The Trypanosoma brucei MitoCarta and its regulation and splicing pattern during development

It has long been known that trypanosomes regulate mitochondrial biogenesis during the life cycle of the parasite; however, the mitochondrial protein inventory (MitoCarta) and its regulation remain unknown. We present a novel computational method for genome-wide prediction of mitochondrial proteins using a support vector machine-based classifier with ∼90% prediction accuracy. Using this method, we predicted the mitochondrial localization of 468 proteins with high confidence and have experimentally verified the localization of a subset of these proteins. We then applied a recently developed parallel sequencing technology to determine the expression profiles and the splicing patterns of a total of 1065 predicted MitoCarta transcripts during the development of the parasite, and showed that 435 of the transcripts significantly changed their expressions while 630 remain unchanged in any of the three life stages analyzed. Furthermore, we identified 298 alternatively splicing events, a small subset of which could lead to dual localization of the corresponding protein

MIClique: An Algorithm to Identify Differentially Coexpressed Disease Gene Subset from Microarray Data

Computational analysis of microarray data has provided an effective way to identify disease-related genes. Traditional disease gene selection methods from microarray data such as statistical test always focus on differentially expressed genes in different samples by individual gene prioritization. These traditional methods might miss differentially coexpressed (DCE) gene subsets because they ignore the interaction between genes. In this paper, MIClique algorithm is proposed to identify DEC gene subsets based on mutual information and clique analysis. Mutual information is used to measure the coexpression relationship between each pair of genes in two different kinds of samples. Clique analysis is a commonly used method in biological network, which generally represents biological module of similar function. By applying the MIClique algorithm to real gene expression data, some DEC gene subsets which correlated under one experimental condition but uncorrelated under another condition are detected from the graph of colon dataset and leukemia dataset

Mandatory vs. voluntary disclosure of management forecast in China

This study examines the difference in management forecast quality under mandatory vs. voluntary disclosure in China’s stock markets in terms of management forecasting error (MFE) and value relevance. The results of MFE tests reveal that the disclosure approach is significantly associated with forecast accuracy, and voluntarily disclosed forecasts are more accurate than mandatorily disclosed forecasts. In terms of value relevance, the results are also consistent with the belief that in China’s stock markets, management forecast quality under voluntary disclosure is higher than that under mandatory disclosure.journal articl

Oxidation-Resistant, Solution-Processed Plasmonic Ni Nanochain-SiO\u3csub\u3ex\u3c/sub\u3e (x \u3c 2) Selective Solar Thermal Absorbers

Metal oxidation at high temperatures has long been a challenge in cermet solar thermal absorbers, which impedes the development of atmospherically stable, high-temperature, high-performance concentrated solar power (CSP) systems. In this work, we demonstrate solution-processed Ni nanochain-SiOx (x \u3c 2) and Ni nanochain-SiO2 selective solar thermal absorbers that exhibit a strong anti-oxidation behavior up to 600 °C in air. The thermal stability is far superior to previously reported Ni nanoparticle-Al2O3 selective solar thermal absorbers, which readily oxidize at 450 °C. The SiOx (x \u3c 2) and SiO2 matrices are derived from hydrogen silsesquioxane and tetraethyl orthosilicate precursors, respectively, which comprise Si-O cage-like structures and Si-O networks. Fourier transform infrared spectroscopy shows that the dissociation of Si-O cage-like structures and Si-O networks at high temperatures have enabled the formation of new bonds at the Ni/SiOx interface to passivate the surface of Ni nanoparticles and prevent oxidation. X-ray photoelectron spectroscopy and Raman spectroscopy demonstrate that the excess Si in the SiOx (x \u3c 2) matrices reacts with Ni nanostructures to form silicides at the interfaces, which further improves the anti-oxidation properties. As a result, Ni-SiOx (x \u3c 2) systems demonstrate better anti-oxidation performance than Ni-SiO2 systems. This oxidation-resistant Ni nanochain-SiOx (x \u3c 2) cermet coating also exhibits excellent high-temperature optical performance, with a high solar absorptance of ∼90% and a low emittance ∼18% measured at 300 °C. These results open the door towards atmospheric stable, high temperature, high-performance solar selective absorber coatings processed by low-cost solution-chemical methods for future generations of CSP systems

AlphaFold2 in biomedical research: facilitating the development of diagnostic strategies for disease

Proteins, as the primary executors of physiological activity, serve as a key factor in disease diagnosis and treatment. Research into their structures, functions, and interactions is essential to better understand disease mechanisms and potential therapies. DeepMind’s AlphaFold2, a deep-learning protein structure prediction model, has proven to be remarkably accurate, and it is widely employed in various aspects of diagnostic research, such as the study of disease biomarkers, microorganism pathogenicity, antigen-antibody structures, and missense mutations. Thus, AlphaFold2 serves as an exceptional tool to bridge fundamental protein research with breakthroughs in disease diagnosis, developments in diagnostic strategies, and the design of novel therapeutic approaches and enhancements in precision medicine. This review outlines the architecture, highlights, and limitations of AlphaFold2, placing particular emphasis on its applications within diagnostic research grounded in disciplines such as immunology, biochemistry, molecular biology, and microbiology

The Trypanosoma \u3ci\u3ebrucei\u3c/i\u3e MitoCarta and its regulation and splicing pattern during development

It has long been known that trypanosomes regulate mitochondrial biogenesis during the life cycle of the parasite; however, the mitochondrial protein inventory (MitoCarta) and its regulation remain unknown. We present a novel computational method for genome-wide prediction of mitochondrial proteins using a support vector machine-based classifier with ~90% prediction accuracy. Using this method, we predicted the mitochondrial localization of 468 proteins with high confidence and have experimentally verified the localization of a subset of these proteins. We then applied a recently developed parallel sequencing technology to determine the expression profiles and the splicing patterns of a total of 1065 predicted MitoCarta transcripts during the development of the parasite, and showed that 435 of the transcripts significantly changed their expressions while 630 remain unchanged in any of the three life stages analyzed. Furthermore, we identified 298 alternatively splicing events, a small subset of which could lead to dual localization of the corresponding proteins