Case study in using linguistic phrases for text categorization on the WWW

Journal ArticleMost learning algorithms that arc applied to text categorization problems rely on a bag-of-words document representation, i.e., each word occurring in the document is considered as a separate feature. In this paper, we investigate the use of linguistic phrases as input features for text categorization problems. These features are based on information extraction patterns that are generated and used by the AUTOSLOG- TS system. We present experimental results on using such features as background knowledge for two machine learning algorithms on a classification task on the WWW. The results show that phrasal features can improve the precision of learned theories at the expense of coverage

Repeat Ablation for Atrial Fibrillation Recurrence Post Cryoballoon or Radiofrequency Ablation in the FIRE and ICE Trial

Background: The FIRE AND ICE trial assessed efficacy and safety of pulmonary vein (PV) isolation using cryoballoon versus radiofrequency current (RFC) ablation in patients with drug refractory, symptomatic, paroxysmal atrial fibrillation (AF). The purpose of the current study was to assess index lesion durability as well as reablation strategy and outcomes in trial patients undergoing a reablation procedure. Methods: Patients with reablation procedures during FIRE AND ICE were retrospectively consented and enrolled at 13 trial centers. The first reablation for each patient was included in the analysis. Documented arrhythmias before reablation, number and location of reconnected PVs, lesions created during reablations, procedural characteristics, and acute as well as long-term outcomes were assessed. Results: Eighty-nine (36 cryoballoon and 53 RFC) patients were included in this study. Paroxysmal atrial fibrillation was the predominant recurrent arrhythmia (69%) before reablation. Reablations occurred at a median of 173 and 182 days (P=0.54) in the cryoballoon and RFC cohorts, respectively. The number of reconnected PVs was significantly higher in the RFC than the cryoballoon group (2.1\ub11.4 versus 1.4\ub11.1; P=0.010), which was driven by significantly more reconnected left superior PVs and markedly more reconnected right superior PVs. The number of (predominantly RFC) lesions applied during reablation was significantly greater in patients originally treated with RFC (3.3\ub11.3 versus 2.5\ub11.5; P=0.015) with no difference in overall acute success (P=0.70). After reablation, no differences in procedure-related rehospitalization or antiarrhythmic drug utilization were observed between cohorts. Conclusions: At reablation, patients originally treated with the cryoballoon had significantly fewer reconnected PVs, which may reflect RFC catheter instability in certain left atrial regions, and thus required fewer lesions for reablation success. Repeat ablations were predominantly performed with RFC and resulted in similar acute success, duration of hospitalization, and antiarrhythmic drug prescription between the study cohorts. Clinical Trial Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT03314753

Machine Learning in Automated Text Categorization

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert manpower, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely document representation, classifier construction, and classifier evaluation.Comment: Accepted for publication on ACM Computing Survey

Differential effects of chlorinated and oxidized phospholipids in vascular tissue:implications for neointima formation

The presence of inflammatory cells and MPO (myeloperoxidase) in the arterial wall after vascular injury could increase neointima formation by modification of phospholipids. The present study investigates how these phospholipids, in particular oxidized and chlorinated species, are altered within injured vessels and how they affect VSMC (vascular smooth muscle cell) remodelling processes. Vascular injury was induced in C57BL/6 mice and high fat-fed ApoE-/- (apolipoprotein E) mice by wire denudation and ligation of the left carotid artery (LCA). Neointimal and medial composition was assessed using immunohistochemistry and ESI-MS. Primary rabbit aortic SMCs (smooth muscle cells) were utilized to examine the effects of modified lipids on VSMC proliferation, viability and migration at a cellular level. Neointimal area, measured as intima-to-media ratio, was significantly larger in wire-injured ApoE-/- mice (3.62±0.49 compared with 0.83±0.25 in C57BL/6 mice, n=3) and there was increased oxidized low-density lipoprotein (oxLDL) infiltration and elevated plasma MPO levels. Relative increases in lysophosphatidylcholines and unsaturated phosphatidylcholines (PCs) were also observed in wire-injured ApoE-/- carotid arteries. Chlorinated lipids had no effect on VSMC proliferation, viability or migration whereas chronic incubation with oxidized phospholipids stimulated proliferation in the presence of fetal calf serum [154.8±14.2% of viable cells at 1 μM PGPC (1-palmitoyl-2-glutaroyl-sn-glycero-3-phosphocholine) compared with control, n=6]. In conclusion, ApoE-/- mice with an inflammatory phenotype develop more neointima in wire-injured arteries and accumulation of oxidized lipids in the vessel wall may propagate this effect

Finding microRNA regulatory modules in human genome using rule induction

Background: MicroRNAs (miRNAs) are a class of small non-coding RNA molecules (20-24 nt), which are believed to participate in repression of gene expression. They play important roles in several biological processes (e.g. cell death and cell growth). Both experimental and computational approaches have been used to determine the function of miRNAs in cellular processes. Most efforts have concentrated on identification of miRNAs and their target genes. However, understanding the regulatory mechanism of miRNAs in the gene regulatory network is also essential to the discovery of functions of miRNAs in complex cellular systems. To understand the regulatory mechanism of miRNAs in complex cellular systems, we need to identify the functional modules involved in complex interactions between miRNAs and their target genes. Results: We propose a rule-based learning method to identify groups of miRNAs and target genes that are believed to participate cooperatively in the post-transcriptional gene regulation, so-called miRNA regulatory modules (MRMs). Applying our method to human genes and miRNAs, we found 79 MRMs. The MRMs are produced from multiple information sources, including miRNA-target binding information, gene expression and miRNA expression profiles. Analysis of two first MRMs shows that these MRMs consist of highly-related miRNAs and their target genes with respect to biological processes. Conclusion: The MRMs found by our method have high correlation in expression patterns of miRNAs as well as mRNAs. The mRNAs included in the same module shared similar biological functions, indicating the ability of our method to detect functionality-related genes. Moreover, review of the literature reveals that miRNAs in a module are involved in several types of human cancer

withdrawn 2017 hrs ehra ecas aphrs solaece expert consensus statement on catheter and surgical ablation of atrial fibrillation

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