Case report of a primary subcutaneous melanoma:a surprising entity for a subcutaneous nodule

INTRODUCTION: A melanoma can originate at the subcutis without any visible skin lesion. CASE PRESENTATION: A 73-year old patient came to the outpatient clinic with a subcutaneous nodule on the right thigh without any visible lesion of the skin. It turned out to be a primary subcutaneous melanoma that could be classified as a primary dermal melanoma (PDM). DISCUSSION: A PDM is a very rare subtype of melanoma that stands out for its excellent prognosis in comparison to cutaneous melanomas. No valid reliable staging system or treatment guideline exists for this entity, Breslow depth might overestimate the clinical aggressiveness possibly leading to overtreatment. CONCLUSION: It is of great importance for the clinician to be familiar with a primary dermal melanoma. It deserves an appropriate place in the current AJCC system and a treatment guideline for this unique melanoma subtype with relativity excellent prognosis would be beneficial

Using state space differential geometry for nonlinear blind source separation

Given a time series of multicomponent measurements of an evolving stimulus, nonlinear blind source separation (BSS) seeks to find a "source" time series, comprised of statistically independent combinations of the measured components. In this paper, we seek a source time series with local velocity cross correlations that vanish everywhere in stimulus state space. However, in an earlier paper the local velocity correlation matrix was shown to constitute a metric on state space. Therefore, nonlinear BSS maps onto a problem of differential geometry: given the metric observed in the measurement coordinate system, find another coordinate system in which the metric is diagonal everywhere. We show how to determine if the observed data are separable in this way, and, if they are, we show how to construct the required transformation to the source coordinate system, which is essentially unique except for an unknown rotation that can be found by applying the methods of linear BSS. Thus, the proposed technique solves nonlinear BSS in many situations or, at least, reduces it to linear BSS, without the use of probabilistic, parametric, or iterative procedures. This paper also describes a generalization of this methodology that performs nonlinear independent subspace separation. In every case, the resulting decomposition of the observed data is an intrinsic property of the stimulus' evolution in the sense that it does not depend on the way the observer chooses to view it (e.g., the choice of the observing machine's sensors). In other words, the decomposition is a property of the evolution of the "real" stimulus that is "out there" broadcasting energy to the observer. The technique is illustrated with analytic and numerical examples.Comment: Contains 14 pages and 3 figures. For related papers, see http://www.geocities.com/dlevin2001/ . New version is identical to original version except for URL in the bylin

Theoretical Studies and Algorithms Regarding the Solution of Non-invertible Nonlinear Source Separation

International audienceIn this paper, we analyse and solve a source separation problem based on a mixing model that is nonlinear and non-invertible at the space of mixtures. The model is relevant considering it may represent the data obtained from ion-selective electrode arrays. We apply a new approach for solving the problems of local stability of the recurrent network previously used in the literature, which allows for a wider range of source concentration. In order to achieve this, we utilize a second-order recurrent network which can be shown to be locally stable for all solutions. Using this new network and the priors that chemical sources are continuous and smooth, our proposal performs better than the previous approach

Hyperspectral unmixing with material variability using social sparsity

International audienceWe apply social-norms for the first time to the problem of hyperspectral unmixing while modeling spectral variability. These norms are built with inter-group penalties which are combined in a global intra-group penalization that can enforce selection of entire endmember bundles; this results in the selection of a few representative materials even in the presence of large endmembers bundles capturing each material's variability. We demonstrate improvements quantitatively on synthetic data and qualitatively on real data for three cases of social norms: group, elitist, and a fractional social norm, respectively. We find that the greatest improvements arise from using either the group or fractional flavor

Steklov problem on differential forms

In this paper we study spectral properties of Dirichlet-to-Neumann map on differential forms obtained by a slight modification of the definition due to Belishev and Sharafutdinov. The resulting operator $\Lambda$ is shown to be self-adjoint on the subspace of coclosed forms and to have purely discrete spectrum there.We investigate properies of eigenvalues of $\Lambda$ and prove a Hersch-Payne-Schiffer type inequality relating products of those eigenvalues to eigenvalues of Hodge Laplacian on the boundary. Moreover, non-trivial eigenvalues of $\Lambda$ are always at least as large as eigenvalues of Dirichlet-to-Neumann map defined by Raulot and Savo. Finally, we remark that a particular case of $p$-forms on the boundary of $2p+2$-dimensional manifold shares a lot of important properties with the classical Steklov eigenvalue problem on surfaces.Comment: 18 page

Wide Field Imaging. I. Applications of Neural Networks to object detection and star/galaxy classification

[Abriged] Astronomical Wide Field Imaging performed with new large format CCD detectors poses data reduction problems of unprecedented scale which are difficult to deal with traditional interactive tools. We present here NExt (Neural Extractor): a new Neural Network (NN) based package capable to detect objects and to perform both deblending and star/galaxy classification in an automatic way. Traditionally, in astronomical images, objects are first discriminated from the noisy background by searching for sets of connected pixels having brightnesses above a given threshold and then they are classified as stars or as galaxies through diagnostic diagrams having variables choosen accordingly to the astronomer's taste and experience. In the extraction step, assuming that images are well sampled, NExt requires only the simplest a priori definition of "what an object is" (id est, it keeps all structures composed by more than one pixels) and performs the detection via an unsupervised NN approaching detection as a clustering problem which has been thoroughly studied in the artificial intelligence literature. In order to obtain an objective and reliable classification, instead of using an arbitrarily defined set of features, we use a NN to select the most significant features among the large number of measured ones, and then we use their selected features to perform the classification task. In order to optimise the performances of the system we implemented and tested several different models of NN. The comparison of the NExt performances with those of the best detection and classification package known to the authors (SExtractor) shows that NExt is at least as effective as the best traditional packages.Comment: MNRAS, in press. Paper with higher resolution images is available at http://www.na.astro.it/~andreon/listapub.htm

A clinical-radiological framework of the right temporal variant of frontotemporal dementia

The concept of the right temporal variant of frontotemporal dementia (rtvFTD) is still equivocal. The syndrome accompanying predominant right anterior temporal atrophy has previously been described as memory loss, prosopagnosia, getting lost and behavioural changes. Accurate detection is challenging, as the clinical syndrome might be confused with either behavioural variant FTD (bvFTD) or Alzheimer’s disease. Furthermore, based on neuroimaging features, the syndrome has been considered a right-sided variant of semantic variant primary progressive aphasia (svPPA). Therefore, we aimed to demarcate the clinical and neuropsychological characteristics of rtvFTD versus svPPA, bvFTD and Alzheimer’s disease. Moreover, we aimed to compare its neuroimaging profile against svPPA, which is associated with predominant left anterior temporal atrophy. Of 619 subjects with a clinical diagnosis of frontotemporal dementia or primary progressive aphasia, we included 70 subjects with a negative amyloid status in whom predominant right temporal lobar atrophy was identified based on blinded visual assessment of their initial brain MRI scans. Clinical symptoms were assessed retrospectively and compared with age- and sex-matched patients with svPPA (n = 70), bvFTD (n = 70) and Alzheimer’s disease (n = 70). Prosopagnosia, episodic memory impairment and behavioural changes such as disinhibition, apathy, compulsiveness and loss of empathy were the most common initial symptoms, whereas during the disease course, patients developed language problems such as word-finding difficulties and anomia. Distinctive symptoms of rtvFTD compared to the other groups included depression, somatic complaints, and motor/mental slowness. Aside from right temporal atrophy, the imaging pattern showed volume loss of the right ventral frontal area and the left temporal lobe, which represented a close mirror image of svPPA. Atrophy of the bilateral temporal poles and the fusiform gyrus were associated with prosopagnosia in rtvFTD. Our results highlight that rtvFTD has a unique clinical presentation. Since current diagnostic criteria do not cover specific symptoms of the rtvFTD, we propose a diagnostic tree to be used to define diagnostic criteria and call for an international validation

Assessing cognition and daily function in early dementia using the cognitive-functional composite:findings from the Catch-Cog study cohort

BackgroundThe cognitive-functional composite (CFC) was designed to improve the measurement of clinically relevant changes in predementia and early dementia stages. We have previously demonstrated its good test-retest reliability and feasibility of use. The current study aimed to evaluate several quality aspects of the CFC, including construct validity, clinical relevance, and suitability for the target population.MethodsBaseline data of the Capturing Changes in Cognition study was used: an international, prospective cohort study including participants with subjective cognitive decline (SCD), mild cognitive impairment (MCI), Alzheimer's disease (AD) dementia, and dementia with Lewy bodies (DLB). The CFC comprises seven existing cognitive tests focusing on memory and executive functions (EF) and the informant-based Amsterdam Instrumental Activities of Daily Living Questionnaire (A-IADL-Q). Construct validity and clinical relevance were assessed by (1) confirmatory factor analyses (CFA) using all CFC subtests and (2) linear regression analyses relating the CFC score (independent) to reference measures of disease severity (dependent), correcting for age, sex, and education. To assess the suitability for the target population, we compared score distributions of the CFC to those of traditional tests (Alzheimer's Disease Assessment Scale-Cognitive subscale, Alzheimer's Disease Cooperative Study-Activities of Daily Living scale, and Clinical Dementia Rating scale).ResultsA total of 184 participants were included (age 71.88.4; 42% female; n=14 SCD, n=80 MCI, n=78AD, and n=12 DLB). CFA showed that the hypothesized three-factor model (memory, EF, and IADL) had adequate fit (CFI=.931, RMSEA=.091, SRMR=.06). Moreover, worse CFC performance was associated with more cognitive decline as reported by the informant (=.61, p</p

The natural history of primary progressive aphasia: beyond aphasia

Introduction: Primary progressive aphasia (PPA) is divided into three prototypical subtypes that are all characterized by their single core symptom of aphasia. Although later in their course, other cognitive, behavioral, and motor domains may become involved, little is known about the progression profile of each subtype relative to the other subtypes. Methods: In this longitudinal retrospective cohort study, based on the recent biomarker-supported diagnostic criteria, 24 subjects diagnosed with semantic variant (svPPA), 22 with non-fluent variant (nfvPPA), and 18 with logopenic variant (lvPPA) were collected and followed up for 1–6 years. Symptom distribution, cognitive test and neuropsychiatric inventory scores, and progression into another syndrome were assessed. Results: Over time, lvPPA progressed with broader language problems (PPA-extended) and nfvPPA progressed to mutism, whereas semantic impairment remained the major problem in svPPA. Apart from linguistic problems, svPPA developed pronounced behavioral disturbances, whereas lvPPA exhibited a greater cognitive decline. By contrast, in nfvPPA motor deficits were more common. Furthermore, within 5 years (IQR = 2.5) after clinical onset, 65.6% of the patients additionally fulfilled the clinical criteria for another neurodegenerative syndrome (PPA-plus). Fourteen out of 24 (58%) svPPA patients additionally met the diagnostic criteria of behavioral variant frontotemporal dementia (5.1 years, IQR = 1.1), whereas the clinical features of 15/18 (83%) lvPPA patients were consistent with Alzheimer disease dementia (4.5 years IQR = 3.4). Furthermore, 12/22 (54%) of the subjects with the nfvPPA progressed to meet the diagnostic criteria of corticobasal syndrome, progressive supranuclear palsy, or motor neuron disease (5.1 years IQR = 3.4). Discussion: Despite aphasia being the initial and unique hallmark of the syndrome, our longitudinal results showed that PPA is not a language limited disorder and progression differs widely for each subtype, both with respect to the nature of symptoms and disease duration