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Sub pulse length event measurement in BOTDR system using slope assisted Brillouin frequency shift

Move over graphene! Scientists forge bismuthene and host of atoms-thick metals

Generative AI lacks the human creativity to achieve scientific discovery from scratch

ULTRAWX: A ubiquitous realtime acoustic gesture information interaction system based on Tiou DODA

Limited effect of antibiotic use on the management of pulmonary ground-glass nodules

Exploring the antibacterial efficacy of Opuntia monacantha in combatting methicillin-resistant Staphylococcus aureus

Influence of curing light irradiance and ceramic thickness on color stability and translucency of cemented ceramic laminate veneers

Impact of pterygium morphological profiles on dry eye parameters

Dual regulation of mitochondrial fusion by Parkin–PINK1 and OMA1

Evaluating the change and trend of construction land in Changsha City based GeoSOS-FLUS model and machine learning methods

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.

Circadian phase inversion causes insulin resistance in a rat model of night work and jet lag

A WAD-YOLOv8-based method for classroom student behavior detection

Experimental study on the disturbance and control of cluster jet pile construction on existing railroad bridge foundations

Author Correction: A study on hybrid-architecture deep learning model for predicting pressure distribution in 2D airfoils

Differential large-scale network functional connectivity in cocaine-use disorder associates with drug-use outcomes

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

Association between body roundness index and osteoarthritis/rheumatoid arthritis: a cross-sectional study

Reconstitution of SPO11-dependent double-strand break formation

Abstract Meiotic recombination starts with SPO11 generation of DNA double-strand breaks (DSBs) 1 . SPO11 is critical for meiosis in most species, but it generates dangerous DSBs with mutagenic 2 and gametocidal 3 potential. Cells must therefore utilize the beneficial functions of SPO11 while minimizing its risks 4 —how they do so remains poorly understood. Here we report reconstitution of DNA cleavage in vitro with purified recombinant mouse SPO11 bound to TOP6BL. SPO11–TOP6BL complexes are monomeric (1:1) in solution and bind tightly to DNA, but dimeric (2:2) assemblies cleave DNA to form covalent 5′ attachments that require SPO11 active-site residues, divalent metal ions and SPO11 dimerization. SPO11 can also reseal DNA that it has nicked. Structure modelling with AlphaFold 3 suggests that DNA is bent prior to cleavage 5 . In vitro cleavage displays a sequence bias that partially explains DSB site preferences in vivo. Cleavage is inefficient on complex DNA substrates, partly because SPO11 is readily trapped in DSB-incompetent (presumably monomeric) binding states that exchange slowly. However, cleavage is improved with substrates that favour dimer assembly or by artificially dimerizing SPO11. Our results inform a model in which intrinsically weak dimerization restrains SPO11 activity in vivo, making it exquisitely dependent on accessory proteins that focus and control DSB formation.