetic wave are applied concurrently on this biophysical training of host cells for enhancing immunology, which requires three emittors (or launchers) of electromagnetic wave to work concurrently. However, the receptors (or cells) of electromagnetic wave can NOT identify more than three different frequencies of electromagnetic wave concurrently (This is the environmental pollution of electromagnetic wave), which is similar to the limitation of three spatial dimensions in direct perception capacity of human species (The cell is not so clever to deduce the equations at more than three dimensions like me!).
In this chapter, pathogen ‘army’ behaves as camouflage, ambush, or other intelligence strategy for invasion, and host cells need to defend punctually and effectively by training for survival (host cells adjust their skills by themselves on the basis of biophysical learning during this ‘war’ until invasive enemy dies) --- this is the

evolutionary physiology of environmental adaptiveness, the foundation subject of environmental science.
Reference:
[1] 注册环保工程师专业考试复习教材(2009). 第二分册. 中国环境科学出版社. ISBN:978-7-5111-0505-9.


Appendix 6. The Synthesis of Biological Antibiotics and Its Application on Biomedicine/生物抗生素合成与在生物医药中的应用
In above appendices, the immunology of host cells becomes the key to resist the invasive pathogen. Nevertheless, there are some exceptions that the immunological potential of host cells, which relies on the synthesis of antibiotics in host cells, may not be sufficient to resist the invasive pathogen (such as congenital defect of rat species against a specific pathogen). Then the vegetation antibiotics is helpful as complementary solution. The steps of synthesis of vegetation antibiotics are similar to appendix 4.
Step 1. N×N samples of a vegetation species, which has been identified to be helpful in biomedicine, are cultivated during simulation of different electromagnetic wave conditions;
Step 2. Different frequency of electromagnetic wave (or different wavelength) are simulated, and labeled as F1, F2, ..., Fn;
Step 3. Metabolomics test is conducted individually after cultivation in F1, F2,...Fn, respectively.
Step 4. Under each simulated frequency of electromagnetic wave, different electromagnetic wave  intensity (AND amplitude) are simulated, and labeled as I1,   I2, ..., and In.
Step 5. Metabolomics test is conducted individually after cultivation in I1, I2,...In, respectively. The amount of N×N metabolomics tests are conducted in total.
Step 6. In total N×N different samples of vegetation antibiotics are abstracted from each different cultivation condition (The method of this abstraction is the same as the preparation of Traditional Chinese Medicine).
Step 7. Each sample of vegetation antibiotics is injected into the invasive simulation of pathogens targeting the host cells of rats respectively, in combination with the training of host cells discussed in chapter 8.
Step 8. The infection of host cells are observed, and the effectiveness of each sample of vegetation antibiotics is decided correspondingly.

It is expected that a combination of antibiotics from both host cells and vegetation leads to the best solution, and a combination of different vegetation antibiotics is more effective. However, the ‘dead’ antibiotics abstracted from vegetation is not as effective as ‘living’ antibiotics in host cells, due to the evolved resistance of  pathogens against the static or constant antibiotics. Actually, there are lots of cases that insect pests frequently evolve into resistance to VERY toxic pesticides, which is the same phenomenon. Please note: the abstraction of vegetation antibiotics here is on the basis of ancient preparation method of Chinese medicine, and the advantages of this is to consider all the vegetation metabolites cultivated in Lab as the whole substances for antibiotics, rather than separating a specific chemistry species from the vegetation metabolites, which can be proven by that plant resistance (or antibiotics)

substances usually contain multiple biochemistry species discussed in chapter 4. Another advantages of ancient preparation of Chinese medicine is to provide additional nutrition for host cells. During effective vegetation antibiotics condition, the invasive pathogens are usually dormant so that the competition in nutrition between host cells and pathogens is minimized. Otherwise the additional nutrition may benefit the pathogens rather than host cells.

There are three kinds of vegetation species selected in future research for better ‘diversity of antibiotics’ (If funding is available): one is the Ganoderma Lucidum (I started to grow this from 2011), another is Anoectochilus roxburghii (Wall.) Lindl.(I started to grow this from 2016. Not only human species know this, but also wild pigs must be keen to look for this vegetation for remediation after injury), and the last one is rhizome of Leguminosae species, because the symbiosis of rhizobium in Leguminosae species leads to antibiotics with higher dynamics from both vegetation cells and rhizobium cells. However, the inoculation of various rhizobium, which successfully lead to tumour in root system as symbiosis, is necessary. The reason of enriching rhizobium biodiversity has been discussed in chapter 8 (the specificity of host-invasion interaction), which results in various antibiotics from both plant cells and microbial cells.
For the shading-habitat plant species, which suits shading environment only for growth, plants' leaves usually turns to be yellow when they are long-termly exposed  to the intensitive sunshine. Inversely, the leaves of sunshine-habitat plant species turn from green into yellow when they are shaded. For the shading habitat plant such as  the Anoectochilus roxburghii (Wall.) Lindl. as well as Ganoderma Lucidum, the intensity of UV-B radiation must be reduced for the cultivation, as compared to the intensity used in Chapter 4.

I will tell this world a story: the host cells from someone who has practiced Qigong (such as my uncles) help to identify the host cells with specific immunology against the pathogen (the biophysical training of host cells in this book is originally from the principle of Qigong), and the vegetation variety originated from adverse condition help to identify the best antibiotics too. Good luck for my lovely rats, I hope I will remedy you!

In addition to the synthesis of vegetation antibiotics for biomedicine, the inoculation of microbial vaccine in animals such us rats, pointed out in chapter 8, also provides effective way of generating antibiotics for biomedicine production against similar genetic strains. However, in this case, symbiosis between microbial vaccine and host cells is not compulsory, which means that the host cells can be ‘eaten up’ by microbial vaccine for biomedicine production. Please note: according to the Traditional Chinese Medicine, the biomedicine made from animal cells tends to be ‘warm,’ possibly due  to too much animal proteins, which need to be incorporated into vegetation biomedicines (which tends to be ‘cool’) as mixtures for best biomedicines. Good Luck!
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 楼主| 发表于 2020-12-20 17:10:13 | 显示全部楼层
Article 1. Assimilation of The Cells’ ‘Memory’ Between Different Phenotypes and Its Implication on Canceration/ 不同表现型细胞间在‘思维’上的的同化机制及在癌变过程的指示意义
Author: Liu Huan, MSc (First Class Honours), The University of Auckland. Published after graduation on 21/01/2016

Methods (All the tables are in PDF version):
As described in chapter 9 in this book, after UV-B simulation process stops, both sample 1 and sample 2 of the same genetic strain are transferred into moisture simulation process.

Step 1. Both sample 1 and sample 2 are cultivated separately and individually in each moisture simulation process (T1, T2, ..., Tn);

Step 2. The samples of even mixture between sample 1 and sample 2 (50% for each sample) are cultivated individually in each moisture simulation process (T1, T2, ..., Tn) as well; The cultivation condition is the same between step 1 and step 2, and step 1 and step 2 is conducted independently;

Step 3. The reproduction rate (or cell division rate) is observed, and the comparison of cell division rates between step 1 and step 2 under the same cultivation condition is conducted: in step 1, the cell division rate of sample 1 and sample 2 is R1 (cell quantity/time) and R2, respectively; if assimilation of the cells’ ‘memory’ does NOT occur, then the cell division rate of mixture sample is 0.5*(R1+R2); however, if assimilation of the cells’ ‘memory’ does occur, then the cell division rate of mixture sample is not equivalent to 0.5*(R1+R2); if the cell division rate of mixture sample is closer to R1, the sample 1 becomes dominant; if the cell division rate of mixture sample is closer to R2, the sample 2 becomes dominant.


Discussion:

Within the cells of the same genetic strain, cells apparently assimilate each other between different phenotypes. It is expected that sample 1 tends to be dominant  during comfortable condition; and sample 2 tends to be dominant during adverse conditions. This theory is applicable on the cancerous tissue: when cancerous cell without immunology becomes dominant in cell assimilation process, the whole tissue (or organ) starts to be canceration, so the prevention of cancerous cell assimilation is the key in pathological study. Appendix of this chapter lists the experiment procedure for blood cell cultivation, further support the discussion of this chapter.

Appendix. The simulation methods for blood cell cultivation

As described by the appendix of chapter 9 in this book, after electromagnetism simulation for cell cultivation process stops, both blood sample 1 and sample 2 of a  rat (or the same genetic strain) are transferred into simulation process of physiological saline:

Step 1. Simulation process of physiological saline: cells are cultivated individually in different concentrations of physiological saline in Lab, and different cell environment (salinity stress of cell environment or ‘thirsty’ simulation) are labeled as T1, T2, ..., Tn.

Step 2. The samples of even mixture between sample 1 and sample 2 are cultivated individually in each different concentrations of physiological saline (T1, T2, ..., Tn) as well; The cultivation condition for blood cell is the same between step 1 and step 2, and step 1 and step 2 is conducted independently.

The other steps are the same as described above. To be continued...


Article 2. The Inversion of Cancerous Gene Mutation in DNA Molecule and Its Implication on Biochronometry of Life Origin/
癌细胞基因突变的逆转及在生命起源中‘生物钟’论的指示意义
Author: Liu Huan, MSc (First Class Honours), The University of Auckland. Published after graduation on 31/01/2016

Methods:

There are three kinds of cells abstracted from the same tissue of the same genetic strain, which are cultivated in physiological saline in Lab:
Step 1. A stream of cells is cultivated in radiation condition, leading to the gene mutation of DNA molecule (sample 1) which can be detected by FISH technology, pointed out by the chapter 1 of this book; another stream of cells is cultivated in moderate electromagnetism condition leading to better immunology (sample 2) without gene mutation in DNA, as described by the chapter 9 of this book; the last stream of cells (sample 3) is cultivated in ‘comfortable’ conditions.

Step 2. The samples of even mixture between sample 1 and sample 2 (50% for each sample) are cultivated together in moderate electromagnetism condition;

Step 3. The samples of even mixture between sample 1 and sample 3 (50% for each sample) are cultivated together in ‘comfortable’ condition;

Step 4. Finally, the gene mutation rate in DNA molecules of mixed cells are calculated by FISH after step 2 and step 3, respectively.

Please note: it is expected to clearly detect gene mutation after ten generations of cell division.

Objectives:
Step 2 leads to lower gene mutation rate in mixed cells due to the assimilation of cells’ ‘memory’ between different phenotypes, as discussed in the chapter 10 of this book, as compared to step 3. This is the inversion of cancerous gene mutation in DNA molecules, due to the self-repair of genome. The electric potential of different cell communities are recorded, as pointed out in chapter 9, and it is expected that the cells of junior stages are more likely to be assimilated, so the advantages of this electromagnetism gradient is to separate the cell communities with different aging stages. As discussed in chapter 9, the electromagnetic field of cells varies during their life cycle, which is consequently a kind of time-varying electromagnetism yielding the bio-electromagnetic wave. This bio-electromagnetic wave is the bio-signal for cells to communicate each other (or for vegetation species to communicate each

other).

Please note: this method is less effective on the cancerous cells caused by virus, due to the ‘intelligence’ of causal factors. The electromagnetism condition is the constant electromagnetism in this book, and the electromagnetic wave is the time-varying electromagnetism. In chapter 9, the intensity of constant electromagnetic fields for training blood cell is discussed. The electromagnetic fields are obviously the constant one in Chapter 11 rather than time-varying electromagnetic waves. Consequently, the method of this chapter is similar to the situation of ‘taking rest quietly and recovery smoothly by coachers in electromagnetic field after injury,’ whereas the method of application of time-varying electromagnetic waves, which aims to defend against invasive pathogens, is similar to the battle music violently, encouraging cells to fight.

Discussion:
Gene mutation, which occurs during DNA replication process, is triggered by the bio-signal perceived by cell. Once the bio-signal is altered, gene mutation occurs. However, the assimilation of cells results in the inversion of gene mutation during positive biophysical simulation in site.


Article 3: The life origin, Death and The Sustainability of
Population /生命起源、终结与种群的延续
Author: Liu Huan, MSc (First Class Honours), The University of Auckland. Published after graduation on 31/01/2016


Further more, this book asserts that: DNA molecule, as the first molecule of biochemistry chains in cell, is NOT the origin of life cycle, but the ‘spirit’ is! The phenomenon, senescence of cells, is NOT a kind of gene trait which is determined by gene sequences in DNA molecule, but is determined by the biochronometry, the invisible ‘spirit’.

The 'Energy Conservation Law' of cell's life:
As discussed in chapter 9, cells move from a specific electric potential (a) to another electric potential (b) due to the variation in bio-electromagnetic field in their life cycle. Consequently, this section hypothesizes that the cells' life cycle complies with the energy conservation law, and the entropy of 'life energy' is constant for each life cycle of cell.
Wab=Epa-Epb; Epa=qa·φA
Epb=qb·φB        [1]
The entropy of life energy =Epa - N*Epb;

Wab is the electric potential energy, φA and φB is the electric potential (a) and (b), respectively; qa and qb is the electric charge in cells located at electric potential (a) and (b), respectively; N is the amount of cells in the final generation. Once cells exhaust the entropy of life energy, life cycle ends. Please note: electric potential (a) and (b) is based on the ideal conditions. The emission of bio-electromagnetic wave is also a kind of energy consumption. The rest entropy of life energy from the first cell is evenly divided into two of its offspring cells; the rest entropy of life energy from two cells of the second generation are evenly divided into four of their offspring cells; ....
Consequently, the cell division rate starts to decrease, because more and more offspring cells share the rest entropy of life energy.

As pointed out by other biologists, continuous gene mutation gives cells infinite life cycle. Consequently, the population of microbes, as the single cell creatures, must  gain sustainability of population only through gene mutation, after  primary life-energy is exhausted by a cell's division cycle. In principal, after gene mutation, this 'new' population of microbes becomes a new 'species' as well. For example, once  a strain of cells exhausts the entropy of life energy at electric potential (b) and gene mutation occurs at this exact time, then a new cell's division cycle, created by gene mutation, starts at a point before electric potential (b) (such as at potential (a) again),

which means 'fresh' life energy can be only charged by gene mutation. According to the 'Energy Conservation Law,' gene mutation must be driven by other sources of electromagnetic waves (such as radiation), which is an environmental adaptiveness. However, for the species of sexual reproduction, gene communication replaces gene mutation as a way 'charging' life energy. For other multiple-cell creatures of asexual reproduction, gene recombination would replace the gene mutation to sustain the population.

Implication for environmental microbe cultivation: the way of sustainability of microbe population reflects the importance of genetic pool conservation with similar environmental traits. The mixed cultivation of various genetic strains  of microbes with similar environmental traits helps to keep constant and sustainable environmental traits as a whole microbe community during gene mutation process due to cell's assimilation in memory, as discussed above.

Gene Therapy and Gene Modification in Nature: It is further deduced that natural and mild gene mutation would occur during biophysical training process  of blood cells discussed above, which is an indicator of improved environmental adaptiveness like microbes. However, this mild and natural gene mutation is neither like cancerous gene mutation, nor like clone cells which have been artificially inserted or deleted by other DNA sequences. As discussed in chapter 8, gene mutation leads to faster cell division rate. It is deduced that gene mutation caused by stronger intensity of electromagnetic waves results in faster cell division rate, so the recommended intensity of electromagnetic waves in chapter 9 is moderate and increases the blood cell activity once gene mutation is caused by this. Please keep in mind like that: gene mutation is the way for cells evolving into environmental adaptiveness in response to environmental change, and positive gene mutation can be directed in Lab, whereas negative gene mutation is prevented. To data it has been noticed that moderate gene mutation occurs after 10 generation cultivations using common environmental microbes in sewage water treatment, which can be detected by the above method. Actually, this is easy to understand in life: let’s compare two different populations: a population of Chinese who survive in city for several generations without much exposure to sunshine and the other population of Chinese who work as farmers for several generations with much exposure to sunshine. As to compare the skin color of their infants, there must be apparent different between these two populations: city populations tend to be white, and rural populations tend to be brown due to more skin melanin. Obviously, the gene-mutation-induced phenotype is able to pass onto next generations as genetic materials.

Additionally, the mixed cultivation of blood cells with different genetic strains (of course, blood types must be the same) helps to improve the immunology as a whole community against invasive pathogens as well, which focus on the optimization of gene pool in blood cell community. Please note: unlike cells of other tissues (or organs), blood cells can be the mixed cultivation from different genetic strains for

‘exchange transfusion’ remediation. Consequently, DNA sequencing technology is used to relate the gene variation to the specific immunology against specific pathogens, which provides basis of optimization of gene pool for cell transplantation. However, it is deduced that cell transplantation is more effective than the whole tissue or organ transplantation, not just because of less genetic or type matching requirement for cell transplantation, but also because cultivation of 'young' cells after transplantation would lead to less resistance against other organs. Please note: the 'young' cells are the cells with more active cell division rate caused by moderate gene mutation, and the advantages of cell transplantation is particularly important to other tissues or organs rather than just blood cells.

Further more, as discussed in chapter 8, ‘the specific frequency of electromagnetic wave simulates the bio-signal regulating gene expression as a specific isozyme family, and the specific electromagnetic wave intensity corresponds to the bio-signal regulating gene expression as a specific enzyme species within an isozyme family, which can be examined by metabolomics tests.’ Consequently the specific gene locus of moderate gene mutation, expressed as specific gene trait, is caused by this biophysical training as well, which restore the specific congenital defect through gene therapy and cell transplantation. Please note: the detection of gene mutation on specific genome locus can be achieved by FISH technology specified in Chapter 1. The criterion of centromere index or the curve degree of chromosome is used to detect the specific locus of gene mutation, which becomes the unique identifier to  distinguish different loci of gene mutation. Of course, it is further deduced that the specific frequency of electromagnetic wave determines the gene mutation in specific gene locus (a stream of the same and repetitive DNA or RNA sequences on chromosome), and the specific electromagnetic wave intensity determines the ‘modification’ of amount of these repetitive DNA or RNA sequences on this specific gene locus. This science rhythm corresponds to the re-definition of isozyme family: the biochemistry molecules contain the same functional group in the same isozyme family, but the amount of repetitive functional groups varies between different  enzyme species’ molecules within an isozyme family. Obviously, specific gene locus (or loci) express as specific isozyme family in this case.

However, it is expected that a specific life function is not determined by one gene locus only, but is determined by multiple gene loci, which means the time-varying electromagnetic wave proposed in the chapter 9 is more reasonable than static electromagnetic wave.
Acknowledge
A cross awarded by a Christchurch church, 2006, New Zealand.
[1] 注册环保工程师专业考试复习教材(2009). 第二分册. 中国环境科学出版社. ISBN:978-7-5111-0505-9.


Article 4. Reproduction Physiology/生殖生理学
Author: Liu Huan, MSc (First Class Honours), The University of Auckland. Published after graduation on 09/09/2016
Cervix or uterine contraction and women senescence.

There is a significant and direct corelation between women senescence (such as after 25 years old) and cervix or uterine contraction (This is similar to the skin contraction and wrinkle during aging), which tends to cause hemorrhoea during delivery process and also results in negative impacts on baby head, directly expressed as increased frequency of convulsion after birth, due to increased contraction force during birthing process. Consequently, do not try to breed after this age! This is my viewpoints about reproduction health! My wife yields my son and daughter before this senescence!


Article 5. Evolutionary Selection/进化选择
Author: Liu Huan, MSc (First Class Honours), The University of Auckland. Published after graduation on 25/09/2016

After the experiment is conducted as indicated in article 2, it is noticed that only one (or a limited proportion) cell of cell community is selected to start a new life cycle through gene mutation under long-term radiation treatment. In other cases, when the population of a plant variety faces epidemic disease, there is always one (or a limited proportion) plant individual selected by population for survival surrounded by dead plants infected by pathogen. At least this is the observation by myself.

However, I don’t think this selection is determined by genetic variation among this population. As discussed previously, the cell or plants populations are able to communicate each other in a certain mean, so that a population try their best to keep  at least one individual selected to survive when this population faces the risks of extinction. This rhythm is also applicable on the host-invasion simulation of blood  cell community. Once the community of blood cells faces pathogen invasion, there are always a small proportion of cells selected to survive, although the whole cell community can hardly sustain the function in body. The survival cell selected by this cell population is the rare samples for cultivation. At least this is the observation by myself.

Let’s re-discuss the life energy experiment in article 2:
Wab=Epa-Epb; Epa=qa·φA
Epb=qb·φB        [1]
The entropy of life energy =Epa - N*Epb;

If one cell is selected by this cell community (the quantity of cells is N1 at this time in this community) at electric potential b to start a new life cycle through gene mutation under radiation treatment. This gene mutation cell starts new life cycle at electric potential c and other cells cease life cycle at potential b; if radiation treatment is not applied, this cell community (the final quantity of cells is N2 at this time) ceases cell division at electric potential d naturally. Then

Epc * 1 = Epb * N1 - Epd * N2

This means that the whole cell community pass on the rest of life energy entropy into a cell to start a new life cycle through gene mutation. This is the natural law in cell evolution. Consequently, cell evolution costs original life energy!

As discussed in chapter 1, population does not only pass on the genome, the genetic resource, but also passes on the ‘memory,’ in terms of identifying the bio-signal triggering the gene expression, onto their offspring. Similarly, after the disaster to population, their offspring, who are selected by this population and survive after disaster, keep ‘memory’ of this disaster. Consequently, the indigenous microbe population settles in underground water environment of earthquake areas, who keep ‘memory’ of this earthquake disaster, must respond to earthquakes before it happen,  as a kind of ‘immunology,’ which becomes an indicator to predict earthquake as well. The metabolomics test listed in this book is the feasible measure to conduct this.

References: the conceptions and terms of biology in this book sources from Wikipedia, the free encyclopedia.
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 楼主| 发表于 2020-12-20 18:45:33 | 显示全部楼层
Article 1. Genetic Marker/遗传标记

1. Classification of Virus by Genetic Marker and Its Theory
The methods of classifying and identifying virus will follow these steps:
Step 1. The molecular cytogenetic karyotype is analyzed by fluorescence in situ hybridization (FISH) technique [1] using transmission electron microscopy;

Step 2. Virus is classified by multivariate cluster analysis and genetic  distance analysis on the basis of molecular cytogenetic karyotype, preliminarily leading to different families of virus [2];

Step 3. The optima sampling units of each virus family, which can well represent the genetic diversity of each virus family, is examined and determined as pointed out by Liu et al.,(2015) [2] for further classification based on DNA (or RNA) molecular marker (SSR or AFLP). The sampling units can be adjusted by changing the concentration of virus solution;

Step 4. Classification of virus families is further conducted on the basis of DNA (or RNA) molecular marker, analyzed by multivariate cluster analysis of UPGMA (unweighted pair group method with arithmetic averages) and genetic distance analysis [2].

Step 5. Gene recombination and gene mutation rate is analyzed by the inconsistence  of classification between molecular cytogenetic karyotype and DNA (or RNA) molecular marker.

There are three hypotheses examined by this research:
Hypothesis 1: the optima numbers of polymorphic SSR primers are examined and screened for each virus family, because we assume that the amount of polymorphic SSR primers, which are assessed on the basis of polymorphism information content (PIC), increases with the increase of total SSR primers selected from Gene Bank, but the increase rate is not constant. Consequently, the optimal number of polymorphic SSR primers is determined at the peak increase rate.

Hypothesis 2: the classification significantly differs between morphological markers and genetic markers.

Hypothesis 3: there are two kinds of multivariate cluster analysis and genetic distance analysis on the basis of SSR markers, resulting in two different classifications of virus families: firstly, the classification of virus families is conducted based on the Nei’ genetic identity[3](or Nei’ genetic similarity[4]) calculated by the total SSR primers from Gene Bank; or then the classification of virus families is conducted based on the genetic identity calculated by the polymorphic SSR primers only. This research aims to examine which classification method leads to better correlation with the incidence of pathological characters recorded.

Discussion:
1.The total SSR primers selected from Gene Bank are the pairs of SSR primers which lead to clear PCR bands for at least one virus family in amplified process;

2.The recommended three criteria of molecular cytogenetic karyotype for the preliminary classification of different virus families include: the ratio of length between the beginning of a short arm and the margin of rDNA probe to the total  length of a chromosome; relative length of chromosome (ratio of each chromosome length to the sum length of all chromosomes examined in research); and centromere index. The average value of each criterion should be calculated for each virus family.

3.The virus samples should be collected in the same area at local scale, which facilitates the differentiation of local virus families due to the unique nature of virus ecosystem.

4.Preparation of DNA samples in one test: �12 uniform samples are abstracted from the same DNA water solution which has been evenly mixed, named as sample 1, sample 2, ..., sample12; � In total 12 different SSR primers are selected in one test, named as primer 1, primer 2,...., primer12, and each different SSR primer is injected into sample 1, sample 2, ..., sample 12 respectively for PCR amplified process; � after PCR amplified process, each sample (12 in total) is electrophoresed separately in each pipe of electrophoresis instrument, and the PCR bands from different virus families, preliminarily drawn by FISH technique, would be clearly separated from each other in a electrophoresis pipe. Consequently, the distance between two PCR bands from two different virus families, which is measured in a pipe of electrophoretogram, represents the genetic distance between these two virus families per SSR primer (or locus). Then the multivariate cluster analysis of UPGMA (unweighted pair group method with arithmetic averages) is conducted on the basis of the average genetic distance between any two different virus families across SSR primers (or loci) which can lead to clear PCR bands in all virus families preliminarily drawn by FISH technique. If the electrophoresis pipe is the vertical one, then the PCR bands around the same horizontal lines represent the same virus families due to the ‘similar weight of molecules’, which can be deduced by the ‘similar length of genomes’ within one virus family identified by FISH step. Please note: both the molecular weight and genome length mentioned above are the relatively weight and relative length, because the DNA molecular weight, shown in the gel electrophoretogram (such as the distance between two PCR bands in a pipe of electrophoretogram), is the relative weight of molecules, which can be consequently deduced by the relative length of genome (the ratio of the sum genome length within a virus family to the sum genome length of all the virus families examined in research).

5.The multivariate cluster analysis for virus family classification is on the basis of the mutual interaction among virus ecosystem. Consequently, there are two criteria of qualitative gene expression (or qualitative trait locus of gene expression), including the ratio of length between the beginning (or end) of a chromosome and rDNA probe to the total length of a chromosome as well as centromere index, and a criterion of quantitative gene expression (or quantitative trait locus of gene expression) in response to the competition mechanism in virus ecosystem, reflected by the relative length of chromosome (ratio of each chromosome length to the sum length of all chromosomes examined in research). Usually, the local virus ecosystem is relatively isolated, due to ‘the absence of gene communication’ among virus ecosystem and the limitations of airborne virus transmission. Consequently, the virus samples should be collected in the same area at local scale. However, the amount of virus families, which result in the impacts on human health, is increasing due to gene recombination and mutation in self-reproduction process.

There is an improved method presented for identification of virus families:
Step 1. The whole genome of a specific virus family, whose DNA (or RNA) molecular weight is examined in Lab[5], is cultivated for reproduction in Lab as standardized DNA molecule.

Step 2. After amplified process in PCR, the DNA fragment samples together with the cultivated genomes in step 1, are transferred into the electrophoretogram procedure, conducted by the discussion 4 above.

Step 3. The standardized DNA (or RNA) molecule should be the molecules of the highest weight; Then the molecular weight of DNA fragments from the other virus families can be calculated per SSR correspondingly[5]. This improved method facilitates the identification of virus families, regardless of variation in virus ecosystem.

Step 4. Identification of virus family with gene mutation: the virus family with gene mutation is firstly identified by FISH technology; then the specific locus of genome, in which gene mutation occurs, is identified by DNA (or RNA) molecular markers (the heterozygous bands of a specific locus is the gene mutation bands, as compared  to the homozygous bands of parental virus family without gene mutation). Please note: the heterozygous or homozygous bands here are just description of band morphology, rather than allelic gene.

Please note: the objects of dyeing procedure in step 1 is protein due to the protein ‘coat’ around virus DNA (or RNA) and the DNA (or RNA) molecules are the molecules with the highest weight in virus physiology, whereas the objects of dyeing procedure in step 3 is nucleic acid molecule. SDS-PAGE for protein separation requires lower voltage than nucleic acid molecules (or isozyme separation), so that the

DNA (or RNA) can hardly take off their protein 'coat.' The weaker clearness of protein ‘coat’s bands, the higher accuracy of this test, which can be adjusted by gradual change of voltage.

Discussion:
In this experiment, the gene mutation virus family is identified in the whole virus ecosystem, analyzed by both multivariate cluster method (FISH technology) and two-paired comparison (between parental virus and gene mutation virus). It is expected that the gene mutation virus family show closer genetic distance to the other virus families, rather than its parental virus family, conducted by FISH technology. However, the conclusion of virus classification is ‘corrected’ by further DNA (or RNA) molecular markers (gene mutation virus family should show closer genetic distance to their parental virus family). This finding will further support the distortive bio-signal caused by gene mutation virus family, which is hardly identified by host cells discussed in chapter 8.



References:
[1]. 刘焕, 张洪初与唐秋盛, 保护遗传学方法在生物多样性监测和评价领域的应用研究.  科技视界, 2014(8);
[2]. Liu, H, Ouyang T. L, Tian Chengqing (2015). Review of Evolutionary Ecology Study and Its Application on Biodiversity Monitoring and Assessment. Science & Technology Vision (6) 2015;
[3]. 陶玲与任珺, 进化生态学的数量研究方法, 2004, 中国林业出版社: 北京市;
[4]. Genuineness and Purity Verification of Potato Seed Tuber - SSR Molecular Marker (GB/T 28660-2012).
[5]. 朱广廉,杨中汉 SDS-聚丙烯酰胺凝胶电泳法测定蛋白质的分子量《植物生理学报》, 1982.

2. Classification of Bacteria by Genetic Marker and Its Theory
However, in addition to the five steps above, a supplementary metabolomics test is advised for further classification of bacteria families, as discussed in the Chapter 7 of this book, resulting in more specific classification of bacteria families related to the incidence of pathological characters. In principal, the more enzyme species variation between pathogenic bacteria families, the higher pathogenicity for the  epidemiological receptors due to the higher environmental adaptiveness of pathogen families. Consequently, this methodology is listed below:

Step 1. Each bacterium is isolated from bacteria samples, and cultivated separately in situ forming a bacterium stream. Then each bacterium stream is named as stream 1, stream 2,. , stream n.

Step 2. The cytoplasm sample is abstracted in each bacterium stream labeled for subsequent step 8, and the abstracting procedure and storage of isozyme is listed in page 47 of isozyme chapter [1]. Then the chromosome sample of each bacterium stream labeled is prepared for step 2.

Step 3. The molecular cytogenetic karyotype of each bacterium stream labeled is analyzed by fluorescence in situ hybridization (FISH) technique[2] using transmission electron microscopy;

Step 4. These bacterium streams are classified by multivariate cluster analysis and genetic distance analysis on the basis of molecular cytogenetic karyotype, preliminarily leading to different families of bacteria[3];

Step 5. The optima sampling units of each bacteria family, which can well represent the genetic diversity of each bacteria family, is examined and determined as pointed out by Liu et al.,(2015) [3] for further classification based on DNA molecular marker (SSR or AFLP);

Step 6. Classification of bacteria families is further conducted on the basis of DNA molecular marker, analyzed by multivariate cluster analysis of UPGMA (unweighted pair group method with arithmetic averages) and genetic distance analysis[3];

Step 7. Gene recombination and gene mutation rate is analyzed by the inconsistence  of classification between molecular cytogenetic karyotype and DNA molecular marker[3];

Step 8. Bacteria family ‘F’ is identified by cluster analysis and genetic distance analysis based on DNA molecular markers (genotype), which results in apparent incidence of pathological characters when exposure dose to bacteria family ‘F’ increases significantly (phenotype);

Step 9. Biochemical samples are abstracted from streams of Bacteria family ‘F,’ leading to various zymogram calculated as the average similarity coefficient across different isozyme families, which is listed in the chapter 7 of this book.

Step 10. Bacteria family ‘F’ is further classified into different sub-families by UPGMA (unweighted pair group method with arithmetic averages) method on the basis of the average similarity coefficient across different isozyme families between any two streams. The UPGMA calculation is listed in page 63 of isozyme chapter [1].

Step 11. Sub-Bacteria families, named as F1, F2 .... Fn, should be more specific in terms of correlation to the incidence of pathological characters.

Note: the above hypotheses and discussion about virus are also required for bacteria ecosystem. However, the DNA preparation procedure in discussion 4 can be changed into the procedure in these case reports instead[3], due to the inconvenience of  labeling bacteria in discussion 4.

Further more, after the Step11, there are some improvements of bacteria classification. Different environmental conditions (such as temperature  and PH) are simulated in  our Lab for cultivation of bacteria streams: this research hypothesizes that there is not absolutely the same enzyme species between two different bacteria sub-families. Consequently, the comparison of one bacteria sub-family between different environmental conditions reveals the total amount of enzyme species within a whole isozyme family expressing under the range of environmental conditions simulated in Lab, and the total amount of enzyme species is the basis for calculation of similarity coefficient in one isozyme family between different bacteria sub-families, as pointed out below. Please note: the comparison of one bacteria sub-family’s zymograms between different environmental cultivation conditions should be conducted in a ‘smooth’ way, which means the comparison should be conducted at two consecutive conditions without significant variation in environmental conditions for bacteria cultivation, otherwise two different zymograms between significantly different conditions are not comparable due to the relative weight of enzyme molecules revealed by the electrophoretogram. The specific environmental condition (or bio-signal), regulating the gene expression as a specific enzyme species, is determined by this ‘smooth’ comparison as well, as further discussed in the Chapter 7 of this book.

The calculation of similarity coefficient between zymogram of different bacteria families is performed within one isozyme family[1]. However, this method is performed on the basis of unweighted average. Hence this book advises the steps of analyzing the zymograms with weighted average in future research:

1.If the electrophoresis pipe is the vertical one, then the horizontal bands in a pipe represent various enzyme species in an isozyme family. The bands at the same horizontal line between different pipes represent the same enzyme species, and the clearness of bands indicates activity of enzyme species (the more clearness, the higher activity of enzyme). Please note: the reproduction rate of microbial streams varies among different environmental cultivation conditions. Consequently, the density of microbial samples should be counted, ensuring the uniform concentration of microbial samples for the enzyme activity observation.

2.The whole environmental conditions (such as temperature) are simulated in situ from T1 to Tn (T1,T2,……,Tn). Within the environmental range [T1, Tn], the range of [T2, Ta] is the environmental range triggering the gene expression of enzyme species A, and the range of [T3, Tb] is the environmental range triggering the gene expression of enzyme species B,… etc. Consequently, the weight of enzyme species A is the ratio of range [T2,Ta] to the total range [T1, Tn], and the weight of enzyme species B is the ratio of range [T2, Tb] to the total range [T1,Tn],… etc. Then the similarity coefficient in one isozyme family between zymogram of different bacteria sub-families is calculated as: similarity coefficient = 2*∑(enzyme i * weight i) /
{∑(enzyme j * weight j) + ∑(enzyme k * weight k)}. In this equation, enzyme j is the enzyme species in bacteria sub-family 1 and weight j is the weight of enzyme species j; enzyme k is the enzyme species in bacteria sub-family 2 and weight k is the weight of enzyme species k; enzyme i is the common (or same) enzyme species between sub-family 1 and sub-family 2.

3.In principle, the gene expression of enzyme species A should start at the environmental condition T2 with increasing activity along the environmental gradient, and the activity should decrease after the peak value until gene expression ceases at environmental condition Ta, which can be observed by the ‘smooth’ comparison of one bacteria sub-family’ zymograms between different bacteria cultivation conditions. However, the comparison of zymograms between different sub-families should be conducted at the same environmental cultivation condition.

Hypothesis: The ‘memory’ of gene expression:
There are two kinds of bacteria cultivation methods conducted independently in Lab: Method 1: Each bacteria sample of the same genetic strain is cultivated separately in different environmental conditions for ten generations (T1, T2, …Tn); Then different bacteria samples are abstracted for metabolomics test.

Method 2: in Step 1, bacteria samples of the same genetic strain as method 1 are cultivated in environmental condition T1. Then some samples are abstracted after this cultivation of reasonable reproduction process (two generations) for metabolomics test, and the rest bacteria samples continues reproduction in T1 condition for ten generations. After this, the rest bacteria are transferred to the next cultivation in a different environmental condition T2; in Step 2, the rest bacteria samples are cultivated in environmental condition T2. Then some samples are abstracted after this cultivation of reasonable reproduction process (two generations) for metabolomics test, and the rest bacteria samples continues reproduction in T2 condition for ten generations. After this, the rest bacteria are transferred to the next cultivation in a different environmental condition T3; ….; Finally, the rest bacteria samples are cultivated in environmental condition Tn. Then some samples are abstracted after this cultivation of reasonable reproduction process (two generations) for metabolomics test, and the rest bacteria samples continues reproduction in Tn condition for ten generations.

This research aims to examine the gene expression difference between these two  kinds of bacteria cultivation methods, although the simulated environmental conditions are the same for bacteria cultivation, which reveals the ‘memory’ of gene expression. This means that the population does not only pass on the genome, the genetic resource, but also passes on the ‘memory,’ in terms of identifying the bio-signal triggering the gene expression, onto their offspring. If these two kinds of bacteria cultivation methods lead to different gene expression types, then the second bacteria cultivation method is closer to the field conditions. Definition of bio-signal in this book as environmental physiology: the signals, emitted from environmental factors (both biotic and abiotic), can be perceived or identified by living beings.

Conclusion and Implication for future Research & Development in Air quality Monitoring:

After a family of pathogenic virus (or bacteria) has been identified by the methodologies above, the unique SSR primers specifically for this family, which can not lead to PCR bands in the other microbial families but result in clear PCR bands in this pathogenic family only, are screened and synthesized into FISH probes for FISH step again. The methods of FISH probe preparation is listed [4]. Then the  concentration of this pathogen family in water solution can be tested by ultraviolet spectrophotometer, yielding a feasible and affordable method for routine air quality monitoring. The steps are listed below. Please note, the specific SSR primer (or the specific locus), leading to the gene mutation bands of virus, is the key for this selection of FISH probe preparation. It is expected that the gene mutation virus family results in unusual and sharp increase of airborne density, as compared to its parental virus family, because the gene mutation significantly increases the genome replication rate discussed in chapter 8. In this case, this specific SSR primer (or the specific locus), leading to the gene mutation bands of virus, may NOT be the unique one, but becomes the key to monitor the density of gene mutation virus.

Step 1. In total five different densities of a virus family (such as the parental virus family of gene mutation one) are cultivated and separated in Lab.

Step 2. Specific FISH probe is prepared for this virus family, and FISH procedure is conducted on five densities of this virus sample without the last drying process, leading to five different water solution concentrations (Sample 1, Sample 2 ..., Sample
5) of virus genomes binding FISH probe.

Step 3. The same volume of virus water solution are abstracted from Sample 1, Sample 2, ..., Sample 5, respectively, and the density of each virus water solution is counted by transmission electron microscopy after dying process.

Step 4. The regression equation for ultraviolet spectrophotometer is consequently worked out by detecting the fluorescence intensity in five different water solution concentrations (Sample 1, Sample 2 ..., Sample 5) of virus genomes binding FISH probe.

References:
[1]. 周延清, 张改娜与杨清香, 生物遗传标记与应用, 2008, 化学工业出版社.
[2]. 刘焕, 张洪初与唐秋盛, 保护遗传学方法在生物多样性监测和评价领域的应用研究.  科技视界, 2014(8).
[3]. Liu, H, Ouyang T. L, Tian Chengqing (2015). Review of Evolutionary Ecology Study and Its Application on Biodiversity Monitoring and Assessment. Science & Technology Vision (6) 2015.
[4]. 郑成木, 植物分子标记原理与方法, 2003, 湖南科学技术出版社.

3. Examination of Environmental Toxicity
Methods of examination of environmental toxicity in heavy metal pollution adhering to aerosol:

Step 1: two parallel samples of rats, as the receptors of heavy metal pollution, are exposed to two kinds of environmental conditions respectively for the same duration: one is adjacent to the transportation road where the main pollution source of heavy metal is diesel; the other is the factory in which the pollution source of heavy metal is the industrial emission. The height of rat samples should be located at the level of people’s breath zone. The rat’s total urine during two hours after exposure experiment is collected for analysis.

Step 2: The cumulative exposure dose of heavy metal pollution adhering to aerosol  are monitored in both sites, and the test of mean heavy metal concentration in the urine after exposure experiment are correspondingly conducted for the analysis of correlation. The standards of monitoring the heavy metal pollution include: GB/T16157, HJ/T 373, and HJ/T48...etc.

Step 3. The ratio of mean heavy metal concentration in urine to the cumulative exposure dose is calculated, for the assessment of the difference in environmental toxicity between two different emission sources, which emit aerosols with different morphology. The mean heavy metal concentration in the urine after exposure experiment is compared with relevant standards of limit value to reveal the degree of health (The higher concentration, the more environmental toxicity).

Step 4. The total content of heavy metal in the urine after exposure experiment is also counted, which is divided by the cumulative exposure dose so that another ratio is worked out for the assessment of environmental toxicity (The higher ratio, the more environmental toxicity).

Step5. 8-hours exposure duration, 24-hours exposure duration and long-term exposure duration are chosen for the investigation of environmental toxicity in heavy metal at different durations.

Step 6. After multiple test, the mean ratio becomes a stable criterion to examine the effects of the environmental toxicity in heavy metal pollution.

Step 7. The mean heavy metal concentration in urine is also tested in people who are working in both sites as ‘clinical trial’, and the correlation between the cumulative exposure dose and mean heavy metal concentration in urine is analyzed. The mean heavy metal concentration in the urine after exposure experiment is compared with relevant standards of limit value to reveal the degree of health.

4. Environmental & Health Standards of Electromagnetic Wave/电磁波污染的环境与卫生标准
As discussed in Chapter 9 of this book, the electromagnetic wave with multiple frequencies (more than 3 frequencies) leads to altered or distortive bio-signals which is detrimental to health. Consequently, this appendix presents novel method to examine the environmental standards of electromagnetic wave for environmental monitoring and assessment:

Step 1. Parallel samples of rats with the same genetic strain are cultivated in Lab as the receptors of electromagnetic wave, and the rhythm of heart is measured as the indicator of health degree;

Step 2. Different frequencies of electromagnetic wave, which can be commonly found in daily life, are simulated in Lab, and more than three emittors of electromagnetic wave works concurrently; Different intensities of each frequency are simulated in Lab as well;

Step 3. The limitations of environmental standards for electromagnetic wave, in terms of limitations on both frequency and intensity, are tested and decided according to the heart rhythm of rat samples.

The above PDF version is the original materials formally published in book.
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 楼主| 发表于 2020-12-20 18:50:54 | 显示全部楼层
Article 2: 3S Technology Application on Biodiversity
Monitoring and Assessment/3S 技术在生物多样性监测与评价领域的应用研究(英文)
Author: Liu Huan, MSc (First Class Honous), The University of Auckland
Published after graduation on 07/08/2015


Abstract
This article presents an improved method for the object-oriented classification of high-resolution remote sensing imagines.

1.Introduction
The application of the object-oriented classification of high-resolution remote sensing imagines on biodiversity monitoring was summarized previously by Liu et al., (2014)[1]. However, the limitation of these cases was not discussed in article. This chapter aims to point out the main limitations of these cases and illustrate the improved methods for object-oriented classification of high-resolution remote sensing imagines.

2.Remote Sensing
Chen etc [2] classified QuickBird remote sensing imagines for shrubs, crops, broadleaf forest and needle leaf forest on the basis of object-oriented and multi-level segmentation methods in HeiShiDing Nature Reserve of GuangDong Province. Compared with pixel-based method, the methods adopted by Chen etc resulted in higher accuracy of classification, more distinct boundary of classification and more internally uniform homogeneity [2]. However, the unsatisfactory accuracy (less than 40%) of classification in this research should be mainly attributed to the absence of geometric precision correction of high resolution remote sensing image. Wang etc [3] used eCognition software to extract the dominant tree crown’s information from Quickbird RS imagines, and the classification method of membership function was selected on the basis of three spectral characteristics (brightness, adjacent characteristic and asymmetry). The extracted tree crown’s information was transformed into vector data, which facilitated statistical analysis on ArcGIS software [3]. However, the geometric precision correction has not been conducted in this research as well, possibly explaining part of inaccuracy of classification in this research. Long etc [4] adopted the object-oriented classification method  by eCognition software for identifying wetland plant species in Natural Reserve of HanShiQiao wetland of Beijing, according to the spectral characteristic analysis of SPOT5 imagines. Spectral characteristic of each plant species  (Phragmites  australis, Echinoch loacrusgallii and Nymphaea tetragona)     was extracted and identified by field survey work; spectral correlation analysis of each plant species was conducted by SPSS software to identify the specific spectral band or combination of bands which reflected the most distinct difference between species; the weight of each spectral band was consequently determined for segmentation of remote  sensing images step in eCognition; as to compare with object-oriented classification without spectral analysis, spectral analysis played a essential role in the improvement of object-oriented classification accuracy [4]. However, compared with Wang etc [3], three spectral characteristics (brightness, adjacent characteristic and asymmetry) have not been considered in Long etc [9] research, which would be a deficiency due to lack of reflecting all spectral bands of high-resolution imagines, particularly for the more complex species composition in a national forest park. Additionally, in a forest park with complex topography, the extraction of spectral characteristics for each species should be sampled separately between sunshine and shading area.

There are five spectral characteristic criteria recommended for the membership function by this article, including the ratio characteristic (three bands for Quickbirds imagines): the ratio of each spectral band value to the sum value of all spectral bands; the brightness characteristic: the sum value of all spectral bands divided by the number of pixels in an object; the adjacent characteristic: the weighted average of brightness difference between an object and the adjacent objects [3].

Table 1. Multivariate cluster analysis is conducted on the basis of these parameters below for QuickBirds imagines (See PDF version):

To estimate the value range of each spectral characteristic for membership function of eCognition software, multivariate cluster analysis on the basis of five spectral characteristic should be conducted for the differentiation among various plant species, with verification of each species (particularly to verify the boundary of spectral value range for each species) in the sampling work. The objects are classified as different species distribution by cluster analysis with field validation (Table 1).

3.Geographic Information System (GIS)
Multivariate cluster analysis on the basis of five spectral characteristic should be conducted by GIS, which also facilitates the calculation of distribution area of each species. By the way, the GIS does not only allows the Chemistry Transport Model (CTM) to incorporate for the assessment of climate change effect (e.g. Ozone depletion (UV-B radiation), or carbon balance) on the temporal and spatial dynamics of vegetation ecosystem [6], but also allows the numerical model, creatively presented in the last article, to integrate for carbon sink calculation. Particularly, the concentration of atmospheric carbon dioxide modeled by CTM would significantly influence the Light Use Efficiency (LUE) calculated by the atmospheric transmissivity. Atmospheric transmissivity is significantly determined by the atmospheric CO2 [7]. However, as discussed by Drayson [7], the inconsistence was reported between theoretical and experiment data, and the influence of absorbing gases with variable mixture ratio on atmospheric transmissivity was further found [8]. Consequently, the site-specific regression model of atmospheric transmissivity based on atmospheric CO2 concentration is advised to integrate into GIS, due to the geographic heterogeity of atmospheric chemistry.

4.Global Position System (GPS)
Geometric precision correction is to eliminate the geometric deformation of remote sensing images, resulting in the error of RS location which is less than 0.5 pixel of high-resolution RS. For the high-resolution RS images (e.g. Quickbirds RS with a resolution of 0.61m), the application of static differential Global Position System (GPS) with inaccuracy of ± 5mm is advantageous, compared with the expensive RKT GPS. The steps of geometric precision correction is specified by Wan et al., (2013) [5], which is essential not only to extract spectral information for object-oriented classification of RS imagines, but also to exactly select objects for verification of classification accuracy. In addition, the application of static differential GPS also provides supportive data for the correction of digital elevation of high-resolution RS, which becomes important for the calculation of drainage area in ecosystem, an element of assessment of ecological function for water conservation.

References:
[1]. Liu Huan, Zhang HongChu, Tang QiuSheng, Li XiLai (2014). A Brief Review of 3S Technology Application on Biodiversity Monitoring and Assessment. Science & Technology Vision (8).
[2]. 陈旭, 徐佐荣与余世孝,  基于对象的QuickBird遥感图像多层次森林分类. 遥感技术与应用,
2009(01): 第22-26+130页.
[3]. 王茹雯等, 利用面向对象的技术进行树冠信息提取研究. 中国农学通报, 2010(15): 第128-134页.
[4]. 龙娟等, 基于光谱特征的湿地湿生植物信息提取研究. 国土资源遥感, 2010(03): 第125-129 页.
[5]. 万本太等,《生态环境遥感监测技术》中国环境出版社。ISBN 978-7-5111-1664-2.
[6]. Liu, H, Ouyang T. L, Tian Chengqing (2015). Review of Evolutionary Ecology Study and Its Application on Biodiversity Monitoring and Assessment. Science & Technology Vision (6) 2015;
[7]SR Drayson (1966)Atmospheric Transmission in the CO_2 Bands Between 12 μ and 18 μ  - 《Applied Optics》;
[8]LM Mcmillin,HE Fleming,ML Hill (1979) Atmospheric transmittance of an absorbing gas. 3: A computationally fast and accurate transmittance model for absorbing gases with variable mixing ratios.《Applied Optics》.
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 楼主| 发表于 2020-12-20 19:02:27 | 显示全部楼层
Article 3: Inter-specific and intra-specific comparison in the responses to UV-B radiation and to water deficit in Trifolium (Leguminosae)/三叶草属(Trifolium)植物种间和种内对  UV-B  辐射和干旱等逆境生理的比较性研究
Author: Liu Huan, MSc (First Class Honours), The University of Auckland, Adviser: Hofmann, R.W., Lincoln University, New Zealand.
Revised after graduation on date 05/08/2015.


Abstract
Caucasian clover (Trifolium ambiguum M. Bieb.) and two populations of white clover (Trifolium repens L.) were grown for 9 weeks with supplemental application of UV-B radiation at a rate of approximately 13 kJ m-2 day-1. Parameters of total aerial biomass, net photosynthesis, conductance, transpiration, water use efficiency, relative chlorophyll content, water solute potential ΨW, canopy temperature were tested in this research. Drought stress was also simulated during the last four weeks. Compared  with the control, the total aerial (Dry Matter) DM production across clovers decreased by 81% under drought condition. However, Caucasian and Tienshan clover showed higher drought tolerance in terms of osmotic adjustment. Under well-water condition, the total aerial biomass yield of Tienshan clover was not significantly affected by UV-B, while Kopu II was sensitive to UV-B. By the intra-specific comparison within white clover species, Tienshan clover, which showed less UV-B sensitivity and higher tolerance to drought, was less productive and had a original habit with multiple forms of stress. Further more, drought stress reduced UV-B sensitivity in both clover species. On the other hand, UV-B treatment also improved water-deficit tolerance across clovers by 43% under drought. In comparison, for Caucasian clover, UV-B increased the total aerial biomass yield by 84% under drought conditions. This indicated that UV-B might lead to a higher improvement of drought tolerance in Caucasian clover than in white clover. However, results also indicated that the pathways  of physiological adjustments would differ between UV-B radiation and drought stress conditions.

论文一:三叶草属(Trifolium)植物种间和种内对  UV-B  辐射和干旱等逆境生理的比较性研究
摘要:一个高加索三叶草 (Trifolium ambiguum M. Bieb.) 的种群和两个白三叶草(Trifolium repens L.) 的不同种群在UV-B 辐射和干旱的模拟实验室条件中栽培了9 个星期。总体生物量干重,初级光合作用,大气传导率,蒸腾量,水利用效率, 树冠温度,叶绿素相对含量、溶质势能等参数在实验室条件下进行了测试。 与适宜生长条件相比,所有三叶草植物在干旱逆境中生长的总体干重平均降低了81%。 然而,高加索三叶草和天山白三叶草以渗透调节的方式显示出了更高的抗旱性。在水分充足的条件下,天山白三叶草生长的总体干重并无显著地受到UV-B 辐射的影响,然而 Kopu II 白三叶草品种却对 UV-B 辐射很敏感,说明天山白三叶草显示出了对 UV-B 辐射和干旱的较强抗逆性。此外,天山白三叶草起源于一个充满各种逆境生理条件的生境中,并且生产力较低。干旱条件降低了三叶草植被对 UV-B 辐射的敏感性。同样的,UV-B 辐射增强了三叶草植被对干旱的抗逆性。比较而言,UV-B 辐射对高加索三叶草在抗旱性的提高更为显著。然而, 实验结果表明了三叶草属植物分别在UV-B辐射和缺水等不同逆境条件下的生理调节机制和路径应该有所不同。

1.Introduction
The increasing level of ultraviolet (UV)-B radiation, as a result of ozone depletion,  has been recognized. The enhanced UV-B radiation may lead to large effects on the productivity of plant (Lindroth et al., 2000). Because the UV-B level in New Zealand is significantly higher than that at comparable latitudes in the northern hemisphere, these consequences caused by increasing UV-B radiation have been paid more attention in NZ (Lindroth et al., 2000).

White clover (Trifolium repens L.), as the most important legume species in NZ, effectively contributes to the soil nitrogen content for pasture growth due to its ability of atmospheric N fixation. It also has a high nutritious value for grazing ruminants. However, the taproot of white clover usually goes to senescence within two years and its persistence in pasture ecosystem mainly relies on the adventitious roots (Black et al., 2006). Consequently, white clover is relatively sensitive to water stress, which has become one of the main limitations to the productivity of pastures based on the white clover in many areas of NZ (Barbour et al., 1999). There were two white clover populations used in this experiment. Kopu II, which has notable characteristics  of high stolon density, long persistence under grazing, high yielding, and large leaf size, is a NZ cultivar for rotational grazing (Kopu II White Clover, 2006). Tienshan, originated from China, evolved under high level of UV-B irradiance with low annual rainfall (Hofmann et al., 2000).

Compared with white clover, Caucasian clover (Trifolium ambiguum M. Bieb.), which produces rhizomes rather than stolons and has a persistent deep taproot, has a longer persistence and higher drought tolerance in temperate pasture ecosystems, although it is less competitive in the establishment, mainly because it allocates more dry matter to the root system, which leads to a reduction of radiation interception by the shoot (Black et al., 2006).

Previous studies on white clover showed the interaction between drought and UV-B. It is suggested that populations evolved under higher natural level of UV-B irradiance were more tolerant to UV-B (Hofmann et al., 2001). Populations, which had well adaptation to drought stress and lower productivity, also had higher UV-B tolerance (Hofmann et al., 2003b). UV-B sensitivity, which was measured mainly by plant growth attributes, decreased with longer duration of drought stress and increasing exposure to UV-B radiation (Hofmann et al., 2003a). UV-B radiation increased levels of UV-B absorbing compounds and flavonol glycosides, which could also be  enhanced by water stress. Both drought and UV-B radiation were able to cause accumulation of osmoregulator proline and lead to reduction of osmotic (solute) potential in leaf cells. Further more, leaf water potential under drought stress could be improved by UV-B radiation (Hofmann et al., 2003b).

However, there were still few studies on the inter-specific comparison in the  responses to UV-B irradiance and to water stress (Hofmann et al., 2001). We hypothesized that clovers (both within and between species) adapted to drought stress might have higher UV-B tolerance. Further more, a combination of drought and UV-B stress might decrease the sensitivity to each of these environmental stresses.

2.Materials and methods (All the tables and Figures in PDF version)

There were two populations of white clover (Trifolium repens L.) used in this experiment: cultivar Kopu II and ecotype Tienshan. Caucasian clover (Trifolium ambiguum M. Bieb.) cultivar Endura was also compared. Plants were established as 4-6 months old seedlings. In the controlled environment, the ratio of day length to night length was 14h:10h at a temperature of 20oC and 15oC, respectively. The light level was set to be approximate 400 μmol m-2 sec-1. The RH was approximately 50%. Clovers were planted in the 1:1 mixture of Wakanui silt loam and pumice. The top 2 cm layer of the pots, each of which was 8.5 L, contained a 4:1 mixture of composted bark:pumice, adding to small amounts of lime, Hydraflo wetting agent, and Osmocote slow-release fertiliser.

There were four treatments in this experiment. Well-water (WW): Plants were  watered at a daily rate of 5% below field capacity of the soil; Drought (DR): Plants were watered when 2% above permanent point wilting occurred; UV+: Plants were exposed to UV-B radiation at a rate of approximate 13 kJ m-2 day-1 from 12 Philips  UV lamps; UV-: The UV- room contained a dummy rig with un-energised lamps. The duration of UV+ treatment was 9 weeks, while drought treatment started from the beginning of the 6th week after establishment. Each UV treatment (UV+ or UV-) was divided into 4 blocks. Each block was subdivided into 2 water treatments (well-watered and drought). Data was analysed using the GENSTAT General Analysis of Variance Procedure.

2.1.Total aerial biomass yield (DM production)
Consistently large and green leaves, some inflorescence (flower cluster) and peduncle (inflorescence stalk) materials, and some stolon materials (in white clover) were harvested using scissors. Then they were dried out in oven at a temperature of 80oC for 48 hours.

2.2.Gas exchange
LICOR 6400 Gas Exchange System was applied to test the gas exchange parameters in a young, fully unfolded leaf per pot. There were four parameters determined: Net photosynthesis (A or Pn) (μmol CO2 m-2 sec-1); Conductance (g) (mol H2O m-2 sec-1); Transpiration (E) (mmol H2O m-2 sec-1); Water use efficiency (WUE) (mmol CO2 / mol H2O).

2.3.Relative chlorophyll content
Relative chlorophyll content was tested in three randomly chosen leaves per pot using SPAD chlorophyll meter.
LICOR 6400 Gas Exchange System

2.4.Canopy temperature (oC)
Canopy leaf temperature per pot was determined using Infrared thermometer.

2.5.Water potential ΨW (MPa)
Water potential ΨW in a young and fully unfolded leaf per pot was determined by pressure bomb.

Pressure bomb

2.6.Corrected osmotic potential ΨS and osmotic adjustment (MPa)
Osmotic adjustment (OA), which is defined as the difference in corrected osmotic

potential S(100)  between control and stressed plants, was calculated by the formula (See PDF version):
Where RWCa is the correction factor for dilution by apoplastic water, which is an estimated value. It is estimated to be approximately 0.1 for most plants in most situations. The osmotic (solute) potential was tested using osmometer.

Osmometer

2.7.Time to reach drought (Day)
The time to reach drought was counted from the well-watered stage to the 2% above permanent point wilting stage.

2.8.Other morphological factors
The inflorescence DM, petiole length, leaf appearance rate, stolon elongation rate, specific leaf mass, % leaf dry mass, leaf damage, leaf senescence, leaf lamina size, and leaf lamina dry mass were also measured in this experiment.


3.Results

Figure 1. Total aerial biomass yield of Kopu II, Tienshan, and Caucasian clover, grown with (UV+) and without (UV-) supplementation of approx. 13 kJ m-2 day-1 UV-B, under well-water (WW) or drought (DR) conditions. Error bars are SE.(See PDF version)

Figure 2. Total aerial biomass yield across clovers grown with (UV+) and without (UV-) supplementation of approx. 13 kJ m-2 day-1 UV-B, under well-water (WW) or drought (DR) conditions. Error bars are SE.(See PDF version)

3.1.Total aerial biomass yield
The total aerial biomass yield across clovers was significantly (P<0.001) decreased by drought (Fig 2). Compared with the control, the total aerial DM production across clovers decreased by 81% under drought condition (UV-DR). In addition, in the control conditions, the total aerial biomass yield of Kopu II was higher than Tienshan clover.

The total aerial biomass yield was significantly (P<0.01) affected by the interaction between UV-B and populations. However, clovers responded to UV-B variously (Fig 1). Under well-water treatment, the total aerial biomass yield of Kopu II decreased by 33% as UV-B was applied. However, under drought condition, Kopu II did not show significant response to UV-B. For Tienshan clover and Caucasian clover, there was no significant UV-B-induced difference in the total aerial biomass yield,  under well-water conditions. However, for Caucasian clover under drought conditions, UV-B increased the total aerial biomass yield by 84%.

3.2.Photosynthesis and transpiration
Drought significantly (P<0.001) affected the net photosynthesis (A), conductance (g), transpiration (E), water use efficiency (WUE) and canopy temperature (Table 1). Compared with the control, the net photosynthesis (A), conductance (g), and transpiration (E) across clovers decreased by an average of 45%, 75%, and 65%, respectively, under drought treatment (UV-DR). Consequently, the water use efficiency (WUE) and canopy temperature across clovers under drought treatment (UV-DR) increased by an average of 67% and 12%, respectively, compared with the control (Table 1).

Net photosynthesis (A) was also significantly (P<0.05) decreased by UV-B (Table 1). Under well-water condition, a decrease of 23% across clovers was caused by UV-B. Further more, under well-water treatment, UV-B significantly (P<0.05) decreased the conductance and transpiration across clovers by 40% and 33%, respectively. However,

there was no significant effect of UV-B on the relative chlorophyll content.
Table 1. Biological responses across clovers, grown with (UV+) and without (UV-) supplementation of approx. 13 kJ m-2 day-1 UV-B, under well-water (WW) or drought (DR) conditions. (See PDF version)
Figure 3. Leaf water potential across clovers grown with (UV+) and without (UV-) supplementation of approx. 13 kJ m-2 day-1 UV-B, under well-water (WW) or drought (DR) conditions. Error bars are SE.(See PDF version)
Figure 4. Leaf osmotic (solute) potential of Kopu II, Tienshan, and Caucasian clover grown with (UV+) and without (UV-) supplementation of approx. 13 kJ m-2 day-1 UV-B, under well-water (WW) or drought (DR) conditions. Error bars are SE.

3.3.Water relations and Osmotic adjustment (OA)
Water potential was significantly (P<0.001) affected by UV-B and drought, and their interaction (Fig 3). In comparison with the control, water potential across clovers was decreased by an average of 172% by drought (UV-DR). However, under drought treatment, UV-B increased water potential across clovers by 43%. Further  more, UV-B significantly (P<0.05) increased the time to reach drought across clovers by 36% (Fig 5). Especially, for Tienshan clover under well-water condition, UV-B increased water potential by 27% (not list).

Osmotic (solute) potential (OA) was significantly (P<0.05) decreased by drought (Fig 4). Compared with the control, a decrease of 19% and 41% in the osmotic potential (OA) was caused by the drought (UV-DR) in the Caucasian clover and Tienshan clover, respectively. However, clover Kopu II did not show significant osmotic adjustment response to drought.

Figure 5. Time to reach drought across clovers grown with (UV+) and without (UV-) supplementation of approx. 13 kJ m-2 day-1 UV-B, under well-water (WW) or drought (DR) conditions. Error bars are SE.

3.4.Plant morphology
The inflorescence DM, petiole length, leaf appearance rate and stolon elongation rate were significantly (P<0.001) reduced by drought (Table 2), as well as leaf lamina size (P<0.01), and leaf lamina dry mass (P<0.05). Especially, under drought condition, Tienshan clover had higher leaf appearance rate than Kopu II (not list).
Both specific leaf mass and % leaf dry mass were significantly (P<0.001) increased  by drought (Table 2). However, UV-B tended to reduce specific leaf mass and leaf dry mass. Leaf damage and leaf senescence across clovers were significantly (P<0.001) increased by both UV-B and drought. However, for the Caucasian clover under drought condition, UV-B led to less leaf damage, and leaf senescence did not show significant response to UV-B.

Table 2. Significance of morphology changes across clovers affected by UV-B and Drought (PDF Version).

4.Discussion

Drought significantly decreased the total aerial biomass yield across clovers (Fig 2). Water deficit reduced water uptake from soil, and hence decreased water potential in leaves (Fig 3), which eliminated cell turgor. This reduction in growth under drought conditions could also be supported by the reduction of net photosynthesis rate (A) (Table 1), which was mainly due to a decrease of carbon dioxide assimilation caused by the reduced stomata conductance. Stomata closure, which was one of the main mechanisms involving in the drought acclimatization in plants, could prevent water vapour loss from transpiration. Consequently, the increase of canopy temperature was caused by the reduction of transpiration (Table 1), which adjusted energy balance in leaves (Taiz & Zeiger, 2006). However, stomata closure led to a higher reduction rate in transpiration than in carbon dioxide assimilation, which caused the increase of water use efficiency (WUE). Barbour et al., (1999) also reported the increased water use efficiency in white clovers by drought stress.

Net photosynthesis (A) was significantly reduced by UV-B (Table 1). This was mainly due to the reduction of stomata conductance caused by UV-B. Salvador et al., (1999) suggested that the damage of PSII caused by UV-B in the guard cells affected the photophosphorylation, which consequently eliminated the K+  influx and stimulated  K+ efflux transport. A second mechanism might be due to a directly UV-B-induced inhibition of the plasmalemma ATPase proton pump. UV-B might also indirectly inhibit the guard cell turgor by modifying the elasticity of the cell walls or the cytoskeleton of guard cells. These effects eliminated the stomata opening, which tended to decrease the stomata conductance.

However, the total aerial biomass yield across clovers was not significantly affected by UV-B (Fig 1), which reflected that photosynthetic system of these clovers might still effectively function. This was supported by the relative chlorophyll content (Table 1), which was not negatively affected by UV-B. These results indicated that these clovers might have adequately photo-protective mechanism, such as enhancing the synthesis of UV-B screening secondary metabolites (Hofmann et al., 2003a). Hofmann et al., (2003a) also reported that photosynthetic pigmentation and photosystem II photochemistry in white clovers were not reduced by UV-B. Under well-water condition (Fig 1), the total aerial DM of Tienshan and Caucasian clovers did not show significant responses to UV-B, while the total aerial biomass yield of Kupo II was reduced by UV-B. These results indicated that Tienshan and Caucasian clovers were less UV-B sensitive than Kupo II, under well-water conditions.

In addition to UV-B sensitivity, Tienshan and Caucasian clovers also showed drought acclimatization in terms of significantly decreased solute potential by osmotic adjustment (OA), whereas Kupo II clover did not (Fig 4). Osmotic adjustment (OA), which decreases water solute potential by accumulation of compatible solutes in cells, such as proline, can maintain cell turgor under water deficit (Taiz & Zeiger, 2006). This was supported by Hofmann et al., (2003a), who reported the increased proline levels in white clover by drought stress. This acclimatization response indicated that Tienshan and Caucasian clovers might also have higher drought tolerance than Kupo II. This higher drought tolerance in Tienshan clover could also be supported by its higher leaf appearance rate than Kupo II under drought conditions. The synthesis of proline is divided into two pathways: glutamic acid and ornithine pathways, which is mainly catalyzed by P5CS & P5CR, as well as Ornithine Aminotransferase (OAT) respectively (Delauney&Verma,1993).

We may be able to conclude that clovers that have higher drought tolerance are also less UV-B sensitive under well-water conditions. Hofmann et al., (2003b) also suggested that the UV-B tolerance under well-water conditions might be related to other forms of stress, such as drought. Further more, within white clover species, compared with Kupo II, Tienshan clover, which was less UV-B sensitive under well-water condition and higher tolerant to drought, had less productivity in terms of less total aerial biomass yield under control conditions (Fig 1), and had a original  habit with multiple forms of stress (see introduction). This relationship was supported by Hofmann et al., (2000), who reported that a higher concentration of flavonols, which functioned as UV-B protective compounds, were found in those white clovers that evolved under multiple forms of stress (Hofmann et al., 2000). Hofmann, et al, (2001, 2003 a, 2003b) also frequently reported similar relationships.

Compared with the well-water treatment, the total aerial biomass yield of Kopu II did not show significant response to UV-B under drought conditions (Fig 1). This reflected that drought stress reduced UV-B sensitivity in Kupo II clover. Hofmann et al., (2003b) also reported the decreased UV-B sensitivity of white clover with the increase of drought duration. Under UV-B treatment, the concentration of both UV-B absorbing compounds and flavonols, including quercetin and kaempferol, were stimulated by the drought stress (Hofmann et al., 2003a). Elsewhere Grace and Logan (2000, cited in Hofmann et al., 2003b) suggested that the adaptation to other forms of stress contributed to UV-B protection mechanisms, probably via the phenylpropanoid pathway.

In addition to the improvement of UV-B tolerance by drought, UV-B increased water potential by 43% across clovers under drought treatment (Fig 3), and prolonged the time to reach drought (Fig 5), which indicated that UV-B stress also helped  to improve the water status during the subsequent drought period. This was supported by Hofmann et al., (2003a), who found that UV-B increased leaf water potential of white clovers by 16% under drought conditions. This improvement of drought tolerance by UV-B was attributed to the UV-B-induced changes of leaf morphology and different growth reduction rate among plant organs (Hofmann et al., 2003a; Rozema et al., 1997). The decreased stomata conductance by UV-B led to a reduction of transpiration, which eliminated the water vapour loss from leaves. This was an advantage for the adaptation to the subsequent drought stress. UV-B also led to an increase of root : shoot ratio, which enhanced the water uptake from the soil, and hence improved the water potential in leaves (Hofmann et al., 2001). Further more, UV-B significantly increased leaf damage and leaf senescence across clovers, which reduced leaf area, and consequently decreased the water vapor loss from transpiration before the subsequent drought stress. This could be supported by the increased water potential in Tienshan clover under UV-B treatment with well-water conditions.

Especially, for Caucasian clover, UV-B increased the total aerial DM  production under drought conditions. This was partially due to less leaf damage and leaf senescence in Caucasian clover under UV-B treatment. Particularly, this result reflected that UV-B might lead to a higher improvement of drought tolerance in Caucasian clover than in white clover.

In addition, Hofmann et al., (2003a) reported that UV-B increased proline levels, which played a role in decreasing the water solute potential for osmotic adjustment under water-deficit condition, in white clover under well-water treatment. However,  in this experiment, UV-B did not significantly decrease the water solute potential under well-water conditions (Figure 3 and 4), which indicated that the pathways of adjusting physiological response would differ between UV-B radiation and water stress. Further more, the synthesis of solute species proline, triggered by the  bio-signal of UV-B, would be more sensitive, explaining the mutual enhancement of UV-B and drought tolerance (however, this was considered as the minor mechanism  as compared to the discussion below), whereas other compatible solute species involving in osmotic adjustment would be less sensitive to UV-B (UV-B even tended to eliminate the synthesis of some solute species revealed by the slight increase of water solute potential in Caucasian clover in Figure 3 and by the increased water solute potential in Tianshan clover by 27% (not listed) during well-water conditions). Nevertheless, the significantly increased water solute potential by UV-B radiation during drought conditions, which has been previously explained by the UV-B-induced changes of leaf morphology and different growth reduction rate among plant organs (Hofmann et al., 2003a; Rozema et al., 1997), would be attributed to the UV-B-induced elimination of synthesis of some solute species in cell as well, but this lead to positive effects on DM production, because it is hypothesized that the accumulation of compatible solutes during water stress condition (without UV-B stress) would be usually excessive for maintaining the cell turgor.

The main limitation in this experiment might be due to the controlled environment, which isolated the individual stress from the field conditions. For example, controlled environment might lead to disproportionate environment conditions such as high ratios of UV-B to PPF (Hofmann et al., 2003a).


5.Conclusion
In our experiment, the total aerial biomass yield in both white clover and Caucasian clover was significantly decreased by drought. However, Caucasian and Tienshan clover showed higher drought tolerance in terms of osmotic adjustment. Consequently, both Caucasian clover and Tienshan clover had less UV-B sensitivity. Within white clover species, Tienshan clover, which had less UV-B sensitivity and higher tolerance to drought, was less productive and had a original habit with multiple forms of stress. Further more, the combination of drought and UV-B stress helped clovers improve the adaptation to each of them. Via the inter-specific comparison, UV-B might lead to a higher improvement of drought tolerance in Caucasian clover than in white clover. These findings have significantly indicative meanings for the pasture production in NZ, where there is a high level of UV-B radiation.

6.Acknowledge
The data of this article sources from the course of ‘Plant Physiology’ in Lincoln University, 2007, New Zealand.


References
Barbour, M., Caradus, J. R., Woodfield, D. R., & Silvester, W. B., (1999). Water stress and water use efficiency of ten white clover cultivars. Grassland Research and Practice Series (6): 159-162.
Black A. D., Moot, D. J., & Lucast, R. J., (2006). Spring and autumn establishment of Caucasian and white clovers with different sowing rates of perennial ryegrass. Grass and Forage Science, 61, 430-441.
Delauney.A.J.,Verma,D.P.S., Proline biosynthesis and osmotic regulation in plants[J].Plant J, 1993, 4(2):215-223.
Hofmann, R.W., Campbell, B. D., Bloor, S. J., Swinny, E. E., Markham, K. R., Ryan, K. G., & Fountain, D. W. (2003). Responses to UV-B radiation in Trifolium repens L.– physiological links to plant productivity and water availability. Plant Cell and Environment (2003) 26, 603-612.
Hofmann, R. W., Campbell, B. D., & Fountain, D. W. (2003). Sensitivity of white clover to UV-B radiation depends on water availability, plant productivity and duration of stress. Global Changes Biology (2003) 9, 473-477.
Hofmann, R. W., Campbell, B. D., Fountain, D. W., Jordan, B. R., Greer D. H., Hunt, D. Y., & Hunt, C.L., (2001). Multivariate analysis of intraspecific responses to UV-B radiation in white clover (Trifolium repens L.). Plant Cell and Environment (2001) 24, 917-927.
Hofmann, R. W., Swinny, E. E., Bloor, S. J., Markham, K. R., Ryan, K. G., Campbell, B. D., Jordan, B. R., & Fountain, D. W. (2000). Responses of nine trifolium repens L. Populations to Ultraviolet-B radiation: Differential flavonol glycoside accumulation and biomass production. Annals of Botany 86: 527-537, 2000.
Kopu II White Clover, (2006). Ampac seed company. Retrieved April 11th , 2007.  from http://www.ampacseed.com/kopu2.htm.
Lindroth, R. L., Hofmann, R.W., Campbell, B. D., McNabb W.C., & Hunt, D. Y., (2000). Population differences in Trifolium repens L. response to ultraviolet-B radiation: foliar chemistry and consequences for two lepidopteran herbivores. Oecologia (2000) 122: 20-28.
Nogues, S., Allen, D. J., Morison, J. I. L., & Baker, N. R. (1999).Characterization of stomatal closure caused by Ultraviolet-B radiation. Plant Physiology, October 1999, Vol. 121, pp. 489–496.
Rozema, J., Staaij, J. Van de., Bjorn, L. O., & Caldwell, M. (1997). UV-B as an environmental factor in plant life: stress and regulation. TREE vol. 12, no. 1 January 1997.
Taiz, L., & Zeiger, E., (2006). Plant physiology, 4th ED. Loughborough, UK: Cambridge University Press.
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 楼主| 发表于 2020-12-20 19:08:23 | 显示全部楼层
Article4: Discussion of Yield components

According to the results sourcing from the ‘Crop Science’ course instructed by Lincoln University NZ in 2007, yield components were also significantly affected by genotypes. The highest values  of pods/plant, seeds/pod, and mean seed weight were achieved from genotype Aragorn, genotype PRO, and genotype Midichi, respectively. However, the total seed yield was not affected by pea genotypes. This result indicated the interdependent compensation mechanism among yield components. Wilson (1987) and Taweekul (1999) also suggested that large variation in one yield component might not lead to changes in total seed yield, due to the ‘plasticity’ of yield components. In addition, the limitation of small sample units in this experiment made it difficult to exactly extrapolate the variation among yield components.


Acknowledge:
Advisers: Hill, G. D. & Mckenzie, B. A.  Lincoln University, New Zealand. 2007.
References:
Ayaz, S., Mckenzie, B. A., Hill, G. D., & McNeil, D. L., (2004). Variability in yield of four grain legume species in a sub-humid temperate environment І. Yields and harvest index. Journal of Agricultural Science (2004), 142, 9-19.
Ayaz, S., Mckenzie, B. A., Hill, G. D., & McNeil, D. L., (2004). Variability in yield of four grain legume species in a sub-humid temperate environment ІІ. Yield components. Journal of Agricultural Science (2004), 142, 9-19.
Moot, D. J., & McNeil, D. L., (1995). Yield components, harvest index and plant type in relation to yield differences in field pea genotypes. Euphytica 86, 31-40.
Moot, D. J. (1997). Theoretical analysis of yield of field pea crops using frequency distributions for individual plant performance. Annals of Botany 79: 429-437. 1997.
TAWEEKUL, N. (1999). Factors affecting seed vigour in field peas (Pisum sativum). Ph.D. thesis, Lincoln University,Canterbury.
WILSON, D. R. (1987). New approaches to understanding the growth and yield of pea crops.
Agronomy Society of New Zealand 6, 23–28.
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 楼主| 发表于 2020-12-20 19:11:08 | 显示全部楼层
Article 5: A Methodology of Assessing Spatial and Functional Heterogeneity in A Ecosystem /生态系统的空间和功能异质性评价方法研究
Author: Liu Huan, MSc (First Class Honours), The University of Auckland Published after graduation on date 06/08/2015.

Abstract
As pointed out by Chinese Biodiversity Conservation Strategy and Action Planning (2010-2030), degradation of ecosystem function is one of three problems threatening biodiversity conservation in China. Under this background, this article presents a matrix to assess the spatial and functional heterogeneity in a ecosystem, which also provides the route of conservation strategy for ecosystem restoration.


1.Introduction and Significance (All the tables are in PDF version)
Liu et al., (2010) applied β Sorenson index on the investigation of the variability of plant communities of grass land in Ordos, Inner Mongolia of China, which was restored from grazing land [2]. However, the β Sorenson index does not well represent the spatial heterogeneity in ecosystem, which has been revised by this research and the species significance has been integrated into the β Sorenson index. To date fewer research have assessed the functional heterogeneity in ecosystem, which has been included in this research. A novel matrix and functional heterogeneity index are presented, reflecting the interaction between ecological function of each botanical species and spatial distribution. In a national forest park representing the background value of a ecosystem, the application of both spatial and functional heterogeneity index helps to select the reference communities for restoration of ecosystem function degradation in local area. Additionally, a new methodology of assessment of water conservation function in a ecosystem is also presented in appendix 1.

2.Assessment of Spatial Heterogeneity
The method to assess spatial heterogeneity of botanical community: Significance of species (S) = Density + Frequency + Dominance [1]. The spatial heterogeneity index between community B and C
= 2×ΣS i1/(ΣSi2+ΣS i3), i1 = 1,2,… a; i2 = 1,2,… b; i3 = 1,2,… c.
In this equation, S i1 is the significance of a species existing concurrently in both community B and C; a is the total amount of common species between community B and C; Si2 is the significance of a species in community B; b is the total amount of species in community B; Si2 is the significance of a species in community C; c is the total amount of species in community C.

3.Assessment of Functional Heterogeneity
All the methods are presented in Tables shown in PDF version.
4.Discussion of Methodology
In this article, the calculation scope of species significance is the whole ecosystem in local area which can be classified into several communities by multivariate cluster analysis. Then, the spatial and functional heterogeneity index can be calculated between any two communities. However, if the two communities are independent community pre-defined for restoration purpose (e.g. a background community and a restored community), then the calculation scope of species significance is within the pre-defined two communities only. In this case, there is an exception that the spatial heterogeneity index would always remain constant when the two communities own the same species, regardless of proportion and spatial distribution of species. Apparently, the spatial heterogeneity index does not work in this case, so it is just a rough indicator to measure  spatial heterogeneity.

Additionally, in Matrix F2, ΣΣValuej×Sj is a simple community indicator of restoration for ecosystem function degradation, although this indicator lacks of reflecting the effect of interactions among species on ecosystem function. However, the application of functional heterogeneity index on restoration process should strengthen the whole ecosystem function (e.g. to select several communities with satisfactory spatial and functional heterogeneity index in a national forest park as the background samples for ecosystem restoration).

References:
[1].环境保护部环境工程评估中心,全国环境影响评价工程师职业资格考试考点要点分析, 2008,中国环境出版社。
[2].刘硕,贺康宁与王晓江,鄂尔多斯沙地不同退牧年限植物群落多样性及变异性研究.西北植物学报, 2010. 30(3).
[3]. 张大勇,王刚,赵松岭(1988). 甘南亚高山草甸弃耕地植物群落演替的数量研究:演替先锋群落的特征分析. 草地生态与牧草生理生化。
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 楼主| 发表于 2020-12-20 19:12:48 | 显示全部楼层
Article 6. Assessment of water conservation function in a ecosystem/生态系统的水源涵养功能的评价方法


The quantitative assessment of ecosystem function for conservation of water will follow these steps: in a national forest park which is the source of a river, the drainage area is calculated by 3S technology; the depth of runoff (= the runoff volume over a period ÷ drainage area) is calculated; estimation of total water evaporation over the drainage area; record of total rainfall volume over the drainage area; the ecological function for conservation of water is assessed by the ratio: (total rainfall volume – total water evaporation) ÷ the depth of runoff. In addition, to assess the effect of topography on conservation of water in ecosystem, the drainage area should be calculated as the area of curved surface by GIS for the depth of runoff, and as the projection area for the total water evaporation and rainfall volume. The methods of GIS application on this calculation is advised [4]. However, the application of static differential GPS on digital elevation correction is also advised before this [5].
References:
[4]. 吴秀芹等 (2007). ArcGIS 9 地理信息系统应用与实践。 清华大学出版社.ISBN 978-7-302-15134-0.
[5]. DOI: 10.13140/RG.2.1.2776.9448.
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 楼主| 发表于 2020-12-20 19:15:31 | 显示全部楼层
Article 7: Conservation Genetics Strategy: Metabolomics --- The Systematic Chemistry Fingerprints Between Genotype and Phenotype /遗传资源的保护策略: 新陈代谢组学---连接基因型和表现型的一项系统化学指纹识别技术
Author: Liu Huan, MSc (First Class Honours), The University of Auckland, Published after graduation on data 26/08/2015

1.Genotype and Genetic Diversity Conservation (All the tables in PDF version)
The feasibility of large-scale application of DNA markers on biodiversity assessment has been discussed by Liu et al.,(2014)[1]. However, the DNA markers suit not only for the classification of plant sub-populations for biodiversity assessment, but also provide the faster and convenient tool to identify the suitable plant varieties (genotype) from wild ecosystem for ecological restoration. The suitable environmental conditions for each variety growth (phenotype) can be identified by the analysis of both community and species interactions with environment as discussed by Liu et al.,(2015)[2]. According to the Environmental Standard on Classifying the Categories of Genetic Resources (HJ 626-2011) in Mainland China, three kinds of DNA  molecular methods have been listed to rank genetic resources (or endangered species) between categoryⅠand categoryⅡ, including assessment of genetic diversity, evolutionarily significant unit (ESU), or genetic contribution rate, which have been substantially discussed by Liu et al.,(2015)[2]. However, it is advised that assessment of genetic diversity would be the first choice in ranking genetic resources (or endangered species), when the total SSR primers are calculated [3]; assessment of genetic variation would be the best method to select the suitable varieties for restoration of endangered species (or other important constructive species as well), when only polymorphic SSR primers are calculated [3]. The optimalization of both sampling units and polymorphic SSR primers, which allows to well  present  the genetic diversity for each variety at reasonable cost, has been pointed out as well [3].

2.Metabolomics and Environmental Adaptivity
However, the supplementary test of biochemical variation in enzyme species among different varieties collected in field, as the indicator for different varieties to adjust metabolism pathways in different environmental conditions, is advised for the conclusion of environmental adaptability between genotype and phenotype (metabolomics analysis). To be more comparable, the biochemical variation in enzyme species within one isozyme family, which catalyze the same metabolism substances, is analyzed according to the similarity coefficient. The function of each isozyme family in plant resistance to different environmental stress is summarized in table 1 below, and the experiment procedure of biochemical test is listed in isozyme chapter [4].To minimize the inaccurate conclusion between genotype and phenotype, the comparison between different varieties should be conducted on the basis of bio-samples collected in the same tissue and development phase of a plant species during the same season. In principle, the higher variation in enzyme species among varieties, the better environmental adaptability for restoration. This can be attributed into two reasons: firstly, the activity of an enzyme species only responds to the specific environmental conditions, and consequently the higher enzyme species variation of an isozyme family would result in the broader environmental conditions triggering the activity of the whole isozyme family; secondly, the gene expression of an enzyme species would be regulated by the specific environmental conditions only, which also explains the higher environmental adaptivity caused by the higher enzyme species variation of an isozyme family due to the broader environmental conditions  for the regulation of gene expression as the whole isozyme family. Both reasons can result in the variation in isozyme electrophoretogram.

Table 1. The Isozyme Function in Plant Resistance to Environmental Stress (See PDF version)
The calculation of similarity coefficient between zymogram of different varieties is performed in one isozyme family[4]. However, the average of similarity coefficient among different isozyme families is calculated to reveal the systematics of environmental adaptability, as metabolomics analysis. The comparison of enzyme species variation between different seasons is required to reveal some resistance characteristics during specific environmental stress (such as cold stress). Compared with Chapter 1 of this book, the simulated environmental conditions of microbial cultivation are not suitable for botany. There are two reasons: firstly, the metabolic enzymes of botanical species is usually less sensitive to environmental conditions in comparison to microbes; secondly, the life cycle of constructive species for ecological restoration of botany communities can be hardly simulated in the controlled Lab.

3.Phenotype and Gene Mapping for Genetic Breeding
Environmental adaptivity is definitely one of the main considerations for plant genetic breeding in restoration work. Nevertheless, as discussed in the Chapter 4 of this book, gene expression traits as higher environmental adaptivity are usually associated with the gene traits of lower biomass productivity (or carbon sink), which means that both gene traits would be located in the same linkage group of genome. However, as discussed in the Chapter 5 of this book, the gene trait of plant drought tolerance would increase the capacity of water & soil conservation due to the  advantageous partitioning for root system, which results in higher ratio of root biomass to the total biomass. For the conservation of endangered birds, the gene  traits as the partitioning of more branches for habitats or suitable fruits would become the major consideration in variety selection as well. As discussed by the appendix 1 of Chapter 4 in this book, in comfortable conditions, the final yield of ‘suitable food’ for endangered  birds should not be significantly influenced by the yield components which are mainly measured by the criteria of branches per plant individual, pods per branch, seeds per pod, and mean seed weight. This would be also explained by the theory that the sets   of gene, underlying the expression as these yield component traits above,  should locate in the same linkage group of genome, so that some agriculture scientists announce that the gene traits of yield components are not useful in breeding selection. However, this book hypothesizes that the gene expressed as partitioning more  branches would locate in the same linkage group as some gene traits of environmental adaptivity (such as drought tolerance and higher capacity of nitrogen fixation in root system), which becomes the objectives of my future study. The infection between microbes of biological nitrogen fixation and botanical roots must be quite specific[15], so the thinner root skin, usually associated with the partitioning of more root branches, would benefit the parasitic infection of microbes, enhancing the biological nitrogen fixation in root system. Additionally, the gene trait of partitioning more branches should result in higher radiation use efficience (RUE) as well, an environmental adaptivity trait in shading side of hills. This gene trait provides not only more suitable shelters for endangered birds, but also higher sustainability of habitats for food.



Appendix

The Observation of DNA Molecules at Three Dimensions

In this article[1], DAPI fluorescence binding technology results in the appearance of AT rich region on chromosome, but the patterns of DAPI binding varies among different plant species. Consequently, this book presents the method to observe the structure of DNA molecules at three dimensions:
If the slide glass is the horizontal plane, and the vertical line is the eyesight line of microscope for DNA molecule observation, then the angle between the planes of AT DNA sequences and the eyesight line observed by microscope is ± α (0°≤ α ≤90°),  and α is generally uniform in the DNA molecules of a species, but varies among different species. If this angle tends to be zero, then the DAPI binding tends to be not observed; If this angle tends to be 90°, then the DAPI binding tends to be more clearly observed by florescence microscope. The structure of DNA molecules can be consequently deduced by the clearness of fluorescence binding.
References:
[1]. Review of Conservation Genetics and Its Application on Biodiversity Monitoring and Assessment. 刘焕, 张洪初与唐秋盛, 保护遗传学方法在生物多样性监测和评价领域的应用研究. 科技视界, 2014(8). DOI: 10.3969/j.issn.2095-2457.2014.08.203
[2]. Liu, H, Ouyang T. L, Tian Chengqing (2015). Review of Evolutionary Ecology Study and Its Application on Biodiversity Monitoring and Assessment. Science & Technology Vision (6) 2015.
[3].  Liu Huan. Epidemiology        A sub-topic: Comparison between indoor and outdoor air quality at
three representative sites in Auckland Center. Appendix 1. Chapter 1. In book Proceedings for Degree of Postgraduate Diploma in Environmental Science. ISBN: 978 – 988 – 12552 – 5 - 9.
[4]. 周延清, 张改娜与杨清香, 生物遗传标记与应用, 2008, 化学工业出版社.
[5].梁艳荣, 胡晓红, 张颖力, 刘湘萍(2003).植物过氧化物酶生理功能研究进展.内蒙古农业大学学报. 第 24 卷第 2 期.
[6].陈金峰, 王宫南, 程素满(2008).过氧化氢酶在植物胁迫响应中的功能研究进展.西北植物学报, 2008, 28(1):0188-0193.
[7].王晓云,毕玉芬(2006). 植物苹果酸脱氢酶研究进展.生物技术通报.2006 年第 4 期.
[8].张计育,王刚,黄胜男,宣继萍,贾晓东,郭忠仁 (2015).乙醇脱氢酶基因家族在植物抵抗非生物胁迫过程中的作用研究进展.中国农学通报2015,31(10):246-250.
[9].郝兆丰 袁进成 刘颖慧.(2012).异柠檬酸脱氢酶在植物抗氧化胁迫中的作用.生物技术通报.2012 年第 6 期.
[10]. 段昌群 王焕校.(1998).Pb+ 2、Cd+ 2、Hg+ 2对蚕豆(Vicia fabaL.)乳酸脱氢酶的影响. 生态学报.第18卷第4期.
[11].于定群,汤浩茹,张勇,罗娅,刘泽静(2012).高等植物葡萄糖-6-磷酸脱氢酶的研究进   展.July 25, 2012, 28(7): 800−812.
[12].黄国存,田波.(2001). 高等植物中的谷氨酸脱氢酶及其生理作用.植物学通报        2001,
18(4):396~ 401.
[13].周滈, 杨传平, 柳参奎(2011).植物苹果酸酶在抵御逆境中的作用.第36 卷第6 期.2011年
11月.
[14].杨泽峰,徐暑晖,王一凡,张恩盈,徐辰武.(2014).禾本科植物β- 淀粉酶基因家族分子进化及响应非生物胁迫的表达模式分析.科技导报2014,32(31).
[15].Liu Huan (2015). Review of Biological Control: The Population Biology of Microbial
Ecosystem/种群生物学原理在微生物生态系统和生物控制技术中的应用研究(英文). Journal of Environmental & Health Science. DOI: 10.13140/RG.2.1.4219.9525
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 楼主| 发表于 2020-12-20 19:19:13 | 显示全部楼层
Article 8. Review of Epidemiology: The Airborne Heavy Metal Pollution & Microbes and Its Impact on Health/病理传播学:环境空气重金属和微生物及其对健康的危害(英文)
Author: Liu Huan, MSc (First Class Honours), The University of Auckland. Published after graduation on 27/09/2015

1.The Heavy Metal Pollution
The pollution sources of heavy metal are mainly through three pathways: the aerosol, diet and water environment[1]. This chapter focus on the atmospheric pathway of heavy metal pollution with the representative pollutants of lead and mercury.

1.1.The Pathology of Heavy Metal Toxicity
The toxic heavy metal associates with the pathology of all organs, with particularly attention to the kidney as the most sensitive organ to the toxicity of heavy metal. There are some identified mechanism of pathology in kidney caused by the toxic heavy metal pollution[2]:
1.1.1.Alteration of permeability and transport function in cellular membrane
Heavy metal inactivates the membrane lipid, leading to the alteration of permeability and transport function in cellular membrane.
1.1.2.Impact on the enzyme and nuclein
In cell, the heavy metal react with organic molecules or the functional group of enzyme, which results in the exchange of essential metal ions or inactivation of enzymes. The heavy metal ions also combine the non-enzymatic protein and nucleic acid, inactivating the biological organs.
1.1.3.Distorting the immunological system
The heavy metals, as half antigens, react with proteins into complex antigens, distorting the immunological system of biological organs.
1.1.4.Secondary pathology
The secondary pathological characteristics associating the heavy metals mainly include methemoglobinemia, hematolysis, shock, anoxia and electrolyte disturbances.

1.2.Pathology of Lead
The main perniciousness of lead is the chronic interstitial nephritis, which has been testified physiologically by the evidence of excessive lead in the inclusion body of renal epithelial cell using animal test. The pathological characteristics of lead toxicity usually include Fanconi-de Toni syndrome, benign glycosuria, amino-aciduria, albuminuria, cylindruria, urine lead ascending and hypertension etc. Approximately 50% of patients of toxic lead are associated with pathological characteristics of hyperuricemia, arthrolithiasis, and osteosclerosis in bone X-ray (typical increase of texture in the end of long bone)[2].

1.3.Pathology of Mercury
The physiological mechanism caused by mercury is that the mercury combines with sulfur hydrogen group of mitochondrial membrane protein, resulting in the decomposition and destruction of mitochondria and nuclei. The compounds of plasma mercury tightly combines with proteins, allowing only 1% of glomerulus to permeate. The accumulation of mercury intensively occurs in the proximal tubule of kidney, manifesting the formation of granule in epithelial cells or vacuolar degeneration with serious pathology as focal tubular rupture. The acute characteristics of pathology include the renal failure, urine dipstick for protein, cast epithelial, the increase of red-blood-cells, diabetes, acidaminuria, and mercury urine, as well as chronic characteristics of nephrosis syndrome[2].

2.The Airborne Microbial Pollution
Acute respiratory infection is divided into anemofrigid cold and anemopyretic cold by Traditional Chinese Medicine (TCM) [3]. The population density and microbial diversity of aerosol samples in oral cavity were compared between the heathy one and patients by Chen et al.,(2005)[3]. However, the patients are diagnosed as anemofrigid cold and anemopyretic cold by TCM separately, which are correspondingly compared independently as well. The background microbial ecosystem are sampled and analyzed in this research for the assessment of meteorological effects on the microbial communities. The conclusion of this research supports the theory of ‘alteration of eco-balance’ in microbial ecosystem revealed by the increase of microbial density and decrease of microbial diversity, which is considered as the causal factor of acute respiratory infection. However, the specific pathogenesis of each microbial species  has not been characterized in this research, and the classification of microbial species is based on the morphological characters only. Particularly, the establishment of pathogens is performed as a microbial community rather than a population of single species in this research, which further supports the improvement of biological control pointed by this book author [4].

References:
[1]. 唐银栋(1987)。重金属的结构与其环境污染和毒性的关系。《内蒙古医学院学报》。
第 9 卷,第 1 期。
[2]许国章,樊军明(1995)。重金属中毒性肾脏病。《新医学》。
[3]. 陈文慧, 袁嘉丽, 韩妮萍, 姚政, 张英凯, 赵鹏 (2005)。 春季时令病邪与空气微生物及呼吸道微生态相关性初步研究。《云南中医学院学报》。第 28 卷第 4 期。
[4].Liu Huan (2015). Review of Biological Control: The Population Biology of Microbial Ecosystem/种群生物学原理在微生物生态系统和生物控制技术中的应用研究(英文). Journal of Environmental & Health Science.
[5]. 周延清, 张改娜与杨清香, 生物遗传标记与应用, 2008, 化学工业出版社.
[6]. 余 敏 新生儿换血疗法的治疗及护理 2011 年第 9 卷第 9 期 《中华现代临床医学杂志》
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