Where Is My Food? Brazilian Flower Fly Steals Prey from Carnivorous Sundews in a Newly Discovered Plant-Animal Interaction

A new interaction between insects and carnivorous plants is reported from Brazil. Larvae of the predatory flower fly Toxomerus basalis (Diptera: Syrphidae: Syrphinae) have been found scavenging on the sticky leaves of several carnivorous sundew species (Drosera, Droseraceae) in Minas Gerais and São Paulo states, SE Brazil. This syrphid apparently spends its whole larval stage feeding on prey trapped by Drosera leaves. The nature of this plant-animal relationship is discussed, as well as the Drosera species involved, and locations where T. basalis was observed. 180 years after the discovery of this flower fly species, its biology now has been revealed. This is (1) the first record of kleptoparasitism in the Syrphidae, (2) a new larval feeding mode for this family, and (3) the first report of a dipteran that shows a kleptoparasitic relationship with a carnivorous plant with adhesive flypaper traps. The first descriptions of the third instar larva and puparium of T. basalis based on Scanning Electron Microscope analysis are provided.

The influence of gene environment interaction on the risk of cognitive impairment: Reducing sexual risk behaviors and alcohol use in HIV-infected adults

Memory deficits and executive dysfunction are highly prevalent among HIV-infected adults. These conditions can affect their quality of life, antiretroviral adherence, and HIV risk behaviors. Several factors have been suggested including the role of genetics in relation to HIV disease progression. This dissertation aimed to determine whether genetic differences in HIV-infected individuals were correlated with impaired memory, cognitive flexibility and executive function and whether cognitive decline moderated alcohol use and sexual transmission risk behaviors among HIV-infected alcohol abusers participating in an NIH-funded clinical trial comparing the efficacy of the adapted Holistic Health Recovery Program (HHRP-A) intervention to a Health Promotion Control (HPC) condition in reducing risk behaviors. ^ A total of 267 individuals were genotyped for polymorphisms in the dopamine and serotonin gene systems. Results yielded significant associations for TPH2, GALM, DRD2 and DRD4 genetic variants with impaired executive function, cognitive flexibility and memory. SNPs TPH2 rs4570625 and DRD2 rs6277 showed a risk association with executive function (odds ratio = 2.5, p = .02; 3.6, p = .001). GALM rs6741892 was associated with impaired memory (odds ratio = 1.9, p = .006). At the six-month follow-up, HHRP-A participants were less likely to report trading sex for food, drugs and money (20.0%) and unprotected insertive or receptive oral (11.6%) or vaginal and/or anal sex (3.2%) than HPC participants (49.4%, p^

The Influence of Gene Environment Interaction on the Risk of Cognitive Impairment: Reducing Sexual Risk Behaviors and Alcohol Use in HIV-infected Adults

Memory deficits and executive dysfunction are highly prevalent among HIV-infected adults. These conditions can affect their quality of life, antiretroviral adherence, and HIV risk behaviors. Several factors have been suggested including the role of genetics in relation to HIV disease progression. This dissertation aimed to determine whether genetic differences in HIV-infected individuals were correlated with impaired memory, cognitive flexibility and executive function and whether cognitive decline moderated alcohol use and sexual transmission risk behaviors among HIV-infected alcohol abusers participating in an NIH-funded clinical trial comparing the efficacy of the adapted Holistic Health Recovery Program (HHRP-A) intervention to a Health Promotion Control (HPC) condition in reducing risk behaviors. A total of 267 individuals were genotyped for polymorphisms in the dopamine and serotonin gene systems. Results yielded significant associations for TPH2, GALM, DRD2 and DRD4 genetic variants with impaired executive function, cognitive flexibility and memory. SNPs TPH2 rs4570625 and DRD2 rs6277 showed a risk association with executive function (odds ratio = 2.5, p = .02; 3.6, p = .001). GALM rs6741892 was associated with impaired memory (odds ratio = 1.9, p = .006). At the six-month follow-up, HHRP-A participants were less likely to report trading sex for food, drugs and money (20.0%) and unprotected insertive or receptive oral (11.6%) or vaginal and/or anal sex (3.2%) than HPC participants (49.4%, p

内蒙古锡林河流域空间生态学研究

本文从物种和景观两个组织水平上研究了气候、土壤、地形等自然环境因子和人类活动因子对生物空间分布格局的影响。基于锡林河流域地理信息系统各环境因子的专题数据，利用空间异质性分析方法研究了锡林河流域环境因子的空间分布格局;基于锡林河流域野外调查数据，运用空间异质性分析方法研究了重要物种的空间分布格局，并采用典范对应分析(Canomc Correspondence Analvsis，CCA)方法分析了物种分布与环境因子的关系：基于锡林河流域地理信息系统各环境因子的专题数据，研究了锡林河流域植被斑块的空间格局特征及其与环境因子的关系，并采用典范对应分析方法分析了植被类型组成与环境因子的关系：基于内蒙古草原生态系统定位研究站放牧样地的样方调查数据．采用空间异质性分析方法，研究了放牧压力对物种空间分布格局的影响：基于多年的卫星遥感数据，采用建模和对比等方法，研究了定居放牧方式下植被状况空间变化规律及植被状况时空变化与人类活动、社会经济发展的关系。通过上述分析，得到的主要结论如下： 1、锡林河流域各个环境因子都具有自己的空间特征尺度，共同形成多尺度等级体系，按特征尺度的大小可以分为如下3个组： ·小尺度组(15km左右)：有机暖、全N的较小的特征尺度 ·中尺度组(30～50km)：T1，碳酸钙含量．PER、全N和海拔高度的较小的特征尺度 ·大尺度组(100km左右)：ANNR，PER、全N和海拔高度的较大的特征尺度多尺度等级的生态学意义是它反映生态变量异质斑块的镶嵌和包含特征，环境因子多尺度等级体系反映共性，具有普遍性：反映生态关系，具有生态学意义。 2、对物种空间异质性的Mantel检验和半方差分析得到了一致的结果产即羊草、糙隐子草和星毛萎菱菜在锡林河流域的空间分布呈现随机特征，而大针茅和冷蒿则表现为十分显著的格局特征。按分布格局的显著程度从大到小排列为冷蒿>大针茅>星毛萎菱菜>糙隐子草>羊草。理论半方差图显示大针茅和冷蒿的空间自相关域分别为30．447公里和30公里。物种空间分布格局是受自然条件、人类活动以及它们自身的生理生态特征综合决定的，物种自身的生理生态特征决定了它们对外界环境变化的适应性反应机制，而自然与人类活动这两种因素在空间的交错配置决定了物种适应性反应的方向和程度，从而综合导致物种空间分布格局的形成。 3、对锡林河流域物种分布与环境因子关系的CCA分析和交叉半方差方法分析显示：1)气候因子(11个指标)、土壤性状因子(3个指标)和地形因子(3个指标)对物种分布的贡献率分别为11．2％、9．5％和11％，三者总和为31．7％。2)各个环境因子对物种分布空间作用方向具有一致性，物种分布与环境因子几乎都在135。和157．5。两个方向上具有相对明显的相关性，从锡林河流域来看，这两个方向反映了气候、土壤以及地形从东南往西北的变化梯度方向。 4、对锡林河流域14个植被景观指数进行的PCA分析表明，锡林河流域植被斑块空间分布的物理特征主要表现在斑块的数目和大小方面，其次是在斑块的多样性方面，并可将它们分为4个组，分别反映锡林河流域植被斑块的不同特征： ·第一组：NP、PRD、LPI、MPS、PSSD和TE，主要反映景观斑块在数量和大小方面的特征; ·第二组：SHDI、SIDI、SHEI和SIEI，主要反映景观斑块的多样性特征; ·第三组：PSCV和[J]．主要反映景观斑块之间的相互邻接程度; ·第四组：MSI和AWMSI，主要反映景观斑块的形状特征。 MPS和PSSD两个指数与环境因子无论是在相关系数的性质还是显著程度上都保持了很好的一致性，它们与纬度(LAT)及可能蒸散率(PER)呈极显著的正相关关系，而与经度(LNG)、海拔高度(ALT)、年平均降水量(ANNR)及土壤有机质含量(0RG)呈极显著的负相关关系：平均形状指数(MSI)只与LAT呈显著的正相关关系;多样性指数和扩散毗连指数与任何一个环境因子都没有表现出显著的相关性。 5、锡林河流域植被分布与环境因子的关系CCA排序方法分析表明，气候因子(11个指标)、土壤性状因子(3个指标)和地形因子(3个指标)对植被分布的贡献率分别为19.8%、11.1%和14.5%，三者总和为45.4％。环境因子在植被和物种两个水平上的贡献率表现了相似的特点，自然环境因子不能完全解释植被的空间分布，人类活动的影响应该受到重视。 6、放牧压力对物种空间分布格局的研究表明： ·牧压对温带典型草原物种的空间分布格局有明显的影响。随着牧云的增大，属于原生群落物种的羊草与大针茅空间分布的随机性减小，空间自相关尺度逐渐增大;而对于退化过程中的入侵物种冷蒿和星毛萎菱菜，其空间分布的随机性逐渐增大．空间自相关尺度也呈增大趋势。在牧压胁迫超过一定水平时，冷蒿空间分布的自相关尺度开始下降，而星毛萎菱菜的空间分布格局则表现出强烈的随机性。 ·物种空间格局的变化是反映群落演替过程较为稳定的特征，适用于不同放牧条件下 群落之间的比较。 7、利用遥感数据对人类活动对植被影响的研究表明： ·定居放牧方式下，NDVI随定居点距离的变化格局经历了3个阶段。第一阶段，草场处于原生阶段，NDVI不随距离变化;第二阶段，定居点附近开始局部退化，NDVI随距离增加而增大：第三阶段，退化区域扩大，NDVI不随距离变化。 ·在草场局部退化阶段，NDVI随距离的变化呈对数函数规律，定居点的放牧区具有放牧半径、原生NDVI值、NDVI变化率等特征。根据这些特征、NDVI对数规律以及NDVI与地上生物量的关系可以推测定居点的总载畜量。 ·锡林河流域从87年到85年NDVI值降低最大的区域为流域的中部和南部，这与这一区域人类活动强度以及社会经济发展具有密切关系。

Effect of floating macrophyte cover on the water quality in fishpond

A study was conducted during 23 days in order to evaluate the impact of floating aquatic macrophyte on the water quality of a fishpond. Water samples were collected in four points, three inside the pond and one in water inlet. Drastic reduction of dissolved oxygen was observed in the pond, down to 0.87 mg/L. No significant differences (P > 0.05) were observed for total CO 2, nitrite and ammonia with respect to inlet water (P1) and inside the pond (P2, P3 e P4). Chlorophyll a displayed an inverse relationship with phosphorus. Among nitrogen compounds, ammonia presented the highest concentrations except in water inlet where nitrate was higher, 513.33 μg/l, as well as the highest conductivity values. The pH was slightly acid. The results obtained showed that the macrophyte cover promoted an adverse effect in the medium. Under control, aquatic plants might impact positively due to its capacity to reduce total phosphorus and nitrate in the water column as observed in this study.

Studying the genetic basis of drought tolerance in sorghum by managed stress trials and adjustments for phenological and plant height differences

Managed environments in the form of well watered and water stressed trials were performed to study the genetic basis of grain yield and stay green in sorghum with the objective of validating previously detected QTL. As variations in phenology and plant height may influence QTL detection for the target traits, QTL for flowering time and plant height were introduced as cofactors in QTL analyses for yield and stay green. All but one of the flowering time QTL were detected near yield and stay green QTL. Similar co-localization was observed for two plant height QTL. QTL analysis for yield, using flowering time/plant height cofactors, led to yield QTL on chromosomes 2, 3, 6, 8 and 10. For stay green, QTL on chromosomes 3, 4, 8 and 10 were not related to differences in flowering time/plant height. The physical positions for markers in QTL regions projected on the sorghum genome suggest that the previously detected plant height QTL, Sb-HT9-1, and Dw2, in addition to the maturity gene, Ma5, had a major confounding impact on the expression of yield and stay green QTL. Co-localization between an apparently novel stay green QTL and a yield QTL on chromosome 3 suggests there is potential for indirect selection based on stay green to improve drought tolerance in sorghum. Our QTL study was carried out with a moderately sized population and spanned a limited geographic range, but still the results strongly emphasize the necessity of corrections for phenology in QTL mapping for drought tolerance traits in sorghum.

Contribution and perspectives of quantitative genetics to plant breeding in Brazil

The purpose of this article is to show how quantitative genetics has contributed to the huge genetic progress obtained in plant breeding in Brazil in the last forty years. The information obtained through quantitative genetics has given Brazilian breeders the possibility of responding to innumerable questions in their work in a much more informative way, such as the use or not of hybrid cultivars, which segregating population to use, which breeding method to employ, alternatives for improving the efficiency of selection programs, and how to handle the data of progeny and/or cultivars evaluations to identify the most stable ones and thus improve recommendations.

Analysis of 16S rRNA and mxaF genes revealing insights into Methylobacterium niche-specific plant association

The genus Methylobacterium comprises pink-pigmented facultative methylotrophic (PPFM) bacteria, known to be an important plant-associated bacterial group. Species of this group, described as plant-nodulating, have the dual capacity of producing cytokinin and enzymes, such as pectinase and cellulase, involved in systemic resistance induction and nitrogen fixation under specific plant environmental conditions. The aim hereby was to evaluate the phylogenetic distribution of Methylobacterium spp. isolates from different host plants. Thus, a comparative analysis between sequences from structural (16S rRNA) and functional mxaF (which codifies for a subunit of the enzyme methanol dehydrogenase) ubiquitous genes, was undertaken. Notably, some Methylobacterium spp. isolates are generalists through colonizing more than one host plant, whereas others are exclusively found in certain specific plant-species. Congruency between phylogeny and specific host inhabitance was higher in the mxaF gene than in the 16S rRNA, a possible indication of function-based selection in this niche. Therefore, in a first stage, plant colonization by Methylobacterium spp. could represent generalist behavior, possibly related to microbial competition and adaptation to a plant environment. Otherwise, niche-specific colonization is apparently impelled by the host plant.

Analysis of 16S rRNA and mxaF genes reveling insights into Methylobacterium niche-specific plant association

The genus Methylobacterium comprises pink-pigmented facultative methylotrophic (PPFM) bacteria, known to be an important plant-associated bacterial group. Species of this group, described as plant-nodulating, have the dual capacity of producing cytokinin and enzymes, such as pectinase and cellulase, involved in systemic resistance induction and nitrogen fixation under specific plant environmental conditions. The aim hereby was to evaluate the phylogenetic distribution of Methylobacterium spp. isolates from different host plants. Thus, a comparative analysis between sequences from structural (16S rRNA) and functional mxaF (which codifies for a subunit of the enzyme methanol dehydrogenase) ubiquitous genes, was undertaken. Notably, some Methylobacterium spp. isolates are generalists through colonizing more than one host plant, whereas others are exclusively found in certain specific plant-species. Congruency between phylogeny and specific host inhabitance was higher in the mxaF gene than in the 16S rRNA, a possible indication of function-based selection in this niche. Therefore, in a first stage, plant colonization by Methylobacterium spp. could represent generalist behavior, possibly related to microbial competition and adaptation to a plant environment. Otherwise, niche-specific colonization is apparently impelled by the host plant.

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

Effect of environment and genotype on bread-making quality of spring-sown spring wheat cultivars in China

Improvement of end-use quality in bread wheat depends on a thorough understanding of current wheat quality and the influences of genotype (G), environment (E), and genotype by environment interaction (G x E) on quality traits. Thirty-nine spring-sown spring wheat (SSSW) cultivars and advanced lines from China were grown in four agro-ecological zones comprising seven locations during the 1998 and 1999 cropping seasons. Data on 12 major bread-making quality traits were used to investigate the effect of G, E, and G x E on these traits. Wide range variability for protein quantity and quality, starch quality parameters and milling quality in Chinese SSSW was observed. Genotype and environment were found to significantly influence all quality parameters as major effects. Kernel hardness, flour yield, Zeleny sedimentation value and mixograph properties were mainly influenced by the genetic variance components, while thousand kernel weight, test weight, and falling number were mostly influenced by the environmental variance components. Genotype, environment, and their interaction had important effects on test weight, mixing development time and RVA parameters. Cultivars originating from Zone VI (northeast) generally expressed high kernel hardness, good starch quality, but poor milling and medium to weak mixograph performance; those from Zone VII (north) medium to good gluten and starch quality, but low milling quality; those from Zone VIII (central northwest) medium milling and starch quality, and medium to strong mixograph performance; those from Zone IX (western/southwestern Qinghai-Tibetan Plateau) medium milling quality, but poor gluten strength and starch parameters; and those from Zone X (northwest) high milling quality, strong mixograph properties, but low protein content. Samples from Harbin are characterized by good gluten and starch quality, but medium to poor milling quality; those from Hongxinglong by strong mixograph properties, medium to high milling quality, but medium to poor starch quality and medium to low protein content; those from Hohhot by good gluten but poor milling quality; those from Linhe by weak gluten quality, medium to poor milling quality; those from Lanzhou by poor bread-making and starch quality; those from Yongning by acceptable bread-making and starch quality and good milling quality; and those from Urumqi by good milling quality, medium gluten quality and good starch pasting parameters. Our findings suggest that Chinese SSSW quality could be greatly enhanced through genetic improvement for targeted well-characterized production environments.

Mixed model formulations for multi-environment trials

When studying genotype X environment interaction in multi-environment trials, plant breeders and geneticists often consider one of the effects, environments or genotypes, to be fixed and the other to be random. However, there are two main formulations for variance component estimation for the mixed model situation, referred to as the unconstrained-parameters (UP) and constrained-parameters (CP) formulations. These formulations give different estimates of genetic correlation and heritability as well as different tests of significance for the random effects factor. The definition of main effects and interactions and the consequences of such definitions should be clearly understood, and the selected formulation should be consistent for both fixed and random effects. A discussion of the practical outcomes of using the two formulations in the analysis of balanced data from multi-environment trials is presented. It is recommended that the CP formulation be used because of the meaning of its parameters and the corresponding variance components. When managed (fixed) environments are considered, users will have more confidence in prediction for them but will not be overconfident in prediction in the target (random) environments. Genetic gain (predicted response to selection in the target environments from the managed environments) is independent of formulation.

Analysis of line x environment interactions for yield in navy beans. 2. Pattern analysis of lines and environment within years

Seven years of multi-environment yield trials of navy bean (Phaseolus vulgaris L.) grown in Queensland were examined. As is common with plant breeding evaluation trials, test entries and locations varied between years. Grain yield data were analysed for each year using cluster and ordination analyses (pattern analyses). These methods facilitate descriptions of genotype performance across environments and the discrimination among genotypes provided by the environments. The observed trends for genotypic yield performance across environments were partly consistent with agronomic and disease reactions at specific environments and also partly explainable by breeding and selection history. In some cases, similarities in discrimination among environments were related to geographic proximity, in others management practices, and in others similarities occurred between geographically widely separated environments which differed in management practices. One location was identified as having atypical line discrimination. The analysis indicated that the number of test locations was below requirements for adequate representation of line x environment interaction. The pattern analyses methods used were an effective aid in describing the patterns in data for each year and illustrated the variations in adaptive patterns from year to year. The study has implications for assessing the number and location of test sites for plant breeding multi-environment trials, and for the understanding of genetic traits contributing to line x environment interactions.

Estimation in a multiplicative mixed model involving a genetic relationship matrix.

Genetic models partitioning additive and non-additive genetic effects for populations tested in replicated multi-environment trials (METs) in a plant breeding program have recently been presented in the literature. For these data, the variance model involves the direct product of a large numerator relationship matrix A, and a complex structure for the genotype by environment interaction effects, generally of a factor analytic (FA) form. With MET data, we expect a high correlation in genotype rankings between environments, leading to non-positive definite covariance matrices. Estimation methods for reduced rank models have been derived for the FA formulation with independent genotypes, and we employ these estimation methods for the more complex case involving the numerator relationship matrix. We examine the performance of differing genetic models for MET data with an embedded pedigree structure, and consider the magnitude of the non-additive variance. The capacity of existing software packages to fit these complex models is largely due to the use of the sparse matrix methodology and the average information algorithm. Here, we present an extension to the standard formulation necessary for estimation with a factor analytic structure across multiple environments.