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.
View Article and Find Full Text PDFObjectives: 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.
Dentomaxillofac Radiol
March 2024
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.
Nanomicro Lett
November 2023
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.
View Article and Find Full Text PDFSingle-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.
View Article and Find Full Text PDFAqueous 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.
View Article and Find Full Text PDFObjective: 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.
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.
View Article and Find Full Text PDFIEEE/ACM Trans Comput Biol Bioinform
April 2023
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.
View Article and Find Full Text PDFAnnu Int Conf IEEE Eng Med Biol Soc
November 2021
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.
View Article and Find Full Text PDFLancet Public Health
December 2021
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.
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