A multi-center study: intra-scan and inter-scan variability of diffusion spectrum imaging.

The objective of this study was to investigate whether it is possible to pool together diffusion spectrum imaging data from four different scanners, located at three different sites. Two of the scanners had identical configuration whereas two did not. To measure the variability, we extracted three scalar maps (ADC, FA and GFA) from the DSI and utilized a region and a tract-based analysis. Additionally, a phantom study was performed to rule out some potential factors arising from the scanner performance in case some systematic bias occurred in the subject study. This work was split into three experiments: intra-scanner reproducibility, reproducibility with twin-scanner settings and reproducibility with other configurations. Overall for the intra-scanner and twin-scanner experiments, the region-based analysis coefficient of variation (CV) was in a range of 1%-4.2% and below 3% for almost every bundle for the tract-based analysis. The uncinate fasciculus showed the worst reproducibility, especially for FA and GFA values (CV 3.7-6%). For the GFA and FA maps, an ICC value of 0.7 and above is observed in almost all the regions/tracts. Looking at the last experiment, it was found that there is a very high similarity of the outcomes from the two scanners with identical setting. However, this was not the case for the two other imagers. Given the fact that the overall variation in our study is low for the imagers with identical settings, our findings support the feasibility of cross-site pooling of DSI data from identical scanners.

On-axis joint transform correlation based on a four-level power spectrum

We propose a method to obtain a single centered correlation with use of a joint transform correlator. We analyze the required setup to carry out the whole process optically, and we also present experimental results.

Reactivity spectrum of 30 monoclonal antimelanoma antibodies to a panel of 28 melanoma and control cell lines.

Thirty monoclonal antibodies from eight laboratories exchanged after the First Workshop on Monoclonal Antibodies to Human Melanoma held in March 1981 at NIH were tested in an antibody-binding radioimmunoassay using a panel of 28 different cell lines. This panel included 12 melanomas, three neuroblastomas, four gliomas, one retinoblastoma, four colon carcinomas, one lung carcinoma, one cervical carcinoma, one endometrial carcinoma, and one breast carcinoma. The reactivity pattern of the 30 monoclonal antibodies tested showed that none of them were directed against antigens strictly restricted to melanoma, but that several of them recognize antigenic structures preferentially expressed on melanoma cells. A large number of antibodies were found to crossreact with gliomas and neuroblastomas. Thus, they seem to recognize neuroectoderm associated differentiation antigens. Four monoclonal antibodies produced in our laboratory were further studied for the immunohistological localization of melanoma associated antigens on fresh tumor material. In a three-layer biotin-avidin-peroxidase system each antibody showed a different staining pattern with the tumor cells, suggesting that they were directed against different antigens.

Photon spectrum produced by the late decay of a cosmic neutrino background

We obtain the photon spectrum induced by a cosmic background of unstable neutrinos. We study the spectrum in a variety of cosmological scenarios and also we allow for the neutrinos having a momentum distribution (only a critical matter-dominated universe and neutrinos at rest have been considered until now). Our results can be helpful when extracting bounds on neutrino electric and magnetic moments from cosmic photon background observations.

Advanced Kernel Methods For Remote Sensing Image Classification

Résumé Suite aux recentes avancées technologiques, les archives d'images digitales ont connu une croissance qualitative et quantitative sans précédent. Malgré les énormes possibilités qu'elles offrent, ces avancées posent de nouvelles questions quant au traitement des masses de données saisies. Cette question est à la base de cette Thèse: les problèmes de traitement d'information digitale à très haute résolution spatiale et/ou spectrale y sont considérés en recourant à des approches d'apprentissage statistique, les méthodes à noyau. Cette Thèse étudie des problèmes de classification d'images, c'est à dire de catégorisation de pixels en un nombre réduit de classes refletant les propriétés spectrales et contextuelles des objets qu'elles représentent. L'accent est mis sur l'efficience des algorithmes, ainsi que sur leur simplicité, de manière à augmenter leur potentiel d'implementation pour les utilisateurs. De plus, le défi de cette Thèse est de rester proche des problèmes concrets des utilisateurs d'images satellite sans pour autant perdre de vue l'intéret des méthodes proposées pour le milieu du machine learning dont elles sont issues. En ce sens, ce travail joue la carte de la transdisciplinarité en maintenant un lien fort entre les deux sciences dans tous les développements proposés. Quatre modèles sont proposés: le premier répond au problème de la haute dimensionalité et de la redondance des données par un modèle optimisant les performances en classification en s'adaptant aux particularités de l'image. Ceci est rendu possible par un système de ranking des variables (les bandes) qui est optimisé en même temps que le modèle de base: ce faisant, seules les variables importantes pour résoudre le problème sont utilisées par le classifieur. Le manque d'information étiquétée et l'incertitude quant à sa pertinence pour le problème sont à la source des deux modèles suivants, basés respectivement sur l'apprentissage actif et les méthodes semi-supervisées: le premier permet d'améliorer la qualité d'un ensemble d'entraînement par interaction directe entre l'utilisateur et la machine, alors que le deuxième utilise les pixels non étiquetés pour améliorer la description des données disponibles et la robustesse du modèle. Enfin, le dernier modèle proposé considère la question plus théorique de la structure entre les outputs: l'intègration de cette source d'information, jusqu'à présent jamais considérée en télédétection, ouvre des nouveaux défis de recherche. Advanced kernel methods for remote sensing image classification Devis Tuia Institut de Géomatique et d'Analyse du Risque September 2009 Abstract The technical developments in recent years have brought the quantity and quality of digital information to an unprecedented level, as enormous archives of satellite images are available to the users. However, even if these advances open more and more possibilities in the use of digital imagery, they also rise several problems of storage and treatment. The latter is considered in this Thesis: the processing of very high spatial and spectral resolution images is treated with approaches based on data-driven algorithms relying on kernel methods. In particular, the problem of image classification, i.e. the categorization of the image's pixels into a reduced number of classes reflecting spectral and contextual properties, is studied through the different models presented. The accent is put on algorithmic efficiency and the simplicity of the approaches proposed, to avoid too complex models that would not be used by users. The major challenge of the Thesis is to remain close to concrete remote sensing problems, without losing the methodological interest from the machine learning viewpoint: in this sense, this work aims at building a bridge between the machine learning and remote sensing communities and all the models proposed have been developed keeping in mind the need for such a synergy. Four models are proposed: first, an adaptive model learning the relevant image features has been proposed to solve the problem of high dimensionality and collinearity of the image features. This model provides automatically an accurate classifier and a ranking of the relevance of the single features. The scarcity and unreliability of labeled. information were the common root of the second and third models proposed: when confronted to such problems, the user can either construct the labeled set iteratively by direct interaction with the machine or use the unlabeled data to increase robustness and quality of the description of data. Both solutions have been explored resulting into two methodological contributions, based respectively on active learning and semisupervised learning. Finally, the more theoretical issue of structured outputs has been considered in the last model, which, by integrating outputs similarity into a model, opens new challenges and opportunities for remote sensing image processing.

The spectrum of opportunistic diseases complicating sarcoidosis.

Sarcoidosis is an inflammatory disease marked by a paradoxical immune status. The anergic state, which results from various immune defects, contrasts with the inflammatory formation of granulomas. Sarcoidosis patients may be at risk for opportunistic infections (OIs) and a substantial number of cases have been reported, even in untreated sarcoidosis. It is not clear how OIs in patients with sarcoidosis are different from other groups at risk. In this review, we discuss the most common OIs: mycobacterial infection (including tuberculosis), cryptococcosis, progressive multifocal leukoencephalopathy, and aspergillosis. Unlike peripheral lymphocytopenia, corticosteroids are a major risk factor for OIs but the occurrence of Ols in untreated patients suggests more complex predisposing mechanisms. Opportunistic infections presenting with extrapulmonary features are often misdiagnosed as new localizations of sarcoidosis. Aspergillomas mostly develop on fibrocystic lungs. Overall, physicians should be aware of the possible occurrence of OIs during sarcoidosis, even in untreated patients.

Rapid cohort generation and analysis of disease spectrum of large animal model of cone dystrophy.

Large animal models are an important resource for the understanding of human disease and for evaluating the applicability of new therapies to human patients. For many diseases, such as cone dystrophy, research effort is hampered by the lack of such models. Lentiviral transgenesis is a methodology broadly applicable to animals from many different species. When conjugated to the expression of a dominant mutant protein, this technology offers an attractive approach to generate new large animal models in a heterogeneous background. We adopted this strategy to mimic the phenotype diversity encounter in humans and generate a cohort of pigs for cone dystrophy by expressing a dominant mutant allele of the guanylate cyclase 2D (GUCY2D) gene. Sixty percent of the piglets were transgenic, with mutant GUCY2D mRNA detected in the retina of all animals tested. Functional impairment of vision was observed among the transgenic pigs at 3 months of age, with a follow-up at 1 year indicating a subsequent slower progression of phenotype. Abnormal retina morphology, notably among the cone photoreceptor cell population, was observed exclusively amongst the transgenic animals. Of particular note, these transgenic animals were characterized by a range in the severity of the phenotype, reflecting the human clinical situation. We demonstrate that a transgenic approach using lentiviral vectors offers a powerful tool for large animal model development. Not only is the efficiency of transgenesis higher than conventional transgenic methodology but this technique also produces a heterogeneous cohort of transgenic animals that mimics the genetic variation encountered in human patients.

Diffusion Spectrum Imaging after stroke shows structural changes in the contra-lateral motor network correlating with functional recovery

There is growing interest in understanding the role of the non-injured contra-lateral hemisphere in stroke recovery. In the experimental field, histological evidence has been reported that structural changes occur in the contra-lateral connectivity and circuits during stroke recovery. In humans, some recent imaging studies indicated that contra-lateral sub-cortical pathways and functional and structural cortical networks are remodeling, after stroke. Structural changes in the contra-lateral networks, however, have never been correlated to clinical recovery in patients. To determine the importance of the contra-lateral structural changes in post-stroke recovery, we selected a population of patients with motor deficits after stroke affecting the motor cortex and/or sub-cortical motor white matter. We explored i) the presence of Generalized Fractional Anisotropy (GFA) changes indicating structural alterations in the motor network of patientsâeuro? contra-lateral hemisphere as well as their longitudinal evolution ii) the correlation of GFA changes with patientsâeuro? clinical scores, stroke size and demographics data iii) and a predictive model.

ROLES OF ACID-SENSING ION CHANNELS (ASICS) IN PERIPHERAL NEUROPEPTIDE SECRETION AND PAIN

Acid-sensing ion channels (ASICs) are non-voltage-gated sodium channels activated by an extracellular acidification. They are widely expressed in neurons of the central and peripheral nervous system. ASICs have a role in learning, the expression of fear, in neuronal death after cerebral ischemia, and in pain sensation. Tissue damage leads to the release of inflammatory mediators. There is a subpopulation of sensory neurons which are able to release the neuropeptides calcitonin gene-related peptide (CGRP) and substance P (SP). Neurogenic inflammation refers to the process whereby peripheral release of the neuropeptides CGRP and SP induces vasodilation and extravasation of plasma proteins, respectively. Our laboratory has previously shown that calcium-permeable homomeric ASIC1a channels are present in a majority of CGRP- or SP-expressing small diameter sensory neurons. In the first part of my thesis, we tested the hypothesis that a local acidification can produce an ASIC-mediated calcium-dependant neuropeptide secretion. We have first verified the co-expression of ASICs and CGRP/SP using immunochemistry and in-situ hybridization on dissociated rat dorsal root ganglion (DRG) neurons. We found that most CGRP/SP-positive neurons also expressed ASIC1a and ASIC3 subunits. Calcium imaging experiments with Fura-2 dye showed that an extracellular acidification can induce an increase of intracellular Ca2+ concentration, which is essential for secretion. This increase of intracellular Ca2+ concentration is, at least in some cells, ASIC-dependent, as it can be prevented by amiloride, an ASIC antagonist, and by Psalmotoxin (PcTx1), a specific ASIC1a antagonist. We identified a sub-population of neurons whose acid-induced Ca2+ entry was completely abolished by amiloride, an amiloride-resistant population which does not express ASICs, but rather another acid-sensing channel, possibly transient receptor potential vanilloïde 1 (TRPV1), and a population expressing both H+-gated channel types. Voltage-gated calcium channels (Cavs) may also mediate Ca2+ entry. Co-application of the Cavs inhibitors (ω-conotoxin MVIIC, Mibefradil and Nifedipine) reduced the Ca2+ increase in neurons expressing ASICs during an acidification to pH 6. This indicates that ASICs can depolarise the neuron and activate Cavs. Homomeric ASIC1a are Ca2+-permeable and allow a direct entry of Ca2+ into the cell; other ASICs mediate an indirect entry of Ca2+ by inducing a membrane depolarisation that activates Cavs. We showed with a secretion assay that CGRP secretion can be induced by extracellular acidification in cultured rat DRG neurons. Amiloride and PcTx1 were not able to inhibit the secretion at acidic pH, but BCTC, a TRPV1 inhibitor was able to decrease the secretion induced by an extracellular acidification in our in vitro secretion assay. In conclusion, these results show that in DRG neurons a mild extracellular acidification can induce a calcium-dependent neuropeptide secretion. Even if our data show that ASICs can mediate an increase of intracellular Ca2+ concentration, this appears not to be sufficient to trigger neuropeptide secretion. TRPV1, a calcium channel whose activation induces a sustained current - in contrary of ASICs - played in our experimental conditions a predominant role in neurosecretion. In the second part of my thesis, we focused on the role of ASICs in neuropathic pain. We used the spared nerve injury (SNI) model which consists in a nerve injury that induces symptoms of neuropathic pain such as mechanical allodynia. We have previously shown that the SNI model modifies ASIC currents in dissociated rat DRG neurons. We hypothesized that ASICs could play a role in the development of mechanical allodynia. The SNI model was performed on ASIC1a, -2, and -3 knock-out mice and wild type littermates. We measured mechanical allodynia on these mice with calibrated von Frey filaments. There were no differences between the wild-type and the ASIC1, or ASIC2 knockout mice. ASIC3 null mice were less sensitive than wild type mice at 21 day after SNI, indicating a role for ASIC3. Finally, to investigate other possible roles of ASICs in the perception of the environment, we measured the baseline heat responses. We used two different models; the tail flick model and the hot plate model. ASIC1a null mice showed increased thermal allodynia behaviour in the hot plate test at three different temperatures (49, 52, 55°C) compared to their wild type littermates. On the contrary, ASIC2 null mice showed reduced thermal allodynia behaviour in the hot plate test compared to their wild type littermates at the three same temperatures. We conclude that ASIC1a and ASIC2 in mice can play a role in temperature sensing. It is currently not understood how ASICs are involved in temperature sensing and what the reason for the opposed effects in the two knockout models is.

Spectrum of gluten sensitive enteropathy in first degree relatives of patients with coeliac disease: clinical relevance of lymphocytic enteritis.

Background: Limited data on a short series of patients suggest that lymphocytic enteritis (classically considered as latent coeliac disease) may produce symptoms of malabsorption, although the true prevalence of this situation is unknown. Serological markers of coeliac disease are of little diagnostic value in identifying these patients. Aims: To evaluate the usefulness of human leucocyte antigen-DQ2 genotyping followed by duodenal biopsy for the detection of gluten-sensitive enteropathy in first-degree relatives of patients with coeliac disease and to assess the clinical relevance of lymphocytic enteritis diagnosed with this screening strategy. Patients and methods: 221 first-degree relatives of 82 DQ2+ patients with coeliac disease were consecutively included. Duodenal biopsy (for histological examination and tissue transglutaminase antibody assay in culture supernatant) was carried out on all DQ2+ relatives. Clinical features, biochemical parameters and bone mineral density were recorded. Results: 130 relatives (58.8%) were DQ2+, showing the following histological stages: 64 (49.2%) Marsh 0; 32 (24.6%) Marsh I; 1 (0.8%) Marsh II; 13 (10.0%) Marsh III; 15.4% refused the biopsy. 49 relatives showed gluten sensitive enteropathy, 46 with histological abnormalities and 3 with Marsh 0 but positive tissue transglutaminase antibody in culture supernatant. Only 17 of 221 relatives had positive serological markers. Differences in the diagnostic yield between the proposed strategy and serology were significant (22.2% v 7.2%, p<0.001). Relatives with Marsh I and Marsh II¿III were more often symptomatic (56.3% and 53.8%, respectively) than relatives with normal mucosa (21.1%; p=0.002). Marsh I relatives had more severe abdominal pain (p=0.006), severe distension (p=0.047) and anaemia (p=0.038) than those with Marsh 0. The prevalence of abnormal bone mineral density was similar in relatives with Marsh I (37%) and Marsh III (44.4%). Conclusions: The high number of symptomatic patients with lymphocytic enteritis (Marsh I) supports the need for a strategy based on human leucocyte antigen-DQ2 genotyping followed by duodenal biopsy in relatives of patients with coeliac disease and modifies the current concept that villous atrophy is required to prescribe a gluten-free diet.

AMCHP Issue Brief: Environmental Scan: State Title V Program Response to Autism Spectrum Disorder and Other Developmental Disabilities, December 2009

The increasing incidence of children identified and diagnosed with Autism Spectrum Disorders (ASD) and other developmental disabilities (DD) poses a major challenge to Title V and other programs as they try to meet the diverse and sometimes complex needs of these children. However, those state that have initiated coordinated efforts to meet the needs of these children cross systems have had the opportunity to form and/or strengthen relationships with new partners. In addition, these coordinated efforts will allow states to develop new policies, programs and financing mechanisms addressing the health of children with ASD, which may also strengthen the system of care for all Children and Youth with Special Health Care Needs.

Caspase-3 and RasGAP: a stress-sensing survival/demise switch.

The final decision on cell fate, survival versus cell death, relies on complex and tightly regulated checkpoint mechanisms. The caspase-3 protease is a predominant player in the execution of apoptosis. However, recent progress has shown that this protease paradoxically can also protect cells from death. Here, we discuss the underappreciated, protective, and prosurvival role of caspase-3 and detail the evidence showing that caspase-3, through differential processing of p120 Ras GTPase-activating protein (RasGAP), can modulate a given set of proteins to generate, depending on the intensity of the input signals, opposite outcomes (survival vs death).

Development and Demonstration of a Freezing Drizzle Algorithm for Roadway Environmental Sensing Systems, Final Report, October 2012

The primary goal of this project is to demonstrate the accuracy and utility of a freezing drizzle algorithm that can be implemented on roadway environmental sensing systems (ESSs). The types of problems related to the occurrence of freezing precipitation range from simple traffic delays to major accidents that involve fatalities. Freezing drizzle can also lead to economic impacts in communities with lost work hours, vehicular damage, and downed power lines. There are means for transportation agencies to perform preventive and reactive treatments to roadways, but freezing drizzle can be difficult to forecast accurately or even detect as weather radar and surface observation networks poorly observe these conditions. The detection of freezing precipitation is problematic and requires special instrumentation and analysis. The Federal Aviation Administration (FAA) development of aircraft anti-icing and deicing technologies has led to the development of a freezing drizzle algorithm that utilizes air temperature data and a specialized sensor capable of detecting ice accretion. However, at present, roadway ESSs are not capable of reporting freezing drizzle. This study investigates the use of the methods developed for the FAA and the National Weather Service (NWS) within a roadway environment to detect the occurrence of freezing drizzle using a combination of icing detection equipment and available ESS sensors. The work performed in this study incorporated the algorithm developed initially and further modified for work with the FAA for aircraft icing. The freezing drizzle algorithm developed for the FAA was applied using data from standard roadway ESSs. The work performed in this study lays the foundation for addressing the central question of interest to winter maintenance professionals as to whether it is possible to use roadside freezing precipitation detection (e.g., icing detection) sensors to determine the occurrence of pavement icing during freezing precipitation events and the rates at which this occurs.