The Clinical Assessment and Remote Administration Tablet

Electronic data capture of case report forms, demographic, neuropsychiatric, or clinical assessments, can vary from scanning hand-written forms into databases to fully electronic systems. Web-based forms can be extremely useful for self-assessment; however, in the case of neuropsychiatric assessments, self-assessment is often not an option. The clinician often must be the person either summarizing or making their best judgment about the subject’s response in order to complete an assessment, and having the clinician turn away to type into a web browser may be disruptive to the flow of the interview. The Mind Research Network has developed a prototype for a software tool for the real-time acquisition and validation of clinical assessments in remote environments. We have developed the clinical assessment and remote administration tablet on a Microsoft Windows PC tablet system, which has been adapted to interact with various data models already in use in several large-scale databases of neuroimaging studies in clinical populations. The tablet has been used successfully to collect and administer clinical assessments in several large-scale studies, so that the correct clinical measures are integrated with the correct imaging and other data. It has proven to be incredibly valuable in confirming that data collection across multiple research groups is performed similarly, quickly, and with accountability for incomplete datasets. We present the overall architecture and an evaluation of its use

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Giant Renal Angiomyolipoma without Fat Density on CT Scan: Case Report and Review of the Literature

Giant renal angiomyolipomas have been reported, but typically have the pathognomonic finding of fat density on CT scan. We present the case of a 53-year-old male with a symptomatic, 35-cm, predominantly cystic renal mass without fat density on CT that on nephrectomy was found to be a fat-poor angiomyolipoma with predominantly epithelioid morphology weighing 17.9 kg. Giant renal angiomyolipoma without macroscopic fat density on CT scan is an exceedingly rare occurrence

Deep Learning for Neuroimaging: a Validation Study

Deep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience discovery, making the methods attractive for porting to a neuroimager’s toolbox. Success of these methods is, in part, explained by the flexibility of deep learning models. However, this flexibility makes the process of porting to new areas a difficult parameter optimization problem. In this work we demonstrate our results (and feasible parameter ranges) in application of deep learning methods to structural and functional brain imaging data. These methods include deep belief networks and their building block the restricted Boltzmann machine. We also describe a novel constraint-based approach to visualizing high dimensional data. We use it to analyze the effect of parameter choices on data transformations. Our results show that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data

New experimental limits for electron decay and charge conservation

Abstract New experimental limits for the decay e− → γ + νe are reported. The lower limit for the half-life of this decay mode is T e 1 2 > 1.63 × 10 25 yr (68% CL). The data were collected for 3199 h by using one of the enriched germanium detectors of the Heidelberg-Moscow ββ Collaboration. This detector has an active volume of 591 cm3. This value is up to now the most stringent laboratory limit for this decay mode. Also charge nonconservation in nuclei is shortly discussed in the GaGe system using the data of gallium solar neutrino experiments

Searching for dark matter with the enriched Ge detectors of the Heidelberg-Moscow ββ experiment

Abstract For the first time a search for dark matter with isotopically enriched material is done, by using the Ge detectors of the Heidelberg-Moscow experiment. A measuring time of 165.6 kg·d is used to set limits on the spin-independent cross section of weakly interacting massive particles (WIMPs). A background level of 0.102±0.005 events/(kg·d·keV) was achieved (average value between 11 keV and 30 keV). It was possible to extend the exclusion range for Dirac neutrino masses up to 4.7 TeV