Pathophysiology and classification of the vibration white finger.

Skin, arteries and nerves of the upper extremities can be affected by vibration exposure. Recent advances in skin and vascular biology as well as new investigative methods, have shown that neurovascular symptoms may be due to different vascular and neurological disorders which should be differentiated if proper management is to be evaluated. Three types of vascular disorder can be observed in the vibration white finger: digital organic microangiopathy, a digital vasospastic phenomenon and arterial thrombosis in the upper extremities. An imbalance between endothelin-1 and calcitonin-gene-related peptide is probably responsible for the vasospastic phenomenon. Moreover, paresthesiae can be due to either a diffuse vibration neuropathy or a carpal tunnel syndrome. A precise diagnosis is then necessary to adapt the treatment to individual cases. A classification describing the type and severity of the vascular lesions is presented. Asymptomatic lesions are included for adequate epidemiological studies and risk assessment of vibrating tools. Monitoring of vibration exposed workers should include not only a questionnaire about symptoms, but also a clinical evaluation including diagnostic tests for the screening of early asymptomatic neurovascular injuries.

Cluster kernels for semisupervised classification of VHR urban images

In this paper, we present and apply a semisupervised support vector machine based on cluster kernels for the problem of very high resolution image classification. In the proposed setting, a base kernel working with labeled samples only is deformed by a likelihood kernel encoding similarities between unlabeled examples. The resulting kernel is used to train a standard support vector machine (SVM) classifier. Experiments carried out on very high resolution (VHR) multispectral and hyperspectral images using very few labeled examples show the relevancy of the method in the context of urban image classification. Its simplicity and the small number of parameters involved make it versatile and workable by unexperimented users.

Revised nomenclature and classification of inherited ichthyoses: results of the First Ichthyosis Consensus Conference in Sorèze 2009.

BACKGROUND: Inherited ichthyoses belong to a large, clinically and etiologically heterogeneous group of mendelian disorders of cornification, typically involving the entire integument. Over the recent years, much progress has been made defining their molecular causes. However, there is no internationally accepted classification and terminology. OBJECTIVE: We sought to establish a consensus for the nomenclature and classification of inherited ichthyoses. METHODS: The classification project started at the First World Conference on Ichthyosis in 2007. A large international network of expert clinicians, skin pathologists, and geneticists entertained an interactive dialogue over 2 years, eventually leading to the First Ichthyosis Consensus Conference held in Sorèze, France, on January 23 and 24, 2009, where subcommittees on different issues proposed terminology that was debated until consensus was reached. RESULTS: It was agreed that currently the nosology should remain clinically based. "Syndromic" versus "nonsyndromic" forms provide a useful major subdivision. Several clinical terms and controversial disease names have been redefined: eg, the group caused by keratin mutations is referred to by the umbrella term, "keratinopathic ichthyosis"-under which are included epidermolytic ichthyosis, superficial epidermolytic ichthyosis, and ichthyosis Curth-Macklin. "Autosomal recessive congenital ichthyosis" is proposed as an umbrella term for the harlequin ichthyosis, lamellar ichthyosis, and the congenital ichthyosiform erythroderma group. LIMITATIONS: As more becomes known about these diseases in the future, modifications will be needed. CONCLUSION: We have achieved an international consensus for the classification of inherited ichthyosis that should be useful for all clinicians and can serve as reference point for future research.

Decision fusion for the classification of hyperspectral data: Outcome of the 2008 GRS-S data fusion contest

The 2008 Data Fusion Contest organized by the IEEE Geoscience and Remote Sensing Data Fusion Technical Committee deals with the classification of high-resolution hyperspectral data from an urban area. Unlike in the previous issues of the contest, the goal was not only to identify the best algorithm but also to provide a collaborative effort: The decision fusion of the best individual algorithms was aiming at further improving the classification performances, and the best algorithms were ranked according to their relative contribution to the decision fusion. This paper presents the five awarded algorithms and the conclusions of the contest, stressing the importance of decision fusion, dimension reduction, and supervised classification methods, such as neural networks and support vector machines.

A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis.

Diagnosis of several neurological disorders is based on the detection of typical pathological patterns in the electroencephalogram (EEG). This is a time-consuming task requiring significant training and experience. Automatic detection of these EEG patterns would greatly assist in quantitative analysis and interpretation. We present a method, which allows automatic detection of epileptiform events and discrimination of them from eye blinks, and is based on features derived using a novel application of independent component analysis. The algorithm was trained and cross validated using seven EEGs with epileptiform activity. For epileptiform events with compensation for eyeblinks, the sensitivity was 65 +/- 22% at a specificity of 86 +/- 7% (mean +/- SD). With feature extraction by PCA or classification of raw data, specificity reduced to 76 and 74%, respectively, for the same sensitivity. On exactly the same data, the commercially available software Reveal had a maximum sensitivity of 30% and concurrent specificity of 77%. Our algorithm performed well at detecting epileptiform events in this preliminary test and offers a flexible tool that is intended to be generalized to the simultaneous classification of many waveforms in the EEG.

HAMAP in 2015: updates to the protein family classification and annotation system.

HAMAP (High-quality Automated and Manual Annotation of Proteins-available at http://hamap.expasy.org/) is a system for the automatic classification and annotation of protein sequences. HAMAP provides annotation of the same quality and detail as UniProtKB/Swiss-Prot, using manually curated profiles for protein sequence family classification and expert curated rules for functional annotation of family members. HAMAP data and tools are made available through our website and as part of the UniRule pipeline of UniProt, providing annotation for millions of unreviewed sequences of UniProtKB/TrEMBL. Here we report on the growth of HAMAP and updates to the HAMAP system since our last report in the NAR Database Issue of 2013. We continue to augment HAMAP with new family profiles and annotation rules as new protein families are characterized and annotated in UniProtKB/Swiss-Prot; the latest version of HAMAP (as of 3 September 2014) contains 1983 family classification profiles and 1998 annotation rules (up from 1780 and 1720). We demonstrate how the complex logic of HAMAP rules allows for precise annotation of individual functional variants within large homologous protein families. We also describe improvements to our web-based tool HAMAP-Scan which simplify the classification and annotation of sequences, and the incorporation of an improved sequence-profile search algorithm.

Introducing a new MRI classification of lumbar spinal stenosis based on cross-sectional morphology of the dural sac : 37

Introduction: Measures of the degree of lumbar spinal stenosis (LSS) such as antero-posterior diameter of the canal, and dural sac cross sectional area vary, and do not correlate with symptoms or results of surgery. We created a grading system, comprised of seven categories, based on the morphology of the dural sac and its contents as seen on T2 axial images. The categories take into account the ratio of rootlet/ CSF content. Grade A indicates no significant compression, grade D is equivalent to a total myelograhic block. We compared this classification with commonly used criteria of severity of stenosis. Methods: Fifty T2 axial MRI images taken at disc level from 27 symptomatic LSS patients undergoing decompressive surgery were classified twice by two radiologists and three spinal surgeons working at different institutions and countries. Dural sac cross-sectional surface area and AP diameter of the canal were measured both at disc and pedicle level from DICOM images using OsiriX software. Intraand inter-observer reliability were assessed using Cohen's, Fleiss' kappa statistics, and t test. Results: For the morphological grading the average intra-and inter observer kappas were 0.76 and 0.69+, respectively, for physicians working in the study originating country. Combining all observers the kappa values were 0.57 ± 0.19. and 0.44 ± 0.19, respectively. AP diameter and dural sac cross-sectional area measurements showed no statistically significant differences between observers. No correlation between morphological grading and AP diameter or dural sac crosssectional areawas observed in 13 (26%) and 8 cases (16%), respectively. Discussion: The proposed morphological grading relies on the identification of the dural sac and CSF better seen on full MRI series. This was not available to the external observers, which might explain the lower overall kappa values. Since no specific measurement tools are needed the grading suits everyday clinical practice and favours communication of degree of stenosis between practising physicians. The absence of a strict correlation with the dural sac surface suggests that measuring the surface alone might be insufficient in defining LSS as it is essentially a mismatch between the spinal canal and its contents. This grading is now adopted in our unit and further studies concentrating on relation between morphology, clinical symptoms and surgical results are underway.