1,720,974 research outputs found
Computational approaches and their applications in cancer: uncovering the role of extracellular vesicles, hypoxia and cancer phenotypes in the tumor microenvironment
No full textCancer phenotypes typically arise from 1) genetic and epigenetic deregulations and 2) altered signaling responses, a consequence of aberrant interactions within the tumor microenvironment (TME). The study of the TME and, broadly speaking, of the tumor complexity is well suited to quantitative approaches, and provides opportunities for methodological developments. In turn, in silico models and integrated analyses are instrumental to predictively model cancer behavior, with the aim of dissecting key mechanisms of tumor initiation, progression, dissemination and drug resistance. The main objectives of this dissertation involve the development and the application of computational methods to disentangle cancer complexity. Several research questions have been addressed, ranging from basic research to translational applications.
A data-driven investigation of extracellular vesicles (EVs)-mediated cell signaling was conducted by characterizing the molecular cargo of tumor-derived EVs released by Neuroblastoma (NB) cells. Predictions were confirmed by in vitro and in vivo validation and revealed a microRNA-signature associated to tumor aggressiveness in hypoxic microenvironments. As a complementary approach, the development of a first-principles model to quantitatively characterize EVs diffusion and uptake was proposed to study the short- and long-range effects of released EVs. A comparative study on feature selection and molecular classification of cancer phenotypes allowed evaluating the impact of algorithmic combinations to predict cancer phenotypes using gene expression profiles. A set of order effects for successful classification of cancer phenotypes stemmed from this study, which constitutes a valuable resource in the view of designing diagnostic and prognostic tools.
However, the elucidation of cancer-associated dysregulations that coordinately shape malignant cell states cannot be uniquely based on reductionist approaches, but requires systems biology. In this work, network-based analysis of genomic data – especially single-cell RNA sequencing (scRNA-Seq) data – made it possible to dissect tumor heterogeneity at single-cell resolution, in the context of several malignancies, including prostate and pancreatic cancer. The analysis of scRNA-Seq poses several computational challenges that require the development of suitable methods. To this aim, an optimization-based framework for graph-based clustering of cell populations and a computational framework to dissect ligand/receptor-mediated paracrine crosstalk between distinct cellular niches are proposed in this dissertation.
Moving to the task of performing phenotypic characterization of in vitro tumor spheroids, an image-analysis based pipeline was developed to quantify cytotoxic effects of mycotoxins on NB cells. This contribution constitutes a valuable tool for the extraction of quantitative features from spheroids images, and was key for the evaluation of several effects such as morphological shifts and decrease in migration following mycotoxins exposure on tumor cells. While remarkable progress has been made towards quantitative cancer research, multiple open questions remain, including many related to understanding the dynamics that take place in the TME. Approaches and results presented and discussed in this dissertation make several steps in pushing research forward, towards a more quantitative understanding of the mechanisms that nurture cancer, with the aim of allowing deeper insights into the complexity of biological systems.Cancer phenotypes typically arise from 1) genetic and epigenetic deregulations and 2) altered signaling responses, a consequence of aberrant interactions within the tumor microenvironment (TME). The study of the TME and, broadly speaking, of the tumor complexity is well suited to quantitative approaches, and provides opportunities for methodological developments. In turn, in silico models and integrated analyses are instrumental to predictively model cancer behavior, with the aim of dissecting key mechanisms of tumor initiation, progression, dissemination and drug resistance. The main objectives of this dissertation involve the development and the application of computational methods to disentangle cancer complexity. Several research questions have been addressed, ranging from basic research to translational applications.
A data-driven investigation of extracellular vesicles (EVs)-mediated cell signaling was conducted by characterizing the molecular cargo of tumor-derived EVs released by Neuroblastoma (NB) cells. Predictions were confirmed by in vitro and in vivo validation and revealed a microRNA-signature associated to tumor aggressiveness in hypoxic microenvironments. As a complementary approach, the development of a first-principles model to quantitatively characterize EVs diffusion and uptake was proposed to study the short- and long-range effects of released EVs. A comparative study on feature selection and molecular classification of cancer phenotypes allowed evaluating the impact of algorithmic combinations to predict cancer phenotypes using gene expression profiles. A set of order effects for successful classification of cancer phenotypes stemmed from this study, which constitutes a valuable resource in the view of designing diagnostic and prognostic tools.
However, the elucidation of cancer-associated dysregulations that coordinately shape malignant cell states cannot be uniquely based on reductionist approaches, but requires systems biology. In this work, network-based analysis of genomic data – especially single-cell RNA sequencing (scRNA-Seq) data – made it possible to dissect tumor heterogeneity at single-cell resolution, in the context of several malignancies, including prostate and pancreatic cancer. The analysis of scRNA-Seq poses several computational challenges that require the development of suitable methods. To this aim, an optimization-based framework for graph-based clustering of cell populations and a computational framework to dissect ligand/receptor-mediated paracrine crosstalk between distinct cellular niches are proposed in this dissertation.
Moving to the task of performing phenotypic characterization of in vitro tumor spheroids, an image-analysis based pipeline was developed to quantify cytotoxic effects of mycotoxins on NB cells. This contribution constitutes a valuable tool for the extraction of quantitative features from spheroids images, and was key for the evaluation of several effects such as morphological shifts and decrease in migration following mycotoxins exposure on tumor cells. While remarkable progress has been made towards quantitative cancer research, multiple open questions remain, including many related to understanding the dynamics that take place in the TME. Approaches and results presented and discussed in this dissertation make several steps in pushing research forward, towards a more quantitative understanding of the mechanisms that nurture cancer, with the aim of allowing deeper insights into the complexity of biological systems
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Variations on the Author
Full text link“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
Feature Selection and Molecular Classification of Cancer Phenotypes: A Comparative Study
Full text linkThe classification of high dimensional gene expression data is key to the development of effective diagnostic and prognostic tools. Feature selection involves finding the best subset with the highest power in predicting class labels. Here, we conducted a comparative study focused on different combinations of feature selectors (Chi-Squared, mRMR, Relief-F, and Genetic Algorithms) and classification learning algorithms (Random Forests, PLS-DA, SVM, Regularized Logistic/Multinomial Regression, and kNN) to identify those with the best predictive capacity. The performance of each combination is evaluated through an empirical study on three benchmark cancer-related microarray datasets. Our results first suggest that the quality of the data relevant to the target classes is key for the successful classification of cancer phenotypes. We also proved that, for a given classification learning algorithm and dataset, all filters have a similar performance. Interestingly, filters achieve comparable or even better results with respect to the GA-based wrappers, while also being easier and faster to implement. Taken together, our findings suggest that simple, well-established feature selectors in combination with optimized classifiers guarantee good performances, with no need for complicated and computationally demanding methodologies
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe 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
ConfMix: Unsupervised Domain Adaptation for Object Detection via Confidence-based Mixing
No full textUnsupervised Domain Adaptation (UDA) for object detection aims to adapt a model trained on a source domain to detect instances from a new target domain for which annotations are not available. Different from traditional approaches, we propose ConfMix, the first method that introduces a sample mixing strategy based on region-level detec- tion confidence for adaptive object detector learning. We mix the local region of the target sample that corresponds to the most confident pseudo detections with a source image, and apply an additional consistency loss term to gradually adapt towards the target data distribution. In order to robustly define a confidence score for a region, we exploit the confidence score per pseudo detection that accounts for both the detector-dependent confidence and the bounding box uncertainty. Moreover, we propose a novel pseudo la- belling scheme that progressively filters the pseudo target detections using the confidence metric that varies from a loose to strict manner along the training. We perform ex- tensive experiments with three datasets, achieving state-of-the-art performance in two of them and approaching the supervised target model performance in the other.This work has been supported by the European Union's Horizon 2020 research and innovation programme under grant agreement No. 957337, and the European Commission Internal Security Fund for Po- lice under grant agreement No. ISFP-2020-AG-PROTECT-101034216- PROTECTOR
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe 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
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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