Search results for "Hine"
showing 10 items of 5088 documents
Autoantibodies in Spondyloarthritis, Focusing on Anti-CD74 Antibodies
2019
Spondyloarthritis (SpA) is an inflammatory rheumatic disease with diverse clinical presentation. The diagnosis of SpA remains a big challenge in daily clinical practice because of the limitation in specific biomarkers of SpA, more biomarkers are still needed for SpA diagnosis and disease activity monitoring. In the past, SpA was considered predominantly as auto-inflammatory disease vs. autoimmune disease. However, in recent years several researches demonstrated a broad autoantibody response in SpA patients. Study also indicated that mice lack of ZAP70 in T cell develop SpA featured inflammation. These studies indicated the autoimmune features of SpA and gave rise to the potential use of aut…
Statistical Explorations and Univariate Timeseries Analysis on COVID-19 Datasets to Understand the Trend of Disease Spreading and Death
2020
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Pantethine Alters Lipid Composition and Cholesterol Content of Membrane Rafts, With Down-Regulation of CXCL12-Induced T Cell Migration
2015
Pantethine, a natural low-molecular-weight thiol, shows broad activity in a large range of essential cellular pathways. It has been long known as a hypolipidemic and hypocholesterolemic agent. We showed recently that it exerts a neuroprotective action in mouse models of cerebral malaria and Parkinson's disease through multiple mechanisms. In the present study we looked at its effects on membrane lipid rafts that serve as platforms for molecules engaged in cell activity, therefore providing a target against inappropriate cell response leading to chronic inflammation. We found that pantethine-treated cells showed a significant change in raft fatty acid composition and cholesterol content, wit…
The Impact of Artificial Intelligence on Traditional Chinese Medicine
2021
Traditional Chinese Medicine (TCM) is a well-established medical system with a long history. Currently, artificial intelligence (AI) is rapidly expanding in many fields including TCM. AI will significantly improve the reliability and accuracy of diagnostics, thus increasing the use of effective therapeutic methods for patients. This systematic review provides an updated overview on the major breakthroughs in the field of AI-assisted TCM four diagnostic methods, syndrome differentiation, and treatment. AI-assisted TCM diagnosis is mainly based on digital data collected by modern electronic instruments, which makes TCM diagnosis more quantitative, objective, and standardized. As a result, th…
Towards identifying drug side effects from social media using active learning and crowd sourcing.
2019
Motivation Social media is a largely untapped source of information on side effects of drugs. Twitter in particular is widely used to report on everyday events and personal ailments. However, labeling this noisy data is a difficult problem because labeled training data is sparse and automatic labeling is error-prone. Crowd sourcing can help in such a scenario to obtain more reliable labels, but is expensive in comparison because workers have to be paid. To remedy this, semi-supervised active learning may reduce the number of labeled data needed and focus the manual labeling process on important information. Results We extracted data from Twitter using the public API. We subsequently use Ama…
Paving the way for synthetic biology-based bioremediation in Europe
2009
Synthetic biology (SB) has a dual definition. It is both the design and construction of new biological parts, devices and systems, and also the re‐design of existing, natural systems for useful purposes. The latter field is maybe one of the major challenges within this discipline, since the promising prospect that biological systems may be used as biomachines will certainly be exploited in the near future. Synthetic biology has challenging conceptual possibilities (Moya et al., 2009a) and impressive progress has already been made in biotechnology following SB approaches (de Lorenzo and Danchin, 2008). Much more is expected in the near future from current efforts aiming to make synthetic gen…
Boosting Signal-to-Noise in Complex Biology: Prior Knowledge Is Power
2011
A major difficulty in the analysis of complex biological systems is dealing with the low signal-to-noise inherent to nearly all large biological datasets. We discuss powerful bioinformatic concepts for boosting signal-to-noise through external knowledge incorporated in processing units we call filters and integrators. These concepts are illustrated in four landmark studies that have provided model implementations of filters, integrators, or both.
Defining classifier regions for WSD ensembles using word space features
2006
Based on recent evaluation of word sense disambiguation (WSD) systems [10], disambiguation methods have reached a standstill. In [10] we showed that it is possible to predict the best system for target word using word features and that using this 'optimal ensembling method' more accurate WSD ensembles can be built (3-5% over Senseval state of the art systems with the same amount of possible potential remaining). In the interest of developing if more accurate ensembles, w e here define the strong regions for three popular and effective classifiers used for WSD task (Naive Bayes – NB, Support Vector Machine – SVM, Decision Rules – D) using word features (word grain, amount of positive and neg…
Building an Optimal WSD Ensemble Using Per-Word Selection of Best System
2006
In Senseval workshops for evaluating WSD systems [1,4,9], no one system or system type (classifier algorithm, type of system ensemble, extracted feature set, lexical knowledge source etc.) has been discovered that resolves all ambiguous words into their senses in a superior way. This paper presents a novel method for selecting the best system for target word based on readily available word features (number of senses, average amount of training per sense, dominant sense ratio). Applied to Senseval-3 and Senseval-2 English lexical sample state-of-art systems, a net gain of approximately 2.5 – 5.0% (respectively) in average precision per word over the best base system is achieved. The method c…
Efficient Online Laplacian Eigenmap Computation for Dimensionality Reduction in Molecular Phylogeny via Optimisation on the Sphere
2019
Reconstructing the phylogeny of large groups of large divergent genomes remains a difficult problem to solve, whatever the methods considered. Methods based on distance matrices are blocked due to the calculation of these matrices that is impossible in practice, when Bayesian inference or maximum likelihood methods presuppose multiple alignment of the genomes, which is itself difficult to achieve if precision is required. In this paper, we propose to calculate new distances for randomly selected couples of species over iterations, and then to map the biological sequences in a space of small dimension based on the partial knowledge of this genome similarity matrix. This mapping is then used …