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Nano-confined controllable crystallization in supramolecular polymeric membranes for ultra-selective desalination
The prognostic value of cortical stimulation induced seizures using stereo EEG in presurgical evaluation of focal epilepsies
Abstract The value of stimulation-induced seizures for multimodal determination of the epileptogenic zone in preoperative epilepsy diagnostics has not yet been sufficiently investigated. Patients with focal pharmacorefractory epilepsy who underwent invasive electroencephalography with cortical 50 Hz stimulation at the Epilepsy Center Erlangen between 2018 and 2023, had at least one stimulation-induced seizure, underwent resective epilepsy surgery, and had a postoperative follow-up ≥ 1 year were analyzed. 20 patients were included, 11 (55.0%) with temporal, 7 (35.0%) with frontal and 2 (10.0%) with parietal lobe epilepsy. 12 patients (60.0%) had a good Engel outcome (Engel 1A). Associated with a good vs. poor (Engel 1B-4) surgical outcome were not only the percentage of resected electrode contacts of the spontaneous seizure onset zone, SOZ (p = 0.005), but also the stimulation SOZ (p = 0.022), as well as stimulation-induced seizure with a typical seizure semiology (p = 0.033), the electrodes inducing a stimulation-induced seizure (p = 0.014), electrodes with an identical seizure onset pattern (p = 0.035), and the occurrence of low voltage fast seizure onset pattern, LVFA (p = 0.015). ROC analyses showed that the AUC for the predictors of the spontaneous SOZ were greatest for the stimulation SOZ (AUC 0.876) and stimulation-induced seizures with LVFA (0.860). Analysis of combined predictors showed higher odds of predicting SOZ for combinations including LVFA. Electroclinical stimulation seizures have prognostic value in determining the epileptogenic zone. Characteristics such as the seizure onset zone, seizure pattern and stimulation seizure semiology predict seizure freedom in case of resection of electrode contacts. Electrodes should be resected where both stimulation seizures have been induced or the seizure pattern has been localized and low voltage fast seizure pattern has occurred.
Dynamic boron-doping switched chitin-based single-atom Pt catalyst for chemo-selective hydrogenation
Advanced fermentation techniques enhance dioxolanone type biopesticide production from Phyllosticta capitalensis
CYP51A1 drives resistance to pH-dependent cell death in pancreatic cancer
Constrained search space selection based optimization approach for enhanced reduced order approximation of interconnected power system models
An obesogenic FTO allele causes accelerated development, growth and insulin resistance in human skeletal muscle cells
Abstract Human GWAS have shown that obesogenic FTO polymorphisms correlate with lean mass, but the mechanisms have remained unclear. It is counterintuitive because lean mass is inversely correlated with obesity and metabolic diseases. Here, we use CRISPR to knock-in FTO rs9939609-A into hESC-derived tissue models, to elucidate potentially hidden roles of FTO during development. We find that among human tissues, FTO rs9939609-A most robustly affect human muscle progenitors’ proliferation, differentiation, senescence, thereby accelerating muscle developmental and metabolic aging. An edited FTO rs9939609-A allele over-stimulates insulin/IGF signaling via increased muscle-specific enhancer H3K27ac, FTO expression and m6A demethylation of H19 lncRNA and IGF2 mRNA, with excessive insulin/IGF signaling leading to insulin resistance upon replicative aging or exposure to high fat diet. This FTO-m6A-H19/IGF2 circuit may explain paradoxical GWAS findings linking FTO rs9939609-A to both leanness and obesity. Our results provide a proof-of-principle that CRISPR-hESC-tissue platforms can be harnessed to resolve puzzles in human metabolism.
An improved synergistic dual-layer feature selection algorithm with two type classifier for efficient intrusion detection in IoT environment
Abstract In an era of increasing sophistication and frequency of cyber threats, securing Internet of Things (IoT) networks has become a paramount concern. IoT networks, with their diverse and interconnected devices, face unique security challenges that traditional methods often fail to address effectively. To tackle these challenges, an Intrusion Detection System (IDS) is specifically designed for IoT environments. This system integrates a multi-faceted approach to enhance security against emerging threats. The proposed IDS encompasses three critical subsystems: data pre-processing, feature selection and detection. The data pre-processing subsystem ensures high-quality data by addressing missing values, removing duplicates, applying one-hot encoding, and normalizing features using min-max scaling. A robust feature selection subsystem, employing Synergistic Dual-Layer Feature Selection (SDFC) algorithm, combines statistical methods, such as mutual information and variance thresholding, with advanced model-based techniques, including Support Vector Machine (SVM) with Recursive Feature Elimination (RFE) and Particle Swarm Optimization (PSO) are employed to identify the most relevant features. The classification subsystem employ two stage classifier namely LightGBM and XGBoost for efficient classification of the network traffic as normal or malicious. The proposed IDS is implemented in MATLAB by using TON-IoT dataset with various performance metrics. The experimental results demonstrate that the proposed SDFC method significantly enhances classifier performance, consistently achieving higher accuracy, precision, recall, and F1 scores compared to other existing methods.
Continuous map of early hematopoietic stem cell differentiation across human lifetime
Abstract Uncovering early gene network changes of human hematopoietic stem cells (HSCs) leading to differentiation induction is of utmost importance for therapeutic manipulation. We employed single cell proteo-transcriptomic sequencing to FACS-enriched bone marrow hematopoietic stem and progenitor cells (HSPCs) from 15 healthy donors. Pseudotime analysis reveals four major differentiation trajectories, which remain consistent upon aging, with an early branching point into megakaryocyte-erythroid progenitors. However, young donors suggest a more productive differentiation from HSPCs to committed progenitors of all lineages. tradeSeq analysis depicts continuous changes in gene expression of HSPC-related genes (DLK1, ADGRG6), and provides a roadmap of gene expression at the earliest branching points. We identify CD273/PD-L2 to be highly expressed in a subfraction of immature multipotent HSPCs with enhanced quiescence. Functional experiments confirm the immune-modulatory function of CD273/PD-L2 on HSPCs in regulating T-cell activation and cytokine release. Here, we present a molecular map of early HSPC differentiation across human life.
Potential of roughening geometric elements as bridge pier local scour countermeasures
An atypical atherogenic chemokine that promotes advanced atherosclerosis and hepatic lipogenesis
Abstract Atherosclerosis is the underlying cause of myocardial infarction and ischemic stroke. It is a lipid-triggered and cytokine/chemokine-driven arterial inflammatory condition. We identify D-dopachrome tautomerase/macrophage migration-inhibitory factor-2 (MIF-2), a paralog of the cytokine MIF, as an atypical chemokine promoting both atherosclerosis and hepatic lipid accumulation. In hyperlipidemic Apoe –/– mice, Mif-2-deficiency and pharmacological MIF-2-blockade protect against lesion formation and vascular inflammation in early and advanced atherogenesis. MIF-2 promotes leukocyte migration, endothelial arrest, and foam-cell formation, and we identify CXCR4 as a receptor for MIF-2. Mif-2-deficiency in Apoe –/– mice leads to decreased plasma lipid levels and suppressed hepatic lipid accumulation, characterized by reductions in lipogenesis-related pathways, tri-/diacylglycerides, and cholesterol-esters, as revealed by hepatic transcriptomics/lipidomics. Hepatocyte cultures and FLIM-FRET-microscopy suggest that MIF-2 activates SREBP-driven lipogenic genes, mechanistically involving MIF-2-inducible CD74/CXCR4 complexes and PI3K/AKT but not AMPK signaling. MIF-2 is upregulated in unstable carotid plaques from atherosclerotic patients and its plasma concentration correlates with disease severity in patients with coronary artery disease. These findings establish MIF-2 as an atypical chemokine linking vascular inflammation to metabolic dysfunction in atherosclerosis.
Role of genetic diversity and salicylic acid in drought stress memory of tall fescue
Unveiling the atomistic mechanism of oxide scale spalling in heat-resistant alloys
Efficient red-NIR laser of Er3+/Yb3+, Er3+/Nd3+, and Er3+/Ce3+ co-doped in oxyfluorophosphate glass
Histidine 73 methylation coordinates β-actin plasticity in response to key environmental factors
3D virtual histology of rodent and primate cochleae with multi-scale phase-contrast X-ray tomography
Abstract Multi-scale X-ray phase contrast tomography (XPCT) enables three-dimensional (3D), non-destructive imaging of intact small animal cochlea and apical cochlear turns. Here we report on post-mortem imaging of excised non-human primate and rodent cochleae at different $${\upmu }$$ -CT and nano-CT synchrotron instruments. We explore different sample embeddings, stainings and imaging regimes. Under optimized conditions of sample preparation, instrumentation, imaging protocol, and phase retrieval, high image quality and detail level can be achieved in 3D reconstructions. The showcased instrumentation and imaging protocols along with the reconstucted volumes can serve as benchmarks and reference for multi-scale microanatomy and 3D histology. The provided benchmarks and imaging protocols of this work cover a wide range of scales and are intended as augmented imaging tools for auditory research.
FIORA: Local neighborhood-based prediction of compound mass spectra from single fragmentation events
Abstract Non-targeted metabolomics holds great promise for advancing precision medicine and biomarker discovery. However, identifying compounds from tandem mass spectra remains a challenging task due to the incomplete nature of spectral reference libraries. Augmenting these libraries with simulated mass spectra can provide the necessary references to resolve unmatched spectra, but generating high-quality data is difficult. In this study, we present FIORA, an open-source graph neural network designed to simulate tandem mass spectra. Our main contribution lies in utilizing the molecular neighborhood of bonds to learn breaking patterns and derive fragment ion probabilities. FIORA not only surpasses state-of-the-art fragmentation algorithms, ICEBERG and CFM-ID, in prediction quality, but also facilitates the prediction of additional features, such as retention time and collision cross section. Utilizing GPU acceleration, FIORA enables rapid validation of putative compound annotations and large-scale expansion of spectral reference libraries with high-quality predictions.