Identification of drought tolerant Chickpea genotypes through multi trait stability index

Drought is a major and constantly increasing abiotic stress factor, thus limiting chickpea production. Like other crops, Kabuli Chickpea genotypes are screened for drought stress through Multi-environment trials (METs). Although, METs analysis is generally executed taking into account only one trait, which provides less significant reliability for the recommendation of genotypes as compared to multi trait-based analysis. Multi trait-based analysis could be used to recommend genotypes across diverse environments. Hence, current research was conducted for selection of superior genotypes through multi-trait stability index (MTSI) by using mixed and fixed effect models under six diverse environments. The genotypic stability was computed for all traits individually using the weighted average of absolute scores from the singular value decomposition of the matrix of best linear unbiased predictions for the genotype vs environment interaction (GEI) effects produced by a linear mixed-effect model index. A superiority index, WAASBY was measured to reflect the MPS (Mean performance and stability). The selection differential for the WAASBY index was 11.2%, 18.49% and 23.30% for grain yield (GY), primary branches per plant (PBP) and Stomatal Conductance (STOMA) respectively. Positive selection differential (0.80% ≤ selection differential ≤ 13.00%) were examined for traits averaged desired to be increased and negative (-0.57% ≤ selection differential ≤ -0.23%) for those traits desired to be reduced. The MTSI may be valuable to the plant breeders for the selection of genotypes based on many characters as being strong and simple selection process. Analysis of MTSI for multiple environments revealed that, the genotypes G20, G86, G31, G28, G116, G12, G105, G45, G50, G10, G30, G117, G81, G48, G85, G17, G32, G4, and G37 were the most stable and high yielding out of 120 chickpea genotypes, probably due to high MPS of selected traits under various environments. It is concluded that identified traits can be utilized as genitors in hybridization programs for the development of drought tolerant Kabuli Chickpea breeding material.     

PCT和NT-proBNP在脓毒血症患者生存状况预测中的应用与评价

目的:探究患者降钙素原(PCT)和N端前体脑利钠肽(NT-proBNP)水平在评价脓毒血症患者生存状况的预测价值。方法:本研究于2013年6月~2015年6月期间,选择在我院治疗的脓毒血症患者81例为研究对象,根据患者纳入研究后1个月的生存状况,将所有研究对象分为存活组(55例)和死亡组(26例)。分别测定患者PCT和NT-proBNP水平,并记录患者急性生理与慢性健康状况(APACHEII)评分,通过ROC曲线探究PCT、NT-proBNP对患者生存预后评估的价值。结果:死亡组患者PCT水平及APACHEII评分均显著高于存活组,NT-pro BNP水平显著低于存活组患者(P0.05);PCT与APACHEII评分存在正相关关系(r=0.311;P0.05);NT-pro BNP水平与APACHEII评分存在负相关关系(r=-0.289;P0.05);ROC曲线显示PCT、NT-proBNP水平预测患者生存状况的敏感性(86.23%、82.01%)及特异性(80.89%、87.39%)均较高。结论:脓毒血症患者血清PCT与NT-proBNP水平在预测患者病情严重程度及生存状况方面具有重要价值。     

Applied simulations and integrated modelling for the understanding of toxic and harmful algal blooms (ASIMUTH): Integrated HAB forecast systems for Europe's Atlantic Arc

Reasons for the emergent interest in HABs are abundant, including concerns associated with human health, adverse effects on biological resources, economic losses attributed to recreation, tourism and seafood related industries, and the cost of maintaining public advisory services and monitoring programs for shellfish toxins and water quality. The impact of HABs can potentially be mitigated by early warning of their development. In this regard the project ASIMUTH (Applied Simulations and Integrated Modelling for the Understanding of Toxic and Harmful algal blooms) was borne in order to develop short term HAB alert systems for Atlantic Europe. This was achieved using information on the most current marine conditions (weather, water characteristics, toxicity, harmful algal presence etc.) combined with high resolution local numerical predictions. This integrated, multidisciplinary, trans-boundary approach to the study of HABs developed during ASIMUTH led to a better understanding of the physical, chemical and ecological factors controlling these blooms, as well as their impact on human activities. The outcome was an appropriate alert system for an effective management of areas that are usually associated with HAB events and where these episodes may have a more significant negative impact on human activities. Specifically for the aquaculture industry, the information provided enabled farmers to adapt their working practices in time to prevent mortalities in finfish farms and/or manage their shellfish harvest more effectively. This paper summarises the modelling and alert developments generated by the ASIMUTH project.     

The value of harmful algal bloom predictions to the nearshore commercial shellfish fishery in the Gulf of Maine

In this study, we develop a framework for measuring the value of harmful algal bloom (HAB) predictions. The framework captures the effects of both private and public responses to HABs. Using data from the New England nearshore commercial shellfish fishery and impact estimates for a large-scale HAB event in 2005, we illustrate how the potential value of HAB forecasts may be estimated. The results of our study suggest that the long-term value of a HAB prediction and tracking system for the Gulf of Maine is sensitive to the frequency of HAB events, the accuracy of predictions, the choice of HAB impact measures, and the effectiveness of public and private responses.     

猕猴(Macaca mulatta)生命表研究

本文研究了猕猴雌性生命表。南湾猕猴各年度全群平均存活率高于93％,特定年龄组逐年存活率直到13岁仍达0.81以上,种群内禀增长率为0.1327,年周增长率为1.1419。用莱斯利矩阵对种群发展作预测,将预测值与1985-1987年的实测值作卡方适应性检验,两者无显著性差异。进一步分析发现该方法预测南湾猕猴种群动态,在7年内是有效的。     

A 30-day-ahead forecast model for grass pollen in north London, United Kingdom

A 30-day-ahead forecast method has been developed for grass pollen in north London. The total period of the grass pollen season is covered by eight multiple regression models, each covering a 10-day period running consecutively from 21 May to 8 August. This means that three models were used for each 30-day forecast. The forecast models were produced using grass pollen and environmental data from 1961 to 1999 and tested on data from 2000 and 2002. Model accuracy was judged in two ways: the number of times the forecast model was able to successfully predict the severity (relative to the 1961–1999 dataset as a whole) of grass pollen counts in each of the eight forecast periods on a scale of 1 to 4; the number of times the forecast model was able to predict whether grass pollen counts were higher or lower than the mean. The models achieved 62.5% accuracy in both assessment years when predicting the relative severity of grass pollen counts on a scale of 1 to 4, which equates to six of the eight 10-day periods being forecast correctly. The models attained 87.5% and 100% accuracy in 2000 and 2002, respectively, when predicting whether grass pollen counts would be higher or lower than the mean. Attempting to predict pollen counts during distinct 10-day periods throughout the grass pollen season is a novel approach. The models also employed original methodology in the use of winter averages of the North Atlantic Oscillation to forecast 10-day means of allergenic pollen counts.     

Time linkages between pollination onsets of different taxa over an 11-year period in Perugia,Central Italy

Times of pollination of different taxa in the atmosphere of Perugia (Central Italy) over an 11-year period (1982–1992) were recorded and analysed by means of a 7-day volumetric Hirst-type pollen trap. For some taxa, the pollination period varied from year to year from a chronological and/or quantitative point of view. Several taxa showed a linkage in their starting dates of pollination. Knowledge of this kind of linkage allows us to build a forecasting model.     

Artificial neural network approaches to one-step weekly prediction of Dinophysis acuminata blooms in Huelva (Western Andalucía, Spain)

On the Atlantic coasts of Andalucía, chronic spring–summer (March–June) diarrhetic shellfish poisoning (DSP) outbreaks are associated with blooms of Dinophysis acuminata, Claparède and Lachmann. Artificial neural networks (ANNs) have been successfully used to model primary production and have recently been tested for the prediction of harmful algae blooms. In this study, we evaluated the performance of feed forward ANN models trained to predict D. acuminata blooms. ANN models were trained and tested using weekly data (5 previous weeks) of D. acuminata cell counts from eight stations of the Andalucía HAB monitoring programme in the coasts of Huelva between 1998 and 2004. Principal component analysis (PCA) were previously carried out to find out possible similarities within time series from each zone with the aim of reducing the number of areas to model. Our results show that ANN models with a low number of input variables are able to reproduce trends in D. acuminata population dynamics.     

In‐depth comparative analysis of Illumina® MiSeq run metrics: Development of a wet‐lab quality assessment tool

Whole genome sequencing of bacterial isolates has become a daily task in many laboratories, generating incredible amounts of data. However, data acquisition is not an end in itself; the goal is to acquire high‐quality data useful for understanding genetic relationships. Having a method that could rapidly determine which of the many available run metrics are the most important indicators of overall run quality and having a way to monitor these during a given sequencing run would be extremely helpful to this effect. Therefore, we compared various run metrics across 486 MiSeq runs, from five different machines. By performing a statistical analysis using principal components analysis and a K‐means clustering algorithm of the metrics, we were able to validate metric comparisons among instruments, allowing for the development of a predictive algorithm, which permits one to observe whether a given MiSeq run has performed adequately. This algorithm is available in an Excel spreadsheet: that is, MiSeq Instrument & Run (In‐Run) Forecast. Our tool can help verify that the quantity/quality of the generated sequencing data consistently meets or exceeds recommended manufacturer expectations. Patterns of deviation from those expectations can be used to assess potential run problems and plan preventative maintenance, which can save valuable time and funding resources.     

Relationships between airborne fungal spore concentration of <Emphasis Type="Italic">Cladosporium</Emphasis> and the summer climate at two sites in Britain

Cladosporium conidia have been shown to be important aeroallergens in many regions throughout the world, but annual spore concentrations vary considerably between years. Understanding these annual fluctuations may be of value in the clinical management of allergies. This study investigates the number of days in summer when spore concentration exceeds the allergenic threshold in relation to regional temperature and precipitation at two sites in England and Wales over 27 years. Results indicate that number of days in summer when the Cladosporium spores are above the allergenic concentration is positively correlated with regional temperature and negatively correlated with precipitation for both sites over the study period. Further analysis used a winter North Atlantic Oscillation index to explore the potential for long-range forecasting of the aeroallergen. For both spore measurement sites, a positive correlation exists between the winter North Atlantic Oscillation index and the number of days in summer above the allergenic threshold for Cladosporium spore concentration.