Association between paramyosin and the heat-induced toughening of adductor muscle in Pacific oysters (Crassostrea gigas).

Background: The phenomenon of increased toughness of adductor muscle of Pacific oysters (Crassostrea gigas) during heat treatment considerably impacts their palatability. Thus, the present study was conducted aiming to investigate the relationship between the adductor muscle proteins and observed toughness.

Results: During heat treatment with boiling, the smooth muscle exhibited greater difficulty in detaching from the shell compared to the striated muscle, indicating more pronounced toughness.

Annular cavity VCSELs: enhanced beam quality and speckle-less illumination.

Vertical-cavity surface-emitting lasers (VCSELs) are valued for their high coupling efficiency, directionality, and low cost. Broad-area (BA)-VCSEL, in particular, offers the possibility of a customizable degree of spatial coherence and high output power for low-speckle imaging, illumination, communication, and augmented reality technologies. However, new designs of BA-VCSEL for scalable fabrication need to be developed.

Molecularly engineered sisal fibers with programmable interfaces for efficient separation of complex emulsions and multipollutants.

Purifying surfactant-stabilized oily wastewater remains significant challenge, as conventional demulsifiers frequently fail to disrupt complex emulsions stabilized by multiple surfactant types. Herein, we developed an innovative molecular engineering strategy that constructs phytic acid-crosslinked β-cyclodextrin (PA-cl-β-CD) networks on sisal fibers (SFs) through a facile one-step process, creating an eco-friendly separation material (SFs@PA-cl-β-CD) with dynamically tunable surface wettability and interfacial properties. This biomass-based material exhibits remarkable versatility, achieving universal high separation efficiency (>99 %) for diverse oil-in-water emulsions regardless of surfactant types (cationic, anionic, or nonionic) through molecularly programmable surface wettability control.

Refractory Gout and Metabolic Syndrome in a Chinese Cohort: A Propensity Score Matching and Nonlinear Relationship Analysis.

Objective: The coexistence of refractory gout and metabolic syndrome may worsen patient prognosis, but the association and the nonlinear relationships with clinical parameters have not been fully elucidated. This study aimed to explore this association and reveal its potential nonlinear patterns in a Chinese cohort.

Methods: This retrospective study included clinical data from 4111 gout patients from a single center in Tianjin, China between 2014 and 20234.

A novel blocker displacement qMSP assay for DNA methylation analysis at single-base resolution.

Accurate quantification of DNA methylation at single-CpG resolution remains a challenge for conventional qMSP, which typically lacks the specificity to discriminate solitary methylated sites. Here, we present a novel blocker displacement qMSP (BD-qMSP) assay that integrates a rationally designed blocker oligonucleotide to suppress unmethylated sequences while enabling robust amplification of single methylated CpGs. Through systematic optimization of blocker design, primer/probe modification, and annealing conditions, BD-qMSP achieved high discrimination efficiency with a detection limit as low as 0.

EZ-FRCNN: A Fast, Accessible and Robust Deep Learning Package for Object Detection Applications from Ethology to Cell Biology.

Advances in high-throughput imaging and experimental automation have dramatically increased the scale of biological datasets, creating a growing need for tools that can efficiently identify and localize features in complex image data. Although deep learning has transformed image analysis, methods such as region-based convolutional neural networks remain underutilized in biology due to technical barriers such as coding requirements and reliance on cloud infrastructure. We present EZ-FRCNN, a locally hosted, user-friendly package that enables the accessible and scalable application of object detection to biological datasets.

Micro-LED-based quantum random number generators.

Quantum random number generators (QRNGs) leverage the inherent unpredictability of quantum mechanical phenomena to produce random numbers. However, the performance of many QRNGs is hindered by limitations such as low generation rates and the bulkiness of systems utilizing single-photon detectors (SPDs). In this study, we present a QRNG that addresses these challenges by using a micro-LED as an entropy source, utilizing the intensity fluctuations of spontaneous emissions.

Distributed salinity sensor based on optical frequency domain reflectometry with polyimide coated single mode fiber.

A distributed optical fiber sensor for salinity sensing is proposed and analyzed. The sensor uses a single mode fiber coated with polyimide and is based on optical frequency domain reflectometry. By applying the polyimide solution to the surface of the fiber, a layer of polyimide film is formed by heating.

Fungal Warriors: Effects of and on CCYV-Carrying Whiteflies.

is a major agricultural pest that affects both greenhouse and field crops by feeding on plant sap, which impairs plant growth, and by secreting honeydew, promotes sooty mold growth that further reduces photosynthesis. Additionally, these insects are vectors for viruses such as the cucurbit chlorotic yellows virus (CCYV), which causes significant damage to cucurbit crops. Traditional chemical pesticide treatments have limitations, including the development of resistance, harm to non-target organisms, and environmental contamination.

Non-Invasive Quality Control of Organoid Cultures Using Mesofluidic CSTR Bioreactors and High-Content Imaging.

Human brain organoids produce anatomically relevant cellular structures and recapitulate key aspects of brain function, which holds great potential to model neurological diseases and screen therapeutics. However, the long growth time of 3D systems complicates the culturing of brain organoids and results in heterogeneity across samples hampering their applications. We developed an integrated platform to enable robust and long-term culturing of 3D brain organoids.

Genotypes of SNPs of key genes regulate susceptibility and drug sensitivity to neovascular AMD in the human population.

Objective: To compare the genetic characteristics of the normal control group to those of neovascular age-related macular degeneration (AMD) patients and to detect single-nucleotide polymorphisms (SNPs) related to the pathogenesis of neovascular AMD and the sensitivity to anti-VEGF drug, combercept.

Method: This is a prospective case-controlled study. A total of 104 neovascular AMD patients were treated with combercept and 106 normal subjects were served as the control group.

The inhibition potentiality of sodium carboxymethyl cellulose on PhIP, Harman, and Norharman formation of fried beef patties at low NaCl level.

The objective of this study was to investigate the inhibitory effect of sodium carboxymethyl cellulose (CMCNa) on the formation of heterocyclic aromatic amines (HAAs) in fried beef patties under low salt conditions. The 0.5-1.

Generative AI for vaccine misbelief correction: Insights from targeting extraversion and pseudoscientific beliefs.

Background: Misinformation about vaccines is a significant barrier to public health, fueling hesitancy and resistance. Generative AI offers a scalable tool for assisting public health communicators in crafting targeted correction messages tailored to audience characteristics. This study investigates the effectiveness of AI-generated messages targeting extraversion and pseudoscientific beliefs compared to high-quality generic and non-vaccine-related messages.

Postmortem Changes of Desmin, Talin-2, Vinculin, and Integrin β1 in Chicken Breast and Their Effects on Tenderness.

Costameres are essential for maintaining the integrity of muscle fibers, which affects the meat tenderness. To explore the pattern of alteration in costameres after slaughter, this study investigated the distribution of costamere proteins (desmin, talin-2, vinculin, and integrin β1), their impact on tenderness, and the involved enzymes. Western blot analysis showed that talin-2 significantly degraded in postmortem, while integrin β1 significantly increased at 48 h ( < 0.

Rapid and slow thawing of Takifugu rubripes fillets: TMT-labeled proteomics analysis, biochemical and morphological comparison.

This study aimed to investigate how running water (rapid, R) and ice-water (slow, I) thawing methods affect the quality of Takifugu rubripes fillets. Thawing shrinkage and changes in extractable proteins quantified by tandem mass tag (TMT)-labeled quantitative proteomics were compared. The results showed that the rapidly thawed fillets were quickly underwent greater shrinkage, and the smaller gap areas were reduced by 7.

Prior knowledge-based multi-task learning network for pulmonary nodule classification.

The morphological characteristics of pulmonary nodule, also known as the attributes, are crucial for classification of benign and malignant nodules. In clinical, radiologists usually conduct a comprehensive analysis of correlations between different attributes, to accurately judge pulmonary nodules are benign or malignant. However, most of pulmonary nodule classification models ignore the inherent correlations between different attributes, leading to unsatisfactory classification performance.

Effects of Baicalin on Gout Based on Network Pharmacology, Molecular Docking, and in vitro Experiments.

Purpose: Baicalin is a flavonoid of Scutellaria baicalensis Georgi. It possesses antipyretic, analgesic, and anti-inflammatory effects. It has great potential to treat gout.

Automated cell annotation in multi-cell images using an improved CRF_ID algorithm.

Cell identification is an important yet difficult process in data analysis of biological images. Previously, we developed an automated cell identification method called CRF_ID and demonstrated its high performance in whole-brain images (Chaudhary et al., 2021).

Unifying community whole-brain imaging datasets enables robust neuron identification and reveals determinants of neuron position in C. elegans.

We develop a data harmonization approach for C. elegans volumetric microscopy data, consisting of a standardized format, pre-processing techniques, and human-in-the-loop machine-learning-based analysis tools. Using this approach, we unify a diverse collection of 118 whole-brain neural activity imaging datasets from five labs, storing these and accompanying tools in an online repository WormID (wormid.

Large-area radiation-modulated thermoelectric fabrics for high-performance thermal management and electricity generation.

Flexible thermoelectric systems capable of converting human body heat or solar heat into sustainable electricity are crucial for the development of self-powered wearable electronics. However, challenges persist in maintaining a stable temperature gradient and enabling scalable fabrication for their commercialization. Herein, we present a facile approach involving the screen printing of large-scale carbon nanotube (CNT)-based thermoelectric arrays on conventional textile.

OSP-1 protects neurons from autophagic cell death induced by acute oxidative stress.

Oxidative stress, caused by the accumulation of reactive oxygen species (ROS), is a pathological factor in several incurable neurodegenerative conditions as well as in stroke. However, our knowledge of the genetic elements that can be manipulated to protect neurons from oxidative stress-induced cell death is still very limited. Here, using Caenorhabditis elegans as a model system, combined with the optogenetic tool KillerRed to spatially and temporally control ROS generation, we identify a previously uncharacterized gene, oxidative stress protective 1 (osp-1), that protects C.

Scale-up construction of stable multifunctional hydrogel interfaces for large-scale purification of complex oil-water emulsions and oil recovery.

Inspired by the multi-level structure of grass clumps in nature, a novel filter with plexiform-structured hydrogel interface was constructed using sepiolite-derived silica nanofiber (SiNF) as the supporter and crosslinked polyvinyl alcohol (cl-PVA) hydrogel as the coating. Experimental test, DFT and MD calculations have confirmed that the addition of SiNF can not only enhance oil-water separation efficiency, but also improve the stability of hydrogel coating. The hydrogel interface with excellent stability and superhydrophilic/underwater superoleophobicity can be manufactured on a large copper mesh (1 m × 1.

For short-segment Hirschsprung disease, daily trans-anal irrigation before pull-through surgery is necessary?

Objective: To explore the role of daily trans-anal irrigation (TAI) before pull-through surgery in preventing Hirschsprung disease-associated enterocolitis (HAEC) and improving other clinical outcomes in short-segment Hirschsprung disease (HSCR).

Methods: We enrolled the children with short-segment HSCR who received primary pull-through surgery from February 2012 to August 2019 at our hospital. They were divided into two groups.

Non-invasive label-free imaging analysis pipeline for in situ characterization of 3D brain organoids.

Brain organoids provide a unique opportunity to model organ development in a system similar to human organogenesis in vivo. Brain organoids thus hold great promise for drug screening and disease modeling. Conventional approaches to organoid characterization predominantly rely on molecular analysis methods, which are expensive, time-consuming, labor-intensive, and involve the destruction of the valuable three-dimensional (3D) architecture of the organoids.

Method for remaining useful life prediction of rolling bearings based on deep reinforcement learning.

In contemporary industrial systems, the prediction of remaining useful life (RUL) is recognized as a valuable maintenance strategy for health management due to its ability to monitor equipment operational status in real time and ensure the safety of industrial production. Current studies have largely concentrated on deep learning (DL) techniques, leading to a shortage of RUL prediction methods that utilize deep reinforcement learning (DRL). To further enhance application and research, this paper introduces a novel approach to RUL prediction based on DRL, specifically using a combination of Convolutional Neural Network-Bidirectional Long Short-Term Memory Network (CNN-BiLSTM) and the Deep Deterministic Policy Gradient (DDPG) algorithm.