Diverging longitudinal resistivity with decreasing temperature is the most apparent transport property of a simple band insulator in distinction to any metals. However, such a distinct feature ceases to stand in various topological insulators due to the existing metallic surface states 1, 2 . Nevertheless there are other transport phenomena, such as resistivity plateau and negative MR, which may distinguish at least ideal TIs and ideal TSMs from topologically trivial materials. In an ideal 3D TI where the bulk states are completely gapped out near the Fermi level, a resistivity plateau can be clearly established because the only participating surface states are TRS protected and thus robust to disorders, leading to a saturation of resistivity in the low temperature regime [3][4][5][6] . In TSMs without coexisting bulk Fermi surfaces such as an ideal WSM, on the other hand, the bulk exci-2 tations come from the two separated Weyl nodes in momentum space which are chiral in nature owing to the lack of TRS or inversion symmetry [30][31][32] . When the applied magnetic field is parallel to the electric field direction, the density of the right/left chiral excitations increases/decreases accordingly as a consequence of the chiral anomaly [33][34][35] , resulting in a non-dissipative current from the left to right nodes along the field direction, hence an unconventional negative MR appears.Because the resistivity plateau and negative MR are opposite consequences for systems with or without TRS in the ideal situations mentioned above, coexistence of the both in a single material is unlikely or very difficult. Of course, these features should be more intriguing but much involved in realistic topological materials, and, TRS itself is not the only origin relevant to the resistivity plateau on a general ground. In two-dimensional electron gases or semiconducting films like graphite 36, 37 , for instance, the resistivity seemingly saturates after a field-induced metal-insulator transition while a clear resistivity plateau at lower temperatures was not reported. More recently, a field-induced plateau has been observed in LaSb, a potentially new candidate of TIs 38 . While the interpretation of all these and related behaviors in topological materials remains a theoretical challenge, materials realizations of these effects are highly desirable not only in confronting this challenge but also in technique applications.Here we report the discovery of a new TSM TaSb 2 , which crystallizes in a monoclinic structure with centrosymmetric space group C 12/m1 (SI- Fig. S1). The longitudinal resistivity and Hall effect were measured for various magnetic fields applied along different directions. The semicon-3 ducting behavior is associated with a low carrier density and a field-induced metal-to-insulator-like transition. High quality of the measured samples is indicated by ultrahigh mobility and XMR. The topological nature of the compound is evidenced by the Shubnikov de Haas (SdH) oscillation measurement as well as band structure calcu...

Farnesoid X receptor (FXR) is a nuclear hormone receptor involved in bile acid synthesis and homeostasis. Dysfunction of FXR is involved in cholestasis and atherosclerosis. FXR is prevalent in liver, gallbladder, and intestine, but it is not yet clear whether it modulates neurobehavior. In the current study, we tested the hypothesis that mouse FXR deficiency affects a specific subset of neurotransmitters and results in an unique behavioral phenotype. The FXR knockout mice showed less depressive-like and anxiety-related behavior, but increased motor activity. They had impaired memory and reduced motor coordination. There were changes of glutamatergic, GABAergic, serotoninergic, and norepinephrinergic neurotransmission in either hippocampus or cerebellum. FXR deletion decreased the amount of the GABA synthesis enzyme GAD65 in hippocampus but increased GABA transporter GAT1 in cerebral cortex. FXR deletion increased serum concentrations of many bile acids, including taurodehydrocholic acid, taurocholic acid, deoxycholic acid (DCA), glycocholic acid (GCA), tauro-α-muricholic acid, tauro-ω-muricholic acid, and hyodeoxycholic acid (HDCA). There were also changes in brain concentrations of taurocholic acid, taurodehydrocholic acid, tauro-ω-muricholic acid, tauro-β-muricholic acid, deoxycholic acid, and lithocholic acid (LCA). Taken together, the results from studies with FXR knockout mice suggest that FXR contributes to the homeostasis of multiple neurotransmitter systems in different brain regions and modulates neurobehavior. The effect appears to be at least partially mediated by bile acids that are known to cross the blood-brain barrier (BBB) inducing potential neurotoxicity.

BackgroundThe increasing availability of whole-genome sequence data is expected to increase the accuracy of genomic prediction. However, results from simulation studies and analysis of real data do not always show an increase in accuracy from sequence data compared to high-density (HD) single nucleotide polymorphism (SNP) chip genotypes. In addition, the sheer number of variants makes analysis of all variants and accurate estimation of all effects computationally challenging. Our objective was to find a strategy to approximate the analysis of whole-sequence data with a Bayesian variable selection model. Using a simulated dataset, we applied a Bayes R hybrid model to analyse whole-sequence data, test the effect of dropping a proportion of variants during the analysis, and test how the analysis can be split into separate analyses per chromosome to reduce the elapsed computing time. We also investigated the effect of imputation errors on prediction accuracy. Subsequently, we applied the approach to a dataset that contained imputed sequences and records for production and fertility traits for 38,492 Holstein, Jersey, Australian Red and crossbred bulls and cows.ResultsWith the simulated dataset, we found that prediction accuracy was highly increased for a breed that was not represented in the training population for sequence data compared to HD SNP data. Either dropping part of the variants during the analysis or splitting the analysis into separate analyses per chromosome decreased accuracy compared to analysing whole-sequence data. First, dropping variants from each chromosome and reanalysing the retained variants together resulted in an accuracy similar to that obtained when analysing whole-sequence data. Adding imputation errors decreased prediction accuracy, especially for errors in the validation population. With real data, using sequence variants resulted in accuracies that were similar to those obtained with the HD SNPs.ConclusionsWe present an efficient approach to approximate analysis of whole-sequence data with a Bayesian variable selection model. The lack of increase in prediction accuracy when applied to real data could be due to imputation errors, which demonstrates the importance of developing more accurate methods of imputation or directly genotyping sequence variants that have a major effect in the prediction equation.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-017-0347-9) contains supplementary material, which is available to authorized users.

BackgroundGenomic prediction of breeding values from dense single nucleotide polymorphisms (SNP) genotypes is used for livestock and crop breeding, and can also be used to predict disease risk in humans. For some traits, the most accurate genomic predictions are achieved with non-linear estimates of SNP effects from Bayesian methods that treat SNP effects as random effects from a heavy tailed prior distribution. These Bayesian methods are usually implemented via Markov chain Monte Carlo (MCMC) schemes to sample from the posterior distribution of SNP effects, which is computationally expensive. Our aim was to develop an efficient expectation–maximisation algorithm (emBayesR) that gives similar estimates of SNP effects and accuracies of genomic prediction than the MCMC implementation of BayesR (a Bayesian method for genomic prediction), but with greatly reduced computation time.MethodsemBayesR is an approximate EM algorithm that retains the BayesR model assumption with SNP effects sampled from a mixture of normal distributions with increasing variance. emBayesR differs from other proposed non-MCMC implementations of Bayesian methods for genomic prediction in that it estimates the effect of each SNP while allowing for the error associated with estimation of all other SNP effects. emBayesR was compared to BayesR using simulated data, and real dairy cattle data with 632 003 SNPs genotyped, to determine if the MCMC and the expectation-maximisation approaches give similar accuracies of genomic prediction.ResultsWe were able to demonstrate that allowing for the error associated with estimation of other SNP effects when estimating the effect of each SNP in emBayesR improved the accuracy of genomic prediction over emBayesR without including this error correction, with both simulated and real data. When averaged over nine dairy traits, the accuracy of genomic prediction with emBayesR was only 0.5% lower than that from BayesR. However, emBayesR reduced computing time up to 8-fold compared to BayesR.ConclusionsThe emBayesR algorithm described here achieved similar accuracies of genomic prediction to BayesR for a range of simulated and real 630 K dairy SNP data. emBayesR needs less computing time than BayesR, which will allow it to be applied to larger datasets.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-014-0082-4) contains supplementary material, which is available to authorized users.

BackgroundBayesian mixture models in which the effects of SNP are assumed to come from normal distributions with different variances are attractive for simultaneous genomic prediction and QTL mapping. These models are usually implemented with Monte Carlo Markov Chain (MCMC) sampling, which requires long compute times with large genomic data sets. Here, we present an efficient approach (termed HyB_BR), which is a hybrid of an Expectation-Maximisation algorithm, followed by a limited number of MCMC without the requirement for burn-in.ResultsTo test prediction accuracy from HyB_BR, dairy cattle and human disease trait data were used. In the dairy cattle data, there were four quantitative traits (milk volume, protein kg, fat% in milk and fertility) measured in 16,214 cattle from two breeds genotyped for 632,002 SNPs. Validation of genomic predictions was in a subset of cattle either from the reference set or in animals from a third breeds that were not in the reference set. In all cases, HyB_BR gave almost identical accuracies to Bayesian mixture models implemented with full MCMC, however computational time was reduced by up to 1/17 of that required by full MCMC. The SNPs with high posterior probability of a non-zero effect were also very similar between full MCMC and HyB_BR, with several known genes affecting milk production in this category, as well as some novel genes. HyB_BR was also applied to seven human diseases with 4890 individuals genotyped for around 300 K SNPs in a case/control design, from the Welcome Trust Case Control Consortium (WTCCC). In this data set, the results demonstrated again that HyB_BR performed as well as Bayesian mixture models with full MCMC for genomic predictions and genetic architecture inference while reducing the computational time from 45 h with full MCMC to 3 h with HyB_BR.ConclusionsThe results for quantitative traits in cattle and disease in humans demonstrate that HyB_BR can perform equally well as Bayesian mixture models implemented with full MCMC in terms of prediction accuracy, but with up to 17 times faster than the full MCMC implementations. The HyB_BR algorithm makes simultaneous genomic prediction, QTL mapping and inference of genetic architecture feasible in large genomic data sets.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3082-7) contains supplementary material, which is available to authorized users.

BackgroundUsing whole genome sequence data might improve genomic prediction accuracy, when compared with high-density SNP arrays, and could lead to identification of casual mutations affecting complex traits. For some traits, the most accurate genomic predictions are achieved with non-linear Bayesian methods. However, as the number of variants and the size of the reference population increase, the computational time required to implement these Bayesian methods (typically with Monte Carlo Markov Chain sampling) becomes unfeasibly long.ResultsHere, we applied a new method, HyB_BR (for Hybrid BayesR), which implements a mixture model of normal distributions and hybridizes an Expectation-Maximization (EM) algorithm followed by Markov Chain Monte Carlo (MCMC) sampling, to genomic prediction in a large dairy cattle population with imputed whole genome sequence data. The imputed whole genome sequence data included 994,019 variant genotypes of 16,214 Holstein and Jersey bulls and cows. Traits included fat yield, milk volume, protein kg, fat% and protein% in milk, as well as fertility and heat tolerance. HyB_BR achieved genomic prediction accuracies as high as the full MCMC implementation of BayesR, both for predicting a validation set of Holstein and Jersey bulls (multi-breed prediction) and a validation set of Australian Red bulls (across-breed prediction). HyB_BR had a ten fold reduction in compute time, compared with the MCMC implementation of BayesR (48 hours versus 594 hours). We also demonstrate that in many cases HyB_BR identified sequence variants with a high posterior probability of affecting the milk production or fertility traits that were similar to those identified in BayesR. For heat tolerance, both HyB_BR and BayesR found variants in or close to promising candidate genes associated with this trait and not detected by previous studies.ConclusionsThe results demonstrate that HyB_BR is a feasible method for simultaneous genomic prediction and QTL mapping with whole genome sequence in large reference populations.

Molecules of C60 covalently connected with N-ethylcarbazole (EtCz) and triphenylamine (TPA) have been synthesized. Photoinduced electron transfer in C60-EtCz and C60-TPA has been studied in polar and nonpolar solvents using time-resolved transient absorption and fluorescence measurements. From the fluorescence lifetimes, the excited singlet state of the C60 moiety (1C60) of C60-TPA generates predominantly C60*--TPA*+, which decays quickly to the ground state within 6 ns even in polar solvents. In the case of C60-EtCz, on the other hand, about half of the 1C60 moiety generates short-lived C60*--EtCz*+, while the other half of the 1C60 moiety is transferred to the 3C60 moiety via intersystem crossing in dimethylformamide, in which the energy level of C60*--EtCz*+ is lower than that of 3C60. Thus, the charge separation takes place via 3C60 generating C60*--EtCz*+, having a lifetime as long as 300 ns, probably because of the triplet spin character of C60*--EtCz*+. A special property of the EtCz moiety to stabilize the hole in the charge-separated state was revealed.

One major focus on the performance researches of pump as turbine is how to enhance the efficiency of energy recovery. While the key point of increasing the efficiency is to improve the performance of the blade profile which is structural basis of the blade geometry. This article presents an optimization method for the blade profile. It contained the parameterization of blade profile, the Latin Hypercube experimental design, the computational fluid dynamics techniques, the back propagation neural network, and genetic algorithm. Specifically, the nonuniform cubic B-spline curve was used to parameterize the blade profile, the Latin Hypercube experimental design method for the acquirement of the sample points of back propagation neural network. The performance analysis of each sample point was accomplished by the computational fluid dynamics techniques. Then, the learning and training of the back propagation neural network was carried out. Finally, the optimization techniques of combining the back propagation neural network and genetic algorithm were used to solve the optimization problems of the blade profile. Based on the above method, the blade profile of a pump as turbine was optimized and improved. The result shows that the efficiency of the optimized pump as turbine under the optimum operating condition was increased by 2.91%, with the constraint condition to ensure that the difference between the head and the initial head of the pump as turbine is less than the specified value. This proves that using the above method to optimize the blade profile is feasible.