Multifunctional Ultrathin TiCT MXene@CuCoO /PE Separator for Ultra-High-Energy-Density and Large-Capacity Lithium-Sulfur Pouch Cells.

The shuttling of lithium polysulfides (LiPSs), sluggish reaction kinetics, and uncontrolled lithium deposition/stripping remain the main challenges in lithium-sulfur batteries (LSBs), which are aggravated under practical working conditions, i.e., high sulfur loading and lean electrolyte in large-capacity pouch cells.

Odontogenic cystic lesion segmentation on cone-beam CT using an auto-adapting multi-scaled UNet.

Objectives: Precise segmentation of Odontogenic Cystic Lesions (OCLs) from dental Cone-Beam Computed Tomography (CBCT) is critical for effective dental diagnosis. Although supervised learning methods have shown practical diagnostic results in segmenting various diseases, their ability to segment OCLs covering different sub-class varieties has not been extensively investigated.

Methods: In this study, we propose a new supervised learning method termed OCL-Net that combines a Multi-Scaled U-Net model, along with an Auto-Adapting mechanism trained with a combined supervised loss.

Panoramic imaging errors in machine learning model development: a systematic review.

Objectives: To investigate the management of imaging errors from panoramic radiography (PAN) datasets used in the development of machine learning (ML) models.

Methods: This systematic literature followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and used three databases. Keywords were selected from relevant literature.

Exploring the Roles of Single Atom in Hydrogen Peroxide Photosynthesis.

This comprehensive review provides a deep exploration of the unique roles of single atom catalysts (SACs) in photocatalytic hydrogen peroxide (HO) production. SACs offer multiple benefits over traditional catalysts such as improved efficiency, selectivity, and flexibility due to their distinct electronic structure and unique properties. The review discusses the critical elements in the design of SACs, including the choice of metal atom, host material, and coordination environment, and how these elements impact the catalytic activity.

scGGAN: single-cell RNA-seq imputation by graph-based generative adversarial network.

Single-cell RNA sequencing (scRNA-seq) data are typically with a large number of missing values, which often results in the loss of critical gene signaling information and seriously limit the downstream analysis. Deep learning-based imputation methods often can better handle scRNA-seq data than shallow ones, but most of them do not consider the inherent relations between genes, and the expression of a gene is often regulated by other genes. Therefore, it is essential to impute scRNA-seq data by considering the regional gene-to-gene relations.

Ultrastable Zinc Anode Enabled by CO-Induced Interface Layer.

Aqueous Zn-ion batteries (AZIBs), being safe, inexpensive, and pollution-free, are a promising candidate for future large-scale sustainable energy storage. However, in a conventional AZIBs setup, the Zn metal anode suffers oxidative corrosion, side reactions with electrolytes, disordered dendrite growth during operation, and consequently low efficiency and short lifespan. In this work, we discover that purging CO gas into the electrolyte could address these issues by eliminating dissolved O, inhibiting side reactions by buffering the local pH change, and preventing dendrite growth by inducing the in situ formation of a ZnCO solid electrolyte interphase layer.

Transcriptome analysis of differentially expressed genes involved in the inflammageing status of gingiva in aged mice.

Objective: To identify immune-inflammation-related genes related to susceptibility to periodontitis in the gingiva of aged mice with RNA sequencing.

Methods: Gingival samples from 18-month-old, 8-week-old healthy mice and 8-week-old mice with periodontitis were taken for RNA-seq. The differentially expressed genes (DEGs) were validated with qRT-PCR using mouse and human gingival samples.

Differentially expressed genes prediction by multiple self-attention on epigenetics data.

Predicting differentially expressed genes (DEGs) from epigenetics signal data is the key to understand how epigenetics controls cell functional heterogeneity by gene regulation. This knowledge can help developing 'epigenetics drugs' for complex diseases like cancers. Most of existing machine learning-based methods suffer defects in prediction accuracy, interpretability or training speed.

Differential Gene Expression Prediction by Ensemble Deep Networks on Histone Modification Data.

Predicting differential gene expression (DGE) from Histone modifications (HM) signal is crucial to understand how HM controls cell functional heterogeneity through influencing differential gene regulation. Most existing prediction methods use fixed-length bins to represent HM signals and transmit these bins into a single machine learning model to predict differential expression genes of single cell type or cell type pair. However, the inappropriate bin length may cause the splitting of the important HM segment and lead to information loss.

Combining CNN with Pathological Information for the Detection of Transmissive Lesions of Jawbones from CBCT Images.

Cone-Beam Computed Tomography (CBCT) imaging modality is used to acquire 3D volumetric image of the human body. CBCT plays a vital role in diagnosing dental diseases, especially cyst or tumour-like lesions. Current computer-aided detection and diagnostic systems have demonstrated diagnostic value in a range of diseases, however, the capability of such a deep learning method on transmissive lesions has not been investigated.

The mental health of transgender and gender non-conforming people in China: a systematic review.

Transgender and gender non-conforming (TGNC) individuals are at a high risk of adverse mental health outcomes due to minority stress-the stress faced by individuals categorised as stigmatised social minority groups. This systematic review sought to summarise the key mental health findings of the research on TGNC individuals in mainland China. We also aimed to consolidate research on the topic, identify specific mental health disparities, and offer new perspectives for future research to inform both policy and clinical practice.