The status of China's international division of labour from Marx's theory of international value.

Based on Marx's theory of international value, this paper analyzes the carrier of unequal exchange and the path of international transfer of surplus value, constructs an international division of labour status index by comprehensively observing the organic composition of capital in the export and import sectors, and tries to accurately grasp the characteristics of the current international division of labour pattern coexisting between horizontal products and vertical products. At the same time, based on the data of all available HS92 six-digit code categories in various countries in the world from 1993 to 2016, the level of international division of labour in China is empirically analysed. The results show that: (1) there is no breakthrough in the situation of China's low status of international division of labour; (2) the high capital-organic composition of the import sector is the main reason for the low status of international division of labour; (3) the continuous decline in the absolute value of captured surplus is a global phenomenon; (4) WTO accession has not significantly improved the status of international division of labour.

Effects of Online Learning Readiness and Online Self-Regulated English Learning on Satisfaction with Online English Learning Experience During the COVID-19 Pandemic.

The COVID-19 pandemic has caused major changes in pedagogical practices worldwide. As COVID-19 cases increased, universities had to move their teaching online, requiring both instructors and students to engage through online learning platforms. This study explored the effects of students' online learning readiness and online self-regulated English learning on their satisfaction with the online English learning experience.

Does Participation in Group Music Activities and Pro-Social Behavior Among College Students Have an Association? A Study of the Interlocking Mediating Effects of Positive Social Connections and Peer Support.

It has been demonstrated that music can be an effective tool for shaping the future of college students. This study aimed to investigate the effects of college students' participation in group music activities on their pro-social behavior, and for the first time introduced the psychological variables of "positive social connections" and "perceived peer support". The results of this study show the following: (1) Group music activity participation is positively associated with pro-social behavior.

The Intrinsic Experience of Tourism Autobiographical Memory on Environmentally Responsible Behavior: A Self-Expansion Perspective.

The existing literature on environmentally responsible behavior in tourists focuses primarily on the factors that influence this behavior, such as tourists' attitudes and negative feelings. However, the intrinsic benefits of conservation for individual and societal well-being are often overlooked. Under the theoretical lens of self-expansion theory, this study examined the influence of Chinese tourists' tourism autobiographical memory on their environmentally responsible behavior using a questionnaire survey (N = 434) with partial least squares structural equation modeling.

Software-aided efficient identification of the components of compound formulae and their metabolites in rats by UHPLC/IM-QTOF-MS and an in-house high-definition MS library: Sishen formula as a case.

Identifying the compound formulae-related xenobiotics in bio-samples is full of challenges. Conventional strategies always exhibit the insufficiencies in overall coverage, analytical efficiency, and degree of automation, and the results highly rely on the personal knowledge and experience. The goal of this work was to establish a software-aided approach, by integrating ultra-high performance liquid chromatography/ion-mobility quadrupole time-of-flight mass spectrometry (UHPLC/IM-QTOF-MS) and in-house high-definition MS library, to enhance the identification of prototypes and metabolites of the compound formulae , taking Sishen formula (SSF) as a template.

Artificial intelligence in peptide-based drug design.

Protein-protein interactions (PPIs) are fundamental to a variety of biological processes, but targeting them with small molecules is challenging because of their large and complex interaction interfaces. However, peptides have emerged as highly promising modulators of PPIs, because they can bind to protein surfaces with high affinity and specificity. Nonetheless, computational peptide design remains difficult, hindered by the intrinsic flexibility of peptides and the substantial computational resources required.

Graph analysis based on SCN reveals novel neuroanatomical targets related to tinnitus distress.

Purpose: Tinnitus is considered a neurological disorder affecting both auditory and nonauditory networks. This study aimed to investigate the structural brain covariance network in tinnitus patients and analyze its altered topological properties.

Materials: Fifty three primary tinnitus patients and 67 age- and sex-matched healthy controls (HCs) were included.

PROTCROWN: A Manually Curated Resource of Protein Corona Data for Unlocking the Potential of Protein-Nanoparticle Interactions.

The emergence of the "Protein Corona" is a pivotal concept in bioinformatics and nanotechnology, crucial for understanding nanomedicine delivery and nanoparticle-biological entity interactions. After entering a biological fluid, such as blood, nanoparticles (NPs, such as nanomedical carriers) are quickly coated with proteins, forming a protein interface layer called the protein corona. An in-depth investigation into the protein corona is essential for elucidating the biological ramifications of NPs and their prospective applications within the medical field and beyond.

Unlocking emotional well-being: Evaluation of a stress mindset intervention with a metacognitive approach.

Although there is a growing body of research on the effects of a stress mindset on health and well-being, knowledge of the effectiveness of stress mindset interventions in real-world situations is limited and primarily supported by evidence from Western cultural contexts. We examined the effects of a stress mindset intervention with a metacognitive approach on emotional well-being (negative affect, positive affect, anxious affect, and life satisfaction) over 4 months (i.e.

Investigating the key principles in two-step heterogeneous transfer learning for early laryngeal cancer identification.

Data scarcity in medical images makes transfer learning a common approach in computer-aided diagnosis. Some disease classification tasks can rely on large homogeneous public datasets to train the transferred model, while others cannot, i.e.

Development and Validation of a Highly Sensitive Isotope-Coded Equivalent Reporter Ion Assay for the Semi-Quantification of Isocoumarins in Complex Matrices.

The accurate quantification of multicomponents using LC-MS is pivotal for ensuring the quality control of herbal medicine, as well as the investigation of their analysis of biological tissue distribution. However, two significant challenges persist: the scarcity of authentic standards and the selection of appropriate internal standards. In this study, we present a highly sensitive isotope-coded equivalent reporter ion assay (iERIA) that combines equivalently quantitative ion and isotope-coded derivatization strategies.

Does the digital economy enhance common prosperity? Examining the mediating influence of resilience in environmental investment.

Background: The link between the digital economy and prosperity is well established. However, the mechanism underlying the relationship between the digital economy and common prosperity is yet to be investigated.

Study Rationale And Purpose: The main purpose of this study is to address this mechanism by examining the extent to which the digital economy has optimized regional investments in protecting the environment, particularly the resilience of environmental investment, thus influencing common prosperity.

The value co-creation mechanism of Public Cultural Services ecosystem in China based on "public value" theory.

The establishment of China's public cultural service system has been explored for nearly two decades, but there is still a significant gap in achieving the goal of equalising and eliminating differences in public value, and it is urgent to systematically explore the public value governance issues contained in it. This paper presents a localised theoretical analysis framework based on the strategic triangle model of public value, with the objective of exploring the endogenous mechanism of China's public cultural services ecosystem (PCSE). Furthermore, it constructs a panel vector autoregression (PVAR) model from a dynamic endogenous perspective, employing the STATA 16.

QM/MM study reveals novel mechanism of KRAS and KRAS catalyzed GTP hydrolysis.

As a crucial drug target, KRAS can regulate most cellular processes involving guanosine triphosphate (GTP) hydrolysis. However, the mechanism of GTP hydrolysis has remained controversial over the past decades. Here, several different GTP hydrolysis mechanisms catalyzed by wild-type KRAS (WT-KRAS) and KRAS mutants were discussed via four QM/MM calculation models.

Multi-Scale Contrastive Learning with Hierarchical Knowledge Synergy for Visible-Infrared Person Re-Identification.

Visible-infrared person re-identification (VI-ReID) is a challenging cross-modality retrieval task to match a person across different spectral camera views. Most existing works focus on learning shared feature representations from the final embedding space of advanced networks to alleviate modality differences between visible and infrared images. However, exclusively relying on high-level semantic information from the network's final layers can restrict shared feature representations and overlook the benefits of low-level details.

LKAFFNet: A Novel Large-Kernel Attention Feature Fusion Network for Land Cover Segmentation.

The accurate segmentation of land cover in high-resolution remote sensing imagery is crucial for applications such as urban planning, environmental monitoring, and disaster management. However, traditional convolutional neural networks (CNNs) struggle to balance fine-grained local detail with large-scale contextual information. To tackle these challenges, we combine large-kernel convolutions, attention mechanisms, and multi-scale feature fusion to form a novel LKAFFNet framework that introduces the following three key modules: LkResNet, which enhances feature extraction through parameterizable large-kernel convolutions; Large-Kernel Attention Aggregation (LKAA), integrating spatial and channel attention; and Channel Difference Features Shift Fusion (CDFSF), which enables efficient multi-scale feature fusion.

Country-specific key lifestyle factors and health outcomes for resource allocation in the general population: a network analysis across 29 countries.

Background: We aimed to identify the central lifestyle, the most impactful among lifestyle factor clusters; the central health outcome, the most impactful among health outcome clusters; and the bridge lifestyle, the most strongly connected to health outcome clusters, across 29 countries to optimise resource allocation for local holistic health improvements.

Methods: From July 2020 to August 2021, we surveyed 16 461 adults across 29 countries who self-reported changes in 18 lifestyle factors and 13 health outcomes due to the pandemic. Three networks were generated by network analysis for each country: lifestyle, health outcome, and bridge networks.

ET-PROTACs: modeling ternary complex interactions using cross-modal learning and ternary attention for accurate PROTAC-induced degradation prediction.

Motivation: Accurately predicting the degradation capabilities of proteolysis-targeting chimeras (PROTACs) for given target proteins and E3 ligases is important for PROTAC design. The distinctive ternary structure of PROTACs presents a challenge to traditional drug-target interaction prediction methods, necessitating more innovative approaches. While current state-of-the-art (SOTA) methods using graph neural networks (GNNs) can discern the molecular structure of PROTACs and proteins, thus enabling the efficient prediction of PROTACs' degradation capabilities, they rely heavily on limited crystal structure data of the POI-PROTAC-E3 ternary complex.

CLP-Net: an advanced artificial intelligence technique for localizing standard planes of cleft lip and palate by three-dimensional ultrasound in the first trimester.

Background: Early diagnosis of cleft lip and palate (CLP) requires a multiplane examination, demanding high technical proficiency from radiologists. Therefore, this study aims to develop and validate the first artificial intelligence (AI)-based model (CLP-Net) for fully automated multi-plane localization in three-dimensional(3D) ultrasound during the first trimester.

Methods: This retrospective study included 418 (394 normal, 24 CLP) 3D ultrasound from 288 pregnant woman between July 2022 to October 2024 from Shenzhen Guangming District People's Hospital during the 11-13 weeks of pregnancy.

Meta-Learning Enables Complex Cluster-Specific Few-Shot Binding Affinity Prediction for Protein-Protein Interactions.

Predicting protein-protein interaction (PPI) binding affinities in unseen protein complex clusters is essential for elucidating complex protein interactions and for the targeted screening of peptide- or protein-based drugs. We introduce MCGLPPI++, a meta-learning framework designed to improve the adaptability of pretrained geometric models in such scenarios. To effectively boost the meta-learning optimization by injecting prior intersample distribution knowledge, three specially designed training sample cluster splitting patterns based on protein interaction interfaces are introduced.

Downregulation of SLC7A11 by Bis(4-Hydroxy-3,5-Dimethylphenyl) Sulfone Induces Ferroptosis in Hepatocellular Carcinoma Cell.

The progression of tumors has been demonstrated to have a strong correlation with ferroptosis. Bis(4-hydroxy-3,5-dimethylphenyl) sulfone (TMBPS) has been shown to effectively inhibit the proliferation of hepatocellular carcinoma (HCC), but its underlying mechanism is not clear. In this study, ferrostatin-1 (Fer-1) was employed to explore whether the death of HCC cells caused by TMBPS is related to ferroptosis.

Benchmark of Data Integration in Single-Cell Proteomics.

Single-cell proteomics (SCP) detected based on different technologies always involves batch-specific variations because of differences in sample processing and other potential biases. How to integrate SCP data effectively has become a great challenge. Integration of SCP data not only requires the conservation of true biological variances, but also realizes the removal of unwanted batch effects.

Are companies responding to minimum wage increases by reducing corporate social responsibility?

Corporate social responsibility (CSR) has been widely discussed. However, the existing literature does not delve into the theoretical mechanism to show how companies adjust their CSR in the face of minimum wage increases. This may be due to the lack of a theoretical framework that clarifies the relationship between minimum wage increases and CSR adjustments.

Enhancing IoT security in smart grids with quantum-resistant hybrid encryption.

Integrating the Internet of Things (IoT) in smart grids has revolutionized the energy sector, enabling real-time data collection and efficient energy distribution. However, this integration also introduces significant security challenges, particularly data encryption. Traditional encryption algorithms used in IoT are vulnerable to various attacks, and the advent of quantum computing exacerbates these vulnerabilities.

Visual-linguistic Diagnostic Semantic Enhancement for medical report generation.

Generative methods are currently popular for medical report generation, as they automatically generate professional reports from input images, assisting physicians in making faster and more accurate decisions. However, current methods face significant challenges: 1) Lesion areas in medical images are often difficult for models to capture accurately, and 2) even when captured, these areas are frequently not described using precise clinical diagnostic terms. To address these problems, we propose a Visual-Linguistic Diagnostic Semantic Enhancement model (VLDSE) to generate high-quality reports.