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Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly related populations in addition to the single-environment prediction. Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.

Methane-oxidizing bacteria (MOB) have long been recognized as an important bioindicator for oil and gas exploration. However, due to their physiological and ecological diversity, the distribution of MOB in different habitats varies widely, making it challenging to authentically reflect the abundance of active MOB in the soil above oil and gas reservoirs using conventional methods. Here, we selected the Puguang gas field of the Sichuan Basin in Southwest China as a model system to study the ecological characteristics of methanotrophs using culture-independent molecular techniques. Initially, by comparing the abundance of the pmoA genes determined by quantitative PCR (qPCR), no significant difference was found between gas well and non-gas well soils, indicating that the abundance of total MOB may not necessarily reflect the distribution of the underlying gas reservoirs. 13C-DNA stable isotope probing (DNA-SIP) in combination with high-throughput sequencing (HTS) furthermore revealed that type II methanotrophic Methylocystis was the absolutely predominant active MOB in the non-gas-field soils, whereas the niche vacated by Methylocystis was gradually filled with type I RPC-2 (rice paddy cluster-2) and Methylosarcina in the surface soils of gas reservoirs after geoscale acclimation to trace- and continuous-methane supply. The sum of the relative abundance of RPC-2 and Methylosarcina was then used as specific biotic index (BI) in the Puguang gas field. A microbial anomaly distribution map based on the BI values showed that the anomalous zones were highly consistent with geological and geophysical data, and known drilling results. Therefore, the active but not total methanotrophs successfully reflected the microseepage intensity of the underlying active hydrocarbon system, and can be used as an essential quantitative index to determine the existence and distribution of reservoirs. Our results suggest that molecular microbial techniques are powerful tools for oil and gas prospecting. [ABSTRACT FROM AUTHOR]

With the growth of university chemistry experiment projects, the corresponding laboratory safety risks are increasing year by year for scientific research personnel, and specialized equipment. However, accident data are not stored systematically for lack of a safety platform to collect accident information, share the causes of accidents, and predict safety risks. To solve these problems, we designed a laboratory accident system to store and share related data, and predict risk levels. In this paper, the majority of chemistry laboratory accidents were manually collected by Python software (version 3.10.11) and were categorized based on their risk level. Moreover, the variable factors that generated risk were analyzed using Spsspro, which facilitates the construction of a meaningful forecasting model of laboratory safety via Stata. It is worth noting that the registered laboratory accident data in the proposed chemistry accident system were based on the data ownership safety architecture. The chemistry accident system can break through data barriers using confirmation and authorization key algorithms to trace non-tampered data sources in a timely manner when an emergency accident happens. Meanwhile, the proposed system can use our designed accident risk model to predict the risk level of any experimental project. It can also be recommended as an appropriate safety education module. [ABSTRACT FROM AUTHOR]

The massive worldwide spread of the SARS-CoV-2 virus is fueling the COVID-19 pandemic. Since the first whole-genome sequence was published in January 2020, a growing database of tens of thousands of viral genomes has been constructed. This offers opportunities to study pathways of molecular change in the expanding viral population that can help identify molecular culprits of virulence and virus spread. Here we investigate the genomic accumulation of mutations at various time points of the early pandemic to identify changes in mutationally highly active genomic regions that are occurring worldwide. We used the Wuhan NC_045512.2 sequence as a reference and sampled 15 342 indexed sequences from GISAID, translating them into proteins and grouping them by month of deposition. The per-position amino acid frequencies and Shannon entropies of the coding sequences were calculated for each month, and a map of intrinsic disorder regions and binding sites was generated. The analysis revealed dominant variants, most of which were located in loop regions and on the surface of the proteins. Mutation entropy decreased between March and April of 2020 after steady increases at several sites, including the D614G mutation site of the spike (S) protein that was previously found associated with higher case fatality rates and at sites of the NSP12 polymerase and the NSP13 helicase proteins. Notable expanding mutations include R203K and G204R of the nucleocapsid (N) protein inter-domain linker region and G251V of the viroporin encoded by ORF3a between March and April. The regions spanning these mutations exhibited significant intrinsic disorder, which was enhanced and decreased by the N-protein and viroporin 3a protein mutations, respectively. These results predict an ongoing mutational shift from the spike and replication complex to other regions, especially to encoded molecules known to represent major β-interferon antagonists. The study provides valuable information for therapeutics and vaccine design, as well as insight into mutation tendencies that could facilitate preventive control. [ABSTRACT FROM AUTHOR]

This article described the preparation of copper calcium titanate (CCTO)-modified poly(vinylidene fluoride) (PVDF) by solution casting, and its dielectric properties, breakdown strength and tensile properties. The phase structure, crystal type and surface morphology of the membrane were determined using the methods of Fourier-transform infrared spectroscopy, differential scanning calorimetry, X-ray diffraction and scanning electron microscopy. The results showed that the comprehensive performance of the CCTO/PVDF membrane treated with a coupling agent with a quality ratio of 2% was better than that of the membrane without coupling agent treatment. When the content of CCTO was increased to 20%, the surface structure of the membrane was compacted, and the membrane’s breakdown strength and dielectric properties were high. [ABSTRACT FROM AUTHOR]

Micro/nanotextured topographies (MNTs) can modulate cell-biomaterial interactions mostly by their controllable geometrics. Among them, TiO 2 nanotubes, regarded as having a highly controllable nanoscale geometry, have been extensively investigated and applied and significantly affect diameter-dependent cell biological behaviors. In this study, we used five typical MNTs decorated with TiO 2 nanotubes with diameters of 30, 50, 70, 100 and 120 nm to explore the optimal nanotube diameter for improving the biofunctional properties and to more deeply understand the underlying mechanisms by which these MNTs affect osteogenic differentiation by revealing the effect of beta1-integrin/Hedgehog-Gli1 signaling on this process. The MNTs affected MG63 osteoblast-like cell spreading, osteogenic gene expression (BMP-2, Runx2 and ALP), mineralization and ALP activity in a diameter-dependent pattern, and the optimal TiO 2 nanotube diameter of 70 nm provided the best microenvironment for osteogenic differentiation as well as beta1-integrin/Hedgehog-Gli1 signaling activation. This enhanced osteogenic differentiation by the optimal-diameter TiO 2 nanotubes of 70 nm was attenuated via suppression of the beta1-integrin/ Hedgehog-Gli1 signaling, which indicated a significant role of this pathway in mediating the diameter-dependent osteogenic differentiation promotional effect of MNTs with different TiO 2 nanotube diameters. These results might provide deeper insights into the signal transduction mechanisms by which different nanoscale geometries influence cellular functions for biomaterial modification and biofunctionalization., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)