Discovery of -((2-Arylthiazol-4-yl)methyl)oxazole-5-carboxamide Derivatives as SDHi for Fungicidal Candidates.

A series of oxazole-5-carboxamide derivatives were designed as succinate dehydrogenase (SDH) inhibitors and synthesized, characterized, and evaluated for their fungicidal activities. Among these compounds, and displayed 0.17 and 0.

EEGDfus: A Conditional Diffusion Model for Fine-Grained EEG Denoising.

Electroencephalogram (EEG) signals are vital in understanding brain activity, but their weak amplitude makes them susceptible to various artifacts. Accurate denoising of EEG data is crucial as a preprocessing step to ensure precise analysis and interpretation. In recent years, the diffusion model has garnered significant attention as a promising approach in generative modeling.

Uncovering the microbial community dynamics and metabolic pathways of primary organic acids in Sichuan Baoning vinegar through metagenomics.

Sichuan Baoning vinegar, a renowned traditional vinegar in China, exhibits a higher lactic acid content compared to acetic acid. The microbiota plays a crucial role in shaping the unique flavor of vinegar, but the species-level succession of key microorganisms and metabolic pathways of major organic acids in this vinegar are still unclear. This study utilized metagenomic sequencing to elucidate microbial succession during fermentation and the functional roles of the microbial community, as well as explore the metabolic network of lactic acid and acetic acid.

Metagenomics and Metagenome-Assembled Genomes: Analysis of from Sichuan Baoning Vinegar, One of the Four Traditional Renowned Vinegars in China.

The microbial community in vinegar has primarily been investigated by analyzing short reads to determine operational taxonomic units, but it is also crucial to identify metagenome-assembled genomes (MAGs). In this study, the microbial diversity and functionality in Sichuan Baoning vinegar were examined through deep metagenomic sequencing and metagenomic binning. Results revealed that the most prevalent phylum was Firmicutes, followed by Proteobacteria and unclassified Bacteria.

IL-15/IL-15Rα-secreting bioengineered adipocytes reactivate NK/CD8 T cells in ovarian and colon cancer ascites.

Malignant ascites (MA) presents a complex clinical challenge, linked inextricably to poor prognosis, chemoresistance, and metastasis of peritoneal carcinomatosis (PC). However, standard therapeutic approaches for managing or preventing MA secondary to PC remain unavailable. Here we display that a bioengineered adipocyte, encapsulating long-chain fatty acids and concurrently secreting IL-15 and IL-15 receptor α (IL-15Rα), markedly extends the half-life and bioactivity of IL-15.

Cross-patient seizure prediction via continuous domain adaptation and similar sample replay.

Seizure prediction based on electroencephalogram (EEG) for people with epilepsy, a common brain disorder worldwide, has great potential for life quality improvement. To alleviate the high degree of heterogeneity among patients, several works have attempted to learn common seizure feature distributions based on the idea of domain adaptation to enhance the generalization ability of the model. However, existing methods ignore the inherent inter-patient discrepancy within the source patients, resulting in disjointed distributions that impede effective domain alignment.

Adaptive responses of Alicyclobacillus acidoterrestris in acidic broth and fruit juices: Focus on the influences of organic acids and temperature conditions.

Acid adaptive response (AAR) is a survival mechanism that allows bacteria to develop enhanced stress tolerance. Our previous research identified AAR in Alicyclobacillus acidoterrestris, a thermo-acidophilic bacterium responsible for fruit juice spoilage. However, the roles of specific acidulants, adaptive temperatures, and acidic juice matrices in triggering AAR remain elusive.

Privacy-preserving multi-source semi-supervised domain adaptation for seizure prediction.

Domain adaptation (DA) has been frequently used to solve the inter-patient variability problem in EEG-based seizure prediction. However, existing DA methods require access to the existing patients' data when adapting the model, which leads to privacy concerns. Besides, most of them treat the whole existing patients' data as one single source and attempt to minimize the discrepancy with the target patient.

Standing-wave atom tweezer.

We report on the experimental realization of a standing-wave atom tweezer (SWAT) by aligning tightly focused dipole laser beams from a commercial objective lens and a metalens on a chip. By independently tuning the laser intensities of the two beams, we demonstrate the controlled loading of multiple atoms into the SWAT. We systematically investigate the influence of the standing-wave potential modulation depth on single-atom loading dynamics and quantitatively estimate the number of atoms in the SWAT by calculating the fluorescence of trapped atoms.

Biosorption of cypermethrin from aqueous solutions by Pediococcus acidilactici: kinetics, isotherms, and mechanisms.

Background: Pediococcus acidilactici is an effective adsorbent for removing of pyrethroid insecticides. This study investigated the biosorption characteristics and mechanisms of P. acidilactici D15 using adsorption measurement, scanning electron microscopy, and Fourier-transform infrared spectroscopy.

A novel cold-adapted pyrethroid-degrading esterase from Bacillus subtilis J6 and its application for pyrethroid-residual alleviation in food matrix.

Prolonged and widespread use of pyrethroid pesticides a significant concern for human health. The initial step in pyrethroid bioremediation involves the hydrolysis of ester-bond. In the present study, the esterase genes est10 and est13, derived from Bacillus subtilis, were successfully cloned and expressed in Escherichia coli.

An interpretable and generalizable deep learning model for iEEG-based seizure prediction using prototype learning and contrastive learning.

Epileptic seizure prediction plays a crucial role in enhancing the quality of life for individuals with epilepsy. Over recent years, a multitude of deep learning-based approaches have emerged to tackle this challenging task, leading to significant advancements. However, the 'black-box' nature of deep learning models and the considerable interpatient variability significantly impede their interpretability and generalization, thereby severely hampering their efficacy in real-world clinical applications.

Programming Hydrogen Bonds for Reversible Elastic-Plastic Phase Transition in a Conductive Stretchable Hydrogel Actuator with Rapid Ultra-High-Density Energy Conversion and Multiple Sensory Properties.

Smart hydrogels have recently garnered significant attention in the fields of actuators, human-machine interaction, and soft robotics. However, when constructing large-scale actuated systems, they usually exhibit limited actuation forces (≈2 kPa) and actuation speeds. Drawing inspiration from hairspring energy conversion mechanism, an elasticity-plasticity-controllable composite hydrogel (PCTA) with robust contraction capabilities is developed.

Enhancing Domain Diversity of Transfer Learning-Based SSVEP-BCIs by the Reconstruction of Channel Correlation.

Objective: The application of transfer learning, specifically pre-training and fine-tuning, in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) has been demonstrated to effectively improve the classification performance of deep learning methods with limited calibration data. However, effectively learning task-related knowledge from source domains during the pre-training phase remains challenging. To address this issue, this study proposes an effective data augmentation method called Reconstruction of Channel Correlation (RCC) to optimize the utilization of the source domain data.

Integrating spatial and temporal features for enhanced artifact removal in multi-channel EEG recordings.

Various artifacts in electroencephalography (EEG) are a big hurdle to prevent brain-computer interfaces from real-life usage. Recently, deep learning-based EEG denoising methods have shown excellent performance. However, existing deep network designs inadequately leverage inter-channel relationships in processing multi-channel EEG signals.

Quantitative softness and texture bimodal haptic sensors for robotic clinical feature identification and intelligent picking.

Replicating human somatosensory networks in robots is crucial for dexterous manipulation, ensuring the appropriate grasping force for objects of varying softness and textures. Despite advances in artificial haptic sensing for object recognition, accurately quantifying haptic perceptions to discern softness and texture remains challenging. Here, we report a methodology that uses a bimodal haptic sensor to capture multidimensional static and dynamic stimuli, allowing for the simultaneous quantification of softness and texture features.

Long-Term Detection of Glycemic Glucose/Hypoglycemia by Microfluidic Sweat Monitoring Patch.

A microfluidic sweat monitoring patch that collects human sweat for a long time is designed to achieve the effect of detecting the rise and fall of human sweat glucose over a long period of time by increasing the use time of a single patch. Five collection pools, four serpentine channels, and two different valves are provided. Among them, the three-dimensional valve has a large burst pressure as a balance between the internal and external air pressures of the patch.

[Factors associated with the care needs of the older adults based on different disability levels].

Objective: To identify the factors associated with the care needs of the older adults aged 65-105 by age groups, and to compare these factors across different age groups.

Methods: A total of 12 244 older adults from the Chinese longitudinal healthy longevity survey (CLHLS) conducted in 2018 were included in the analyses. The participants were categorized into three age groups: young-old (aged 65-79), middle-old (aged 80-89), and oldest-old (aged 90-105).

Quantitative analysis of sweat evaporation loss in epidermal microfluidic patches.

Sweat analysis is identified as a promising biochemical technique for the non-invasive assessment of human health status. Epidermal microfluidic patches are the predominant sweat sampling and sensing devices. However, the sweat stored inside the patches may suffer from evaporation loss of moisture, which can increase the concentration of biomarkers and cause the biochemical analysis results of sweat to deviate from the actual results.

Microfluidic sweat patch based on capillary force and evaporation pump for real-time continuous sweat analysis.

In addition to the common blood and urine, fresh sweat contains a diverse range of physiological indicators that can effectively reflect changes in the body's state. Wearable sweat sensors are crucial for understanding human physiological health; however, real-time measurement of multiple biomarkers in sweat remains a significant challenge. Here, we propose a wearable microfluidic patch featuring an integrated microfluidic channel and evaporation pump for accelerated and continuous sweat collection, eliminating the need for additional sweat storage cavities that typically impede real-time detection.

The porcine skin microbiome exhibits broad fungal antagonism.

The skin and its microbiome function to protect the host from pathogen colonization and environmental stressors. In this study, using the Wisconsin Miniature Swine™ model, we characterize the porcine skin fungal and bacterial microbiomes, identify bacterial isolates displaying antifungal activity, and use whole-genome sequencing to identify biosynthetic gene clusters encoding for secondary metabolites that may be responsible for the antagonistic effects on fungi. Through this comprehensive approach of paired microbiome sequencing with culturomics, we report the discovery of novel species of Corynebacterium and Rothia.

fMRI-based spatio-temporal parcellations of the human brain.

Purpose Of Review: Human brain parcellation based on functional magnetic resonance imaging (fMRI) plays an essential role in neuroscience research. By segmenting vast and intricate fMRI data into functionally similar units, researchers can better decipher the brain's structure in both healthy and diseased states. This article reviews current methodologies and ideas in this field, while also outlining the obstacles and directions for future research.