Influence of Drugs and Toxins on Decomposition Dynamics: Forensic Implications.

Drug and toxin-related deaths are common worldwide, making it essential to detect the postmortem concentration of various toxic substances at different stages of decomposition in a corpse. Indeed, determining the postmortem interval (PMI) and cause of death in an advanced stage of decomposed corpses has been a significant challenge in forensic investigations. Notably, the presence of drugs or toxins can have a significant impact on the microbial profile, potentially altering the succession of microbial communities and subsequent production of volatile organic compounds (VOCs), which, in turn, affect insect colonization patterns.

Pulsed Eddy Current Imaging of Partially Missing Solder in Brazing Joints of Stainless Steel Core Plates.

Stainless steel core plates (SSCPs) show great potential for modular construction due to their superiority of excellent mechanical properties, light weight, and low cost over traditional concrete and honeycomb structures. During the brazing process of SSCP joints which connect the skin panel and core tubes, it is difficult to keep an even heat flow of inert gas in the vast furnace, which can lead to partially missing solder defects in brazing joints. Pulsed eddy current imaging (PECI) has demonstrated feasibility for detecting missing solder defects, but various factors including lift-off variation and image blurring can deteriorate the quality of C-scan images, resulting in inaccurate evaluation of the actual state of the brazed joints.

Visualization of Moisture Content, Reducing Sugars, and Chewiness in Bread During Oral Processing Based on Hyperspectral Imaging Technology.

This study evaluated the differences in oral processing and texture perception of breads with varying compositions. The research investigated the dynamic changes in moisture content (MC), reducing sugars (RSs), and chewiness of the bolus formed from white bread (B0) and 50% whole-wheat bread (B50) during oral processing. Hyperspectral imaging (HSI) combined with chemometric methods was used to establish quantitative prediction models for MC, RSs, and chewiness, and to create visual distribution maps of these parameters.

A Comprehensive Method for Example-Based Color Transfer with Holistic-Local Balancing and Unit-Wise Riemannian Information Gradient Acceleration.

Color transfer, an essential technique in image editing, has recently received significant attention. However, achieving a balance between holistic color style transfer and local detail refinement remains a challenging task. This paper proposes an innovative color transfer method, named BHL, which stands for Balanced consideration of both Holistic transformation and Local refinement.

Mobile Application and Machine Learning-Driven Scheme for Intelligent Diabetes Progression Analysis and Management Using Multiple Risk Factors.

Diabetes mellitus is a chronic disease that affects over 500 million people worldwide, necessitating personalized health management programs for effective long-term control. Among the various biomarkers, glycated hemoglobin (HbA1c) is a crucial indicator for monitoring long-term blood glucose levels and assessing diabetes progression. This study introduces an innovative approach to diabetes management by integrating a mobile application and machine learning.

Push-pull effects of basal ganglia network in Parkinson's disease inferred by functional MRI.

Deep brain stimulation (DBS) can ameliorate motor symptoms in Parkinson's disease (PD), but its mechanism remains unclear. This work constructs a multi-scale brain model using the fMRI data from 27 PD patients with subthalamic DBS and 30 healthy controls. The model fits microscopic coupling parameters in the cortico-basal ganglia-thalamic neural loop to match individual connectivity, finding the "push-pull" effect of basal ganglia network.

sc2GWAS: a comprehensive platform linking single cell and GWAS traits of human.

Identifying cell populations associated with risk variants is essential for uncovering cell-specific mechanisms that drive disease development and progression. Integrating genome-wide association studies (GWAS) with single-cell RNA sequencing (scRNA-seq) has become an effective strategy for detecting trait-cell relationships. The accumulation of trait-related single cell data has led to an urgent need for its comprehensively processing.

A method of nucleus image segmentation and counting based on TC-UNet++ and distance watershed.

Nucleus segmentation and counting play a crucial role in many cell analysis applications. However, the dense distribution and blurry boundaries of nucleus make nucleus segmentation tasks challenging. This paper proposes a novel segmentation and counting method.

Attention-based cross-frequency graph convolutional network for driver fatigue estimation.

Fatigue driving significantly contributes to global vehicle accidents and fatalities, making driver fatigue level estimation crucial. Electroencephalography (EEG) is a proven reliable predictor of brain states. With Deep Learning (DL) advancements, brain state estimation algorithms have improved significantly.

Rotor angle stability of a microgrid generator through polynomial approximation based on RFID data collection and deep learning.

The article proposes a novel approach to assess rotor angle stability in microgrids by enhancing the Modified Galerkin Method (MGM), which is based on the Polynomial Approximation, using real-time RFID data acquisition. Due to their reliance on assumptions, traditional rotor angle stability methodologies frequently fail in online transient stability testing. MGM successfully captures the dynamic behavior of microgrids by approximating state variables using a sequence of polynomials and coefficients.

TSOM: Small object motion detection neural network inspired by avian visual circuit.

Detecting small moving objects in complex backgrounds from an overhead perspective is a highly challenging task for machine vision systems. As an inspiration from nature, the avian visual system is capable of processing motion information in various complex aerial scenes, and the Retina-OT-Rt visual circuit of birds is highly sensitive to capturing the motion information of small objects from high altitudes. However, more needs to be done on small object motion detection algorithms based on the avian visual system.

Localizing the seizure onset zone and predicting the surgery outcomes in patients with drug-resistant epilepsy: A new approach based on the causal network.

Background And Objective: Accurate localization of the seizure onset zone (SOZ) is crucial for surgical treatment in patients with drug-resistant epilepsy (DRE). However, clinical identification of SOZ often relies on physician experience and has a certain subjectivity. Therefore, it is emergent to develop quantitative computational tools to assist clinicians in identifying SOZ.

PKC inhibitors promote breast cancer immune evasion by maintaining PD-L1 stability.

Protein kinase C (PKC) regulates diverse biological functions of cancer cells and is a promising therapeutic target. However, clinical trials of PKC-targeted therapies have not yielded satisfactory results. Recent studies have also indicated a tumor-suppressive role of PKCs unclear molecular mechanisms.

Advancing ischemic stroke diagnosis and clinical outcome prediction using improved ensemble techniques in DSC-PWI radiomics.

Ischemic stroke is a leading global cause of death and disability and is expected to rise in the future. The present diagnostic techniques, like CT and MRI, have some limitations in distinguishing acute from chronic ischemia and in early ischemia detection. This study investigates the function of ensemble models based on the dynamic radiomics features (DRF) from the dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) ischemic stroke diagnosis, neurological impairment assessment, and modified Rankin Scale (mRS) outcome prediction).

Dual Ce@g-CN-Photoredox/Chlorine Catalysis: Cross-Dehydrogenative Coupling of -Heteroarenes and Alkanes/Ethers with H Evolution.

With Ce@g-CN as a heterogeneous semiconductor photocatalyst, BuNCl as both a redox catalyst and a hydrogen atom transfer catalyst, the first example of semiheterogeneous photocatalytic cross-dehydrogenative coupling of -heteroarenes and alkanes/ethers with H evolution was developed. Both a diverse array of high-value alkylated -heteroarenes and clean H can be efficiently coproduced under sacrificial reagent- and chemical oxidant/reductant-free conditions. Combining both the reversible Ce/Ce redox pair and the reversible Cl̅/Cl ̇ redox pair can considerably improve the photocatalytic efficiency.

Reinforcing Photogenerated Carrier Extraction of Environment-Friendly InP/ZnSeS Quantum Dots for High-Performing Photoelectrochemical Photodetection and Solar Energy Conversion.

Colloidal InP/ZnSeS-based quantum dots (QDs) are considered promising building blocks for light-emitting devices due to their environmental friendliness, high quantum yield (QY), and narrow emission. However, the intrinsic type-I band structure severely hinders potential photoelectrochemical (PEC) applications requiring efficient photoexcited carrier separation and transfer. In this study, the optoelectronic properties of InP/ZnSeS QDs are tailored by introducing Al dopants in the ZnSeS layer, which concurrently passivate the surface defects and act as shallow donor states for suppressed non-radiative recombination and improved charge extraction efficiency.

Determination of aflatoxin B1 in peanuts based on millimetre wave.

This study introduces a novel method for swift detection of AFB in peanuts using millimetre wave technology. The research team devised a portable millimetre-wave detection device employing a double-external-difference mixing structure. The device measured millimetre-wave transmission coefficients in the 20 GHz - 40 GHz frequency range for peanut samples.

PD-YOLO: A study of daily behavioural detection in housed sheep.

Sheep behavior recognition helps to monitor the health status of sheep and prevent the outbreak of infectious diseases. Aiming at the problems of low detection accuracy and slow speed due to the crowding of sheep in real farming scenarios, which can easily obscure each other, this study proposes a lightweight sheep behavior recognition model based on the YOLOv8n model. First, the Convolutional Block Attention Module (CBAM) is introduced and improved in the YOLOv8n model, and the channel attention module and spatial attention module are changed from serial to parallel to construct a novel attention mechanism, PCBAM, to enhance the network's attention to the sheep and eliminate redundant background information; second, the ordinary convolution in the backbone network is replaced with depth-separable convolution, which effectively reduces the number of parameters in the model and reduces the computational complexity.

Discovery of 5-Nitro--(3-(trifluoromethyl)phenyl) Pyridin-2-amine as a Novel Pure Androgen Receptor Antagonist against Antiandrogen Resistance.

The transformation of clinical androgen receptor (AR) antagonists into agonists driven by AR mutations poses a significant challenge in treating prostate cancer (PCa). Novel anti-AR therapeutics combating mutation-induced resistance are required. Herein, by combining structure-based virtual screening and biological evaluation, a high-affinity agonist was first discovered.

Imaging of permeability defect distribution by electromagnetic tomography with hybrid L1 norm and nuclear norm penalty terms.

Electromagnetic tomography (EMT), with the advantages of being non-contact, non-invasiveness, low cost, simple structure, and fast imaging speed, is a multi-functional tomography technique based on boundary measurement voltages to image the conductivity distribution within the sensing field. EMT is widely used in industrial and biomedical fields. Currently, there are few studies on the application of EMT in magnetic permeability materials, which makes it difficult to obtain high-quality reconstructed images due to its own properties that lead to obvious attenuation of electromagnetic waves during propagation, as well as the ill-posed and ill-conditioned characteristics of EMT.

Green Apple Detection Method Based on Multidimensional Feature Extraction Network Model and Transformer Module.

To enhance the fast and accurate detection of pollution-free green apples for food safety, this paper uses the DETR network as a framework to propose a new method for pollution-free green apple detection based on a multidimensional feature extraction network and Transformer module. Firstly, an improved DETR network main feature extraction module adopts the ResNet18 network and replaces some residual layers with deformable convolutions (DCNv2), enabling the model to better adapt to pollution-free fruit changes at different scales and angles, while eliminating the impact of microbial contamination on fruit testing; Subsequently, the extended spatial pyramid pooling model (DSPP) and multiscale residual aggregation module (FRAM) are integrated, which help reduce feature noise and minimize the loss of underlying features during the feature extraction process. The fusion of the two modules enhances the model's ability to detect objects of different scales, thereby improving the accuracy of near-color fruit detection.

Determination of aflatoxin B1 in wheat using Raman spectroscopy combined with chemometrics.

Aflatoxin B1 (AFB1) is carcinogenic and highly susceptible to production in wheat. In this study, the quantitative detection of contaminant AFB1 in wheat was investigated by Raman spectroscopy combined with chemometric method realization. Firstly, Savitzky-Golay smoothing (SG) and baseline calibration methods were used to perform the necessary preprocessing of the collected raw Raman spectra.

A multi-center big-data approach for precise PICC-RVT prognosis and identification of major risk factors in clinical practice.

Background: The Peripherally Inserted Central Catheter (PICC) is a widely used technique for delivering intravenous fluids and medications, especially in critical care units. PICC may induce venous thrombosis (PICC-RVT), which is a frequent and serious complication. In clinical practice, Color Doppler Flow Imaging (CDFI) is regarded as the gold standard for diagnosing PICC-RVT.