Ellogon: A New Text Engineering Platform

This paper presents Ellogon, a multi-lingual, cross-platform, general-purpose text engineering environment. Ellogon was designed in order to aid both researchers in natural language processing, as well as companies that produce language engineering systems for the end-user. Ellogon provides a powerful TIPSTER-based infrastructure for managing, storing and exchanging textual data, embedding and managing text processing components as well as visualising textual data and their associated linguistic information. Among its key features are full Unicode support, an extensive multi-lingual graphical user interface, its modular architecture and the reduced hardware requirements.Comment: 7 pages, 9 figures. Will be presented to the Third International Conference on Language Resources and Evaluation - LREC 200

Stacking classifiers for anti-spam filtering of e-mail

We evaluate empirically a scheme for combining classifiers, known as stacked generalization, in the context of anti-spam filtering, a novel cost-sensitive application of text categorization. Unsolicited commercial e-mail, or "spam", floods mailboxes, causing frustration, wasting bandwidth, and exposing minors to unsuitable content. Using a public corpus, we show that stacking can improve the efficiency of automatically induced anti-spam filters, and that such filters can be used in real-life applications

Learning to Filter Spam E-Mail: A Comparison of a Naive Bayesian and a Memory-Based Approach

We investigate the performance of two machine learning algorithms in the context of anti-spam filtering. The increasing volume of unsolicited bulk e-mail (spam) has generated a need for reliable anti-spam filters. Filters of this type have so far been based mostly on keyword patterns that are constructed by hand and perform poorly. The Naive Bayesian classifier has recently been suggested as an effective method to construct automatically anti-spam filters with superior performance. We investigate thoroughly the performance of the Naive Bayesian filter on a publicly available corpus, contributing towards standard benchmarks. At the same time, we compare the performance of the Naive Bayesian filter to an alternative memory-based learning approach, after introducing suitable cost-sensitive evaluation measures. Both methods achieve very accurate spam filtering, outperforming clearly the keyword-based filter of a widely used e-mail reader

Facial skin metastasis due to small-cell lung cancer: a case report

<p>Abstract</p> <p>Introduction</p> <p>Cutaneous metastases in the facial region occur in less than 0.5% of patients with metastatic cancer. They are an important finding and are not often the first sign leading to diagnosis.</p> <p>Case presentation</p> <p>We describe the case of a 64-year-old male patient who presented with dyspnea, pleuritic pain, loss of weight and a nodule on his left cheek. A chest X-ray revealed a left upper lobe mass with mediastinal lymphadenopathy. Excision biopsy of the facial nodule revealed small-cell lung carcinoma. Palliative chemo-radiotherapy was administered and the patient survived for 12 months.</p> <p>Conclusion</p> <p>A high index of suspicion is necessary for the early detection of facial cutaneous metastases. Appropriate treatment may prolong patient survival.</p

Machine learning methods applied to genotyping data capture interactions between single nucleotide variants in late onset Alzheimer's disease

Introduction Genome-wide association studies (GWAS) in late onset Alzheimer's disease (LOAD) provide lists of individual genetic determinants. However, GWAS do not capture the synergistic effects among multiple genetic variants and lack good specificity. Methods We applied tree-based machine learning algorithms (MLs) to discriminate LOAD (>700 individuals) and age-matched unaffected subjects in UK Biobank with single nucleotide variants (SNVs) from Alzheimer's disease (AD) studies, obtaining specific genomic profiles with the prioritized SNVs. Results MLs prioritized a set of SNVs located in genes PVRL2, TOMM40, APOE, and APOC1, also influencing gene expression and splicing. The genomic profiles in this region showed interaction patterns involving rs405509 and rs1160985, also present in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. rs405509 located in APOE promoter interacts with rs429358 among others, seemingly neutralizing their predisposing effect. Discussion Our approach efficiently discriminates LOAD from controls, capturing genomic profiles defined by interactions among SNVs in a hot-spot region

Efficient GF(p m ) Arithmetic Architectures for Cryptographic Applications

Recently, there has been a lot of interest on cryptographic applications based on fields OF(p&quot;), for p &gt; 2. This contribution presents OF(p TM) multipliers architectures, where p is odd. We present designs which trade area for performance based on the number of coefficients that the multiplier processes at one time. Families of irreducible polynomials are introduced to reduce the complexity of the modulo reduction operation and, thus, improved the efficiency of the multiplier. We, then, specialize to fields OF(3 TM) and provide the first cubing architecture pre- sented in the literature. We synthesize our architectures for the special case of OF(397) on the XCV1000-8-FG1156 and XC2VP20-7-FF1156 FPGAs and provide area/performance numbers and comparisons to previous OF(3 TM) and OF(2 TM) implementations. Finally, we provide tables of irreducible polynomials over OF(3) of degree m with 2 _&lt; m _&lt; 255

Cdc42 Regulates Apical Junction Formation in Human Bronchial Epithelial Cells through PAK4 and Par6B

A systematic screen of Cdc42 targets was carried out in human bronchial epithelial cells. Two kinases, PAK4 and Par6B/aPKC, were identified and are required for maturation of primordial junctions into apical junctions. PAK4 recruitment to primordial junctions is Cdc42-dependent, but maintenance at junctions during maturation is Par6B-dependent