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Abstract Knockout of GAS2 (growth arrest-specific protein 2), causes disorganization and destabilization of microtubule bundles in supporting cells of the cochlear duct, leading to hearing loss in vivo. However, the molecular mechanism through which GAS2 variant results in hearing loss remains unknown. By Whole-exome sequencing, we identified a novel heterozygous splicing variant in GAS2 (c.616–2 A > G) as the only candidate mutation segregating with late-onset and progressive nonsyndromic hearing loss (NSHL) in a large dominant family. This splicing mutation causes an intron retention and produces a C-terminal truncated protein (named GAS2mu). Mechanistically, the degradation of GAS2mu via the ubiquitin-proteasome pathway is enhanced, and cells expressing GAS2mu exhibit disorganized microtubule bundles. Additionally, GAS2mu further promotes apoptosis by increasing the Bcl-xS/Bcl-xL ratio instead of through the p53-dependent pathway as wild-type GAS2 does, indicating that GAS2mu acts as a toxic molecule to exacerbate apoptosis. Our findings demonstrate that this novel variant of GAS2 promotes its own protein degradation, microtubule disorganization and cellular apoptosis, leading to hearing loss in carriers. This study expands the spectrum of GAS2 variants and elucidates the underlying pathogenic mechanisms, providing a foundation for future investigations of new therapeutic strategies to prevent GAS2-associated progressive hearing loss.

Point-of-care testing (POCT) of pathogenic bacteria at low concentrations is vital to early warning of bacterial contaminations. A disposable centrifuge tube was reconstructed in this study for sensitive Salmonella detection in large-volume samples, where the entire bacterial detection progress from separation to detection, was performed within the tube. The bottom of this centrifuge tube was assembled with a flexible stirrer containing a pair of circular magnets, which was rapidly rotated using a DC motor to produce the vortex for simultaneous mixing and capture of target bacteria. Besides, immune manganese dioxide nanoflowers were synthesized and used to label target bacteria, followed by mimicking catalyze colorless TMB substrate into blue TMBox product. The product image was captured and analyzed by a smartphone App to quantitatively determine the target bacteria. This POCT centrifuge tube effectively achieved a separation efficiency of approximately 80 % for target bacteria from a 10 mL sample, enabling the detection of target bacteria within the range of 1.3 × 101 to 1.3 × 104 CFU/mL in 1 h, with a low detection limit of 13 CFU/mL. More importantly, this reconstructed centrifuge tube demonstrated its potential as a laboratory consumable for bacterial detection in routine screening owing to the features of low cost, easy operation and high integration.

Accumulating multiomics studies have been developed to gain new insights into complex diseases, including mental stress-induced diseases and myocardial ischemia. Multiomics techniques integrate multiple layers of biological data, such as genomics, transcriptomics, proteomics, and metabolomics, to obtain a more comprehensive understanding of the molecular mechanisms underlying these diseases. Despite the potential benefits of applying multiomics approaches to the study of mental stress-induced myocardial ischemia (MSIMI), such studies are relatively limited. The etiology of MSIMI remains poorly understood, highlighting the need for further research in this field. This review first discusses the current state of knowledge on MSIMI and highlights the research gaps in this field. Then, we provide an overview of recent studies that have used multiomics approaches to expand insights into mental stress-induced diseases and myocardial ischemia, respectively. Finally, we propose possible research directions that can be pursued to improve our knowledge of MSIMI and the potential benefits of applying multiomics approaches to this domain. While still in its early stages, multiomics research holds great promise for improving the recognition of MSIMI and developing more effective clinical interventions.

Empirical tropospheric models can improve the performance of GNSS precise point positioning (PPP) by providing a priori zenith tropospheric delay (ZTD) information. However, existing models experience insufficient ZTD profile refinement, inadequate correction for systematic bias between the ZTD used in empirical modelling and the GNSS ZTD, and low time efficiency in model updating as more data become available. Therefore, a refined spatiotemporal empirical ZTD model was developed in this study on the basis of the fifth generation of European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5) data and GNSS data. First, an ENM-R profile model was established by refining the modelling height of the negative exponential function model (ENM). Second, a regression kriging interpolation method was designed to model the systematic bias correction between the ERA5 ZTD and the GNSS ZTD. Last, the final refined ZTD model, ENM-RS, was established by introducing systematic bias correction into ENM-R. Experiments suggest that, compared with the ENM-R and GPT3 models, ENM-RS can effectively suppress systematic bias and improve ZTD modelling accuracy by 10~17%. To improve model update efficiency, the idea of updating an empirical model with sequential least square (SLSQ) adjustment is proposed for the first time. When ENM-RS is modelled via 12 years of ERA data, our method can reduce the time consumption to one-fifth of that of the traditional method. The benefits of our ENM-RS model are evaluated with PPP. The results show that relative to PPP solutions with ENM-R- and GPT3-derived ZTD constraints as well as no constraint, the ENM-RS ZTD constraint can decrease PPP convergence time by approximately 10~30%.

Maize residue retention is an effective agricultural practice for improving soil fertility in black soil region, where suffered from long freezing-thawing periods and intense freeze-thawing (FT) cycles. However, very few studies have examined the influence of maize residue retention on soil microbial communities under FT cycles. We investigated the response of soil microbial communities and co-occurrence networks to maize residue retention at different FT intensities over 12 cycles using a microcosm experiment conditioned in a temperature incubator. Our results indicated that maize residue retention induced dramatic shifts in soil archaeal, bacterial and fungal communities towards copiotroph-dominated communities. Maize residue retention consistently reduced soil fungal richness across all cycles, but this effect was weaker for archaea and bacteria. Normalized stochastic ratio analysis revealed that maize residue retention significantly enhanced the deterministic process of archaeal, bacterial and fungal communities. Although FT intensity significantly impacted soil respiration, it did not induce profound changes in soil microbial diversity and community composition. Co-occurrence network analysis revealed that maize residue retention simplified prokaryotic network, while did not impact fungal network complexity. The network robustness index suggested that maize residue retention enhanced the fungal network stability, but reduced prokaryotic network stability. Moreover, the fungal network in severe FT treatment harbored the most abundant keystone taxa, mainly being cold-adapted fungi. By identifying modules in networks, we observed that prokaryotic Module #1 and fungal Module #3 were enhanced by maize residue retention and contributed greatly to soil quality. Together, our results showed that maize residue retention exerted stronger influence on soil microbial communities and co-occurrence network patterns than FT intensity and highlighted the potential of microbial interactions in improving soil functionality.

Summary: Accurate detection of pathogens, particularly distinguishing between Gram-positive and Gram-negative bacteria, could improve disease treatment. Host gene expression can capture the immune system’s response to infections caused by various pathogens. Here, we present a deep neural network model, bvnGPS2, which incorporates the attention mechanism based on a large-scale integrated host transcriptome dataset to precisely identify Gram-positive and Gram-negative bacterial infections as well as viral infections. We performed analysis of 4,949 blood samples across 40 cohorts from 10 countries using our previously designed omics data integration method, iPAGE, to select discriminant gene pairs and train the bvnGPS2. The performance of the model was evaluated on six independent cohorts comprising 374 samples. Overall, our deep neural network model shows robust capability to accurately identify specific infections, paving the way for precise medicine strategies in infection treatment and potentially also for identifying subtypes of other diseases.

Due to a unique imaging mechanism, Synthetic Aperture Radar (SAR) images typically exhibit degradation phenomena. To enhance image quality and support real-time on-board processing capabilities, we propose a lightweight deep generative network framework, namely, the Lightweight Super-Resolution Generative Adversarial Network (LSRGAN). This method introduces Depthwise Separable Convolution (DSConv) in residual blocks to compress the original Generative Adversarial Network (GAN) and uses the SeLU activation function to construct a lightweight residual module (LRM) suitable for SAR image characteristics. Furthermore, we combine the LRM with an optimized Coordinated Attention (CA) module, enhancing the lightweight network’s capability to learn feature representations. Experimental results on spaceborne SAR images demonstrate that compared to other deep generative networks focused on SAR image super-resolution reconstruction, LSRGAN achieves compression ratios of 74.68% in model storage requirements and 55.93% in computational resource demands. In this work, we significantly reduce the model complexity, improve the quality of spaceborne SAR images, and validate the effectiveness of the SAR image super-resolution algorithm as well as the feasibility of real-time on-board processing technology.