Viruses possess specific conserved regions known as RNA recognition motifs, which are shared within taxonomic groups. Applying these to the Baltimore virus classification system, there are genes/proteins that serve as...
Viruses possess specific conserved regions known as RNA recognition motifs, which are shared within taxonomic groups. Applying these to the Baltimore virus classification system, there are genes/proteins that serve as biomarkers for each of the seven groups. Mutations in conserved sequence regions can have a significant impact on the function of a virus, especially if these conserved regions are part of a major protein. In this study, we analyzed the RdRp gene, known to be a representative RRM of RNA viruses, from viruses of five families (Alternaviridae, Chrysoviridae, Totiviridae, Sedoreoviridae, and Spinareoviridae) within the phylum Duplornaviricota. Data were retrieved from the NCBI non-redundant (nr) database (https://***/protein) applying a cut-off of 300 amino acids in length. RdRp protein sequences were aligned using PROMALS3D, and PhyML (v3.0) was used for phylogenetic analysis. In the next step, we utilized the officially provided ICTV taxonomy and bioinformatics tools to assign the phylogenetic position of RdRp. Although this analysis focused solely on RdRp, it can be concluded that using this gene as a marker may be sufficient for taxonomic assignment. Subsequent analyses will target DNA viruses and RT viruses to perform a more comprehensive assessment. This extensive analysis aims to validate the usefulness of marker genes in the overall viral taxonomy.
Reducing the use of animal models in drug development and safety assessment has long been supported by the *** and Drug Administration(FDA).The report by Royal Society for the Prevention of Cruelty to Animals indicate...
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Reducing the use of animal models in drug development and safety assessment has long been supported by the *** and Drug Administration(FDA).The report by Royal Society for the Prevention of Cruelty to Animals indicates that in 2020,experiments involved the use of over 100 million animals,with the United States leading the list by utilizing 20 million *** ethical considerations associated with animal testing and the costs in terms of time and money,animal models are not always effective in predicting human reactions to drug *** animal testing has been the traditional method for assessing the safety and efficacy of drugs.
Summary: Named entity recognition (NER) is a fundamental part of extracting information from documents in biomedical applications. A notable advantage of NER is its consistency in extracting biomedical entities in a d...
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Bone destruction induced by breast cancer metastasis causes severe complications,including death,in breast cancer *** between cancer cells and skeletal cells in metastatic bone microenvironments is a principal element...
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Bone destruction induced by breast cancer metastasis causes severe complications,including death,in breast cancer *** between cancer cells and skeletal cells in metastatic bone microenvironments is a principal element that drives tumor progression and ***-derived factors play fundamental roles in this form of *** identify soluble factors released from cancer cells in bone metastasis,we established a highly bone-metastatic subline of MDA-MB-231 breast cancer *** subline(mtMDA)showed a markedly elevated ability to secrete S100A4 protein,which directly stimulated osteoclast formation via surface receptor *** S100A4 stimulated osteoclastogenesis in vitro and bone loss in *** medium from mtMDA cells in which S100A4 was knocked down had a reduced ability to stimulate ***,the S100A4 knockdown cells elicited less bone destruction in mice than the control knockdown *** addition,administration of an anti-S100A4 monoclonal antibody(mAb)that we developed attenuated the stimulation of osteoclastogenesis and bone loss by mtMDA in *** together,our results suggest that S100A4 released from breast cancer cells is an important player in the osteolysis caused by breast cancer bone metastasis.
MERS-CoV, which belongs to the beta-coronaviruses together with SARS-CoV-2, although it has received relatively less attention by the COVID-19 pandemic, there is a sufficient possibility of new MERS-CoV lineages and v...
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ISBN:
(纸本)9798400708343
MERS-CoV, which belongs to the beta-coronaviruses together with SARS-CoV-2, although it has received relatively less attention by the COVID-19 pandemic, there is a sufficient possibility of new MERS-CoV lineages and variants. Previous studies have discussed the possibility of frequent recombination of MERS-CoV. We thus present a highly accurate method for the phylogenetic analysis and classification of MERS-CoV including recombinant sequences. We collected the sequences of S protein from MERS-CoV and divided them into five phylogenetic groups, of which recombinant sequences were divided into seven types. Physicochemical properties of amino acids were then calculated from the S protein sequences, and the results were used for the random forest model, Naïve Bayes classification, and k-nearest neighbor method. We also constructed several feature subsets based on the ranked amino acid properties and applied them to the random forest model. In each dataset, the amino acid physicochemical properties were ranked differently. Using this information, classification of MERS-CoV based on machine learning algorithms showed that the random forest model had the best accuracy and area under the curve compared with the k-nearest neighbor and Naïve Bayes classification methods. Several feature subsets were constructed using the correlation feature selection algorithm and applied to the random forest model. Overall, the performance of the classifier was improved compared to that when using all features. Coronaviruses including MERS-CoV continue to evolve into new forms through recombination or mutation. We thus present a method to increase the accuracy of their classification using additional information of the viral protein sequence, and confirm that a subunit consisting of optimal prominent features can improve the performance of the classifier by removing the unnecessary characteristic information.
Motivation: Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from b...
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Many researchers both in academia and industry have long been interested in the stock market. Numerous approaches were developed to accurately predict future trends in stock prices. Recently, there has been a growing ...
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Cellular senescence,a persistent state of cell cycle arrest,accumulates in aged organisms,contributes to tissue dysfunction,and drives age-related *** clearance of senescent cells is expected to decrease chronic,low-g...
Cellular senescence,a persistent state of cell cycle arrest,accumulates in aged organisms,contributes to tissue dysfunction,and drives age-related *** clearance of senescent cells is expected to decrease chronic,low-grade inflammation and improve tissue repair capacity,thus attenuating the decline of physical function in aged ***,selective and effective clearance of senescent cells of different cell types has proven ***,we developed a prodrug strategy to design a new compound based on the increased activity of lysosomalβ-galactosidase (β-gal),a primary characteristic of senescent *** prodrug SSK1 is specifically activated by β-gal and eliminates mouse and human senescent cells independently of senescence inducers and cell *** aged mice,our compound effectively cleared senescent cells in different tissues,decreased the senescence-and age-associated gene signatures,attenuated low-grade local and systemic inflammation,and restored physical *** results demonstrate that lysosomalβ-gal can be effectively leveraged to selectively eliminate senescent cells,providing a novel strategy to develop anti-aging interventions.
The GDNQ motif is mainly found in nonsegmented (-)ssRNA viruses and plays a role in the catalytic and polymerase activity pathways. However, in some recently reported dsRNA mycoviruses, the GDNQ motif of the (-)ssRNA ...
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ISBN:
(纸本)9781665480468
The GDNQ motif is mainly found in nonsegmented (-)ssRNA viruses and plays a role in the catalytic and polymerase activity pathways. However, in some recently reported dsRNA mycoviruses, the GDNQ motif of the (-)ssRNA virus group is found instead of the GDD motif commonly found in the dsRNA virus group. Therefore, in this study, data mining techniques were used to explore the mutation tendency of a specific region in the virus taxonomic groups. Viral protein sequences were obtained from the NCBI Virus data repository, and the performance of several known string-searching algorithms (BF, KMP, and BM) was evaluated to establish an optimal strategy for extracting useful information from sequence big data. Then, the pattern-matching performance of the algorithm was tested to detect whether the conserved region sequence was a common motif type or a mutated motif type. As a result of the analysis, it was confirmed that the BF method produced the fastest results. However, the BM method recorded higher accuracy. Through the virus mutation tendency derived from the analysis results, it was confirmed that 7.1% of dsRNA viruses had a mutated motif type based on the total data analyzed and that it was relatively recent that ssRNA virus motif variants began to be discovered in dsRNA viruses. It is expected that additional in-depth analysis of the host range and infection route of viruses with the mutation will help understand the evolution of dsRNA and ssRNA mycoviruses.
To profiling flavonoids in wild soybean (WB;Glycine soja Sieb & Zucc.) and cultivated soybean (CB;Glycine max (L.) Merr.), liquid chromatography-high resolution mass spectrometry (LC-HRMS), antioxidant assays, and...
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