Self-patterning Gd nano-fibers in Mg-Gd alloys

Manipulating the shape and distribution of strengthening units, e.g. particles, fibers, and precipitates, in a bulk metal, has been a widely applied strategy of tailoring their mechanical properties. Here, we report self-assembled patterns of Gd nano-fibers in Mg-Gd alloys for the purpose of improving their strength and deformability. 1-nm Gd nano-fibers, with a 〈c〉-rod shape, are formed and hexagonally patterned in association with Gd segregations along dislocations that nucleated during hot extrusion. Such Gd-fiber patterns are able to regulate the relative activities of slips and twinning, as a result, overcome the inherent limitations in strength and ductility of Mg alloys. This nano-fiber patterning approach could be an effective method to engineer hexagonal metals

Factors Affecting the Accuracy of Genomic Selection for Agricultural Economic Traits in Maize, Cattle, and Pig Populations

Genomic Selection (GS) has been proved to be a powerful tool for estimating genetic values in plant and livestock breeding. Newly developed sequencing technologies have dramatically reduced the cost of genotyping and significantly increased the scale of genotype data that used for GS. Meanwhile, state-of-the-art statistical methods were developed to make the best use of high marker density genotype data. In this study, 14 traits from four data sets of three species (maize, cattle, and pig) and five influential factors that affect the prediction accuracy were evaluated, including marker density (from 1 to ~600 k), statistical method (GBLUP-A, GBLUP-AD, and BayesR), minor allele frequency (MAF), heritability, and genetic architecture. Results indicate that in the GBLUP method, higher marker density leads to a higher prediction accuracy. In contrast, BayesR method needs more Monte Carlo Markov Chain (MCMC) iterations to reach the convergence and get reliable prediction values. BayesR outperforms GBLUP in predicting high or medium heritability trait that affected by one or several genes with large effects, while GBLUP performs similarly or slightly better than BayesR in predicting low heritability trait that controlled by a large amount of genes with minor effects. Prediction accuracy of trait with complex genetic architecture can be improved by increasing the marker density. Interestingly, for simple traits that controlled by one or several genes with large effects, higher marker density can cause a lower prediction accuracy if the QTN is included, but leads to a higher prediction accuracy if the QTN is excluded. The quantity of genetic markers with low MAF would not significantly affect the prediction accuracy of GBLUP, but results in a bad prediction accuracy performance of BayesR method. Compared with GBLUP-A, GBLUP-AD didn't show any advantages in capturing the non-additive variance for the traits with high heritability. The factors that affected prediction accuracy are discussed in this study and indicate that a combination of either GBLUP or BayesR method with moderate marker density and favorable polymorphism single nucleotide polymorphisms (SNPs) (~25 k SNPs) would always produce a good and stable prediction accuracy with acceptable breeding and computational costs

Research Advances in Major Allergenic Soybean Proteins and Their Detection Techniques

Soybean is one of the common food allergens and can cause severe allergic reactions. Currently, due to the lack of a definite treatment for soybean allergy, people with soybean allergy have to avoid eating foods containing soybean protein. However, the addition of different food ingredients and complex processing during food production can make it difficult to accurately detect the presence of soybean allergens in some foods. Therefore, researching methods for the detection of soybean allergens in foods is particularly important. This paper focuses on the major allergenic proteins in soybean and their structural properties, and summarizes the current major techniques for the detection of soybean allergens. It is expected that this review will be of significance for effectively avoiding soybean allergy and ensuring the life and health of people with soybean allergy

Efficacy of radiomics model based on the concept of gross tumor volume and clinical target volume in predicting occult lymph node metastasis in non-small cell lung cancer

ObjectiveThis study aimed to establish a predictive model for occult lymph node metastasis (LNM) in patients with clinical stage I-A non-small cell lung cancer (NSCLC) based on contrast-enhanced CT.MethodsA total of 598 patients with stage I–IIA NSCLC from different hospitals were randomized into the training and validation group. The “Radiomics” tool kit of AccuContour software was employed to extract the radiomics features of GTV and CTV from chest-enhanced CT arterial phase pictures. Then, the least absolute shrinkage and selection operator (LASSO) regression analysis was applied to reduce the number of variables and develop GTV, CTV, and GTV+CTV models for predicting occult lymph node metastasis (LNM).ResultsEight optimal radiomics features related to occult LNM were finally identified. The receiver operating characteristic (ROC) curves of the three models showed good predictive effects. The area under the curve (AUC) value of GTV, CTV, and GTV+CTV model in the training group was 0.845, 0.843, and 0.869, respectively. Similarly, the corresponding AUC values in the validation group were 0.821, 0.812, and 0.906. The combined GTV+CTV model exhibited a better predictive performance in the training and validation group by the Delong test (p<0.05). Moreover, the decision curve showed that the combined GTV+CTV predictive model was superior to the GTV or CTV model.ConclusionThe radiomics prediction models based on GTV and CTV can predict occult LNM in patients with clinical stage I–IIA NSCLC preoperatively, and the combined GTV+CTV model is the optimal strategy for clinical application

Transposable element-initiated enhancer-like elements generate the subgenome-biased spike specificity of polyploid wheat

Transposable elements (TEs) comprise ~85% of the common wheat genome, which are highly diverse among subgenomes, possibly contribute to polyploid plasticity, but the causality is only assumed. Here, by integrating data from gene expression cap analysis and epigenome profiling via hidden Markov model in common wheat, we detect a large proportion of enhancer-like elements (ELEs) derived from TEs producing nascent noncoding transcripts, namely ELE-RNAs, which are well indicative of the regulatory activity of ELEs. Quantifying ELE-RNA transcriptome across typical developmental stages reveals that TE-initiated ELE-RNAs are mainly from RLG_famc7.3 specifically expanded in subgenome A. Acquisition of spike-specific transcription factor binding likely confers spike-specific expression of RLG_famc7.3-initiated ELE-RNAs. Knockdown of RLG_famc7.3-initiated ELE-RNAs resulted in global downregulation of spike-specific genes and abnormal spike development. These findings link TE expansion to regulatory specificity and polyploid developmental plasticity, highlighting the functional impact of TE-driven regulatory innovation on polyploid evolution

Neuron Navigator 1 (Nav1) regulates the response to cocaine in mice

Article describes how genetic variation accounts for much of the risk for developing a substance use disorder, but the underlying genetic factors and their genetic effector mechanisms are mostly unknown. To test their genetic hypothesis, the authors generated and characterized Nav1 knockout mice

Strain-Specific Antagonism of the Human H1N1 Influenza A Virus against Equine Tetherin

Tetherin/BST-2/CD317 is an interferon-induced host restriction factor that can block the budding of enveloped viruses by tethering them to the cell surface. Many viruses use certain proteins to counteract restriction by tetherin from their natural hosts, but not from other species. The influenza A virus (FLUAV) has a wide range of subtypes with different host tropisms. Human tetherin (huTHN) has been reported to restrict only specific FLUAV strains and the viral hemagglutinin (HA) and neuraminidase (NA) genes determine the sensitivity to huTHN. Whether tetherins from other hosts can block human FLUAV is still unknown. Here, we evaluate the impact of equine tetherin (eqTHN) and huTHN on the replication of A/Sichuan/1/2009 (H1N1) and A/equine/Xinjiang/1/2007 (H3N8) strains. Our results show that eqTHN had higher restriction activity towards both viruses, and its shorter cytoplasmic tail contributed to that activity. We further demonstrated that HA and NA of A/Hamburg/4/2009 (H1N1) could counteract eqTHN. Notably, our results indicate that four amino acids, 13T and 49L of HA and 32T and 80V of NA, were involved in blocking the restriction activity of eqTHN. These findings reveal interspecies restriction by eqTHN towards FLUAV, and the role of the HA and NA proteins in overcoming this restriction

Application of Multiple Locus Linear Mixed Model in Linkage Analyses and Association Studies

Quantitative trait locus (QTL) mapping and genome-wide association studies (GWAS) are still the necessary first steps towards gene discovery. With the ever-growing number of genetic markers, more efficient algorithms for genetic mapping are necessary, especially in the big data era when QTL mapping and GWAS are to be conducted simultaneously for thousand traits, e.g., metabolomic traits. Furthermore, the conventional genomic scanning approaches that detect one locus at a time are subject to many problems, including large matrix inversion, over-conservativeness for tests after Bonferroni correction and difficulty in evaluation of the total genetic contribution to a trait’s variance. Targeting these problems, we take a further step and investigate the multiple locus model that detects all markers simultaneously in a single model.The ordinary ridge regression (ORR) is well known for its high computational efficiency and analysis of the data with multicollinearity. However, ORR has never been widely applied to QTL mapping and GWAS due to its severe shrinkage on the estimated effects. Here we introduce a degree of freedom for each parameter and use it to deshrink both the estimated effect and its estimation error so that the Wald test is brought back to the same level as the Wald test of typical GWAS methods, such as efficient mixed model association (EMMA). The new method is called deshrinking ridge regression (DRR). Using sample data of small, medium and large model sizes, we demonstrate that DRR is efficient for all three model sizes while EMMA only works for medium and large models. We also developed a sparse Bayesian learning (SBL) method for QTL mapping and GWAS. This new method adopts coordinate descent algorithm to estimate parameters by updating one parameter at a time conditional on current values of all other parameters. It uses an L2 type of penalty that allows the method to handle extremely large sample sizes (>100,000). Simulation studies show that SBL often has higher statistical powers and the simulated true loci are often detected with extremely small p-values, indicating that SBL is insensitive to stringent thresholds in significance testing

How Regions React to Economic Crisis: Regional Economic Resilience in a Chinese Perspective

The severity of the 2007–2008 global financial crisis and the spatial heterogeneity of its impact have accelerated the study of regional economic resilience. However, few have investigated whether pre-crisis determinants impact regional economic resilience. This study explores the factors influencing regional economic resilience across 284 Chinese cities from 2003 to 2019. We use data from the National Bureau of Statistics in China and apply a multilevel logistic regression model. The results indicate the magnitude of the province effects on regional performance during the financial crisis. The results show that regional economic resilience is significantly shaped by provincial trajectories and region size. Furthermore, economic agglomeration, manufacturing, education, infrastructure, and financial development make regions less susceptible to external shocks and more resilient to financial crises. The results provide supportive evidence for governments to adopt region-based policies and thereby improve their performance