Potato β-aminobutyric acid receptor IBI1 manipulates VOZ1 and VOZ2 transcription factor activity to promote disease resistance.

Upon infection with nonpathogenic microorganisms or treatment with natural or synthetic compounds, plants exhibit a more rapid and potent response to both biotic and abiotic stresses. However, the molecular mechanisms behind this phenomenon, known as defense priming, are poorly understood. β-minobutyric acid (BABA) is an endogenous stress metabolite that enhances plant tolerance to various abiotic stresses and primes plant defense responses, providing the ability to resist a variety of pathogens (broad-spectrum resistance).

Study on the Damage Characteristics of Wheat Kernels under Continuous Compression Conditions.

Peeling wheat yields higher-quality flour. During processing in a flaking machine, wheat kernels undergo continuous compression within the machine's chamber. As this compression persists, damage to the kernels intensifies and accumulates, eventually leading to kernel breakage.

Predictive modeling of rice milling degree for three typical Chinese rice varieties using interpretative machine learning methods.

Brown rice over-milling causes high economic and nutrient loss. The rice degree of milling (DOM) detection and prediction remain a challenge for moderate processing. In this study, a self-established grain image acquisition platform was built.

Research on the castor oil pressing extraction mechanism based on multi-physics coupling simulation.

Castor oil has been widely used in various fields due to its properties, leading to large attention for its extraction mechanism. To research the castor oil extraction mechanism during pressing, a self-developed uniaxial compression device combined with an in situ observation is established. The effects of pressure, loading speed, and creep time are investigated, and a finite element model coupling with multi-physics is established for castor oil pressing extraction, verified by the seed cake experimental compression strain matching with numerical simulation under the same condition.

Development and validation of a predictive model for end-stage renal disease in systemic lupus erythematosus patients.

Systemic lupus erythematosus (SLE) affects many populations. This study aims to develop a predictive model and create a nomogram for assessing the risk of end-stage renal disease (ESRD) in patients diagnosed with SLE. Data from electronic health records of SLE patients treated at the Affiliated Hospital of North Sichuan Medical College between 2013 and 2023 were collected.

ELF5-Regulated lncRNA-TTN-AS1 Alleviates Myocardial Cell Injury via Recruiting PCBP2 to Increase CDK6 Stability in Myocardial Infarction.

Myocardial infarction (MI) seriously threatens the health of elderly people, and reducing myocardial injury is of great significance for the treatment of MI. LncRNA-TTN-AS1 shows protective effects on cardiomyocyte injury, while the role of TTN-AS1 in MI remains unknown. CCK8, flow cytometry, and JC-1 staining assessed cell viability, apoptosis and mitochondrial membrane potential (MMP), respectively.

A metric to quantify the time-varying characteristics of underwater acoustic communication channels.

Underwater acoustic communication signals suffer from time dispersion due to time-varying multipath propagation in the ocean. This leads to intersymbol interference, which in turn degrades the performance of the communication system. Typically, the channel correlation functions are employed to describe these characteristics.

Portraying on-road CO concentrations using street view panoramas and ensemble learning.

A significant reduction in carbon dioxide (CO) emissions caused by transportation is essential for attaining sustainable urban development. Carbon concentrations from road traffic in urban areas exhibit complex spatial patterns due to the impact of street configurations, mobile sources, and human activities. However, a comprehensive understanding of these patterns, which involve complex interactions, is still lacking due to the human perspective of road interface characteristics has not been taken into account.

Study on the Correlation Between Patent Foramen Ovale and Migraine Based on Meta Analysis.

Objective: This meta-analysis systematically investigates the association between Patent Foramen Ovale (PFO) and the prevalence of migraine. Our goal is to quantify this relationship and evaluate its implications for clinical practice and future research.

Methods: An extensive literature search was carried out in various databases, such as PubMed, Embase, The Cochrane Library, Web of Science, CNKI, VIP, WanFang Data, and CBM, up to November 2023.

Comprehensive genome-wide analysis of wheat xylanase inhibitor protein (XIP) genes: unveiling their role in Fusarium head blight resistance and plant immune mechanisms.

In this comprehensive genome-wide study, we identified and classified 83 Xylanase Inhibitor Protein (XIP) genes in wheat, grouped into five distinct categories, to enhance understanding of wheat's resistance to Fusarium head blight (FHB), a significant fungal threat to global wheat production. Our analysis reveals the unique distribution of XIP genes across wheat chromosomes, particularly at terminal regions, suggesting their role in the evolutionary expansion of the gene family. Several XIP genes lack signal peptides, indicating potential alternative secretion pathways that could be pivotal in plant defense against FHB.

Model fusion for predicting unconventional proteins secreted by exosomes using deep learning.

Unconventional secretory proteins (USPs) are vital for cell-to-cell communication and are necessary for proper physiological processes. Unlike classical proteins that follow the conventional secretory pathway via the Golgi apparatus, these proteins are released using unconventional pathways. The primary modes of secretion for USPs are exosomes and ectosomes, which originate from the endoplasmic reticulum.

Characteristics of Damage to Brown Rice Kernels under Single and Continuous Mechanical Compression Conditions.

During the rice milling process, single and continuous compression occurs between brown rice and the processing parts. When the external load exceeds the yield limit of brown rice, brown rice kernels are damaged; with an increase in compression deformation or the extent of compression, the amount of damage to the kernels expands and accumulates, ultimately leading to the fracture and breakage of kernels. In order to investigate the mechanical compression damage characteristics of brown rice kernels under real-world working conditions, this study constructs an elastic-plastic compression model and a continuous damage model of brown rice kernels based on Hertz theory and continuous damage theory; the accuracy of this model is verified through experiments, and the relevant processing critical parameters are calculated.

Identifying diagnostic indicators for type 2 diabetes mellitus from physical examination using interpretable machine learning approach.

Background: Identification of patients at risk for type 2 diabetes mellitus (T2DM) can not only prevent complications and reduce suffering but also ease the health care burden. While routine physical examination can provide useful information for diagnosis, manual exploration of routine physical examination records is not feasible due to the high prevalence of T2DM.

Objectives: We aim to build interpretable machine learning models for T2DM diagnosis and uncover important diagnostic indicators from physical examination, including age- and sex-related indicators.

Household Decision-Making Choices: Investment in Children's Education or Self-Consumption.

Analyzing the sustainable decision-making mechanism between household consumption and education investment can theoretically develop education. This study uses the continuous-time utility model to demonstrate the independent characteristics of consumption and education investment, as well as the principle of decision incompatibility in the decision-making process of the utility maximization problem. Then, we establish a three-phase logarithmic utility model to obtain the intertemporal decision-making path of a family.

Constructing a 3D BiWO/ZnInS direct Z-scheme heterostructure for improved photocatalytic CO reduction performance.

Developing efficient heterojunction photocatalysts with enhanced charge transfer and reduced recombination rates of photogenerated carriers is crucial for harnessing solar energy in the photocatalytic CO reduction into renewable fuels. This study employed electrostatic self-assembly techniques to construct a 3D BiWO/ZnInS direct Z-scheme heterojunctions. The unique 3D structure provided abundant active sites and facilitated CO adsorption.

Research on rice starch gel preparation and crosslink network structure-rheological property based on direct-writing 3D printing.

Amylopectin and amylose components are natural polymers within rice starch granules, intertwined in specific conditions to form gel polymerized with pore crosslink network, has potential printing properties. In this study, a rice starch gel preparation scheme is proposed for stable properties, and starch granule phase transition mechanism is analyzed based on RVA test during preparation, it can be divided into four-stage, swelling, reacting, homogenizing and self-assembling stages. Gel surface tension and contact angle tested with starch concentration effect, a correlation is developed, reflecting a competition result to gel droplet macro-morphology between the intermolecular cohesion and crosslink network.

Transcription factor FOXP4 inversely governs tumor suppressor genes and contributes to thyroid cancer progression.

Objective: In recent decades, thyroid cancer (TC) has exhibited a rising incidence pattern. Elevated levels of the transcription factor FOXP4 have been strongly linked to the progression of diverse tumors; nevertheless, its specific role in thyroid cancer remains underexplored. The primary objective of this study was to elucidate the functions of FOXP4 and its associated target gene, FBXW7, in the context of thyroid cancer.

EnsembleDL-ATG: Identifying autophagy proteins by integrating their sequence and evolutionary information using an ensemble deep learning framework.

Autophagy is a primary mechanism for maintaining cellular homeostasis. The synergistic actions of autophagy-related (ATG) proteins strictly regulate the whole autophagic process. Therefore, accurate identification of ATGs is a first and critical step to reveal the molecular mechanism underlying the regulation of autophagy.

Bidirectional negative feedback actions of DNMT3A and miR-145 in regulating autophagy in cardiac fibroblasts and affecting myocardial fibrosis.

Epigenetic regulation has crucial implications for myocardial fibrosis. It has been reported that autophagy, regulated by miR-145, is implicated in the proliferation and fibrosis of cardiac fibroblasts (CFs). However, how it works during the process remains unclear.

Research on microstructural-mechanical and shearing properties of castor seed during mechanical extraction.

Castor seed oil, as an important biomass fuel, has attracted extensive attention worldwide due to inclusive applications. Castor seed screw mechanical extraction is in fact seed shear damage and oil output. Seed shearing mechanism has been investigated with a developed tribometer.

Targeting of cold-inducible RNA-binding protein alleviates sepsis via alleviating inflammation, apoptosis and oxidative stress in heart.

A systemic inflammatory response is observed in patients undergoing shock and sepsis. This study aimed to explore the effects of cold-inducible RNA-binding protein (CIRP) on sepsis-associated cardiac dysfunction and the underlying mechanism. In vivo and in vitro lipopolysaccharide (LPS)-induced sepsis models were established in mice and neonatal rat cardiomyocytes (NRCMs), respectively.

Fast and Efficient Design of Deep Neural Networks for Predicting N-Methylguanosine Sites Using autoBioSeqpy.

N-Methylguanosine (mG) is a crucial post-transcriptional RNA modification that plays a pivotal role in regulating gene expression. Accurately identifying mG sites is a fundamental step in understanding the biological functions and regulatory mechanisms associated with this modification. While whole-genome sequencing is the gold standard for RNA modification site detection, it is a time-consuming, expensive, and intricate process.

Evaluation and development of deep neural networks for RNA 5-Methyluridine classifications using autoBioSeqpy.

Post-transcriptionally RNA modifications, also known as the epitranscriptome, play crucial roles in the regulation of gene expression during development. Recently, deep learning (DL) has been employed for RNA modification site prediction and has shown promising results. However, due to the lack of relevant studies, it is unclear which DL architecture is best suited for some pyrimidine modifications, such as 5-methyluridine (mU).