Au-based catalysts have established a new important field of catalysis, revealing specific properties in terms of both high activity and selectivity for many reactions. However, the correlation between the morphology and the activity of the catalyst is not always clear although much effort has been addressed to this task. To some extent the problem relates to the complexity of the characterisation techniques that can be applied to Au catalyst and the broad range of ways in which they can be prepared. Indeed, in many reports only a few characterization techniques have been used to investigate the potential nature of the active sites. The aim of this review is to provide a critical description of the techniques that are most commonly used as well as the more advanced characterization techniques available for this task. The techniques that we discuss are (i) transmission electron microscopy methods, (ii) X-ray spectroscopy techniques, (iii) vibrational spectroscopy techniques and (iv) chemisorption methods. The description is coupled with developing an understanding of a number of preparation methods. In the final section the example of the supported AuPd alloy catalyst is discussed to show how the techniques can gain an understanding of an active oxidation catalyst.
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Brief Bioinform
November 2024
Institute for Molecular Bioscience, The University of Queensland, 306 Carmody Road, St Lucia, Brisbane, QLD 4072, Australia.
Regulatory genes are critical determinants of cellular responses in development and disease, but standard RNA sequencing (RNA-seq) analysis workflows, such as differential expression analysis, have significant limitations in revealing the regulatory basis of cell identity and function. To address this challenge, we present the TRIAGE R package, a toolkit specifically designed to analyze regulatory elements in both bulk and single-cell RNA-seq datasets. The package is built upon TRIAGE methods, which leverage consortium-level H3K27me3 data to enrich for cell-type-specific regulatory regions.
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November 2024
State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, 2 Sipailou, Xuanwu District, Nanjing 210096, China.
Spatial transcriptomics technologies have been extensively applied in biological research, enabling the study of transcriptome while preserving the spatial context of tissues. Paired with spatial transcriptomics data, platforms often provide histology and (or) chromatin images, which capture cellular morphology and chromatin organization. Additionally, single-cell RNA sequencing (scRNA-seq) data from matching tissues often accompany spatial data, offering a transcriptome-wide gene expression profile of individual cells.
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November 2024
College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China.
The role of cell-cell communications (CCCs) is increasingly recognized as being important to differentiation, invasion, metastasis, and drug resistance in tumoral tissues. Developing CCC inference methods using traditional experimental methods are time-consuming, labor-intensive, cannot handle large amounts of data. To facilitate inference of CCCs, we proposed a computational framework, called CellMsg, which involves two primary steps: identifying ligand-receptor interactions (LRIs) and measuring the strength of LRIs-mediated CCCs.
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November 2024
School of Computer Science and Technology, Harbin Institute of Technology, HIT Campus, Shenzhen University Town, Nanshan District, Shenzhen 518055, Guangdong, China.
Antimicrobial peptides (AMPs) emerge as a type of promising therapeutic compounds that exhibit broad spectrum antimicrobial activity with high specificity and good tolerability. Natural AMPs usually need further rational design for improving antimicrobial activity and decreasing toxicity to human cells. Although several algorithms have been developed to optimize AMPs with desired properties, they explored the variations of AMPs in a discrete amino acid sequence space, usually suffering from low efficiency, lack diversity, and local optimum.
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November 2024
Department of Electronic Engineering, Tsinghua University, 100084 Beijing, China.
Single-cell multi-omics techniques, which enable the simultaneous measurement of multiple modalities such as RNA gene expression and Assay for Transposase-Accessible Chromatin (ATAC) within individual cells, have become a powerful tool for deciphering the intricate complexity of cellular systems. Most current methods rely on motif databases to establish cross-modality relationships between genes from RNA-seq data and peaks from ATAC-seq data. However, these approaches are constrained by incomplete database coverage, particularly for novel or poorly characterized relationships.
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