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Macrophage-derived amphiregulin induces myofibroblast transition in adipogenic lineage precursors near Staphylococcus aureus abscess in bone marrow
Epileptic seizure detection from electroencephalogram signals based on 1D CNN-LSTM deep learning model using discrete wavelet transform
Abstract Excessive electrical activity in the brain causes epileptic seizures which can be detected through Electroencephalogram (EEG) signals. The research aims to identify epileptic seizures using EEG records automatically. Firstly, EEG bands are extracted using Discrete Wavelet Transform (DWT) and concatenated. Secondly, the resulting feature vector is fed into a 1-dimensional Convolutional Neural Network (CNN) to extract spatial information. The Long-Short Term Memory (LSTM) layer then receives the feature maps in order to extract the temporal information. Ultimately, a fully connected layer will use the generated spatiotemporal features as input to categorize the signal. Results show that the suggested model performs well on the following datasets: the TUSZ corpus, which has 94.32% accuracy, 86.08% Kappa value, and 79.01% GDR; the BONN dataset, which has 97.24% accuracy, 97.92% Kappa value, and 99.18% GDR; and the CHB-MIT dataset, which has 96.94% accuracy, 94.33% Kappa value, and 96.36% GDR. The computational complexity for BONN, CHB-MIT, and TUSZ datasets are $$\:3.07\times\:{10}^{7}$$ , $$\:1.67\times\:{10}^{6}$$ and $$\:1.67\times\:{10}^{6}$$ respectively. The performance of several popular machine learning classifiers is compared with the proposed model. The results show that the model outperforms existing approaches. The model’s strong performance is largely due to the CNN’s ability to effectively extract meaningful spatial features.
Neurodevelopmental commonalities in cognitive control networks for mathematics and reading in meta-analysis of 3308 participants
Feasibility to obtain vessel occlusion using vessel loop
Facilitating analysis and dissemination of proteomics data through metadata integration in MaxQuant
Abstract Metadata plays an essential role in the analysis and dissemination of proteomics data. It annotates sample information for output tables from library searches and displays sample information from data files in public repositories. However, integrating metadata into data analysis can be time-consuming and is not well standardized. Inconsistent metadata formats in public repositories hinder other researchers’ ability to reproduce and reuse these public datasets. Here we present the metadata integration in MaxQuant, which provides a user-friendly way to export metadata as SDRF, the standard format that maps sample properties to proteomics data files. We also implemented the annotation of output tables with the SDRF file, enabling users to perform seamless downstream data analysis with annotated output tables. These features provide a simple and standardized approach to creating and leveraging standardized metadata, thereby facilitating data analysis and improving the reusability and reproducibility of public proteomics datasets.
Lysosome-related molecular subtypes reveal prognostic signatures and immunotherapeutic implications in ovarian cancer
Super-silencers are crucial for development and carcinogenesis in B cells
Abstract The strength of the repressive histone H3K27me3 signal varies across silencers. Focusing on regions with unusually strong signals—super-silencers—we show that B-cell super-silencers are initially linked to gene upregulation in development, with target genes highly expressed in stem cells. About 13% of B-cell super-silencers convert to super-enhancers in B-cell lymphoma; 22% of these recur in over half of patients. Genes like BCL6 and BACH2 tied to these conversions are downregulated faster by JQ1, a super-enhancer-disrupting anti-cancer agent. Super-silencers are enriched for B-cell cancer-associated variants—both somatic and germline—and translocation breakpoints, exceeding levels in other regulatory elements like CTCF binding sites. Over 80% of B-cell lymphoma t(3;14)(q27;q32) translocations fuse BCL6 super-silencers with enhancer-rich regions. Super-silencer repression depends on CpG content: CpG-rich elements block promoter–enhancer contacts; CpG-poor — inhibit looping. These findings highlight super-silencers’ key role in B-cell regulation and suggest their alteration may be a primary factor of B-cell carcinogenesis.
Integrating transcriptomic and epigenomic data to identify potential biomarkers in gestational diabetes mellitus patients
Itinerant and topological excitations in a honeycomb spiral spin liquid candidate
A nationwide comparative study of pediatric and adult intussusception in emergency departments during the COVID-19
Doping with phosphorus reduces anion vacancy disorder in CdSeTe semiconductors enabling higher solar cell efficiency
Physical parameter optimization and band alignment approach for efficiency improvement in Cs2AgBiBr6 based lead-free perovskite solar cells
Editorial Expression of Concern: Recovery of learning and memory is associated with chromatin remodelling
Corticonigral projections recruit substantia nigra pars lateralis dopaminergic neurons for auditory threat memories
Abstract Dopaminergic neurons (DANs) in the substantia nigra pars lateralis (SNL) project to the tail of striatum, where they contribute to threat behaviors. Auditory cortex contributes to threat conditioning, but whether it directly modulates DANs is unclear. Here, we show that SNL DANs fire irregularly, achieve rapid maximal firing rates, exhibit distinct ionic conductances, and receive predominantly excitatory input. This contrasts with substantia nigra pars compacta (SNc) DANs that fire regularly and receive mainly inhibitory input, establishing SNL DANs as a physiologically distinct dopaminergic subpopulation. Functional mapping revealed robust excitatory input from auditory and temporal association cortices to SNL DANs, but not SNc DANs. In behavioral experiments, inhibiting neurotransmitter release from either SNL DANs or cortical afferents to SNL resulted in impaired auditory threat conditioning. Thus, our work reveals robust functional corticonigral projections to SNL DANs which directly regulate threat behaviors.
Retractions can reshape scientists’ careers in unexpected ways
Molecular-resolution imaging of ice crystallized from liquid water by cryogenic liquid-cell TEM
Abstract Despite the ubiquity of ice, a molecular-resolution image of nanoscopic defects or microstructures in ice crystallized from liquid water has never been obtained. This is mainly due to the difficulties in preparing and preserving crystalline ice samples that can survive under high-resolution imaging conditions. Here, we report the stabilization and Å-resolution electron imaging of ice Ih crystallized from liquid water by developing cryogenic liquid-cell transmission electron microscopy (CRYOLIC-TEM). We combine lattice mapping with molecular dynamics simulations to reveal that ice formation is highly tolerant to nanoscale defects such as misoriented subdomains and trapped gas bubbles, which are stabilized by molecular-scale structural motifs. Importantly, bubble surfaces adopt low-energy nanofacets and create negligible strain fields in the surrounding crystal. These bubbles can dynamically nucleate, grow, migrate, dissolve, and coalesce under electron irradiation and be monitored in situ near a steady state. This work improves our understanding of water crystallization behaviors at a molecular spatial resolution.