1,721,006 research outputs found
Effect of silica-coated magnetic nanoparticles on rigidity sensing of human embryonic kidney cells
Get PDFBACKGROUND: Nanoparticles (NPs) can enter cells and cause cellular dysfunction. For example, reactive oxygen species generated by NPs can damage the cytoskeleton and impair cellular adhesion properties. Previously, we reported that cell spreading and protrusion structures such as lamellipodia and filopodia was reduced when cells are treated with silica-coated magnetic nanoparticles incorporating rhodamine B isothiocyanate (MNPs@SiO2(RITC)), even at 0.1 mug/muL. These protruded structures are involved in a cell's rigidity sensing, but how these NPs affect rigidity sensing is unknown. RESULTS: Here, we report that the rigidity sensing of human embryonic kidney (HEK293) cells was impaired even at 0.1 mug/muL of MNPs@SiO2(RITC). At this concentration, cells were unable to discern the stiffness difference between soft (5 kPa) and rigid (2 MPa) flat surfaces. The impairment of rigidity sensing was further supported by observing the disappearance of locally contracted elastomeric submicron pillars (900 nm in diameter, 2 mum in height, 24.21 nN/mum in stiffness k) under MNPs@SiO2(RITC) treated cells. A decrease in the phosphorylation of paxillin, which is involved in focal adhesion dynamics, may cause cells to be insensitive to stiffness differences when they are treated with MNPs@SiO2(RITC). CONCLUSIONS: Our results suggest that NPs may impair the rigidity sensing of cells even at low concentrations, thereby affecting cell adhesion and spreading
Quantifying intracellular trafficking of silica-coated magnetic nanoparticles in live single cells by site-specific direct stochastic optical reconstruction microscopy
Get PDFBackground: Nanoparticles have been used for biomedical applications, including drug delivery, diagnosis, and imaging based on their unique properties derived from small size and large surface-to-volume ratio. However, concerns regarding unexpected toxicity due to the localization of nanoparticles in the cells are growing. Herein, we quantified the number of cell-internalized nanoparticles and monitored their cellular localization, which are critical factors for biomedical applications of nanoparticles. Methods: This study investigates the intracellular trafficking of silica-coated magnetic nanoparticles containing rhodamine B isothiocyanate dye [MNPs@SiO2(RITC)] in various live single cells, such as HEK293, NIH3T3, and RAW 264.7 cells, using site-specific direct stochastic optical reconstruction microscopy (dSTORM). The time-dependent subdiffraction-limit spatial resolution of the dSTORM method allowed intracellular site-specific quantification and tracking of MNPs@SiO2(RITC). Results: The MNPs@SiO2(RITC) were observed to be highly internalized in RAW 264.7 cells, compared to the HEK293 and NIH3T3 cells undergoing single-particle analysis. In addition, MNPs@SiO2(RITC) were internalized within the nuclei of RAW 264.7 and HEK293 cells but were not detected in the nuclei of NIH3T3 cells. Moreover, because of the treatment of the MNPs@SiO2(RITC), more micronuclei were detected in RAW 264.7 cells than in other cells. Conclusion: The sensitive and quantitative evaluations of MNPs@SiO2(RITC) at specific sites in three different cells using a combination of dSTORM, transcriptomics, and molecular biology were performed. These findings highlight the quantitative differences in the uptake efficiency of MNPs@SiO2(RITC) and ultra-sensitivity, varying according to the cell types as ascertained by subdiffraction-limit super-resolution microscopy. Graphical Abstract: [Figure not available: see fulltext.]
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Computational prediction of species-specific yeast DNA replication origin via iterative feature representation
Get PDFDeoxyribonucleic acid replication is one of the most crucial tasks taking place in the cell, and it has to be precisely regulated. This process is initiated in the replication origins (ORIs), and thus it is essential to identify such sites for a deeper understanding of the cellular processes and functions related to the regulation of gene expression. Considering the important tasks performed by ORIs, several experimental and computational approaches have been developed in the prediction of such sites. However, existing computational predictors for ORIs have certain curbs, such as building only single-feature encoding models, limited systematic feature engineering efforts and failure to validate model robustness. Hence, we developed a novel species-specific yeast predictor called yORIpred that accurately identify ORIs in the yeast genomes. To develop yORIpred, we first constructed optimal 40 baseline models by exploring eight different sequence-based encodings and five different machine learning classifiers. Subsequently, the predicted probability of 40 models was considered as the novel feature vector and carried out iterative feature learning approach independently using five different classifiers. Our systematic analysis revealed that the feature representation learned by the support vector machine algorithm (yORIpred) could well discriminate the distribution characteristics between ORIs and non-ORIs when compared with the other four algorithms. Comprehensive benchmarking experiments showed that yORIpred achieved superior and stable performance when compared with the existing predictors on the same training datasets. Furthermore, independent evaluation showcased the best and accurate performance of yORIpred thus underscoring the significance of iterative feature representation. To facilitate the users in obtaining their desired results without undergoing any mathematical, statistical or computational hassles, we developed a web server for the yORIpred predictor, which is available at: http://thegleelab.org/yORIpred
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Evolution of Machine Learning Algorithms in the Prediction and Design of Anticancer Peptides
No full textPeptides act as promising anticancer agents due to their ease of synthesis and modifications, enhanced tumor penetration, and less systemic toxicity. However, only limited success has been achieved so far, as experimental design and synthesis of anticancer peptides (ACPs) are prohibitively costly and time-consuming. Furthermore, the sequential increase in the protein sequence data via highthroughput sequencing makes it difficult to identify ACPs only through experimentation, which often involves months or years of speculation and failure. All these limitations could be overcome by applying machine learning (ML) approaches, which is a field of artificial intelligence that automates analytical model building for rapid and accurate outcome predictions. Recently, ML approaches hold great promise in the rapid discovery of ACPs, which could be witnessed by the growing number of MLbased anticancer prediction tools. In this review, we aim to provide a comprehensive view on the existing ML approaches for ACP predictions. Initially, we will briefly discuss the currently available ACP databases. This is followed by the main text, where state-of-the-art ML approaches working principles and their performances based on the ML algorithms are reviewed. Lastly, we discuss the limitations and future directions of the ML methods in the prediction of ACPs
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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