Method and apparatus for positioning a robotic end effector

A robotic end effector and operation protocol for a reliable grasp of a target object irrespective of the target's contours is disclosed. A robotic hand includes a plurality of jointed fingers, one of which, like a thumb, is in opposed relation to the other. Each finger is comprised of at least two jointed sections, and provided with reflective proximity sensors, one on the inner surface of each finger section. Each proximity sensor comprises a transmitter of a beam of radiant energy and means for receiving reflections of the transmitted energy when reflected by a target object and for generating electrical signals responsive thereto. On the fingers opposed to the thumb, the proximity sensors on the outermost finger sections are aligned in an outer sensor array and the sensors on the intermediate finger sections and sensors on the innermost finger sections are similarly arranged to form an intermediate sensor array and an inner sensor array, respectively. The invention includes a computer system with software and/or circuitry for a protocol comprising the steps in sequence of: (1) approach axis alignment to maximize the number of outer layer sensors which detect the target; (2) non-contact contour following the target by the robot fingers to minimize target escape potential; and (3) closing to rigidize the target including dynamically re-adjusting the end effector finger alignment to compensate for target motion. A signal conditioning circuit and gain adjustment means are included to maintain the dynamic range of low power reflection signals

Method of Making an Electrically Conductive Strain Gauge Material

An improved elastomeric electrically conductive strain gauge for use in virtual reality systems is disclosed which involves the flash heating of a doped ethylene vinyl acetate elastomer

Application of dexterous space robotics technology to myoelectric prostheses

Future space missions will require robots equipped with highly dexterous robotic hands to perform a variety of tasks. A major technical challenge in making this possible is an improvement in the way these dexterous robotic hands are remotely controlled or teleoperated. NASA is currently investigating the feasibility of using myoelectric signals to teleoperate a dexterous robotic hand. In theory, myoelectric control of robotic hands will require little or no mechanical parts and will greatly reduce the bulk and weight usually found in dexterous robotic hand control devices. An improvement in myoelectric control of multifinger hands will also benefit prosthetics users. Therefore, as an effort to transfer dexterous space robotics technology to prosthetics applications and to benefit from existing myoelectric technology, NASA is collaborating with the Limbs of Love Foundation, the Institute for Rehabilitation and Research, and Rice University in developing improved myoelectric control multifinger hands and prostheses. In this paper, we will address the objectives and approaches of this collaborative effort and discuss the technical issues associated with myoelectric control of multifinger hands. We will also report our current progress and discuss plans for future work

A targeted gene panel that covers coding, non-coding and short tandem repeat regions improves the diagnosis of patients with neurodegenerative diseases

Genetic testing for neurodegenerative diseases (NDs) is highly challenging because of genetic heterogeneity and overlapping manifestations. Targeted-gene panels (TGPs), coupled with next-generation sequencing (NGS), can facilitate the profiling of a large repertoire of ND-related genes. Due to the technical limitations inherent in NGS and TGPs, short tandem repeat (STR) variations are often ignored. However, STR expansions are known to cause such NDs as Huntington\u27s disease and spinocerebellar ataxias type 3 (SCA3). Here, we studied the clinical utility of a custom-made TGP that targets 199 NDs and 311 ND-associated genes on 118 undiagnosed patients. At least one known or likely pathogenic variation was found in 54 patients; 27 patients demonstrated clinical profiles that matched the variants; and 16 patients whose original diagnosis were refined. A high concordance of variant calling were observed when comparing the results from TGP and whole-exome sequencing of four patients. Our in-house STR detection algorithm has reached a specificity of 0.88 and a sensitivity of 0.82 in our SCA3 cohort. This study also uncovered a trove of novel and recurrent variants that may enrich the repertoire of ND-related genetic markers. We propose that a combined comprehensive TGPs-bioinformatics pipeline can improve the clinical diagnosis of NDs

Decadal changes in summertime reactive oxidized nitrogen and surface ozone over the Southeast United States

Widespread efforts to abate ozone (O3) smog have significantly reduced emissions of nitrogen oxides (NOx) over the past 2 decades in the Southeast US, a place heavily influenced by both anthropogenic and biogenic emissions. How reactive nitrogen speciation responds to the reduction in NOx emissions in this region remains to be elucidated. Here we exploit aircraft measurements from ICARTT (July–August 2004), SENEX (June–July 2013), and SEAC4RS (August–September 2013) and long-term ground measurement networks alongside a global chemistry–climate model to examine decadal changes in summertime reactive oxidized nitrogen (RON) and ozone over the Southeast US. We show that our model can reproduce the mean vertical profiles of major RON species and the total (NOy) in both 2004 and 2013. Among the major RON species, nitric acid (HNO3) is dominant (∼ 42–45%), followed by NOx (31%), total peroxy nitrates (ΣPNs; 14%), and total alkyl nitrates (ΣANs; 9–12%) on a regional scale. We find that most RON species, including NOx, ΣPNs, and HNO3, decline proportionally with decreasing NOx emissions in this region, leading to a similar decline in NOy. This linear response might be in part due to the nearly constant summertime supply of biogenic VOC emissions in this region. Our model captures the observed relative change in RON and surface ozone from 2004 to 2013. Model sensitivity tests indicate that further reductions of NOxemissions will lead to a continued decline in surface ozone and less frequent high-ozone events

Reducing the duration of untreated psychosis and its impact in the U.S.: the STEP-ED study

Background: Early intervention services for psychotic disorders optimally interlock strategies to deliver: (i) Early Detection (ED) to shorten the time between onset of psychotic symptoms and effective treatment (i.e. Duration of Untreated Psychosis, DUP); and (ii) comprehensive intervention during the subsequent 2 to 5 years. In the latter category, are teams (‘First-episode Services’ or FES) that integrate several empirically supported treatments and adapt their delivery to younger patients and caregivers. There is an urgent need to hasten access to established FES in the U.S. Despite improved outcomes for those in treatment, these FES routinely engage patients a year or more after psychosis onset. The Scandinavian TIPS study was able to effectively reduce DUP in a defined geographic catchment. The guiding questions for this study are: can a U.S. adaptation of the TIPS approach to ED substantially reduce DUP and improve outcomes beyond existing FES? Methods/Design The primary aim is to determine whether ED can reduce DUP in the US, as compared to usual detection. ED will be implemented by one FES (STEP) based in southern Connecticut, and usual detection efforts will continue at a comparable FES (PREPR) serving the greater Boston metropolitan area. The secondary aim is to determine whether DUP reduction can improve presentation, engagement and early outcomes in FES care. A quasi-experimental design will compare the impact of ED on DUP at STEP compared to PREPR over 3 successive campaign years. The campaign will deploy 3 components that seek to transform pathways to care in 8 towns surrounding STEP. Social marketing approaches will inform a public education campaign to enable rapid and effective help-seeking behavior. Professional outreach and detailing to a wide variety of care providers, including those in the healthcare, educational and judicial sectors, will facilitate rapid redirection of appropriate patients to STEP. Finally, performance improvement measures within STEP will hasten engagement upon referral. Discussion STEP-ED will test an ED campaign adapted to heterogeneous U.S. pathways to care while also improving our understanding of these pathways and their impact on early outcomes. Trial registration ClinicalTrials.gov: NCT02069925. Registered 20 February 2014

Updating Maryland\u27s Sea-level Rise Projections

With its 3,100 miles of tidal shoreline and low-lying rural and urban lands, The Free State is one of the most vulnerable to sea-level rise. Historically, Marylanders have long had to contend with rising water levels along its Chesapeake Bay and Atlantic Ocean and coastal bay shores. Shorelines eroded and low-relief lands and islands, some previously inhabited, were inundated. Prior to the 20th century, this was largely due to the slow sinking of the land since Earth’s crust is still adjusting to the melting of large masses of ice following the last glacial period. Over the 20th century, however, the rate of rise of the average level of tidal waters with respect to land, or relative sea-level rise, has increased, at least partially as a result of global warming. Moreover, the scientific evidence is compelling that Earth’s climate will continue to warm and its oceans will rise even more rapidly. Recognizing the scientific consensus around global climate change, the contribution of human activities to it, and the vulnerability of Maryland’s people, property, public investments, and natural resources, Governor Martin O’Malley established the Maryland Commission on Climate Change on April 20, 2007. The Commission produced a Plan of Action1 that included a comprehensive climate change impact assessment, a greenhouse gas reduction strategy, and strategies for reducing Maryland’s vulnerability to climate change. The Plan has led to landmark legislation to reduce the state’s greenhouse gas emissions and a variety of state policies designed to reduce energy consumption and promote adaptation to climate change

Classification and biomarker identification using gene network modules and support vector machines

<p>Abstract</p> <p>Background</p> <p>Classification using microarray datasets is usually based on a small number of samples for which tens of thousands of gene expression measurements have been obtained. The selection of the genes most significant to the classification problem is a challenging issue in high dimension data analysis and interpretation. A previous study with SVM-RCE (Recursive Cluster Elimination), suggested that classification based on groups of correlated genes sometimes exhibits better performance than classification using single genes. Large databases of gene interaction networks provide an important resource for the analysis of genetic phenomena and for classification studies using interacting genes.</p> <p>We now demonstrate that an algorithm which integrates network information with recursive feature elimination based on SVM exhibits good performance and improves the biological interpretability of the results. We refer to the method as SVM with Recursive Network Elimination (SVM-RNE)</p> <p>Results</p> <p>Initially, one thousand genes selected by t-test from a training set are filtered so that only genes that map to a gene network database remain. The Gene Expression Network Analysis Tool (GXNA) is applied to the remaining genes to form <it>n </it>clusters of genes that are highly connected in the network. Linear SVM is used to classify the samples using these clusters, and a weight is assigned to each cluster based on its importance to the classification. The least informative clusters are removed while retaining the remainder for the next classification step. This process is repeated until an optimal classification is obtained.</p> <p>Conclusion</p> <p>More than 90% accuracy can be obtained in classification of selected microarray datasets by integrating the interaction network information with the gene expression information from the microarrays.</p> <p>The Matlab version of SVM-RNE can be downloaded from <url>http://web.macam.ac.il/~myousef</url></p

The Seventh Data Release of the Sloan Digital Sky Survey

This paper describes the Seventh Data Release of the Sloan Digital Sky Survey (SDSS), marking the completion of the original goals of the SDSS and the end of the phase known as SDSS-II. It includes 11663 deg^2 of imaging data, with most of the roughly 2000 deg^2 increment over the previous data release lying in regions of low Galactic latitude. The catalog contains five-band photometry for 357 million distinct objects. The survey also includes repeat photometry over 250 deg^2 along the Celestial Equator in the Southern Galactic Cap. A coaddition of these data goes roughly two magnitudes fainter than the main survey. The spectroscopy is now complete over a contiguous area of 7500 deg^2 in the Northern Galactic Cap, closing the gap that was present in previous data releases. There are over 1.6 million spectra in total, including 930,000 galaxies, 120,000 quasars, and 460,000 stars. The data release includes improved stellar photometry at low Galactic latitude. The astrometry has all been recalibrated with the second version of the USNO CCD Astrograph Catalog (UCAC-2), reducing the rms statistical errors at the bright end to 45 milli-arcseconds per coordinate. A systematic error in bright galaxy photometr is less severe than previously reported for the majority of galaxies. Finally, we describe a series of improvements to the spectroscopic reductions, including better flat-fielding and improved wavelength calibration at the blue end, better processing of objects with extremely strong narrow emission lines, and an improved determination of stellar metallicities. (Abridged)Comment: 20 pages, 10 embedded figures. Accepted to ApJS after minor correction

GENE-Counter: A Computational Pipeline for the Analysis of RNA-Seq Data for Gene Expression Differences

GENE-counter is a complete Perl-based computational pipeline for analyzing RNA-Sequencing (RNA-Seq) data for differential gene expression. In addition to its use in studying transcriptomes of eukaryotic model organisms, GENE-counter is applicable for prokaryotes and non-model organisms without an available genome reference sequence. For alignments, GENE-counter is configured for CASHX, Bowtie, and BWA, but an end user can use any Sequence Alignment/Map (SAM)-compliant program of preference. To analyze data for differential gene expression, GENE-counter can be run with any one of three statistics packages that are based on variations of the negative binomial distribution. The default method is a new and simple statistical test we developed based on an over-parameterized version of the negative binomial distribution. GENE-counter also includes three different methods for assessing differentially expressed features for enriched gene ontology (GO) terms. Results are transparent and data are systematically stored in a MySQL relational database to facilitate additional analyses as well as quality assessment. We used next generation sequencing to generate a small-scale RNA-Seq dataset derived from the heavily studied defense response of Arabidopsis thaliana and used GENE-counter to process the data. Collectively, the support from analysis of microarrays as well as the observed and substantial overlap in results from each of the three statistics packages demonstrates that GENE-counter is well suited for handling the unique characteristics of small sample sizes and high variability in gene counts