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Dual regulation of mitochondrial fusion by Parkin–PINK1 and OMA1
Nonlinear microbial thermal response and its implications for abrupt soil organic carbon responses to warming
Evaluating the change and trend of construction land in Changsha City based GeoSOS-FLUS model and machine learning methods
High-order Michaelis-Menten equations allow inference of hidden kinetic parameters in enzyme catalysis
A high performance hybrid LSTM CNN secure architecture for IoT environments using deep learning
Abstract The growing use of IoT has brought enormous safety issues that constantly demand stronger hide from increasing risks of intrusions. This paper proposes an Advanced LSTM-CNN Secure Framework to optimize real-time intrusion detection in the IoT context. It adds LSTM layers, which allow for temporal dependencies to be learned, and CNN layers to decompose spatial features which makes this model efficient in identifying threats. It is important to note that the used BoT-IoT dataset involves various cyber attack typologies like DDoS, botnet, reconnaissance, and data exfiltration. These outcomes present that the proposed LSTM-CNN model has 99.87% accuracy, 99.89% precision, and 99.85% recall with a low false positive rate of 0.13% and exceeds CNN, RNN, Standard LSTM, BiLSTM, GRU deep learning models. In addition, the model has 90.2% accuracy in conditions of adversarial attack proving that the model is robust and can be used for practical purposes. Based on feature importance analysis using SHAP, the work finds that packet size, connection duration, and protocol type should be the possible indicators for threat detection. These outcomes suggest that the Hybrid LSTM-CNN model could be useful in improving the security of IoT devices to provide increased reliability with low false alarm rates.
Refined weathering CO2 budget of the Tibetan Plateau strongly modulated by sulphide oxidation
Abstract Estimation of net CO2 consumption by weathering in orogen is complicated as high erosion rate promotes competing processes of CO2 consumption (silicate weathering) and releasing (sulfuric acid (H2SO4) dissolution of carbonate). Quantification of H2SO4 disturbing on weathering is missing in the Tibetan Plateau, hindering the understanding of Himalayan orogenesis impact on global carbon cycle. Here we calculate the riverine solute contributions from both carbonic and sulfuric acid mediated weathering, and their weathering fluxes with major river geochemistry dataset from the Tibetan Plateau. We find that silicate weathering is not anomalous, while carbonate weathering flux is 2.09% of the global value with 1.01% drainage area. Over 80% H2SO4 originated from pyrite oxidation is consumed by carbonate weathering, which counteracts ~58% of the CO2 consumption flux by silicate weathering. The refined weathering CO2 budget in this work provides quantitative modern evidence for pyrite weathering in orogen serving as negative feedback on atmospheric pCO2.
Circadian phase inversion causes insulin resistance in a rat model of night work and jet lag
Structure and unusual binding mechanism of the hyaluronan receptor LYVE-1 mediating leucocyte entry to lymphatics
Abstract Immune surveillance involves the continual migration of antigen-scavenging immune cells from the tissues to downstream lymph nodes via lymphatic vessels. To enable such passage, cells first dock with the lymphatic entry receptor LYVE-1 on the outer surface of endothelium, using their endogenous hyaluronan glycocalyx, anchored by a second hyaluronan receptor, CD44. Why the process should require two different hyaluronan receptors and by which specific mechanism the LYVE-1•hyaluronan interaction enables lymphatic entry is however unknown. Here we describe the crystal structures and binding mechanics of murine and human LYVE-1•hyaluronan complexes. These reveal a highly unusual, sliding mode of ligand interaction, quite unlike the conventional sticking mode of CD44, in which the receptor grabs free hyaluronan chain-ends and winds them in through conformational re-arrangements in a deep binding cleft, lubricated by a layer of structured waters. Our findings explain the mode of action of a dedicated lymphatic entry receptor and define a distinct, low tack adhesive interaction that enables migrating immune cells to slide through endothelial junctions with minimal resistance, while clinging onto their hyaluronan glycocalyx for essential downstream functions.
A WAD-YOLOv8-based method for classroom student behavior detection
Regulatory T cells in the mouse hypothalamus control immune activation and ameliorate metabolic impairments in high-calorie environments
Abstract The hypothalamus in the central nervous system (CNS) has important functions in controlling systemic metabolism. A calorie-rich diet triggers CNS immune activation, impairing metabolic control and promoting obesity and Type 2 Diabetes (T2D), but the mechanisms driving hypothalamic immune activation remain unclear. Here we identify regulatory T cells (Tregs) as key modulators of hypothalamic immune responses. In mice, calorie-rich environments activate hypothalamic CD4 + T cells, infiltrating macrophages and microglia while reducing hypothalamic Tregs. mRNA profiling of hypothalamic CD4 + T cells reveals a Th1-like activation state, with increased Tbx21 , Cxcr3 and Cd226 but decreased Ccr7 and S1pr1 . Importantly, results from Treg loss-of function and gain-of-function experiments show that Tregs limit hypothalamic immune activation and reverse metabolic impairments induced by hyper-caloric feeding. Our findings thus help refine the current model of Treg-centered immune-metabolic crosstalk in the brain and may contribute to the development of precision immune modulation for obesity and diabetes.
Experimental study on the disturbance and control of cluster jet pile construction on existing railroad bridge foundations
Ensemble hot-spots in 3D supercrystals of plasmonic octahedral nanoparticles in tip-to-tip configured superlattices
Author Correction: A study on hybrid-architecture deep learning model for predicting pressure distribution in 2D airfoils
Location-specific neural facilitation in marmoset auditory cortex
Differential large-scale network functional connectivity in cocaine-use disorder associates with drug-use outcomes
TopEC: prediction of Enzyme Commission classes by 3D graph neural networks and localized 3D protein descriptor
Abstract Tools available for inferring enzyme function from general sequence, fold, or evolutionary information are generally successful. However, they can lead to misclassification if a deviation in local structural features influences the function. Here, we present TopEC, a 3D graph neural network based on a localized 3D descriptor to learn chemical reactions of enzymes from enzyme structures and predict Enzyme Commission (EC) classes. Using message-passing frameworks, we include distance and angle information to significantly improve the predictive performance for EC classification (F-score: 0.72) compared to regular 2D graph neural networks. We trained networks without fold bias that can classify enzyme structures for a vast functional space (>800 ECs). Our model is robust to uncertainties in binding site locations and similar functions in distinct binding sites. We observe that TopEC networks learn from an interplay between biochemical features and local shape-dependent features. TopEC is available as a repository on GitHub: https://github.com/IBG4-CBCLab/TopEC and https://doi.org/10.25838/d5p-66.
Gauge equivariant convolutional neural networks for diffusion MRI
Abstract Diffusion MRI (dMRI) is an imaging technique widely used in neuroimaging research, where the signal carries directional information of underlying neuronal fibres based on the diffusivity of water molecules. One of the shortcomings of dMRI is that numerous images, sampled at gradient directions on a sphere, must be acquired to achieve a reliable angular resolution for model-fitting, which translates to longer scan times, higher costs, and barriers to clinical adoption. In this work we introduce gauge equivariant convolutional neural network (gCNN) layers for dMRI that overcome the challenges associated with the signal being acquired on a sphere with antipodal points identified. This is done by noting that the domain is equivalent to the real projective plane, $${\mathbb {R}}P^2$$ , which is a non-euclidean and a non-orientable manifold. This is in stark contrast to a rectangular grid which typical convolutional neural networks (CNNs) are designed for. We apply our method to upsample angular resolution for predicting diffusion tensor imaging (DTI) parameters from just six diffusion gradient directions. The symmetries introduced allow gCNNs the ability to train with fewer subjects as compared to a baseline model that involves only 3D convolutions.
Bad romance: male octopuses inject deadly venom into their mates
Lipid packing and cholesterol content regulate membrane wetting and remodeling by biomolecular condensates
Abstract Biomolecular condensates play a central role in cellular processes by interacting with membranes driving wetting transitions and inducing mutual remodeling. While condensates are known to locally alter membrane properties such as lipid packing and hydration, it remains unclear how membrane composition and phase state in turn affect condensate affinity. Here, we show that it is not only the membrane phase itself, but rather the degree of lipid packing that determines the condensate affinity for membranes. Increasing lipid chain length, saturation, or cholesterol content, enhances lipid packing, thereby decreasing condensate interaction. This regulatory mechanism is consistent across various condensate-membrane systems, highlighting the critical role of the membrane interface. In addition, protein adsorption promotes extensive membrane remodeling, including the formation of tubes and double-membrane sheets. Our findings reveal a mechanism by which membrane composition fine-tunes condensate wetting, highlighting its potential impact on cellular functions and organelle interactions.