Soil phosphorus dynamic, balance and critical P values in longterm fertilization experiment in Taihu Lake region, China

Phosphorus（P） is an important macronutrient for plant but can also cause potential environmental risk. In this paper, we studied the long-term fertilizer experiment（started 1980） to assess the soil P dynamic, balance, critical P value and the crop yield response in Taihu Lake region, China. To avoid the effect of nitrogen（N） and potassium（K）, only the following treatments were chosen for subsequent discussion, including： C0（control treatment without any fertilizer or organic manure）, CNK treatment（mineral N and K only）, CNPK（balanced fertilization with mineral N, P and K）, MNK（integrated organic manure and mineral N and K）, and MNPK（organic manure plus balanced fertilization）. The results revealed that the response of wheat yield was more sensitive than rice, and no significant differences of crop yield had been detected among MNK, CNPK and MNPK until 2013. Dynamic and balance of soil total P（TP） and Olsen-P showed soil TP pool was enlarged significantly over consistent fertilization. However, the diminishing marginal utility of soil Olsen-P was also found, indicating that high-level P application in the present condition could not increase soil Olsen-P contents anymore. Linear-linear and Mitscherlich models were used to estimate the critical value of Olsen-P for crops. The average critical P value for rice and wheat was 3.40 and 4.08 mg kg^（–1）, respectively. The smaller critical P value than in uplands indicated a stronger ability of P supply for crops in this paddy soil. We concluded that no more mineral P should be applied in rice-wheat system in Taihu Lake region if soil Olsen-P is higher than the critical P value. The agricultural technique and management referring to activate the plant-available P pool are also considerable, such as integrated use of low-P organic manure with mineral N and K.

Meta-analysis of bivariate P values

AIM: To propose a new meta-analysis method for bivariate P value which account for the paired structure.METHODS: Studies that look to test two different features from the same sample gives rise to bivariate P value.A relevant example of this is testing for periodicity as well expression from time-course gene expression studies.Kocak et al(2010) uses George and Mudholkar'(1983) "Difference of Two Logit-Sums" method to pool bivariate P value across independent experiments,assuming independence within a pair.As bivariate P value need not to be independent within a given study,we propose a new meta-analysis approach for pooling bivariate P value across independent experiments,which accounts for potential correlation between paired P-values.We compare the "Difference of Two Logit Sums"method with our novel approach in terms of their sensitivity and specificity through extensive simulations by generating P value samples from most commonly used tests namely,Z test,t test,chi-square test,and F test,with varying sample sizes and correlation structure.RESULTS: The simulations results showed that our new meta-analysis approach for correlated and uncorrelated bivariate P value has much more desirable sensitivity and specificity features compared to the existing method,which treats each member of the paired P value as independent.We also compare these meta-analysis approaches on bivariate P value from periodicity and expression tests of 4936 S.Pombe genes from 10 independent time-course experiments and we showed that our new approach ranks the periodic,conserved,and cycling genes significantly higher,and detects many more periodic,"conserved" and "cycling" genes among the top 100 genes,compared to the ‘Difference of Two Logit-Sums' method.Finally,we used our metaanalytic approach to compare the relative evidence in the association of pre-term birth with preschool wheezing versus pre-school asthma.CONCLUSION: The new meta-analysis method has much better sensitivity and specific characteristics compared to the "Difference of Two-Logit Sums" method and it is not computationally more expensive.

Why a P value<0.05 does not necessarily mean statistical significance:controversy over overall survival results of the ORIENT-11 trial

On February 10,2022,the U.S.Food and Drug Administration(FDA)’s Oncologic Drugs Advisory Committee voted 14-1 against using data from the ORIENT-11 trial to support a biologics license application for sintilimab injection plus pemetrexed and platinum-based chemotherapy as first-line treatment for patients with nonsquamous non-small cell lung cancer(NSCLC)1.One major reason was that the FDA claimed overall survival(OS)was not statistically tested in ORIENT-11,while previous regular approvals were granted on the basis of statistically significant improvements in OS2.This may be a surprise to some physicians,as the ORIENT-11 trial previously reported improved OS with a hazard ratio(HR)of 0.60(95%confidence interval[CI]:0.45–0.79)and a P value of 0.00033,which is far below the commonly accepted threshold of 0.05 for declaring statistical significance.Some may argue that the OS results of ORIENT-11 were not considered statistically significant by the FDA because OS was only a secondary endpoint.However,in the KEYNOTE-024 trial4,which compared pembrolizumab with chemotherapy in patients with previously untreated advanced NSCLC with PD-L1 expression on at least 50%of tumor cells,OS was also a secondary endpoint.Nevertheless,the OS benefit of the KEYNOTE-024 trial was acknowledged by the FDA and included in the drug label5.The fundamental reason why OS results of ORIENT-11 were not considered statistically significant by the FDA is that the OS endpoint was not included in the multiplicity control strategy.

Effect size in papers published by the Journal of Forestry Research:A missing treasure?

P values based on standard hypothesis testing are commonly reported in articles published by the Journal of Forestry Research(JFR).However,effect sizes are barely used and reported,even if they are of direct relevance to the primary questions of many of the published studies.The incorporation of effect sizes in studies published by JFR should be encouraged and promoted.Inclusion of effect sizes as a requirement in the journal guidelines will facilitate a major change in the way data are tested and interpreted,with the ultimate goal to exempt researchers from the custom of drawing conclusions merely based upon a dichotomous statistical result(P value).Such a policy can also lead to more informed decisions of whether identified effects are of practical relevance to the forestry.

The Hjek-Rnyi Inequlity For Banach Space Valued Random Variable Sequences and Its Application

In this paper we prove the following Hajek Renyi inequality:Let 0<p≤1 ,then for any Banach space B , any L p integrable B valued random variable sequence {D n,n≥1} ,any real number sequence {b n,n≥1} with 0<b n↑∞ ,any integer n≥1 ,there exits a constant C=C p>0 (only depending on p ) such thatP( sup j≥nji=1D ib j≥ε)≤Cε -p (∞j=n+1E‖D j‖ pb p j+nj=1E‖D j‖ pb p n) In the other direction,we prove some strong laws of large numbers and the integrability of the maximal functions for B valued random variable sequences by using this inequality and the Hajeck Renyi inequality we have obtained recently.Some known results are extended and improved.

The design of aided software for osmotic stress responding genes mining in plant genome

A software and algorithm which based on random sequence model uses osmotic stress responding cis elements from existing information sources of biology was designed. It can infer the genic downstream function of Arabidopsis thaliana through analyzing its promoter region, and can offer effective aided analysis to mine osmotic stress responding genes in Arabidopsis thatiana genome. The practical application proves that this software can aid to analyze vast genic data and offer important data evidence.

利用蛋白质的二面角序列对蛋白质结构比对

蛋白质结构比对对理解蛋白质功能和进化关系非常重要。提出一种基于蛋白质残基的二面角的结构比对算法。通过动态时间规整算法比对二面角序列,来比较蛋白质的结构,拟合两蛋白结构距离的分布后,利用p-value来评价比对的好坏。主要结果有:利用动态时间规整算法计算得出的结构距离是一个很好的蛋白质结构相似性度量;结构距离服从参数为μ=94.7697,σ=41.5837,ξ=0.1925的广义的极值分布;和其他结构比对算法相比,该算法比CTSS的搜索结果要好。

Analysis of groundwater level trend in Jakham River Basin of Southern Rajasthan

Groundwater accounts for about half of the water use for irrigation in India.The fluctuation pattern of the groundwater level is examined by observing rainfall replenishment and monitoring wells.The southern part of Rajasthan has experienced abrupt changes in rainfall and has been highly dependent on groundwater over decades.This study presents the impact of over-dependence on groundwater usage for irrigation and other purposes,spatially and temporally.Hence,the objective of this study is to examine the groundwater level trend by using statistical analysis and geospatial technique.Rainfall factor was also studied in groundwater level fluctuation during 2009-2019.To analyze the influence of each well during recharge or withdrawal of groundwater,thiessien polygonswere generated from them.In the Jakham River basin,75 wells have been identified for water level trend study using the Mann-Kendall statistical test.The statistics of trend analysis show that 15%wells are experiencing water level decline in pre-monsoon,while very low percentage of wells have such trend during post-monsoon season.The average rate of water level decline is 0.245 m/a in pre-monsoon and 0.05 m/a in post-monsoon.The aquifer recharge potential is also decreasing by year.it is expected that such type of studies will help the policy makers to adopt advanced management practices to ensure sustainable groundwater resource management.

Determining the optimum range of soil Olsen P for high P use efficiency, crop yield, and soil fertility in three typical cropland soils

Soil Olsen P level has a major influence on crop yield,efficient P utilization,and soil fertility.In this study,the optimum Olsen P range was determined from long-term(1990–2012)field experiments in three typical soil types of China under single cropping of maize or double cropping of maize and wheat.The critical soil Olsen P value for crop yield was evaluated using three different models,and the relationships among P use efficiency(PUE),Olsen P,and total P were analyzed.The agronomic critical soil Olsen P values obtained from the three models for the neutral soil of Gongzhuling and the calcareous soil of Zhengzhou were similar;however,the values from the linear-linear and linear-plateau models for both maize and wheat were substantially lower than those from the Mitscherlich model for the acidic soil of Qiyang.The PUE response change rates(linear equation slopes)under different soil Olsen P levels were small,indicating slight or no changes in the PUE as the soil Olsen P increased in all three soils.A comparison of the Olsen P levels that achieved the maximal PUE with the agronomic critical values derived from the three models indicated that the linear-plateau model exhibited the best performance.The regression equation coefficients of Olsen P response to total P decreased as follows:Zhengzhou(73 mg g-1)>Qiyang(65 mg g-1)>Gongzhuling(55 mg g-1).The Olsen P level increased as the total P increased,which may result in a decrease in PUE.To achieve a relatively high crop yield,PUE,and soil fertility,the optimum Olsen P range should be 13–40,10–40,and 29–40 mg kg-1 at Gongzhuling,Zhengzhou,and Qiyang,respectively.

Bayesian functional enrichment analysis for the Reactome database

The first step in the analysis of high-throughput experiment results is often to identify genes orproteins with certain characteristics, such as genes being differentially expressed (DE). To gainmore insights into the underlying biology, functional enrichment analysis is then conductedto provide functional interpretation for the identified genes or proteins. The hypergeometricP value has been widely used to investigate whether genes from predefined functional terms,e.g., Reactome, are enriched in the DE genes. The hypergeometric P value has several limitations: (1) computed independently for each term, thus neglecting biological dependence;(2) subject to a size constraint that leads to the tendency of selecting less-specific terms. In this paper,a Bayesian approach is proposed to overcome these limitations by incorporating the interconnected dependence structure of biological functions in the Reactome database through a CARprior in a Bayesian hierarchical logistic model. The inference on functional enrichment is thenbased on posterior probabilities that are immune to the size constraint. This method can detectmoderate but consistent enrichment signals and identify sets of closely related and biologicallymeaningful functional terms rather than isolated terms. The performance of the Bayesian methodis demonstrated via a simulation study and a real data application.