Dry sliding wear behaviour of Ta/NbC filled glass-epoxy composites at elevated temperatures

In this work an attempt was made to evaluate wear loss, specific wear rate and coefficient of friction of Glass-Epoxy (G-E) composites with and without Tantalum Niobium Carbide (Ta/NbC) filler. A vacuum assisted resin transfer moulding (VARTM) technique was employed to fabricate the composite specimens. The fabricated wear specimens were tested by using pin-on-disk test rig at various temperatures viz., 30, 60, 90 and 120° C at normal applied loads of 10 N and 20 N. Sliding velocity of the disc of 1.5 m/s was maintained and test was continued for each sample up to a sliding distance of 5000 m. The wear loss in both the composites increases with increase in temperature and applied normal load. However, Ta/NbC particulate filler incorporated G-E composite exhibits lower wear rate and higher coefficient of friction as compared to unfilled G-E composite. The features of worn surfaces of the specimens were examined under scanning electron microscopy (SEM) and findings are analysed

Enhancing Heart Disease Prediction With Reinforcement Learning and Data Augmentation

The study presents a novel method to improve the prediction accuracy of cardiac disease by combining data augmentation techniques with reinforcement learning. The complex nature of cardiac data frequently presents challenges for traditional machine learning models, which results in subpar performance. In response, our fusion methodology improves predictive capabilities by augmenting data and utilizing reinforcement learning\u27s skill at sequential decision-making. Our method predicts cardiac disease with an astounding 94 % accuracy rate, which is an outstanding result. This significant improvement outperforms existing techniques and shows a deeper comprehension of intricate data relationships. The amalgamation of reinforcement learning and data augmentation not only yields superior predictive accuracy but also bears noteworthy consequences for patient care and accurate cardiac diagnosis. Through the efficient combination of these approaches, our method provides a powerful response to the difficulties presented by complicated cardiac data. The potential to transform illness prediction and prevention techniques and ultimately improve patient outcomes is demonstrated by this integration\u27s success

Distinct RNA profiles in subpopulations of extracellular vesicles: apoptotic bodies, microvesicles and exosomes

Introduction: In recent years, there has been an exponential increase in the number of studies aiming to understand the biology of exosomes, as well as other extracellular vesicles. However, classification of membrane vesicles and the appropriate protocols for their isolation are still under intense discussion and investigation. When isolating vesicles, it is crucial to use systems that are able to separate them, to avoid cross-contamination. Method: EVs released from three different kinds of cell lines: HMC-1, TF-1 and BV-2 were isolated using two centrifugation-based protocols. In protocol 1, apoptotic bodies were collected at 2,000×g, followed by filtering the supernatant through 0.8 µm pores and pelleting of microvesicles at 12,200×g. In protocol 2, apoptotic bodies and microvesicles were collected together at 16,500×g, followed by filtering of the supernatant through 0.2 µm pores and pelleting of exosomes at 120,000×g. Extracellular vesicles were analyzed by transmission electron microscopy, flow cytometry and the RNA profiles were investigated using a Bioanalyzer®. Results: RNA profiles showed that ribosomal RNA was primary detectable in apoptotic bodies and smaller RNAs without prominent ribosomal RNA peaks in exosomes. In contrast, microvesicles contained little or no RNA except for microvesicles collected from TF-1 cell cultures. The different vesicle pellets showed highly different distribution of size, shape and electron density with typical apoptotic body, microvesicle and exosome characteristics when analyzed by transmission electron microscopy. Flow cytometry revealed the presence of CD63 and CD81 in all vesicles investigated, as well as CD9 except in the TF-1-derived vesicles, as these cells do not express CD9. Conclusions: Our results demonstrate that centrifugation-based protocols are simple and fast systems to distinguish subpopulations of extracellular vesicles. Different vesicles show different RNA profiles and morphological characteristics, but they are indistinguishable using CD63-coated beads for flow cytometry analysis

Spatially explicit database on crop-livestock management, soil, climate, greenhouse gas emissions and mitigation potential for all of Bangladesh

Acknowledgments: The International Maize and Wheat Improvement Center (CIMMYT) carried out this work with support of the CGIAR research program on Climate Change, Agriculture and Food Security (CCAFS) and the Climate Services for Resilient Development (CSRD; https://ccafs.cgiar.org/research/projects/climate-services-resilient-development-south-asia) for South Asia project supported by USAID. This work was also supported by the USAID and Bill and Melinda Gates Foundation (BMGF) supported Cereal Systems Initiative for South Asia (CSISA; https://csisa.org) CCAFS’ work is supported by CGIAR Fund Donors and through bilateral funding agreements. For details please visit https://ccafs.cgiar.org/donors. The views expressed in this paper cannot be taken to reflect the official opinions of CCAFS, USAID, or BMGF, and shall not be used for advertising.Peer reviewedPublisher PD

Quantifying opportunities for greenhouse gas emissions mitigation using big data from smallholder crop and livestock farmers across Bangladesh

Funding Information: The International Maize and Wheat Improvement Center (CIMMYT) carried out this work with support of the CGIAR research program on Climate Change, Agriculture and Food Security (CCAFS) and the Climate Services for Resilient Development (CSRD; https://ccafs.cgiar.org/research/projects/climate-services-resilient-development-south-asia ) for South Asia project supported by USAID . This work was also supported by the USAID and Bill and Melinda Gates Foundation (BMGF) supported Cereal Systems Initiative for South Asia (CSISA; https://csisa.org ). CCAFS' work is supported by CGIAR Fund Donors and through bilateral funding agreements. For details please visit https://ccafs.cgiar.org/donors . The views expressed in this paper cannot be taken to reflect the official opinions of CCAFS, USAID, or BMGF, and shall not be used for advertising. We sincerely acknowledge the input and support provided by various stakeholders in Bangladesh during stakeholder meetings. We are thankful to Robel Takele and Sanjay Pothireddy for graphics assistance.Peer reviewedPublisher PD

NCBI Peptidome: a new repository for mass spectrometry proteomics data

Peptidome is a public repository that archives and freely distributes tandem mass spectrometry peptide and protein identification data generated by the scientific community. Data from all stages of a mass spectrometry experiment are captured, including original mass spectra files, experimental metadata and conclusion-level results. The submission process is facilitated through acceptance of data in commonly used open formats, and all submissions undergo syntactic validation and curation in an effort to uphold data integrity and quality. Peptidome is not restricted to specific organisms, instruments or experiment types; data from any tandem mass spectrometry experiment from any species are accepted. In addition to data storage, web-based interfaces are available to help users query, browse and explore individual peptides, proteins or entire Samples and Studies. Results are integrated and linked with other NCBI resources to ensure dissemination of the information beyond the mass spectroscopy proteomics community. Peptidome is freely accessible at http://www.ncbi.nlm.nih.gov/peptidome

Microparticle-mediated transfer of the viral receptors CAR and CD46, and the CFTR channel in a CHO cell model confers new functions to target cells

Cell microparticles (MPs) released in the extracellular milieu can embark plasma membrane and intracellular components which are specific of their cellular origin, and transfer them to target cells. The MP-mediated, cell-to-cell transfer of three human membrane glycoproteins of different degrees of complexity was investigated in the present study, using a CHO cell model system. We first tested the delivery of CAR and CD46, two monospanins which act as adenovirus receptors, to target CHO cells. CHO cells lack CAR and CD46, high affinity receptors for human adenovirus serotype 5 (HAdV5), and serotype 35 (HAdV35), respectively. We found that MPs derived from CHO cells (MP-donor cells) constitutively expressing CAR (MP-CAR) or CD46 (MP-CD46) were able to transfer CAR and CD46 to target CHO cells, and conferred selective permissiveness to HAdV5 and HAdV35. In addition, target CHO cells incubated with MP-CD46 acquired the CD46-associated function in complement regulation. We also explored the MP-mediated delivery of a dodecaspanin membrane glycoprotein, the CFTR to target CHO cells. CFTR functions as a chloride channel in human cells and is implicated in the genetic disease cystic fibrosis. Target CHO cells incubated with MPs produced by CHO cells constitutively expressing GFP-tagged CFTR (MP-GFP-CFTR) were found to gain a new cellular function, the chloride channel activity associated to CFTR. Time-course analysis of the appearance of GFP-CFTR in target cells suggested that MPs could achieve the delivery of CFTR to target cells via two mechanisms: the transfer of mature, membrane-inserted CFTR glycoprotein, and the transfer of CFTR-encoding mRNA. These results confirmed that cell-derived MPs represent a new class of promising therapeutic vehicles for the delivery of bioactive macromolecules, proteins or mRNAs, the latter exerting the desired therapeutic effect in target cells via de novo synthesis of their encoded proteins

Rule-based modeling of biochemical systems with BioNetGen

Totowa, NJ. Please cite this article when referencing BioNetGen in future publications. Rule-based modeling involves the representation of molecules as structured objects and molecular interactions as rules for transforming the attributes of these objects. The approach is notable in that it allows one to systematically incorporate site-specific details about proteinprotein interactions into a model for the dynamics of a signal-transduction system, but the method has other applications as well, such as following the fates of individual carbon atoms in metabolic reactions. The consequences of protein-protein interactions are difficult to specify and track with a conventional modeling approach because of the large number of protein phosphoforms and protein complexes that these interactions potentially generate. Here, we focus on how a rule-based model is specified in the BioNetGen language (BNGL) and how a model specification is analyzed using the BioNetGen software tool. We also discuss new developments in rule-based modeling that should enable the construction and analyses of comprehensive models for signal transduction pathways and similarly large-scale models for other biochemical systems. Key Words: Computational systems biology; mathematical modeling; combinatorial complexity; software; formal languages; stochastic simulation; ordinary differential equations; protein-protein interactions; signal transduction; metabolic networks. 1

An approach for the identification of targets specific to bone metastasis using cancer genes interactome and gene ontology analysis

Metastasis is one of the most enigmatic aspects of cancer pathogenesis and is a major cause of cancer-associated mortality. Secondary bone cancer (SBC) is a complex disease caused by metastasis of tumor cells from their primary site and is characterized by intricate interplay of molecular interactions. Identification of targets for multifactorial diseases such as SBC, the most frequent complication of breast and prostate cancers, is a challenge. Towards achieving our aim of identification of targets specific to SBC, we constructed a 'Cancer Genes Network', a representative protein interactome of cancer genes. Using graph theoretical methods, we obtained a set of key genes that are relevant for generic mechanisms of cancers and have a role in biological essentiality. We also compiled a curated dataset of 391 SBC genes from published literature which serves as a basis of ontological correlates of secondary bone cancer. Building on these results, we implement a strategy based on generic cancer genes, SBC genes and gene ontology enrichment method, to obtain a set of targets that are specific to bone metastasis. Through this study, we present an approach for probing one of the major complications in cancers, namely, metastasis. The results on genes that play generic roles in cancer phenotype, obtained by network analysis of 'Cancer Genes Network', have broader implications in understanding the role of molecular regulators in mechanisms of cancers. Specifically, our study provides a set of potential targets that are of ontological and regulatory relevance to secondary bone cancer.Comment: 54 pages (19 pages main text; 11 Figures; 26 pages of supplementary information). Revised after critical reviews. Accepted for Publication in PLoS ON