Sacrificial-template-free synthesis of core-shell C@Bi2S3 heterostructures for efficient supercapacitor and H-2 production applications

Core-shell heterostructures have attracted considerable attention owing to their unique properties and broad range of applications in lithium ion batteries, supercapacitors, and catalysis. Conversely, the effective synthesis of Bi2S3 nanorod core@ amorphous carbon shell heterostructure remains an important challenge. In this study, C@Bi2S3 core-shell heterostructures with enhanced supercapacitor performance were synthesized via sacrificial-template-free one-pot-synthesis method. The highest specific capacities of the C@Bi2S3 core shell was 333.43 F g(-1) at a current density of 1 A g(-1). Core-shell-structured C@Bi2S3 exhibits 1.86 times higher photocatalytic H-2 production than the pristine Bi2S3 under simulated solar light irradiation. This core-shell feature of C@Bi2S3 provides efficient charge separation and transfer owing to the formed heterojunction and a short radial transfer path, thus efficiently diminishing the charge recombination; it also facilitates plenty of active sites for the hydrogen evolution reaction owing to its mesoporous nature. These outcomes will open opportunities for developing low-cost and noble-metal-free efficient electrode materials for water splitting and supercapacitor applications

Multiple Genome Wide Association Mapping Models Identify Quantitative Trait Nucleotides for Brown Planthopper (Nilaparvata lugens) Resistance in MAGIC Indica Population of Rice

Brown planthopper (BPH), one of the most important pests of the rice (Oryza sativa) crop, becomes catastrophic under severe infestations and causes up to 60% yield loss. The highly disastrous BPH biotype in the Indian sub-continent is Biotype 4, which also known as the South Asian Biotype. Though many resistance genes were mapped until now, the utility of the resistance genes in the breeding programs is limited due to the breakdown of resistance and emergence of new biotypes. Hence, to identify the resistance genes for this economically important pest, we have used a multi-parent advanced generation intercross (MAGIC) panel consisting of 391 lines developed from eight indica founder parents. The panel was phenotyped at the controlled conditions for two consecutive years. A set of 27,041 cured polymorphic single nucleotide polymorphism (SNPs) and across-year phenotypic data were used for the identification of marker–trait associations. Genome-wide association analysis was performed to find out consistent associations by employing four single and two multi-locus models. Sixty-one SNPs were consistently detected by all six models. A set of 190 significant marker-associations identified by fixed and random model circulating probability unification (FarmCPU) were considered for searching resistance candidate genes. The highest number of annotated genes were found in chromosome 6 followed by 5 and 1. Ninety-two annotated genes identified across chromosomes of which 13 genes are associated BPH resistance including NB-ARC (nucleotide binding in APAF-1, R gene products, and CED-4) domain-containing protein, NHL repeat-containing protein, LRR containing protein, and WRKY70. The significant SNPs and resistant lines identified from our study could be used for an accelerated breeding program to develop new BPH resistant cultivars

Assessing the genetic overlap between BMI and cognitive function

Obesity and low cognitive function are associated with multiple adverse health outcomes across the life course. They have a small phenotypic correlation (r=-0.11; high body mass index (BMI)-low cognitive function), but whether they have a shared genetic aetiology is unknown. We investigated the phenotypic and genetic correlations between the traits using data from 6815 unrelated, genotyped members of Generation Scotland, an ethnically homogeneous cohort from five sites across Scotland. Genetic correlations were estimated using the following: same-sample bivariate genome-wide complex trait analysis (GCTA)-GREML; independent samples bivariate GCTA-GREML using Generation Scotland for cognitive data and four other samples (n=20 806) for BMI; and bivariate LDSC analysis using the largest genome-wide association study (GWAS) summary data on cognitive function (n=48 462) and BMI (n=339 224) to date. The GWAS summary data were also used to create polygenic scores for the two traits, with within- and cross-trait prediction taking place in the independent Generation Scotland cohort. A large genetic correlation of -0.51 (s.e. 0.15) was observed using the same-sample GCTA-GREML approach compared with -0.10 (s.e. 0.08) from the independent-samples GCTA-GREML approach and -0.22 (s.e. 0.03) from the bivariate LDSC analysis. A genetic profile score using cognition-specific genetic variants accounts for 0.08% (P=0.020) of the variance in BMI and a genetic profile score using BMI-specific variants accounts for 0.42% (P=1.9 × 10 -7) of the variance in cognitive function. Seven common genetic variants are significantly associated with both traits at

Genomic prediction of complex human traits: relatedness, trait architecture and predictive meta-models

We explore the prediction of individuals' phenotypes for complex traits using genomic data. We compare several widely used prediction models, including Ridge Regression, LASSO and Elastic Nets estimated from cohort data, and polygenic risk scores constructed using published summary statistics from genome-wide association meta-analyses (GWAMA). We evaluate the interplay between relatedness, trait architecture and optimal marker density, by predicting height, body mass index (BMI) and high-density lipoprotein level (HDL) in two data cohorts, originating from Croatia and Scotland. We empirically demonstrate that dense models are better when all genetic effects are small (height and BMI) and target individuals are related to the training samples, while sparse models predict better in unrelated individuals and when some effects have moderate size (HDL). For HDL sparse models achieved good across-cohort prediction, performing similarly to the GWAMA risk score and to models trained within the same cohort, which indicates that, for predicting traits with moderately sized effects, large sample sizes and familial structure become less important, though still potentially useful. Finally, we propose a novel ensemble of whole-genome predictors with GWAMA risk scores and demonstrate that the resulting meta-model achieves higher prediction accuracy than either model on its own. We conclude that although current genomic predictors are not accurate enough for diagnostic purposes, performance can be improved without requiring access to large-scale individual-level data. Our methodologically simple meta-model is a means of performing predictive meta-analysis for optimizing genomic predictions and can be easily extended to incorporate multiple population-level summary statistics or other domain knowledge

A predictive assessment of genetic correlations between traits in chickens using markers

International audienceAbstractBackgroundGenomic selection has been successfully implemented in plant and animal breeding programs to shorten generation intervals and accelerate genetic progress per unit of time. In practice, genomic selection can be used to improve several correlated traits simultaneously via multiple-trait prediction, which exploits correlations between traits. However, few studies have explored multiple-trait genomic selection. Our aim was to infer genetic correlations between three traits measured in broiler chickens by exploring kinship matrices based on a linear combination of measures of pedigree and marker-based relatedness. A predictive assessment was used to gauge genetic correlations.MethodsA multivariate genomic best linear unbiased prediction model was designed to combine information from pedigree and genome-wide markers in order to assess genetic correlations between three complex traits in chickens, i.e. body weight at 35 days of age (BW), ultrasound area of breast meat (BM) and hen-house egg production (HHP). A dataset with 1351 birds that were genotyped with the 600 K Affymetrix platform was used. A kinship kernel (K) was constructed as K = λG + (1 − λ)A, where A is the numerator relationship matrix, measuring pedigree-based relatedness, and G is a genomic relationship matrix. The weight (λ) assigned to each source of information varied over the grid λ = (0, 0.2, 0.4, 0.6, 0.8, 1). Maximum likelihood estimates of heritability and genetic correlations were obtained at each λ, and the “optimum” λ was determined using cross-validation.ResultsEstimates of genetic correlations were affected by the weight placed on the source of information used to build K. For example, the genetic correlation between BW–HHP and BM–HHP changed markedly when λ varied from 0 (only A used for measuring relatedness) to 1 (only genomic information used). As λ increased, predictive correlations (correlation between observed phenotypes and predicted breeding values) increased and mean-squared predictive error decreased. However, the improvement in predictive ability was not monotonic, with an optimum found at some 0 < λ < 1, i.e., when both sources of information were used together.ConclusionsOur findings indicate that multiple-trait prediction may benefit from combining pedigree and marker information. Also, it appeared that expected correlated responses to selection computed from standard theory may differ from realized responses. The predictive assessment provided a metric for performance evaluation as well as a means for expressing uncertainty of outcomes of multiple-trait selection

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

A novel high force density linear segmented switched reluctance machine

Linear switched reluctance machine (LSRM) has low force density, high acoustic noise and low energy conversion ratio. In this paper a novel linear segmented switched reluctance machine (LSSRM) having high force density and energy conversion ratio is proposed. This machine has segmented rotor and full pitch winding on the stator. This machine is the linear counterpart of segmented switched reluctance machine (SSRM). It is proved through finite element based simulation study (FEM) that LSSRM gives approximately double the force, for the same frame size as LSRM. This is achieved by decreasing the reluctance in the aligned position without affecting saliency ratio. The reluctance of LSSRM is decreased by reducing the flux path length and increasing the air gap area. This is achieved by modifying the winding arrangement from concentrated to full-pitched, changing translator pole width (TPW) and stator pole shoe width (SPW). The FEM results are validated analytically. The geometric parameters affecting the output force are optimized to get the maximum ratio of propulsive force to normal force. Also, converter volt-ampere rating is determined

A novel high power density segmented switched reluctance machine

In switched reluctance machine (SRM), the power density can be increased by using full pitched winding instead of concentrated winding. It is reported that full pitched segmented switched reluctance machine (SSRM) can give 40% higher torque than conventional SRM for the same frame size. In this paper, it is shown that SSRM can give approximately double the torque for the same frame size. This is because flux in the magnetic circuit of SSRM is approximately double that in VRSRM. But, in order to operate both machines at the same degree of saturation, the yoke dimensions of SSRM need to be doubled. This leads to increase in weight. In this paper, a novel high power density segmented switched reluctance machine with circular slots (CSSSRM) is proposed. Making the slots circular confines the flux to circular paths, resulting in reduction of active iron weight. Further, geometric parameters affecting the torque of CSSSRM are determined. Optimal ranges of these parameters to get maximum torque are found out using search method. Finally, efficiency and radial forces of CSSSRM are estimated

Delay Sensitive TDMA Slot Assignment in Ad Hoc Wireless Networks

Time slot assignments in a TDMA ad hoc wireless network (AWN) is either centrally coordinated by a root node or distributed among all the nodes in the network. In the centralized TDMA network, the root node uses the global knowledge of the network to assign slots, but becomes more challenging in case of distributed network, as each node is expected to assign a slot for itself without conflicting other nodes' slot selection. There is plenty of literature on how slots are assigned in a centralized TDMA network but only a few on distributed. Quality of Service (QoS) is critically important in AWNs and a good slot assignment scheme prioritizes its QoS metrics during the process of slot assignments.\ud \ud Real-time communications require end-to-end delay and jitter within acceptable limits for better overall QoS. This paper proposes a delay sensitive approach to TDMA Slot assignment problem in distributed AWNs. The proposed approach does a balancing act between end-to-end delay and spatial reuse. The experimental results demonstrate that the proposed approach obtains quality results in terms of call acceptance rate, end-to-end delay and spatial reusability