Noncoding RNAs evolutionarily extend animal lifespan

The mechanisms underlying lifespan evolution in organisms have long been mysterious. However, recent studies have demonstrated that organisms evolutionarily gain noncoding RNAs (ncRNAs) that carry endogenous profound functions in higher organisms, including lifespan. This study unveils ncRNAs as crucial drivers driving animal lifespan evolution. Species in the animal kingdom evolutionarily increase their ncRNA length in their genomes, coinciding with trimming mitochondrial genome length. This leads to lower energy consumption and ultimately lifespan extension. Notably, during lifespan extension, species exhibit a gradual acquisition of long-life ncRNA motifs while concurrently losing short-life motifs. These longevity-associated ncRNA motifs, such as GGTGCG, are particularly active in key tissues, including the endometrium, ovary, testis, and cerebral cortex. The activation of ncRNAs in the ovary and endometrium offers insights into why women generally exhibit longer lifespans than men. This groundbreaking discovery reveals the pivotal role of ncRNAs in driving lifespan evolution and provides a fundamental foundation for the study of longevity and aging.Comment: 13 pages and 4 figure

Computational Models for Transplant Biomarker Discovery.

Translational medicine offers a rich promise for improved diagnostics and drug discovery for biomedical research in the field of transplantation, where continued unmet diagnostic and therapeutic needs persist. Current advent of genomics and proteomics profiling called "omics" provides new resources to develop novel biomarkers for clinical routine. Establishing such a marker system heavily depends on appropriate applications of computational algorithms and software, which are basically based on mathematical theories and models. Understanding these theories would help to apply appropriate algorithms to ensure biomarker systems successful. Here, we review the key advances in theories and mathematical models relevant to transplant biomarker developments. Advantages and limitations inherent inside these models are discussed. The principles of key -computational approaches for selecting efficiently the best subset of biomarkers from high--dimensional omics data are highlighted. Prediction models are also introduced, and the integration of multi-microarray data is also discussed. Appreciating these key advances would help to accelerate the development of clinically reliable biomarker systems

A Systemic Receptor Network Triggered by Human cytomegalovirus Entry

Virus entry is a multistep process that triggers a variety of cellular pathways interconnecting into a complex network, yet the molecular complexity of this network remains largely unsolved. Here, by employing systems biology approach, we reveal a systemic virus-entry network initiated by human cytomegalovirus (HCMV), a widespread opportunistic pathogen. This network contains all known interactions and functional modules (i.e. groups of proteins) coordinately responding to HCMV entry. The number of both genes and functional modules activated in this network dramatically declines shortly, within 25 min post-infection. While modules annotated as receptor system, ion transport, and immune response are continuously activated during the entire process of HCMV entry, those for cell adhesion and skeletal movement are specifically activated during viral early attachment, and those for immune response during virus entry. HCMV entry requires a complex receptor network involving different cellular components, comprising not only cell surface receptors, but also pathway components in signal transduction, skeletal development, immune response, endocytosis, ion transport, macromolecule metabolism and chromatin remodeling. Interestingly, genes that function in chromatin remodeling are the most abundant in this receptor system, suggesting that global modulation of transcriptions is one of the most important events in HCMV entry. Results of in silico knock out further reveal that this entire receptor network is primarily controlled by multiple elements, such as EGFR (Epidermal Growth Factor) and SLC10A1 (sodium/bile acid cotransporter family, member 1). Thus, our results demonstrate that a complex systemic network, in which components coordinating efficiently in time and space contributes to virus entry.Comment: 26 page

Evaluation Of Feasibility And Performance Of Foamed Fire-Resistant Coating Materials

A preliminary study found high-performance cement mortar, geopolymer mortar, and magnesium phosphate cement mortar (MPCM) have the potential as new fire-resistant materials. In this study, foam was added to these three fire-resistant materials to further improve their rheological, mechanical, and fire-resistant performance and reduce costs. Systematic design and experimental programs were conducted. The results showed the addition of foam enhanced workability, adhesiveness, and fire resistance, allowing the materials to withstand higher temperatures and further delay heat transfer. A mixture of 70% MPCM and 30% foam was identified as the optimum design, which could withstand 1000 °C with low heat transfer rates

A Web-Server of Cell Type Discrimination System

Discriminating cell types is a daily request for stem cell biologists. However, there is not a user-friendly system available to date for public users to discriminate the common cell types, embryonic stem cells (ESCs), induced pluripotent stem cells (iPSCs), and somatic cells (SCs). Here, we develop WCTDS, a web-server of cell type discrimination system, to discriminate the three cell types and their subtypes like fetal versus adult SCs. WCTDS is developed as a top layer application of our recent publication regarding cell type discriminations, which employs DNA-methylation as biomarkers and machine learning models to discriminate cell types. Implemented by Django, Python, R, and Linux shell programming, run under Linux-Apache web server, and communicated through MySQL, WCTDS provides a friendly framework to efficiently receive the user input and to run mathematical models for analyzing data and then to present results to users. This framework is flexible and easy to be expended for other applications. Therefore, WCTDS works as a user-friendly framework to discriminate cell types and subtypes and it can also be expended to detect other cell types like cancer cells