DancingLines: An Analytical Scheme to Depict Cross-Platform Event Popularity

Nowadays, events usually burst and are propagated online through multiple modern media like social networks and search engines. There exists various research discussing the event dissemination trends on individual medium, while few studies focus on event popularity analysis from a cross-platform perspective. Challenges come from the vast diversity of events and media, limited access to aligned datasets across different media and a great deal of noise in the datasets. In this paper, we design DancingLines, an innovative scheme that captures and quantitatively analyzes event popularity between pairwise text media. It contains two models: TF-SW, a semantic-aware popularity quantification model, based on an integrated weight coefficient leveraging Word2Vec and TextRank; and wDTW-CD, a pairwise event popularity time series alignment model matching different event phases adapted from Dynamic Time Warping. We also propose three metrics to interpret event popularity trends between pairwise social platforms. Experimental results on eighteen real-world event datasets from an influential social network and a popular search engine validate the effectiveness and applicability of our scheme. DancingLines is demonstrated to possess broad application potentials for discovering the knowledge of various aspects related to events and different media

Automatic alignment of surgical videos using kinematic data

Over the past one hundred years, the classic teaching methodology of "see one, do one, teach one" has governed the surgical education systems worldwide. With the advent of Operation Room 2.0, recording video, kinematic and many other types of data during the surgery became an easy task, thus allowing artificial intelligence systems to be deployed and used in surgical and medical practice. Recently, surgical videos has been shown to provide a structure for peer coaching enabling novice trainees to learn from experienced surgeons by replaying those videos. However, the high inter-operator variability in surgical gesture duration and execution renders learning from comparing novice to expert surgical videos a very difficult task. In this paper, we propose a novel technique to align multiple videos based on the alignment of their corresponding kinematic multivariate time series data. By leveraging the Dynamic Time Warping measure, our algorithm synchronizes a set of videos in order to show the same gesture being performed at different speed. We believe that the proposed approach is a valuable addition to the existing learning tools for surgery.Comment: Accepted at AIME 201

Self-labeling techniques for semi-supervised time series classification: an empirical study

An increasing amount of unlabeled time series data available render the semi-supervised paradigm a suitable approach to tackle classification problems with a reduced quantity of labeled data. Self-labeled techniques stand out from semi-supervised classification methods due to their simplicity and the lack of strong assumptions about the distribution of the labeled and unlabeled data. This paper addresses the relevance of these techniques in the time series classification context by means of an empirical study that compares successful self-labeled methods in conjunction with various learning schemes and dissimilarity measures. Our experiments involve 35 time series datasets with different ratios of labeled data, aiming to measure the transductive and inductive classification capabilities of the self-labeled methods studied. The results show that the nearest-neighbor rule is a robust choice for the base classifier. In addition, the amending and multi-classifier self-labeled-based approaches reveal a promising attempt to perform semi-supervised classification in the time series context

Adult-onset Alexander disease with typical "tadpole" brainstem atrophy and unusual bilateral basal ganglia involvement: a case report and review of the literature

<p>Abstract</p> <p>Background</p> <p>Alexander disease (ALX) is a rare neurological disorder characterized by white matter degeneration and cytoplasmic inclusions in astrocytes called Rosenthal fibers, labeled by antibodies against glial fibrillary acidic protein (GFAP). Three subtypes are distinguished according to age at onset: infantile (under age 2), juvenile (age 2 to 12) and adult (over age 12). Following the identification of heterozygous mutations in <it>GFAP </it>that cause this disease, cases of adult-onset ALX have been increasingly reported.</p> <p>Case Presentation</p> <p>We present a 60-year-old Japanese man with an unremarkable past and no family history of ALX. After head trauma in a traffic accident at the age of 46, his character changed, and dementia and dysarthria developed, but he remained independent. Spastic paresis and dysphagia were observed at age 57 and 59, respectively, and worsened progressively. Neurological examination at the age of 60 revealed dementia, pseudobulbar palsy, left-side predominant spastic tetraparesis, axial rigidity, bradykinesia and gaze-evoked nystagmus. Brain MRI showed tadpole-like atrophy of the brainstem, caused by marked atrophy of the medulla oblongata, cervical spinal cord and midbrain tegmentum, with an intact pontine base. Analysis of the <it>GFAP </it>gene revealed a heterozygous missense mutation, c.827G>T, p.R276L, which was already shown to be pathogenic in a case of pathologically proven hereditary adult-onset ALX.</p> <p>Conclusion</p> <p>The typical tadpole-like appearance of the brainstem is strongly suggestive of adult-onset ALX, and should lead to a genetic investigation of the <it>GFAP </it>gene. The unusual feature of this patient is the symmetrical involvement of the basal ganglia, which is rarely observed in the adult form of the disease. More patients must be examined to confirm, clinically and neuroradiologically, extrapyramidal involvement of the basal ganglia in adult-onset ALX.</p

All-Trans Retinoic Acid Promotes TGF-β-Induced Tregs via Histone Modification but Not DNA Demethylation on Foxp3 Gene Locus

It has been documented all-trans retinoic acid (atRA) promotes the development of TGF-β-induced CD4(+)Foxp3(+) regulatory T cells (iTreg) that play a vital role in the prevention of autoimmune responses, however, molecular mechanisms involved remain elusive. Our objective, therefore, was to determine how atRA promotes the differentiation of iTregs.Addition of atRA to naïve CD4(+)CD25(-) cells stimulated with anti-CD3/CD28 antibodies in the presence of TGF-β not only increased Foxp3(+) iTreg differentiation, but maintained Foxp3 expression through apoptosis inhibition. atRA/TGF-β-treated CD4(+) cells developed complete anergy and displayed increased suppressive activity. Infusion of atRA/TGF-β-treated CD4(+) cells resulted in the greater effects on suppressing symptoms and protecting the survival of chronic GVHD mice with typical lupus-like syndromes than did CD4(+) cells treated with TGF-β alone. atRA did not significantly affect the phosphorylation levels of Smad2/3 and still promoted iTreg differentiation in CD4(+) cells isolated from Smad3 KO and Smad2 conditional KO mice. Conversely, atRA markedly increased ERK1/2 activation, and blockade of ERK1/2 signaling completely abolished the enhanced effects of atRA on Foxp3 expression. Moreover, atRA significantly increased histone methylation and acetylation within the promoter and conserved non-coding DNA sequence (CNS) elements at the Foxp3 gene locus and the recruitment of phosphor-RNA polymerase II, while DNA methylation in the CNS3 was not significantly altered.We have identified the cellular and molecular mechanism(s) by which atRA promotes the development and maintenance of iTregs. These results will help to enhance the quantity and quality of development of iTregs and may provide novel insights into clinical cell therapy for patients with autoimmune diseases and those needing organ transplantation

DGW: an exploratory data analysis tool for clustering and visualisation of epigenomic marks

Background Functional genomic and epigenomic research relies fundamentally on sequencing based methods like ChIP-seq for the detection of DNA-protein interactions. These techniques return large, high dimensional data sets with visually complex structures, such as multi-modal peaks extended over large genomic regions. Current tools for visualisation and data exploration represent and leverage these complex features only to a limited extent. Results We present DGW, an open source software package for simultaneous alignment and clustering of multiple epigenomic marks. DGW uses Dynamic Time Warping to adaptively rescale and align genomic distances which allows to group regions of interest with similar shapes, thereby capturing the structure of epigenomic marks. We demonstrate the effectiveness of the approach in a simulation study and on a real epigenomic data set from the ENCODE project. Conclusions Our results show that DGW automatically recognises and aligns important genomic features such as transcription start sites and splicing sites from histone marks. DGW is available as an open source Python package

Clustering Imputation for Air Pollution Data

Air pollution is a global problem. The assessment of air pollution concentration data is important for evaluating human exposure and the associated risk to health. Unfortunately, air pollution monitoring stations often have periods of missing data or do not measure all pollutants. In this study, we experiment with different approaches to estimate the whole time series for a missing pollutant at a monitoring station as well as missing values within a time series. The main goal is to reduce the uncertainty in air quality assessment. To develop our approach we combine single and multiple imputation, nearest neighbour geographical distance methods and a clustering algorithm for time series. For each station that measures ozone, we produce various imputations for this pollutant and measure the similarity/error between the imputed and the real values. Our results show that imputation by average based on clustering results combined with multiple imputation for missing values is the most reliable and is associated with lower average error and standard deviation

Unregulated miR-96 Induces Cell Proliferation in Human Breast Cancer by Downregulating Transcriptional Factor FOXO3a

FOXO transcription factors are key tumor suppressors in mammalian cells. Until now, suppression of FOXOs in cancer cells was thought to be mainly due to activation of multiple onco-kinases by a phosphorylation-ubiquitylation-mediated cascade. Therefore, it was speculated that inhibition of FOXO proteins would naturally occur through a multiple step post-translational process. However, whether cancer cells may downregulate FOXO protein via an alternative regulatory mechanism is unclear. In the current study, we report that expression of miR-96 was markedly upregulated in breast cancer cells and breast cancer tissues compared with normal breast epithelial cells (NBEC) and normal breast tissues. Ectopic expression of miR-96 induced the proliferation and anchorage-independent growth of breast cancer cells, while inhibition of miR-96 reduced this effect. Furthermore, upregulation of miR-96 in breast cancer cells resulted in modulation of their entry into the G1/S transitional phase, which was caused by downregulation of cyclin-dependent kinase (CDK) inhibitors, p27Kip1 and p21Cip1, and upregulation of the cell-cycle regulator cyclin D1. Moreover, we demonstrated that miR-96 downregulated FOXO3a expression by directly targeting the FOXO3a 3′-untranslated region. Taken together, our results suggest that miR-96 may play an important role in promoting proliferation of human breast cancer cells and present a novel mechanism of miRNA-mediated direct suppression of FOXO3a expression in cancer cells