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As the demand for high-energy batteries to power electric vehicles continues to grow, Ni-rich cathode materials have emerged as promising candidates due to their high capacity. However, these materials are prone to rapid degradation under increased voltages, posing significant challenges for their long-term stability and safety. In this study, we investigate the effects of tantalum (Ta) doping on the performance and stability of LiNi 0.80 Mn 0.1 Co 0.1 O 2 (NMC811) cathode materials. Using a combined theoretical and experimental approach, we employ density functional theory (DFT) and cluster expansion models to analyze the electronic structure and oxygen vacancy formation enthalpy in Ta-doped NMC811. Experimental validation is conducted using cycling and gas measurements via on-line electrochemical mass spectrometry (OEMS) on in-house synthesized cathode active materials. Both theoretical and experimental approaches show an improvement in oxygen binding due to tantalum doping, with the DFT results highlighting the impact of Ni 4+ concentration on the proximity of the vacancy. Our results suggest that Ta doping inhibits the formation of oxygen vacancy-induced side phases, reducing cracking and enhancing the longevity and safety of Ni-rich cathodes.

Background: In recent years, the introduction of single-cell RNA sequencing (scRNA-seq) has enabled the analysis of a cell's transcriptome at an unprecedented granularity and processing speed. The experimental outcome of applying this technology is a [Formula: see text] matrix containing aggregated mRNA expression counts of M genes and N cell samples. From this matrix, scientists can study how cell protein synthesis changes in response to various factors, for example, disease versus non-disease states in response to a treatment protocol. This technology's critical challenge is detecting and accurately recording lowly expressed genes. As a result, low expression levels tend to be missed and recorded as zero - an event known as dropout. This makes the lowly expressed genes indistinguishable from true zero expression and different than the low expression present in cells of the same type. This issue makes any subsequent downstream analysis difficult., Results: To address this problem, we propose an approach to measure cell similarity using consensus clustering and demonstrate an effective and efficient algorithm that takes advantage of this new similarity measure to impute the most probable dropout events in the scRNA-seq datasets. We demonstrate that our approach exceeds the performance of existing imputation approaches while introducing the least amount of new noise as measured by clustering performance characteristics on datasets with known cell identities., Conclusions: ccImpute is an effective algorithm to correct for dropout events and thus improve downstream analysis of scRNA-seq data. ccImpute is implemented in R and is available at https://github.com/khazum/ccImpute ., (© 2022. The Author(s).)

This report describes an improved protocol to generate stranded, barcoded RNA-seq libraries to capture the whole transcriptome. By optimizing the use of duplex specific nuclease (DSN) to remove ribosomal RNA reads from stranded barcoded libraries, we demonstrate improved efficiency of multiplexed next generation sequencing (NGS). This approach detects expression profiles of all RNA types, including miRNA (microRNA), piRNA (Piwi-interacting RNA), snoRNA (small nucleolar RNA), lincRNA (long non-coding RNA), mtRNA (mitochondrial RNA) and mRNA (messenger RNA) without the use of gel electrophoresis. The improved protocol generates high quality data that can be used to identify differential expression in known and novel coding and non-coding transcripts, splice variants, mitochondrial genes and SNPs (single nucleotide polymorphisms)., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Gene networks are important tools in studying gene-gene relationships and gene function. Understanding the relationships within these networks is an important challenge. Ontologies are a critical tool in helping deal with these data. The use of the Gene Ontology, for example, has become routine in methods for validation, discovery, etc. Here we present a novel algorithm that synthesizes an ontology by considering both extant annotation terms and also the connections between genes in gene networks. The process is efficient and produces easily inspectable ontologies. Because the relationships drawn between terms are heavily influenced by data, we call these "Data-Driven" Ontologies. We apply this algorithm to both discover new relationships between biological processes and as a tool to compare sets of genes across microrarray experiments. Supplemental data and source code are available at: http://www.ddont.org

The motif prediction problem is to predict short, conserved subsequences that are part of a family of sequences, and it is a very important biological problem. Gibbs is one of the first successful motif algorithms and it runs very fast compared with other algorithms, and its search behavior is based on the well-studied Gibbs random sampling. However, motif prediction is a very difficult problem and Gibbs may not predict true motifs in some cases. Thus, the authors explored a possibility of improving the prediction accuracy of Gibbs while retaining its fast runtime performance. In this paper, the authors considered Gibbs only for proteins, not for DNA binding sites. The authors have developed iGibbs, an integrated motif search framework for proteins that employs two previous techniques of their own: one for guiding motif search by clustering sequences and another by pattern refinement. These two techniques are combined to a new double clustering approach to guiding motif search. The unique feature of their framework is that users do not have to specify the number of motifs to be predicted when motifs occur in different subsets of the input sequences since it automatically clusters input sequences into clusters and predict motifs from the clusters. Tests on the PROSITE database show that their framework improved the prediction accuracy of Gibbs significantly. Compared with more exhaustive search methods like MEME, iGibbs predicted motifs more accurately and runs one order of magnitude faster., (2006 Wiley-Liss, Inc.)