Genetic Ablation of GIGYF1, Associated With Autism, Causes Behavioral and Neurodevelopmental Defects in Zebrafish and Mice.

BACKGROUND: Autism spectrum disorder is characterized by deficits in social communication and restricted or repetitive behaviors. Due to the extremely high genetic and phenotypic heterogeneity, it is critical to pinpoint the genetic factors for understanding the pathology of these disorders. METHODS: We analyzed the exomes generated by the SPARK (Simons Powering Autism Research) project and performed a meta-analysis with previous data. We then generated 1 zebrafish knockout model and 3 mouse knockout models to examine the function of GIGYF1 in neurodevelopment and behavior. Finally, we performed whole tissue and single-nucleus transcriptome analysis to explore the molecular and cellular function of GIGYF1. RESULTS: GIGYF1 variants are significantly associated with various neurodevelopmental disorder phenotypes, including autism, global developmental delay, intellectual disability, and sleep disturbance. Loss of GIGYF1 causes similar behavioral effects in zebrafish and mice, including elevated levels of anxiety and reduced social engagement, which is reminiscent of the behavioral deficits in human patients carrying GIGYF1 variants. Moreover, excitatory neuron-specific Gigyf1 knockout mice recapitulate the increased repetitive behaviors and impaired social memory, suggesting a crucial role of Gigyf1 in excitatory neurons, which correlates with the observations in single-nucleus RNA sequencing. We also identified a series of downstream target genes of GIGYF1 that affect many aspects of the nervous system, especially synaptic transmission. CONCLUSIONS: De novo variants of GIGYF1 are associated with neurodevelopmental disorders, including autism spectrum disorder. GIGYF1 is involved in neurodevelopment and animal behavior, potentially through regulating hippocampal CA2 neuronal numbers and disturbing synaptic transmission.

A Deep-Learning Model With the Attention Mechanism Could Rigorously Predict Survivals in Neuroblastoma.

BACKGROUND: Neuroblastoma is one of the most devastating forms of childhood cancer. Despite large amounts of attempts in precise survival prediction in neuroblastoma, the prediction efficacy remains to be improved. METHODS: Here, we applied a deep-learning (DL) model with the attention mechanism to predict survivals in neuroblastoma. We utilized 2 groups of features separated from 172 genes, to train 2 deep neural networks and combined them by the attention mechanism. RESULTS: This classifier could accurately predict survivals, with areas under the curve of receiver operating characteristic (ROC) curves and time-dependent ROC reaching 0.968 and 0.974 in the training set respectively. The accuracy of the model was further confirmed in a validation cohort. Importantly, the two feature groups were mapped to two groups of patients, which were prognostic in Kaplan-Meier curves. Biological analyses showed that they exhibited diverse molecular backgrounds which could be linked to the prognosis of the patients. CONCLUSIONS: In this study, we applied artificial intelligence methods to improve the accuracy of neuroblastoma survival prediction based on gene expression and provide explanations for better understanding of the molecular mechanisms underlying neuroblastoma.

First-principles investigations of hydrogen trapping in Y2O3 and the Y2O3|bcc Fe interface.

Based on first-principles calculations, the binding energy of hydrogen atom to Y2O3 and Y2O3|bcc Fe interface (relative to bcc Fe side) with cube-on-cube orientation is at least 0.45 eV, if hydrogen substitutional is considered, or at least 0.26 eV if only hydrogen interstitial is considered. The calculated binding energies do not have a unique fixed value, because they are dependent on the interface structure, the Fermi level of Y2O3 near the interface and the chemical potential of Y/O. Hydrogen substitutional is more stable than hydrogen interstitial near the interface for Fermi level around calculated Schottky barrier height (SBH) at equilibrium. The Y2O3 particle interior can be an effective trapping site for hydrogen. Hydrogen interstitial, hydrogen substitutional and Y/O vacancy have a much lower energy near the interface than within the Y2O3 particle, presumably due to image charge interaction related to their non-zero charge state. For neutral impurities or defects, the energy near interface and that far away from the interface are similar (⩽0.1 eV difference) for a perfect coherent interface. The Y2O3|bcc Fe interface should provide effective trapping sites for hydrogen atoms in oxide dispersion strengthened (ODS) steels.

Targeted capture and sequencing of 1245 SNPs for forensic applications.

Next generation sequencing (NGS) technologies have enabled the possibility of analyzing a large number of SNPs simultaneously from multiple samples in a single experiment, for complementing the shortcomings of STR based methods. To efficiently genotype the desired SNPs, it is critical to optimize the library construction procedures. In this study, we formulated a strategy combining the molecular inversion probe (MIP) based target region capture method and NGS for genotyping 1245 SNPs. All the SNPs we selected exhibited high heterozygosity (minor allele frequency (MAF) > 0.3) according to 1000 genomes data. We applied the method to genotype a population of 210 unrelated individuals from the Hubei province of China and assessed the allele frequencies, Hardy-Weinberg equilibrium and linkage disequilibrium. The MAFs of more than 95% of the SNPs were ≥0.2, and no significant deviation or strong linkage was observed for 98% of the SNPs. The data indicated that, even within a relatively confined region, our SNP panel is suitable for individual identifications. Furthermore, we performed paternity test for 7 trio families using low quality DNA samples. The conclusions are in total agreement with these of STR-based analyses, with higher confidence indexes. Finally, we evaluated the performance of the MIP-NGS method with mock degraded DNA samples. We were able to genotype most of the SNPs even when the genomic DNA was sonicated to ˜100 bp range. In summary, we established a highly accurate and cost-effective method of SNP genotyping, which is potentially capable of solving complex issues encountered in forensic practices.

First-principles study of the structure and band structure of Ga2Se3.

Our first-principles calculations show that the ordering of stoichiometric cation vacancies in Ga2Se3 has a large influence on the bandgap, up to 0.55 eV. Therein, the zigzag-line vacancy-ordered Ga2Se3 has the maximum bandgap (∼2.56 eV direct bandgap), and the straight-line vacancy-ordered Ga2Se3 has the minimum bandgap (∼1.99 eV indirect bandgap) at 0 K, according to scGW calculations. The bandgap difference (0.55 eV) is almost the same for normal density functional theory (DFT) calculations, hybrid DFT calculations and GW calculations. The calculation results are consistent with the experimental bandgap range of 2.0-2.6 eV at room temperature. Also, hydrostatic pressure (<9 GPa) tends to increase the bandgap, consistent with the experiments in the literature.

First-principles study of bubble nucleation and growth behaviors in α U-Zr.

Bubble nucleation and growth is responsible for swelling in metallic fuels such as U-Zr. Computational modeling is useful for understanding and ultimately developing mitigation strategies for the swelling behavior of the fuel. However, the relevant fundamental parameters are not currently available. In our previous work, the formation energy and migration barrier of uranium vacancies and interstitials in α U have been obtained by first-principles calculations, and the calculated diffusion activation energy agrees reasonably well with the experimental results, within 0.1 eV (Huang and Wirth 2011 J. Phys.: Condens. Matter 23 205402). In this paper, the formation energy and migration barrier of Xe, Zr, Pu, in addition to the binding energy of small vacancy clusters, Xe-vacancy clusters, and small interstitial clusters are investigated. These are among the essential data essential for the analysis and computational modeling of swelling in metallic nuclear fuel.

First-principles study of diffusion of interstitial and vacancy in α U-Zr.

Metallic uranium-zirconium alloys are of interest for a variety of fast reactor designs, and there is substantial experience with the behavior of metallic fuels. Yet, there remain a number of questions regarding the mechanisms controlling fission-gas-driven swelling in these alloys. Here we present results of ab initio calculations of the diffusion behavior of interstitial and vacancy point defects in α U-Zr alloys. The formation energy and migration barrier of vacancy and interstitial defects, and the influence of Zr on these values, is obtained and compared with experimental results. Our results confirm that self-diffusion in pure α U is via a simple vacancy mechanism, and shows anisotropic character. The calculated values of activation energy are consistent with the experimental results in the literature. For interstitial diffusion, the kick-out mechanism was found to have the smallest energy barrier. The calculations of point defects, and later Xe, in U-Zr alloys will provide a foundation for computational modeling of fission gas bubble nucleation and growth.

First-principles study of diffusion of Li, Na, K and Ag in ZnO.

Based on ab initio total energy calculations, Li, Na and Ag interstitials are found to be stable with at least a 1.56 eV energy barrier to transform to a zinc substitutional site in ZnO, whereas K interstitial has a relatively small energy barrier at 0.79 eV. The isolated dopant substitutional defects (Li(Zn), Na(Zn), K(Zn) and Ag(Zn)) are found to be rather stable, with at least a 3.4 eV energy barrier to transform to an interstitial site. All of the dopant interstitials (Li(i), Na(i), K(i) and Ag(i)) are fast diffusers. The diffusion of Li interstitial is isotropic, whereas the diffusion of Na, K and Ag interstitials is highly anisotropic. Fundamental processes of the vacancy-assisted mechanisms are systematically investigated and specific values of the energy barriers are obtained.

First-principles study of diffusion of oxygen vacancies and interstitials in ZnO.

A comprehensive investigation of oxygen vacancy and interstitial diffusion in ZnO has been performed using ab initio total energy calculations with both the local density approximation (LDA) and the generalized gradient approximation (GGA). Based on our calculation results, oxygen octahedral interstitials are fast diffusers, contributing to annealing processes, as well as being responsible for the self-diffusion of oxygen for n-type ZnO, and oxygen vacancies are responsible for the self-diffusion of oxygen for p-type ZnO.