Interacting and joint effects of assisted reproductive technology and gestational diabetes mellitus on preterm birth and the mediating role of gestational diabetes mellitus: a cohort study using a propensity score.

Objective: To investigate whether gestational diabetes mellitus (GDM) mediates the association between assisted reproductive technology (ART) and preterm birth (PTB), and to examine the interaction and joint effects of ART and GDM on PTB.

Methods: This retrospective cohort study utilized data from 20,721 mothers with singleton live births at Sichuan Jinxin Xinan Women and Children's Hospital from January 2020 to December 2023. The exposures were ART and GDM, and the outcome was PTB.

Energy-Efficient and Trust-Based Autonomous Underwater Vehicle Scheme for 6G-Enabled Internet of Underwater Things.

This paper introduces a novel energy-efficient lightweight, void hole avoidance, localization, and trust-based scheme, termed as Energy-Efficient and Trust-based Autonomous Underwater Vehicle (EETAUV) protocol designed for 6G-enabled underwater acoustic sensor networks (UASNs). The proposed scheme addresses key challenges in UASNs, such as energy consumption, network stability, and data security. It integrates a trust management framework that enhances communication security through node identification and verification mechanisms utilizing normal and phantom nodes.

GFA-Net: Geometry-Focused Attention Network for Six Degrees of Freedom Object Pose Estimation.

Six degrees of freedom (6-DoF) object pose estimation is essential for robotic grasping and autonomous driving. While estimating pose from a single RGB image is highly desirable for real-world applications, it presents significant challenges. Many approaches incorporate supplementary information, such as depth data, to derive valuable geometric characteristics.

A SERS and colorimetric dual-mode biosensor based on stimulus-responsive DNA/MOF-bound bimetallic nanozyme for the ultrasensitive detection of chloramphenicol in food.

A dual-mode detection platform utilizing colorimetric and Raman was developed based on the exponential amplification reaction (EXPAR) strategy and a "core-satellite" structure constructed by bimetallic nanozymes to detect chloramphenicol (CAP). Initially, DNA-gated metal-organic frameworks (MOFs) incorporating cascaded amplification were used to be nanocarriers for the colorimetric and Raman reporter molecules (3,3',5,5'-tetramethylbiphenyl; TMB). Subsequently, assembled DNA served as gatekeepers to create a stimulus-responsive DNA-gated MOF (TMB@DNA/MOF).

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.

Comparative chloroplast genome analysis of five widespread species ( L.) and development of molecular markers for their discrimination.

Background: Zanthoxylum L., an important genus in the Rutaceae family, has great edible and medical values. However, the high degree of morphological similarity among species and the lack of sufficient chloroplast (cp) genomic resources have greatly impeded germplasm identification and phylogenetic analyses of

Methods: Here we assembled cp genomes of five widespread species (, , , and ) in China as a case study, comparative analysis of these assembled cp genomes.

Simultaneous measurement of liquid level and R.I. sensor using POF based on twisted structure.

In this paper, a dual-parameter liquid level and refractive index (R.I.) sensor is fabricated using three pieces of bare polymer optical fibers (POFs), which can independently and simultaneously sense the liquid level and R.

Style Transfer of Chinese Wuhu Iron Paintings Using Hierarchical Visual Transformer.

Within the domain of traditional art, Chinese Wuhu Iron Painting distinguishes itself through its distinctive craftsmanship, aesthetic expressiveness, and choice of materials, presenting a formidable challenge in the arena of stylistic transformation. This paper introduces an innovative Hierarchical Visual Transformer (HVT) framework aimed at achieving effectiveness and precision in the style transfer of Wuhu Iron Paintings. The study begins with an in-depth analysis of the artistic style of Wuhu Iron Paintings, extracting key stylistic elements that meet technical requirements for style conversion.

Study on the Carrier Transport Process in Deep Ultraviolet Light-Emitting Diodes with Al-Content-Varied AlGaN Composite Last Quantum Barrier.

Serious electron leakage and poor hole injection efficiency are still challenges for deep ultraviolet AlGaN-based light-emitting diodes with a traditional structure in achieving high performance. Currently, the majority of research works concentrate on optimizing the structures of the electron blocking layer (EBL) and last quantum barrier (LQB) separately, rather than considering them as an integrated structure. Therefore, in this study, an Al-content-varied AlGaN composite last quantum barrier (CLQB) layer is proposed to replace the traditional EBL and LQB layers.

Prevalence and risk factors for type 2 diabetes mellitus in women with gestational diabetes mellitus: a systematic review and meta-analysis.

Introduction: This study aims to explore the risk factors in the progression of gestational diabetes mellitus (GDM) to type 2 diabetes mellitus (T2DM).

Material And Methods: Relevant studies were comprehensively searched from PubMed, Web of Science, Cochrane Library, and Embase up to March 12. Data extraction was performed.

Energy consumption forecasting for oil and coal in China based on hybrid deep learning.

The consumption forecasting of oil and coal can help governments optimize and adjust energy strategies to ensure energy security in China. However, such forecasting is extremely challenging because it is influenced by many complex and uncertain factors. To fill this gap, we propose a hybrid deep learning approach for consumption forecasting of oil and coal in China.

CoGraphNet for enhanced text classification using word-sentence heterogeneous graph representations and improved interpretability.

Text Graph Representation Learning through Graph Neural Networks (TG-GNN) is a powerful approach in natural language processing and information retrieval. However, it faces challenges in computational complexity and interpretability. In this work, we propose CoGraphNet, a novel graph-based model for text classification, addressing key issues.

Integrated bioinformatics and multi-omics to investigate the mechanism of Rhododendron molle Flos-induced hepatotoxicity.

Ethnopharmacological Relevance: Drug-induced liver injury (DILI) is an important and common adverse drug event. Rhododendron molle Flos (RMF), as one of toxic Traditional Chinese medicines (TCMs), holds a prominent position in clinical practice for treating rheumatoid arthritis. However, the toxicity of RMF limits its safe.

Co-culture of natural killer cells and tumor spheroids on a heterogeneous multilayer paper stack.

Multilayer paper-based cell culture, as an in vitro three-dimensional (3D) cell culture method, has been frequently used to research drug bioavailability, therapeutic efficacy, and dose-limiting toxicity in malignant tumors. This paper proposes a heterogenous multilayer paper stacking co-culture system to establish a model of natural killer (NK) cells moving through the endothelium layer and attacking tumor spheroids. This system consists of three layers: a bottom tumor-spheroid layer, a middle invasion layer, and a top endothelium layer.

Study protocol: A national cross-sectional study on psychology and behavior investigation of Chinese residents in 2023.

Introduction: This study protocol specifies the primary research line and theoretical framework of the 2023 Survey of the Psychology and Behavior of the Chinese Population. It aims to establish a consistent database of Chinese residents' psychological and behavioral surveys through multi-center and large-sample cross-sectional surveys to provide robust data support for developing research in related fields. It will track the public's physical and psychological health more comprehensively and systematically.

Development and external validation of a multi-task feature fusion network for CTV segmentation in cervical cancer radiotherapy.

Background And Purpose: Accurate segmentation of the clinical target volume (CTV) is essential to deliver an effective radiation dose to tumor tissues in cervical cancer radiotherapy. Also, although automated CTV segmentation can reduce oncologists' workload, challenges persist due to the microscopic spread of tumor cells undetectable in CT imaging, low-intensity contrast between organs, and inter-observer variability. This study aims to develop and validate a multi-task feature fusion network (MTF-Net) that uses distance-based information to enhance CTV segmentation accuracy.

Retraction notice to "Advanced insights on removal of antibiotics by microalgae-bacteria consortia: A state-of-the-art review and emerging prospects"[Chemosphere 307 (2022) 136117].

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/policies/article-withdrawal).

An efficient surface electromyography-based gesture recognition algorithm based on multiscale fusion convolution and channel attention.

In the field of rehabilitation, although deep learning have been widely used in multitype gesture recognition via surface electromyography (sEMG), their higher algorithmic complexity often leads to low computationally inefficient, which compromise their practicality. To achieve more efficient multitype recognition, We propose the Residual-Inception-Efficient (RIE) model, which integrates Inception and efficient channel attention (ECA). The Inception, which is a multiscale fusion convolutional module, is adopted to enhance the ability to extract sEMG features.

Targeted metals extraction from spent LiNiCoMnO batteries via thermochemical-induced sulfation roasting: Thermodynamics & kinetics.

To alleviate the energy crisis and control environmental pollution raised by spent lithium-ion batteries (LIBs), the development of efficient and economic methods for their recycling is crucial for sustainable development of new energy industry. Herein, a combined pyro - hydrometallurgical process was adopted for recovery of valuable metal elements for spent LiNiCoMnO (NCM523). Different from conventional pyrometallurgical methods with high temperature and energy consumption, the NHHSO roasting strategy works at 400 °C and achieves remarkable leaching efficiencies of Li, Co, Mn, and Ni achieved 97.

MFC-ACL: Multi-view fusion clustering with attentive contrastive learning.

Multi-view clustering can better handle high-dimensional data by combining information from multiple views, which is important in big data mining. However, the existing models which simply perform feature fusion after feature extraction for individual views, mostly fails to capture the holistic attribute information of multi-view data due to ignoring the significant disparities among views, which seriously affects the performance of multi-view clustering. In this paper, inspired by the attention mechanism, an approach called Multi-View Fusion Clustering with Attentive Contrastive Learning (MFC-ACL) is proposed to tackle these issues.

Heteroatomic molecules for coordination engineering towards advanced Pb-free Sn-based perovskite photovoltaics.

As an ideal eco-friendly Pb-free optoelectronic material, Sn-based perovskites have made significant progress in the field of photovoltaics, and the highest power conversion efficiency (PCE) of Sn-based perovskite solar cells (PSCs) has been currently approaching 16%. In the course of development, various strategies have been proposed to improve the PCE and stability of Sn-based PSCs by solving the inherent problems of Sn, including high Lewis acidity and easy oxidation. Notably, the recent breakthrough comes from the development of heteroatomic coordination molecules to control the characteristics of Sn-based perovskites, which are considered to be vital for realizing efficient PSCs.