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Enhanced understanding of nitrogen fixing bacteria through DNA extraction with polyvinylidene fluoride membrane
Abstract The rhizobiota, particularly nitrogen-fixing bacteria, play a crucial role in plant functioning by providing essential nutrients and defense against pathogens. This study investigated the diversity of nitrogen-fixing bacteria in a relatively understudied habitat: technosoils developed from industrial soda production. To analyze the bacterial diversity in the rhizosphere soils of wheat (Triticum aestivum L.) and aster (Tripolium pannonicum Jacq.), regions of the nifH gene were amplified and sequenced from the resident bacterial communities. A polyvinylidene fluoride (PVDF) membrane was employed for metagenomic DNA extraction, enhancing the detection of nitrogen-fixing bacteria. Prior to standard DNA extraction, an enrichment step was conducted in nitrogen-free JMV medium at 26 °C for 24 h, with a modification that replaced soil with the PVDF membrane. This approach enabled a more comprehensive analysis of the rhizosphere bacterial community, revealing that unique amplicon sequence variants (ASVs) in aster and wheat membrane samples accounted for a notable proportion of all ASVs in the dataset (8.5% and 23%, respectively) that were not captured using the standard method. Additionally, our findings demonstrated higher alpha diversity of nitrogen-fixing bacteria in the wheat rhizosphere compared to the aster rhizosphere. In wheat, the dominant genus was Insolitispirillum (38.80%), followed by unclassified genera within Gammaproteobacteria (9.76%) and Rhodospirillaceae (4.74%). In contrast, the aster rhizosphere was predominantly occupied by Azotobacter (95.69%).
Palmitoylation prevents B7-H4 lysosomal degradation sustaining tumor immune evasion
Abstract B7-H4 functions as an immune checkpoint in the tumor microenvironment (TME). However, the post-translational modification (PTM) of B7-H4 and its translational potential in cancer remains incompletely understood. We find that ZDHHC3, a zinc finger DHHC-type palmitoyltransferase, palmitoylates B7-H4 at Cys130 in breast cancer cells, preventing its lysosomal degradation and sustaining B7-H4-mediated immunosuppression. Knockdown of ZDHHC3 in tumors results in robust anti-tumor immunity and reduces tumor progression in murine models. Moreover, abemaciclib, a CDK4/6 inhibitor, primes lysosome activation and promotes lysosomal degradation of B7-H4 independently of the tumor cell cycle. Treatment with abemaciclib results in T cell activation and mitigates B7-H4-mediated immune suppression via inducing B7-H4 degradation in preclinical tumor models. Thus, B7-H4 palmitoylation is an important PTM controlling B7-H4 protein stability and abemaciclib may be repurposed to promote B7-H4 degradation, thereby treating patients with B7-H4 expressing tumors.
Safety risk assessment of weak tunnel construction with rich groundwater using an improved weighting cloud model
Time for adults to finally act like adults on climate change
Optical and acoustic plasmons in the layered material Sr2RuO4
Abstract The perfect linear temperature dependence of the electrical resistivity in a variety of “strange” metals is a real puzzle in condensed matter physics. For these materials also other non-Fermi liquid properties are predicted or detected. In particular we mention the results derived from holographic theories which conclude that plasmons should be overdamped due to a low energy continuum in the electronic susceptibility. These predictions were supported by electron energy-loss spectroscopy in reflection on cuprates and ruthenates. Here we use electron energy-loss spectroscopy in transmission to study collective charge excitations in the layer metal Sr2RuO4. This metal has a transition from a perfect Fermi liquid below T ≈ 30 K into a “strange” metal phase above T ≈ 800 K. In this compound we cover a complete range between in-phase and out-of-phase oscillations. Outside the classical range of electron-hole excitations, leading to a Landau damping, we observe well-defined plasmons. The optical (acoustic) plasmon due to an in-phase (out-of-phase) charge oscillation of neighbouring layers exhibits a quadratic (linear) positive dispersion. Using a model for the Coulomb interaction of the charges in a layered system, it is possible to describe the range of optical plasmon excitations at high energies in a mean-field random phase approximation without taking correlation effects into account. In contrast, resonant inelastic X-ray scattering data show at low energies an enhancement of the acoustic plasmon velocity due to correlation effects. This difference can be explained by an energy dependent effective mass which changes from ≈ 3.5 at low energy to 1 at high energy near the optical plasmon energy. There are no signs of over-damped plasmons predicted by holographic theories.
Leveraging retinanet based object detection model for assisting visually impaired individuals with metaheuristic optimization algorithm
Why China needs to review its approach to research evaluation
Rational design of 19F NMR labelling sites to probe protein structure and interactions
Abstract Proteins are investigated in increasingly more complex biological systems, where 19 F NMR is proving highly advantageous due to its high gyromagnetic ratio and background-free spectra. Its application has, however, been hindered by limited chemical shift dispersions and an incomprehensive relationship between chemical shifts and protein structure. Here, we exploit the sensitivity of 19 F chemical shifts to ring currents by designing labels with direct contact to a native or engineered aromatic ring. Fifty protein variants predicted by AlphaFold and molecular dynamics simulations show 80–90% success rates and direct correlations of their experimental chemical shifts with the magnitude of the engineered ring current. Our method consequently improves the chemical shift dispersion and through simple 1D experiments enables structural analyses of alternative conformational states, including ribosome-bound folding intermediates, and in-cell measurements of protein-protein interactions and thermodynamics. Our strategy thus provides a simple and sensitive tool to extract residue contact restraints from chemical shifts for previously intractable systems.
Predicting high-need high-cost pediatric hospitalized patients in China based on machine learning methods
The contribution of de novo coding mutations to meningomyelocele
Impact of dhps mutations on sulfadoxine-pyrimethamine protective efficacy and implications for malaria chemoprevention
Abstract Sulfadoxine-pyrimethamine (SP) is recommended for perennial malaria chemoprevention in young children in high burden areas across Africa. Mutations in the dihydropteroate synthase (dhps) gene (437 G/540E/581 G) associated with sulfadoxine resistance vary regionally, but their effect on SP protective efficacy is unclear. We retrospectively analyse time to microscopy and PCR-confirmed re-infection in seven efficacy trials including 1639 participants in 12 sites across Africa. We estimate the duration of SP protection against parasites with different genotypes using a Bayesian mathematical model that accounts for variation in transmission intensity and genotype frequencies. The longest duration of SP protection is >42 days against dhps sulfadoxine-susceptible parasites and 30.3 days (95%Credible Interval (CrI):17.1-45.1) against the West-African genotype dhps GKA (437G-K540-A581). A shorter duration of protection is estimated against parasites with additional mutations in the dhps gene, with 16.5 days (95%CrI:11.2-37.4) protection against parasites with the east-African genotype dhps GEA (437G-540E-A581) and 11.7 days (95%CrI:8.0-21.9) against highly resistant parasites carrying the dhps GEG (437G-540E−581G) genotype. Using these estimates and modelled genotype frequencies we map SP protection across Africa. This approach and our estimated parameters can be directly applied to any setting using local genomic surveillance data to inform decision-making on where to scale-up SP-based chemoprevention or consider alternatives.
Preparation of MOF-5 imprinted chromium ferrite and its application in decontaminating metronidazole and penicillin G contaminated water system
Economic and environmental competitiveness of multiple hydrogen production pathways in China
Abstract This study utilises the optimization method to ascertain the levelized cost of hydrogen and life cycle carbon emissions of four water electrolysis hydrogen production systems across 31 provinces and regions of China, and compares these with hydrogen production from coal, natural gas and industrial by-products. The findings indicate that the grid-connected water electrolysis hydrogen production system has low-carbon advantages only in certain provinces, and time-of-use electricity prices can improve its economic competitiveness. The off-grid water electrolysis hydrogen production system can achieve near-zero carbon emissions, although additional investment is required to configure larger capacities for electricity energy storage and hydrogen storage. Projections indicate that by 2045–2050, this system could emerge as the most cost-effective to hydrogen production, a milestone that could be advanced by 5–15 years through the implementation of specific carbon reduction incentives or production subsidies. Prior to this timeframe, hydrogen production through industry by-products emerges as a viable alternative for the development of hydrogen energy.
Machine learning assisted in Silico discovery and optimization of small molecule inhibitors targeting the Nipah virus glycoprotein
Climate risk for younger generations is set to soar
scMINER: a mutual information-based framework for clustering and hidden driver inference from single-cell transcriptomics data
Abstract Single-cell transcriptomics data present challenges due to their inherent stochasticity and sparsity, complicating both cell clustering and cell type-specific network inference. To address these challenges, we introduce scMINER (single-cell Mutual Information-based Network Engineering Ranger), an integrative framework for unsupervised cell clustering, transcription factor and signaling protein network inference, and identification of hidden drivers from single-cell transcriptomic data. scMINER demonstrates superior accuracy in cell clustering, outperforming five state-of-the-art algorithms and excelling in distinguishing closely related cell populations. For network inference, scMINER outperforms three established methods, as validated by ATAC-seq and CROP-seq. In particular, it surpasses SCENIC in revealing key transcription factor drivers involved in T cell exhaustion and Treg tissue specification. Moreover, scMINER enables the inference of signaling protein networks and drivers with high accuracy, which presents an advantage in multimodal single cell data analysis. In addition, we establish scMINER Portal, an interactive visualization tool to facilitate exploration of scMINER results.
Influence of surface coal mining on carbon storage in semi-arid steppe
Author Correction: In vivo imaging in mouse spinal cord reveals that microglia prevent degeneration of injured axons
An automated hip fracture detection, classification system on pelvic radiographs and comparison with 35 clinicians
Abstract Accurate diagnosis of orthopedic injuries, especially pelvic and hip fractures, is vital in trauma management. While pelvic radiographs (PXRs) are widely used, misdiagnosis is common. This study proposes an automated system that uses convolutional neural networks (CNNs) to detect potential fracture areas and predict fracture conditions, aiming to outperform traditional object detection-based systems. We developed two deep learning models for hip fracture detection and prediction, trained on PXRs from three hospitals. The first model utilized automated hip area detection, cropping, and classification of the resulting patches. The images were preprocessed using the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm. The YOLOv5 architecture was employed for the object detection model, while three different pre-trained deep neural network (DNN) architectures were used for classification, applying transfer learning. Their performance was evaluated on a test dataset, and compared with 35 clinicians. YOLOv5 achieved a 92.66% accuracy on regular images and 88.89% on CLAHE-enhanced images. The classifier models, MobileNetV2, Xception, and InceptionResNetV2, achieved accuracies between 94.66% and 97.67%. In contrast, the clinicians demonstrated a mean accuracy of 84.53% and longer prediction durations. The DNN models showed significantly better accuracy and speed compared to human evaluators (p < 0.0005, p < 0.01). These DNN models highlight promising utility in trauma diagnosis due to their high accuracy and speed. Integrating such systems into clinical practices may enhance the diagnostic efficiency of PXRs.