Differences in excito-repellent behavior between susceptible and permethrin-resistant strains of Aedes aegypti upon exposure to insecticide-treated fabric.

BACKGROUND: An important component of the biological activity of pyrethroids, when used in disease vector control, is excito-repellency. In this study, behavioral differences between insecticide susceptible (Orlando) and pyrethroid resistant (Puerto Rican) strains of Aedes aegypti were explored in a round glass arena using fabrics treated with permethrin, etofenprox, deltamethrin, or DDT. Repellency was evaluated across several variables, including the time to first flight (TFF), number of landings (NOL), total flight time (TFT), and maximum surface contact (MSC), all by video analysis. RESULTS: Results from the Orlando strain indicated they were less likely than the Puerto Rican strain to tolerate tarsal contact with the treated fabrics. All four response variables indicated that the mosquito flight and landing behavior was most affected by pyrethroid resistance [knockdown resistance (kdr)] status. In other experiments, mosquitoes were surgically altered, with antennae ablated bilaterally, and these mosquitoes were more likely to stay on the treated surfaces for longer periods of time, irrespective of any chemical exposure. There were also differences in the responses to antennal ablation between the two strains of mosquitoes, indicating that resistance factors, probably kdr, influence the reactivity of mosquitoes to pyrethroid and DDT treatments, and that it was not completely negated by antennal ablation. CONCLUSIONS: These findings confirm the role of antennal olfactory components in the expression of excito-repellent behaviors, and also support the hypothesis that excito-repellency from pyrethroid/DDT exposure is probably due to a combination of sublethal neurotoxic excitation and interactions with the olfactory system. © 2023 Society of Chemical Industry.

Increasing genomic prediction accuracy for unphenotyped full-sib families by modeling additive and dominance effects with large datasets in white spruce.

Introduction: Genomic selection is becoming a standard technique in plant breeding and is now being introduced into forest tree breeding. Despite promising results to predict the genetic merit of superior material based on their additive breeding values, many studies and operational programs still neglect non-additive effects and their potential for enhancing genetic gains. Methods: Using two large comprehensive datasets totaling 4,066 trees from 146 full-sib families of white spruce (Picea glauca (Moench) Voss), we evaluated the effect of the inclusion of dominance on the precision of genetic parameter estimates and on the accuracy of conventional pedigree-based (ABLUP-AD) and genomic-based (GBLUP-AD) models. Results: While wood quality traits were mostly additively inherited, considerable non-additive effects and lower heritabilities were detected for growth traits. For growth, GBLUP-AD better partitioned the additive and dominance effects into roughly equal variances, while ABLUP-AD strongly overestimated dominance. The predictive abilities of breeding and total genetic value estimates were similar between ABLUP-AD and GBLUP-AD when predicting individuals from the same families as those included in the training dataset. However, GBLUP-AD outperformed ABLUP-AD when predicting for new unphenotyped families that were not represented in the training dataset, with, on average, 22% and 53% higher predictive ability of breeding and genetic values, respectively. Resampling simulations showed that GBLUP-AD required smaller sample sizes than ABLUP-AD to produce precise estimates of genetic variances and accurate predictions of genetic values. Still, regardless of the method used, large training datasets were needed to estimate additive and non-additive genetic variances precisely. Discussion: This study highlights the different quantitative genetic architectures between growth and wood traits. Furthermore, the usefulness of genomic additive-dominance models for predicting new families should allow practicing mating allocation to maximize the total genetic values for the propagation of elite material.

Optimizing Sparse Testing for Genomic Prediction of Plant Breeding Crops.

While sparse testing methods have been proposed by researchers to improve the efficiency of genomic selection (GS) in breeding programs, there are several factors that can hinder this. In this research, we evaluated four methods (M1-M4) for sparse testing allocation of lines to environments under multi-environmental trails for genomic prediction of unobserved lines. The sparse testing methods described in this study are applied in a two-stage analysis to build the genomic training and testing sets in a strategy that allows each location or environment to evaluate only a subset of all genotypes rather than all of them. To ensure a valid implementation, the sparse testing methods presented here require BLUEs (or BLUPs) of the lines to be computed at the first stage using an appropriate experimental design and statistical analyses in each location (or environment). The evaluation of the four cultivar allocation methods to environments of the second stage was done with four data sets (two large and two small) under a multi-trait and uni-trait framework. We found that the multi-trait model produced better genomic prediction (GP) accuracy than the uni-trait model and that methods M3 and M4 were slightly better than methods M1 and M2 for the allocation of lines to environments. Some of the most important findings, however, were that even under a scenario where we used a training-testing relation of 15-85%, the prediction accuracy of the four methods barely decreased. This indicates that genomic sparse testing methods for data sets under these scenarios can save considerable operational and financial resources with only a small loss in precision, which can be shown in our cost-benefit analysis.

Mr.Bean: a comprehensive statistical and visualization application for modeling agricultural field trials data.

Crop improvement efforts have exploited new methods for modeling spatial trends using the arrangement of the experimental units in the field. These methods have shown improvement in predicting the genetic potential of evaluated genotypes. However, the use of these tools may be limited by the exposure and accessibility to these products. In addition, these new methodologies often require plant scientists to be familiar with the programming environment used to implement them; constraints that limit data analysis efficiency for decision-making. These challenges have led to the development of Mr.Bean, an accessible and user-friendly tool with a comprehensive graphical visualization interface. The application integrates descriptive analysis, measures of dispersion and centralization, linear mixed model fitting, multi-environment trial analysis, factor analytic models, and genomic analysis. All these capabilities are designed to help plant breeders and scientist working with agricultural field trials make informed decisions more quickly. Mr.Bean is available for download at https://github.com/AparicioJohan/MrBeanApp.

Dynamic QTL-based ecophysiological models to predict phenotype from genotype and environment data.

BACKGROUND: Predicting the phenotype from the genotype is one of the major contemporary challenges in biology. This challenge is greater in plants because their development occurs mostly post-embryonically under diurnal and seasonal environmental fluctuations. Most current crop simulation models are physiology-based models capable of capturing environmental fluctuations but cannot adequately capture genotypic effects because they were not constructed within a genetics framework. RESULTS: We describe the construction of a mixed-effects dynamic model to predict time-to-flowering in the common bean (Phaseolus vulgaris L.). This prediction model applies the developmental approach used by traditional crop simulation models, uses direct observational data, and captures the Genotype, Environment, and Genotype-by-Environment effects to predict progress towards time-to-flowering in real time. Comparisons to a traditional crop simulation model and to a previously developed static model shows the advantages of the new dynamic model. CONCLUSIONS: The dynamic model can be applied to other species and to different plant processes. These types of models can, in modular form, gradually replace plant processes in existing crop models as has been implemented in BeanGro, a crop simulation model within the DSSAT Cropping Systems Model. Gene-based dynamic models can accelerate precision breeding of diverse crop species, particularly with the prospects of climate change. Finally, a gene-based simulation model can assist policy decision makers in matters pertaining to prediction of food supplies.

Revealing the Complex Relationship Among Hyperspectral Reflectance, Photosynthetic Pigments, and Growth in Norway Spruce Ecotypes.

Norway spruce has a wide natural distribution range, harboring substantial physiological and genetic variation. There are three altitudinal ecotypes described in this species. Each ecotype has been shaped by natural selection and retains morphological and physiological characteristics. Foliar spectral reflectance is readily used in evaluating the physiological status of crops and forest ecosystems. However, underlying genetics of foliar spectral reflectance and pigment content in forest trees has rarely been investigated. We assessed the reflectance in a clonal bank comprising three ecotypes in two dates covering different vegetation season conditions. Significant seasonal differences in spectral reflectance among Norway spruce ecotypes were manifested in a wide-ranging reflectance spectrum. We estimated significant heritable variation and uncovered phenotypic and genetic correlations among growth and physiological traits through bivariate linear models utilizing spatial corrections. We confirmed the relative importance of the red edge within the context of the study site's ecotypic variation. When interpreting these findings, growth traits such as height, diameter, crown length, and crown height allowed us to estimate variable correlations across the reflectance spectrum, peaking in most cases in wavelengths connected to water content in plant tissues. Finally, significant differences among ecotypes in reflectance and other correlated traits were detected.

Skin microbiome alters attractiveness to Anopheles mosquitoes.

BACKGROUND: Some people produce specific body odours that make them more attractive than others to mosquitoes, and consequently are at higher risk of contracting vector-borne diseases. The skin microbiome can break down carbohydrates, fatty acids and peptides on the skin into volatiles that mosquitoes can differentiate. RESULTS: Here, we examined how skin microbiome composition of women differs in relation to level of attractiveness to Anopheles coluzzii mosquitoes, to identify volatiles in body odour and metabolic pathways associated with individuals that tend to be poorly-attractive to mosquitoes. We used behavioural assays to measure attractiveness of participants to An. coluzzii mosquitoes, 16S rRNA amplicon sequencing of the bacteria sampled from the skin and gas chromatography of volatiles in body odour. We found differences in skin microbiome composition between the poorly- and highly-attractive groups, particularly eight Amplicon Sequence Variants (ASVs) belonging to the Proteobacteria, Actinobacteria and Firmicutes phyla. Staphylococcus 2 ASVs are four times as abundant in the highly-attractive compared to poorly-attractive group. Associations were found between these ASVs and volatiles known to be attractive to Anopheles mosquitoes. Propanoic pathways are enriched in the poorly-attractive participants compared to those found to be highly-attractive. CONCLUSIONS: Our findings suggest that variation in attractiveness of people to mosquitoes is related to the composition of the skin microbiota, knowledge that could improve odour-baited traps or other next generation vector control tools.

Using trained dogs and organic semi-conducting sensors to identify asymptomatic and mild SARS-CoV-2 infections: an observational study.

BACKGROUND: A rapid, accurate, non-invasive diagnostic screen is needed to identify people with SARS-CoV-2 infection. We investigated whether organic semi-conducting (OSC) sensors and trained dogs could distinguish between people infected with asymptomatic or mild symptoms, and uninfected individuals, and the impact of screening at ports-of-entry. METHODS: Odour samples were collected from adults, and SARS-CoV-2 infection status confirmed using RT-PCR. OSC sensors captured the volatile organic compound (VOC) profile of odour samples. Trained dogs were tested in a double-blind trial to determine their ability to detect differences in VOCs between infected and uninfected individuals, with sensitivity and specificity as the primary outcome. Mathematical modelling was used to investigate the impact of bio-detection dogs for screening. RESULTS: About, 3921 adults were enrolled in the study and odour samples collected from 1097 SARS-CoV-2 infected and 2031 uninfected individuals. OSC sensors were able to distinguish between SARS-CoV-2 infected individuals and uninfected, with sensitivity from 98% (95% CI 95-100) to 100% and specificity from 99% (95% CI 97-100) to 100%. Six dogs were able to distinguish between samples with sensitivity ranging from 82% (95% CI 76-87) to 94% (95% CI 89-98) and specificity ranging from 76% (95% CI 70-82) to 92% (95% CI 88-96). Mathematical modelling suggests that dog screening plus a confirmatory PCR test could detect up to 89% of SARS-CoV-2 infections, averting up to 2.2 times as much transmission compared to isolation of symptomatic individuals only. CONCLUSIONS: People infected with SARS-CoV-2, with asymptomatic or mild symptoms, have a distinct odour that can be identified by sensors and trained dogs with a high degree of accuracy. Odour-based diagnostics using sensors and/or dogs may prove a rapid and effective tool for screening large numbers of people.Trial Registration NCT04509713 (clinicaltrials.gov).

Genomic prediction in family bulks using different traits and cross-validations in pine.

Genomic prediction integrates statistical, genomic, and computational tools to improve the estimation of breeding values and increase genetic gain. Due to the broad diversity in mating systems, breeding schemes, propagation methods, and unit of selection, no universal genomic prediction approach can be applied in all crops. In a genome-wide family prediction (GWFP) approach, the family is the basic unit of selection. We tested GWFP in two loblolly pine (Pinus taeda L.) datasets: a breeding population composed of 63 full-sib families (5-20 individuals per family), and a simulated population with the same pedigree structure. In both populations, phenotypic and genomic data was pooled at the family level in silico. Marker effects were estimated to compute genomic estimated breeding values (GEBV) at the individual and family (GWFP) levels. Less than six individuals per family produced inaccurate estimates of family phenotypic performance and allele frequency. Tested across different scenarios, GWFP predictive ability was higher than those for GEBV in both populations. Validation sets composed of families with similar phenotypic mean and variance as the training population yielded predictions consistently higher and more accurate than other validation sets. Results revealed potential for applying GWFP in breeding programs whose selection unit are family, and for systems where family can serve as training sets. The GWFP approach is well suited for crops that are routinely genotyped and phenotyped at the plot-level, but it can be extended to other breeding programs. Higher predictive ability obtained with GWFP would motivate the application of genomic prediction in these situations.

The dehydrins gene expression differs across ecotypes in Norway spruce and relates to weather fluctuations.

Norway spruce has a broad natural distribution range, which results in a substantial variety of its physiological and genetic variation. There are three distinct altitudinal ecotypes described in this tree species. The physiological optimum of each ecotype may be shifted due to ongoing climate change, especially in traits associated with water demand that might be crucial for adaptation. Dehydrins are proteins that help to mitigate the adverse effects of dehydration. Dehydrin gene expression patterns appeared to be a suitable marker for plant stress assessment. Genetically determined differences in response between individuals and populations were formerly studied, however, mainly in controlled conditions. We evaluated ecotypic variation in dehydrin gene expression in a clonal bank comprised of all three ecotypes. A genetic relationship among targeted trees was uncovered utilizing GBS (Genotyping by Sequencing) platform. We sampled 4-6 trees of each ecotype throughout 15 months period. Subsequently, we assessed the RNA expression of dehydrin genes by qRT-PCR. For this study, we deliberately selected dehydrins from different categories. Our findings detected significant differences among ecotypes in dehydrin expression. The association of recorded climatic variables and individual gene expression across the study period was evaluated and revealed, for certain genes, a correlation between dehydrin gene expression and precipitation, temperature, and day-length.

Genetic dissection of heat-responsive physiological traits to improve adaptation and increase yield potential in soft winter wheat.

BACKGROUND: Climate change, including higher temperatures (HT) has a detrimental impact on wheat productivity and modeling studies predict more frequent heat waves in the future. Wheat growth can be impaired by high daytime and nighttime temperature at any developmental stage, especially during the grain filling stage. Leaf chlorophyll content, leaf greenness, cell membrane thermostability, and canopy temperature have been proposed as candidate traits to improve crop adaptation and yield potential of wheat under HT. Nonetheless, a significant gap exists in knowledge of genetic backgrounds associated with these physiological traits. Identifying genetic loci associated with these traits can facilitate physiological breeding for increased yield potential under high temperature stress condition in wheat. RESULTS: We conducted genome-wide association study (GWAS) on a 236 elite soft wheat association mapping panel using 27,466 high quality single nucleotide polymorphism markers. The panel was phenotyped for three years in two locations where heat shock was common. GWAS identified 500 significant marker-trait associations (MTAs) (p ≤ 9.99 × 10- 4). Ten MTAs with pleiotropic effects detected on chromosomes 1D, 2B, 3A, 3B, 6A, 7B, and 7D are potentially important targets for selection. Five MTAs associated with physiological traits had pleiotropic effects on grain yield and yield-related traits. Seventy-five MTAs were consistently expressed over several environments indicating stability and more than half of these stable MTAs were found in genes encoding different types of proteins associated with heat stress. CONCLUSIONS: We identified 500 significant MTAs in soft winter wheat under HT stress. We found several stable loci across environments and pleiotropic markers controlling physiological and agronomic traits. After further validation, these MTAs can be used in marker-assisted selection and breeding to develop varieties with high stability for grain yield under high temperature.

Assessment of unconventional antimicrobial compounds for the control of 'Candidatus Liberibacter asiaticus', the causative agent of citrus greening disease.

In this study, newly identified small molecules were examined for efficacy against 'Candidatus Liberibacter asiaticus' in commercial groves of sweet orange (Citrus sinensis) and white grapefruit (Citrus paradisi) trees. We used benzbromarone and/or tolfenamic acid delivered by trunk injection. We evaluated safety and efficacy parameters by performing RNAseq of the citrus host responses, 16S rRNA gene sequencing to characterize citrus-associated microbial communities during treatment, and qRT-PCR as an indirect determination of 'Ca. L. asiaticus' viability. Analyses of the C. sinensis transcriptome indicated that each treatment consistently induced genes associated with normal metabolism and growth, without compromising tree viability or negatively affecting the indigenous citrus-associated microbiota. It was found that treatment-associated reduction in 'Ca. L. asiaticus' was positively correlated with the proliferation of several core taxa related with citrus health. No symptoms of phytotoxicity were observed in any of the treated trees. Trials were also performed in commercial groves to examine the effect of each treatment on fruit productivity, juice quality and efficacy against 'Ca. L. asiaticus'. Increased fruit production (15%) was observed in C. paradisi following twelve months of treatment with benzbromarone and tolfenamic acid. These results were positively correlated with decreased 'Ca. L. asiaticus' transcriptional activity in root samples.

Exploring Deep Learning for Complex Trait Genomic Prediction in Polyploid Outcrossing Species.

Genomic prediction (GP) is the procedure whereby the genetic merits of untested candidates are predicted using genome wide marker information. Although numerous examples of GP exist in plants and animals, applications to polyploid organisms are still scarce, partly due to limited genome resources and the complexity of this system. Deep learning (DL) techniques comprise a heterogeneous collection of machine learning algorithms that have excelled at many prediction tasks. A potential advantage of DL for GP over standard linear model methods is that DL can potentially take into account all genetic interactions, including dominance and epistasis, which are expected to be of special relevance in most polyploids. In this study, we evaluated the predictive accuracy of linear and DL techniques in two important small fruits or berries: strawberry and blueberry. The two datasets contained a total of 1,358 allopolyploid strawberry (2n=8x=112) and 1,802 autopolyploid blueberry (2n=4x=48) individuals, genotyped for 9,908 and 73,045 single nucleotide polymorphism (SNP) markers, respectively, and phenotyped for five agronomic traits each. DL depends on numerous parameters that influence performance and optimizing hyperparameter values can be a critical step. Here we show that interactions between hyperparameter combinations should be expected and that the number of convolutional filters and regularization in the first layers can have an important effect on model performance. In terms of genomic prediction, we did not find an advantage of DL over linear model methods, except when the epistasis component was important. Linear Bayesian models were better than convolutional neural networks for the full additive architecture, whereas the opposite was observed under strong epistasis. However, by using a parameterization capable of taking into account these non-linear effects, Bayesian linear models can match or exceed the predictive accuracy of DL. A semiautomatic implementation of the DL pipeline is available at https://github.com/lauzingaretti/deepGP/.

Independent Validation of Genomic Prediction in Strawberry Over Multiple Cycles.

The University of Florida strawberry (Fragaria × ananassa) breeding program has implemented genomic prediction (GP) as a tool for choosing outstanding parents for crosses over the last five seasons. This has allowed the use of some parents 1 year earlier than with traditional methods, thus reducing the duration of the breeding cycle. However, as the number of breeding cycles increases over time, greater knowledge is needed on how multiple cycles can be used in the practical implementation of GP in strawberry breeding. Advanced selections and cultivars totaling 1,558 unique individuals were tested in field trials for yield and fruit quality traits over five consecutive years and genotyped for 9,908 SNP markers. Prediction of breeding values was carried out using Bayes B models. Independent validation was carried out using separate trials/years as training (TRN) and testing (TST) populations. Single-trial predictive abilities for five polygenic traits averaged 0.35, which was reduced to 0.24 when individuals common across trials were excluded, emphasizing the importance of relatedness among training and testing populations. Training populations including up to four previous breeding cycles increased predictive abilities, likely due to increases in both training population size and relatedness. Predictive ability was also strongly influenced by heritability, but less so by changes in linkage disequilibrium and effective population size. Genotype by year interactions were minimal. A strategy for practical implementation of GP in strawberry breeding is outlined that uses multiple cycles to predict parental performance and accounts for traits not included in GP models when constructing crosses. Given the importance of relatedness to the success of GP in strawberry, future work could focus on the optimization of relatedness in the design of TRN and TST populations to increase predictive ability in the short-term without compromising long-term genetic gains.

Understanding the Genetic Basis of Spike Fertility to Improve Grain Number, Harvest Index, and Grain Yield in Wheat Under High Temperature Stress Environments.

Moderate heat stress accompanied by short episodes of extreme heat during the post-anthesis stage is common in most US wheat growing areas and causes substantial yield losses. Sink strength (grain number) is a key yield limiting factor in modern wheat varieties. Increasing spike fertility (SF) and improving the partitioning of assimilates can optimize sink strength which is essential to improve wheat yield potential under a hot and humid environment. A genome-wide association study (GWAS) allows identification of novel quantitative trait loci (QTLs) associated with SF and other partitioning traits that can assist in marker assisted breeding. In this study, GWAS was performed on a soft wheat association mapping panel (SWAMP) comprised of 236 elite lines using 27,466 single nucleotide polymorphisms (SNPs). The panel was phenotyped in two heat stress locations over 3 years. GWAS identified 109 significant marker-trait associations (MTAs) (p ≤ 9.99 x 10-5) related to eight phenotypic traits including SF (a major component of grain number) and spike harvest index (SHI, a major component of grain weight). MTAs detected on chromosomes 1B, 3A, 3B, and 5A were associated with multiple traits and are potentially important targets for selection. More than half of the significant MTAs (60 out of 109) were found in genes encoding different types of proteins related to metabolism, disease, and abiotic stress including heat stress. These MTAs could be potential targets for further validation study and may be used in marker-assisted breeding for improving wheat grain yield under post-anthesis heat stress conditions. This is the first study to identify novel QTLs associated with SF and SHI which represent the major components of grain number and grain weight, respectively, in wheat.

Sticky Trap Design Considerations for Entrapping Bed Bugs.

Little evidence has been presented on the usefulness of sticky traps for monitoring bed bugs, Cimex lectularius. We examined how the surface roughness around the adhesive of a sticky trap affects both bed bug behavior and adhesive entrapment. In the first assay, bed bugs were placed onto acetate paper discs with different roughness averages (Ra). Each disc was surrounded by sticky trap adhesive and number of captured bed bugs were recorded. The second assay was set up similarly to the first assay except that the outer portion of the acetate disc had a different Ra than the center. In the third assay, bed bugs were placed into circular acetate arenas where they were surrounded by different Ra treatments. The number of times the bed bugs contacted the Ra treatment but did not cross onto the treatment was recorded. Results of these assays showed that as the acetate surfaces got smoother (lower Ra), bed bugs were more likely to get trapped in sticky trap adhesives but also less likely to travel across the smoother surfaces they encountered. A sticky trap design with a smooth plastic film around the adhesive was tested in the field to see if it could capture bed bugs in apartments with known bed bug activity. This trap was not only able to capture bed bugs but was also able to detect unknown German cockroach, Blattela germanica, infestations. Sticky trap designs with smooth surfaces around an adhesive could be used to monitor not only bed bugs but also German cockroaches.

Developmental Pleiotropy Shaped the Roots of the Domesticated Common Bean (Phaseolus vulgaris).

Roots have been omitted from previous domestication analyses owing mostly to their subterranean nature. We hypothesized that domestication-associated changes in common bean (Phaseolus vulgaris) roots were due to direct selection for some aboveground traits that also affect roots, and to indirect selection of root traits that improved aboveground plant performance. To test this hypothesis, we compared the root traits of wild and domesticated accessions and performed a multistep quantitative trait locus (QTL) analysis of an intra-Andean recombinant inbred family derived from a landrace and a wild accession. Multivariate analysis of root traits distinguished wild from domesticated accessions and showed that seed weight affects many root traits of young seedlings. Sequential and methodical scanning of the genome confirmed the significant effect of seed weight on root traits and identified QTLs that control seed weight, root architecture, shoot and root traits, and shoot traits alone. The root domestication syndrome in the common bean was associated with genes that were directly selected to increase seed weight but had a significant effect on early root growth through a developmental pleiotropic effect. The syndrome was also associated with genes that control root system architecture and that were apparently the product of indirect selection.

Methionine as a safe and effective novel biorational mosquito larvicide.

BACKGROUND: Mosquito larvicides provide a source-reduction strategy to diminish adult females that bite and potentially spread pathogens. Demands are mounting for new and innovative effective biorational larvicides, due to the development of resistance to some currently utilized mosquito larvicides, undesirable non-target effects, and US Environmental Protection Agency (EPA) restrictions. Methionine is a human nutrient essential amino acid that unexpectedly has been shown to be a valuable safe pest management tool against select insect pests that possess alkaline gut physiology. The present study evaluated larvicidal toxicity of methionine in several pestiferous mosquito (Diptera: Culicidae) genera. RESULTS: Concentration-dependent DL-methionine kinetics assays of survival and pupation were conducted in larvae of Aedes albopictus Skuse, Anopheles quadrimaculatus Say, and Culex tarsalis Coquillett in glass jars. High concentrations of DL-methionine yielded 100% mortality for all test species and prevented pupation at a rate equivalent to Bacillus thuringiensis israelensis (Bti) treatments. Concentration kinetics indicated that An. quadrimaculatus was 10-fold more sensitive to DL-methionine than Ae. albopictus and Cx. tarsalis. CONCLUSIONS: EPA regulations currently exempt methionine in pesticide formulations applied to agricultural crops. This study demonstrates that methionine is a highly effective mosquito larvicide that can provide a beneficial new biorational, environmentally sustainable tool to control pestiferous mosquitoes. © 2018 Society of Chemical Industry.

Independent and Joint-GWAS for growth traits in Eucalyptus by assembling genome-wide data for 3373 individuals across four breeding populations.

Genome-wide association studies (GWAS) in plants typically suffer from limited statistical power. An alternative to the logistical and cost challenge of increasing sample sizes is to gain power by meta-analysis using information from independent studies. We carried out GWAS for growth traits with six single-marker models and regional heritability mapping (RHM) in four Eucalyptus breeding populations independently and by Joint-GWAS, using gene and segment-based models, with data for 3373 individuals genotyped with a communal EUChip60KSNP platform. While single-single nucleotide polymorphism (SNP) GWAS hardly detected significant associations at high-stringency in each population, gene-based Joint-GWAS revealed nine genes significantly associated with tree height. Associations detected using single-SNP GWAS, RHM and Joint-GWAS set-based models explained on average 3-20% of the phenotypic variance. Whole-genome regression, conversely, captured 64-89% of the pedigree-based heritability in all populations. Several associations independently detected for the same SNPs in different populations provided unprecedented GWAS validation results in forest trees. Rare and common associations were discovered in eight genes involved in cell wall biosynthesis and lignification. With the increasing adoption of genomic prediction of complex phenotypes using shared SNPs and much larger tree breeding populations, Joint-GWAS approaches should provide increasing power to pinpoint discrete associations potentially useful toward tree breeding and molecular applications.

Sex Modulates Lactobacillus johnsonii N6.2 and Phytophenol Effectiveness in Reducing High Fat Diet Induced mTOR Activation in Sprague-Dawley Rats.

Metabolic syndrome (MetS) is the underlying cause of some devastating diseases, including type 2 diabetes and cardiovascular disease. These diseases have been associated with over-activation of the mechanistic Target of Rapamycin (mTOR) pathway. This study utilizes a high fat diet (HFD) to induce MetS and to dissect the effects of a beneficial bacterium, L. johnsonii N6.2, and natural phenolics on mTOR complex 1 (mTORC1) expression compared to a reduced energy density diet (REDD). HFD significantly elevated MetS markers in males, as noted through an increase in weight, glucose levels, and triglyceride levels. Treatments were effective in reducing mTORC1-activating phosphorylation of pAKT-T308 and pAKT-S473 (p = 0.0012 and 0.0049, respectively) in HFD-fed females, with the combined treatments of L. johnsonii and phytophenols reducing phosphorylation below REDD-fed control levels, and significantly below HFD-fed control levels. Meanwhile, diet was the significant factor influencing male mTORC1-activating phosphorylation (p < 0.0001), as treatments were only effective in reducing phosphorylation in REDD-fed animals. Downstream analysis of mTORC1 activated genes phosphogluconate dehydrogenase (pgd) and phosphofructose kinase (pfk) followed this similar trend, enforcing the significant effect sex has on a treatments' ability to modulate diet induced abnormalities. Analyzing mTORC1 stimulators such as insulin, inflammatory cytokines, and tryptophan, revealed no significant differences among groups. These results indicate that the effects observed on mTORC1 are a direct consequence of the treatments, and not exerted indirectly via the modulation of stimuli. This study highlights the potential use of commensal microorganisms and natural compounds in reducing the onset of metabolic diseases through mTORC1.