K-Nearest Oracles Borderline Dynamic Classifier Ensemble Selection

Dynamic Ensemble Selection (DES) techniques aim to select locally competent classifiers for the classification of each new test sample. Most DES techniques estimate the competence of classifiers using a given criterion over the region of competence of the test sample (its the nearest neighbors in the validation set). The K-Nearest Oracles Eliminate (KNORA-E) DES selects all classifiers that correctly classify all samples in the region of competence of the test sample, if such classifier exists, otherwise, it removes from the region of competence the sample that is furthest from the test sample, and the process repeats. When the region of competence has samples of different classes, KNORA-E can reduce the region of competence in such a way that only samples of a single class remain in the region of competence, leading to the selection of locally incompetent classifiers that classify all samples in the region of competence as being from the same class. In this paper, we propose two DES techniques: K-Nearest Oracles Borderline (KNORA-B) and K-Nearest Oracles Borderline Imbalanced (KNORA-BI). KNORA-B is a DES technique based on KNORA-E that reduces the region of competence but maintains at least one sample from each class that is in the original region of competence. KNORA-BI is a variation of KNORA-B for imbalance datasets that reduces the region of competence but maintains at least one minority class sample if there is any in the original region of competence. Experiments are conducted comparing the proposed techniques with 19 DES techniques from the literature using 40 datasets. The results show that the proposed techniques achieved interesting results, with KNORA-BI outperforming state-of-art techniques.Comment: Paper accepted for publication on IJCNN 201

An Ensemble Generation Method Based on Instance Hardness

In Machine Learning, ensemble methods have been receiving a great deal of attention. Techniques such as Bagging and Boosting have been successfully applied to a variety of problems. Nevertheless, such techniques are still susceptible to the effects of noise and outliers in the training data. We propose a new method for the generation of pools of classifiers based on Bagging, in which the probability of an instance being selected during the resampling process is inversely proportional to its instance hardness, which can be understood as the likelihood of an instance being misclassified, regardless of the choice of classifier. The goal of the proposed method is to remove noisy data without sacrificing the hard instances which are likely to be found on class boundaries. We evaluate the performance of the method in nineteen public data sets, and compare it to the performance of the Bagging and Random Subspace algorithms. Our experiments show that in high noise scenarios the accuracy of our method is significantly better than that of Bagging.Comment: Paper accepted for publication on IJCNN 201

Living biointerfaces based on non-pathogenic bacteria to direct cell differentiation

Genetically modified Lactococcus lactis, non-pathogenic bacteria expressing the FNIII7-10 fibronectin fragment as a protein membrane have been used to create a living biointerface between synthetic materials and mammalian cells. This FNIII7-10 fragment comprises the RGD and PHSRN sequences of fibronectin to bind α5β1 integrins and triggers signalling for cell adhesion, spreading and differentiation. We used L. lactis strain to colonize material surfaces and produce stable biofilms presenting the FNIII7-10 fragment readily available to cells. Biofilm density is easily tunable and remains stable for several days. Murine C2C12 myoblasts seeded over mature biofilms undergo bipolar alignment and form differentiated myotubes, a process triggered by the FNIII7-10 fragment. This biointerface based on living bacteria can be further modified to express any desired biochemical signal, establishing a new paradigm in biomaterial surface functionalisation for biomedical applications

Focal non granulomatous orchitis in a patient with Crohn’s disease

Crohn’s disease is a systemic disease and sometimes involves the testicle, usually leading to granulomatous lesions. We report herein a case of focal non-granulomatous orchitis in a 21-year-old patient with active Crohn’s disease treated by an anti-tumor necrosis factor monoclonal antibody. This circumscribed testicular lesion mimicked a tumor, leading to orchiectomy. Pre-operative blood tests (i.e. alpha-fetoprotein, lactate dehydrogenase and human chorionic gonadotrophin) were strictly normal Pathological examination of the testicle revealed a focal inflammatory infiltrate predominantly composed of lymphocytes accompanied by few plasma cells, lacking giant cells or granulomas. Importantly, intratubular germ cell neoplasia, atrophy or lithiasis were not observed. After discussing and excluding other plausible causes (burnt-out /regressed germ cell tumor, infection, vascular or traumatic lesions, iatrogenic effects), we concluded that this particular case of orchitis was most likely an extra-digestive manifestation of inflammatory bowel disease. To our knowledge, this is the first described case of focal non-granulomatous orchitis associated with Crohn’s disease. Virtual Slides: The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/211774728416011

ОБНАРУЖЕНИЕ ОБЪЕКТОВ СИСТЕМАМИ КОМПЬЮТЕРНОГО ЗРЕНИЯ: ПОДХОД НА ОСНОВЕ ВИЗУАЛЬНОЙ САЛИЕНТНОСТИ

A combined approach of object detection in image and eye fixation probability map calculation is proposed. This approach can be used in applied tasks of autonomous object detection. Experimental results show viability and efficiency of this approach as compared with state-of-art algorithms, and predict its usability on the broader class of tasks - applied variations of eye fixation problem.Представлен комбинированный алгоритм выделения объекта на изображении и расчета карты вероятности фиксации взгляда, который можно использовать для прикладных задач автономного обнаружения. Экспериментальные результаты показывают жизнеспособность алгоритма и его эффективность в сравнении с несколькими state-of-art алгоритмами, предполагая его применимость в более широком классе задач - прикладных вариациях задачи прогноза фиксации взгляда

FAK/src-Family Dependent Activation of the Ste20-Like Kinase SLK Is Required for Microtubule-Dependent Focal Adhesion Turnover and Cell Migration

Cell migration involves a multitude of signals that converge on cytoskeletal reorganization, essential for development, immune responses and tissue repair. Using knockdown and dominant negative approaches, we show that the microtubule-associated Ste20-like kinase SLK is required for focal adhesion turnover and cell migration downstream of the FAK/c-src complex. Our results show that SLK co-localizes with paxillin, Rac1 and the microtubules at the leading edge of migrating cells and is activated by scratch wounding. SLK activation is dependent on FAK/c-src/MAPK signaling, whereas SLK recruitment to the leading edge is src-dependent but FAK independent. Our results show that SLK represents a novel focal adhesion disassembly signal