Structures and functions of suberin in postharvest fruit and vegetable and its regulation: A comprehensive review.

Wound suberin formation is integral to the healing process of postharvest fruit injuries. A comprehensive understanding of suberin synthesis mechanisms and their regulatory factors is essential to address the increasing preservation demands. This review systematically examines the biosynthetic pathways, regulatory elements, and functional roles of suberin in postharvest contexts.

scPEGSSC:Proximity Enhanced Graph Convolutional Sparse Subspace Clustering Method for scRNA-seq Data.

The identification of cell types by clustering singlecell RNA sequencing (scRNA-seq) data is a fundamental step in the downstream analysis of single-cell data. However, great challenges remain owing to the inherent characteristics of scRNAseq data, including high dimensionality, high noise, and high sparsity. In this study, we propose a proximity enhanced graph convolutional sparse subspace clustering method scPEGSSC for scRNA-seq data.

Band Gap Engineering Based on X-Site Ion Tuning in Copper Organic-Inorganic Hybrid Perovskite.

This paper reports two copper-based organic-inorganic hybrid perovskite single crystals, (2A5MP)CuX ( = Cl, Br), synthesized by a halogen-regulated method. Through comprehensive characterization (single-crystal XRD, powder XRD, FTIR, Raman spectroscopy, TGA, and UV-vis DRS), we elucidated crystal structures, intermolecular forces, functional groups, bond configurations, optical properties, and thermal stability. Both compounds crystallized in the monoclinic space group .

Meta-Learning Task Relations for Ensemble-Based Temporal Domain Generalization in Sensor Data Forecasting.

Temporal domain generalization is crucial for the temporal forecasting of sensor data due to the non-stationary and evolving nature of most sensor-generated time series. However, temporal dynamics vary in scale, semantics, and structure, leading to distribution shifts that a single model cannot easily generalize over. Additionally, conflicts between temporal domain-specific patterns and limited model capacity make it difficult to learn shared parameters that work universally.

Active interaction strategy generation for human-robot collaboration based on trust.

In human-robot collaborative tasks, human trust in robots can reduce resistance to them, thereby increasing the success rate of task execution. However, most existing studies have focused on improving the success rate of human-robot collaboration (HRC) rather than on enhancing collaboration efficiency. To improve the overall collaboration efficiency while maintaining a high success rate, this study proposes an active interaction strategy generation for HRC based on trust.

Ultrasonic intervention of Lactobacillus plantarum for quality enhancement of fermented jujube juice: Physicochemical and flavor properties.

In this study, ultrasonic intervention (UI) was used to treat Lactobacillus plantarum CICC 20022 (L. plantarum CICC 20022) at different phases and fermentation experiments were conducted with jujube juice: lag phase (UI 1), logarithmic phase (UI 2), stationary phase (UI 3) and decline phase (UI 4). Scanning electron microscope (SEM) observations and biochemical activity analyses revealed that UI 2 treatment significantly altered the cell surface structure from smooth to porous, while increasing the activities of H-ATPase and β-glucosidase (41.

Physicochemical and functional characteristics of Eucommia ulmoides seed meal protein hydrolysates and their isolates.

Background: Eucommia ulmoides Oliv. seed meal (ESM) as a potential edible protein source as this by-product is usually underutilized or discarded. This study aimed to isolate antioxidant ESM protein hydrolysates (ESMHs) fractions and investigate their physicochemical properties to explore their potential applications.

FDC: Feature Dropout Consistency for unsupervised domain adaptation semantic segmentation.

In Unsupervised Domain Adaptation Semantic Segmentation (UDASS), while self-training techniques have become one of the most effective methods to date, the absence of target labels makes models susceptible to overfitting. To address this problem, consistency techniques regularize the model by perturbing the target domain and enforcing consistent pixel predictions before and after the perturbation. Consistency techniques are based on the idea that a well-performing model should yield consistent predictions for the target data stream before and after perturbations.

scPEDSSC: proximity enhanced deep sparse subspace clustering method for scRNA-seq data.

It is a significant step for single cell analysis to identify cell types through clustering single-cell RNA sequencing (scRNA-seq) data. However, great challenges still remain due to the inherent high-dimensionality, noise, and sparsity of scRNA-seq data. In this study, scPEDSSC, a deep sparse subspace clustering method based on proximity enhancement, is put forward.

GTIGNet: Global Topology Interaction Graphormer Network for 3D hand pose estimation.

Estimating 3D hand poses from monocular RGB images presents a series of challenges, including complex hand structures, self-occlusions, and depth ambiguities. Existing methods often fall short of capturing the long-distance dependencies of skeletal and non-skeletal connections for hand joints. To address these limitations, we introduce the Global Topology Interaction Graphormer Network (GTIGNet), a novel deep learning architecture designed to improve 3D hand pose estimation.

Identification of novel PHGDH inhibitors based on computational investigation: an all-in-one combination strategy to develop potential anti-cancer candidates.

Objective: Biological studies have elucidated that phosphoglycerate dehydrogenase (PHGDH) is the rate-limiting enzyme in the serine synthesis pathway in humans that is abnormally expressed in numerous cancers. Inhibition of the PHGDH activity is thought to be an attractive approach for novel anti-cancer therapy. The development of structurally diverse novel PHGDH inhibitors with high efficiency and low toxicity is a promising drug discovery strategy.

Multiview Spatial-Temporal Meta-Learning for Multivariate Time Series Forecasting.

Multivariate time series modeling has been essential in sensor-based data mining tasks. However, capturing complex dynamics caused by intra-variable (temporal) and inter-variable (spatial) relationships while simultaneously taking into account evolving data distributions is a non-trivial task, which faces accumulated computational overhead and multiple temporal patterns or distribution modes. Most existing methods focus on the former direction without adaptive task-specific learning ability.

GC-STCL: A Granger Causality-Based Spatial-Temporal Contrastive Learning Framework for EEG Emotion Recognition.

EEG signals capture information through multi-channel electrodes and hold promising prospects for human emotion recognition. However, the presence of high levels of noise and the diverse nature of EEG signals pose significant challenges, leading to potential overfitting issues that further complicate the extraction of meaningful information. To address this issue, we propose a Granger causal-based spatial-temporal contrastive learning framework, which significantly enhances the ability to capture EEG signal information by modeling rich spatial-temporal relationships.

Eye Gaze Guided Cross-Modal Alignment Network for Radiology Report Generation.

The potential benefits of automatic radiology report generation, such as reducing misdiagnosis rates and enhancing clinical diagnosis efficiency, are significant. However, existing data-driven methods lack essential medical prior knowledge, which hampers their performance. Moreover, establishing global correspondences between radiology images and related reports, while achieving local alignments between images correlated with prior knowledge and text, remains a challenging task.

Dual modality prompt learning for visual question-grounded answering in robotic surgery.

With recent advancements in robotic surgery, notable strides have been made in visual question answering (VQA). Existing VQA systems typically generate textual answers to questions but fail to indicate the location of the relevant content within the image. This limitation restricts the interpretative capacity of the VQA models and their ability to explore specific image regions.

Active substance and mechanisms of Actinidia chinensis Planch for the treatment of breast cancer was explored based on network pharmacology and in silico study.

In this paper, our objective was to investigate the potential mechanisms of Actinidia chinensis Planch (ACP) for breast cancer treatment with the application of network pharmacology, molecular docking, and molecular dynamics. "Mihoutaogen" was used as a key word to query the Traditional Chinese Medicine Systems Pharmacology database for putative ingredients of ACP and its related targets. DrugBank, GeneCards, Online Mendelian Inheritance in Man, and therapeutic target databases were used to search for genes associated with "breast cancer.

Efficient data reconstruction: The bottleneck of large-scale application of DNA storage.

Over the past decade, the rapid development of DNA synthesis and sequencing technologies has enabled preliminary use of DNA molecules for digital data storage, overcoming the capacity and persistence bottlenecks of silicon-based storage media. DNA storage has now been fully accomplished in the laboratory through existing biotechnology, which again demonstrates the viability of carbon-based storage media. However, the high cost and latency of data reconstruction pose challenges that hinder the practical implementation of DNA storage beyond the laboratory.

A signal transmission strategy driven by gap-regulated exonuclease hydrolysis for hierarchical molecular networks.

Exonucleases serve as efficient tools for signal processing and play an important role in biochemical reactions. Here, we identify the mechanism of cooperative exonuclease hydrolysis, offering a method to regulate the cooperative hydrolysis driven by exonucleases through the modulation of the number of bases in gap region. A signal transmission strategy capable of producing amplified orthogonal DNA signal is proposed to resolve the polarity of signals and byproducts, which provides a solution to overcome the signal attenuation.

Bubble DNA tweezer: A triple-conformation sensor responsive to concentration-ratios.

DNA tweezers, with their elegant simplicity and flexibility, have been pivotal in biosensing and DNA computing. However, conventional tweezers are confined to a binary transformation pre/post target signal recognition, limiting them to presence/absence judgments. This study introduces bubble DNA tweezers (BDT), capable of three distinct conformations based on variable target signal ratios.

KAMPNet: multi-source medical knowledge augmented medication prediction network with multi-level graph contrastive learning.

Backgrounds: Predicting medications is a crucial task in intelligent healthcare systems, aiding doctors in making informed decisions based on electronic medical records (EMR). However, medication prediction faces challenges due to complex relations within heterogeneous medical data. Existing studies primarily focus on the supervised mining of hierarchical relations between homogeneous codes in medical ontology graphs, such as diagnosis codes.

Distractor-Aware Event-Based Tracking.

Event cameras, or dynamic vision sensors, have recently achieved success from fundamental vision tasks to high-level vision researches. Due to its ability to asynchronously capture light intensity changes, event camera has an inherent advantage to capture moving objects in challenging scenarios including objects under low light, high dynamic range, or fast moving objects. Thus event camera are natural for visual object tracking.

Temporal logic circuits implementation using a dual cross-inhibition mechanism based on DNA strand displacement.

Molecular circuits crafted from DNA molecules harness the inherent programmability and biocompatibility of DNA to intelligently steer molecular machines in the execution of microscopic tasks. In comparison to combinational circuits, DNA-based temporal circuits boast supplementary capabilities, allowing them to proficiently handle the omnipresent temporal information within biochemical systems and life sciences. However, the lack of temporal mechanisms and components proficient in comprehending and processing temporal information presents challenges in advancing DNA circuits that excel in complex tasks requiring temporal control and time perception.

Design, synthesis, and biological evaluation of THIQ as antidepressive agents.

7-substituted tetrahydroisoquinolines derivatives were designed, synthesized, and evaluated for neuroprotective properties. We summarized the preliminary structure activity relationships (SAR). Compound 3i was screened as a hit compound and its antidepressant activity was evaluated by employing the forced swimming test, tail suspension test.

Wounding induces suberin deposition, relevant gene expressions and changes of endogenous phytohormones in Chinese yam () tubers.

Wounds on Chinese yam (Dioscorea opposita ) tubers can ocurr during harvest and handling, and rapid suberisation of the wound is required to prevent pathogenic infection and desiccation. However, little is known about the causal relationship among suberin deposition, relevant gene expressions and endogenous phytohormones levels in response to wounding. In this study, the effect of wounding on phytohormones levels and the expression profiles of specific genes involved in wound-induced suberisation were determined.

Development of a multispectral fluorescence LiDAR for point cloud segmentation of plants.

The accelerating development of high-throughput plant phenotyping demands a LiDAR system to achieve spectral point cloud, which will significantly improve the accuracy and efficiency of segmentation based on its intrinsic fusion of spectral and spatial data. Meanwhile, a relatively longer detection range is required for platforms e.g.