Prediction of Muscle Torque Production for the Control of a Paralyzed Arm

Functional electrical stimulation (FES) is a method of restoring function to muscles using electrical pulses delivered through an implanted controller. FES has shown potential for enabling people with high spinal cord injuries to perform basic reaching motions essential to everyday tasks. In order to determine the necessary muscle activations an FES neuroprosthesis must produce to cause a desired arm motion, we must first be able to predict the amount of torque that muscles can produce at each joint. The torque production varies depending on the state of the system. Gaussian Process Regression models were trained with data gathered using a dynamic arm simulator in MATLAB that includes models of joint and muscle groups within the shoulder and arm. The Gaussian Process Regression models are able to predict, with acceptable accuracy, the torque at a given joint due to the activation of a certain muscle group. These predictions can be used to develop a method to calculate the muscle activations that will produce the torques necessary to move the arm along a specified trajectory.https://engagedscholarship.csuohio.edu/u_poster_2016/1057/thumbnail.jp

Fuel channel bore estimation for onload pressurised fuel grab load trace data

Accurate fuel channel bore estimation enables information about the health of the graphite core of an advanced gas-cooled reactor to be inferred. This was extensively explored previously for offload depressurised fuel grab load trace (FGLT) data: by isolating the frictional component of the FGLT and using inspection data as a ground truth, a linear regression model was trained to estimate the fuel channel bore. However, when data gathered during onload refuelling has the added complication of the interaction between the fuel assembly and coolant gas, the same process cannot be used. This paper describes the process for removing the aerodynamic effects of the coolant gas in the core from onload pressurised FGLT data. This effect cannot be directly measured, so initially, an empirical model was created by comparing the response from both offload depressurised and onload pressurised events. This model is then used to estimate the offload equivalent FGLT response, and by using a bore estimation model, trained on offload data, it is possible to produce bore estimations for onload FGLT data

Using Audit and Feedback to Improve Antimicrobial Prescribing in Emergency Departments: A Multicenter Quasi-Experimental Study in the Veterans Health Administration

Background: In this pilot trial, we evaluated whether audit-and-feedback was a feasible strategy to improve antimicrobial prescribing in emergency departments (EDs). Methods: We evaluated an audit-and-feedback intervention using a quasi-experimental interrupted time-series design at 2 intervention and 2 matched-control EDs; there was a 12-month baseline, 1-month implementation, and 11-month intervention period. At intervention sites, clinicians received (1) a single, one-on-one education about antimicrobial prescribing for common infections and (2) individualized feedback on total and condition-specific (uncomplicated acute respiratory infection [ARI]) antimicrobial use with peer-to-peer comparisons at baseline and every quarter. The primary outcome was the total antimicrobial-prescribing rate for all visits and was assessed using generalized linear models. In an exploratory analysis, we measured antimicrobial use for uncomplicated ARI visits and manually reviewed charts to assess guideline-concordant management for 6 common infections. Results: In the baseline and intervention periods, intervention sites had 28 016 and 23 164 visits compared to 33 077 and 28 835 at control sites. We enrolled 27 of 31 (87.1%) eligible clinicians; they acknowledged receipt of 33.3% of feedback e-mails. Intervention sites compared with control sites had no absolute reduction in their total antimicrobial rate (incidence rate ratio = 0.99; 95% confidence interval, 0.98-1.01). At intervention sites, antimicrobial use for uncomplicated ARIs decreased (68.6% to 42.4%; P < .01) and guideline-concordant management improved (52.1% to 72.5%; P < .01); these improvements were not seen at control sites. Conclusions: At intervention sites, total antimicrobial use did not decrease, but an exploratory analysis showed reduced antimicrobial prescribing for viral ARIs. Future studies should identify additional targets for condition-specific feedback while exploring ways to make electronic feedback more acceptable

Exploratory 7-Tesla magnetic resonance spectroscopy in Huntington’s disease provides in vivo evidence for impaired energy metabolism

Huntington’s disease (HD) is a neurodegenerative genetic disorder that affects the brain. Atrophy of deep grey matter structures has been reported and it is likely that underlying pathologic processes occur before, or in concurrence with, volumetric changes. Measurement of metabolite concentrations in these brain structures has the potential to provide insight into pathological processes. We aim to gain understanding of metabolite changes with respect to the disease stage and pathophysiological changes. We studied five brain regions using magnetic resonance spectroscopy (MRS) using a 7-Tesla MRI scanner. Localized proton spectra were acquired to obtain six metabolite concentrations. MRS was performed in the caudate nucleus, putamen, thalamus, hypothalamus, and frontal lobe in 44 control subjects, premanifest gene carriers and manifest HD. In the caudate nucleus, HD patients display lower NAA (p = 0.009) and lower creatine concentration (p = 0.001) as compared to controls. In the putamen, manifest HD patients show lower NAA (p = 0.024), lower creatine concentration (p = 0.027), and lower glutamate (p = 0.013). Although absolute values of NAA, creatine, and glutamate were lower, no significant differences to controls were found in the premanifest gene carriers. The lower concentrations of NAA and creatine in the caudate nucleus and putamen of early manifest HD suggest deficits in neuronal integrity and energy metabolism. The changes in glutamate could support the excitotoxicity theory. These findings not only give insight into neuropathological changes in HD but also indicate that MRS can possibly be applied in future clinical trails to evaluate medication targeted at specific metabolic processes

Image Processing Algorithms for Digital Mammography: A Pictorial Essay

Digital mammography systems allow manipulation of fine differences in image contrast by means of image processing algorithms. Different display algorithms have advantages and disadvantages for the specific tasks required in breast imaging—diagnosis and screening. Manual intensity windowing can produce digital mammograms very similar to standard screen-film mammograms but is limited by its operator dependence. Histogram-based intensity windowing improves the conspicuity of the lesion edge, but there is loss of detail outside the dense parts of the image. Mixture-model intensity windowing enhances the visibility of lesion borders against the fatty background, but the mixed parenchymal densities abutting the lesion may be lost. Contrast-limited adaptive histogram equalization can also provide subtle edge information but might degrade performance in the screening setting by enhancing the visibility of nuisance information. Unsharp masking enhances the sharpness of the borders of mass lesions, but this algorithm may make even an indistinct mass appear more circumscribed. Peripheral equalization displays lesion details well and preserves the peripheral information in the surrounding breast, but there may be flattening of image contrast in the nonperipheral portions of the image. Trex processing allows visualization of both lesion detail and breast edge information but reduces image contrast

MONAI: An open-source framework for deep learning in healthcare

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.Comment: www.monai.i

Defining the Effect of the 16p11.2 Duplication on Cognition, Behavior, and Medical Comorbidities

IMPORTANCE The 16p11.2 BP4-BP5 duplication is the copy number variant most frequently associated with autism spectrum disorder (ASD), schizophrenia, and comorbidities such as decreased body mass index (BMI). OBJECTIVES To characterize the effects of the 16p11.2 duplication on cognitive, behavioral, medical, and anthropometric traits and to understand the specificity of these effects by systematically comparing results in duplication carriers and reciprocal deletion carriers, who are also at risk for ASD. DESIGN, SETTING, AND PARTICIPANTS This international cohort study of 1006 study participants compared 270 duplication carriers with their 102 intrafamilial control individuals, 390 reciprocal deletion carriers, and 244 deletion controls from European and North American cohorts. Data were collected from August 1, 2010, to May 31, 2015 and analyzed from January 1 to August 14, 2015. Linear mixed models were used to estimate the effect of the duplication and deletion on clinical traits by comparison with noncarrier relatives. MAIN OUTCOMES AND MEASURES Findings on the Full-Scale IQ (FSIQ), Nonverbal IQ, and Verbal IQ; the presence of ASD or other DSM-IV diagnoses; BMI; head circumference; and medical data. RESULTS Among the 1006 study participants, the duplication was associated with a mean FSIQ score that was lower by 26.3 points between proband carriers and noncarrier relatives and a lower mean FSIQ score (16.2-11.4 points) in nonproband carriers. The mean overall effect of the deletion was similar (-22.1 points; P 100) compared with the deletion group (P < .001). Parental FSIQ predicted part of this variation (approximately 36.0% in hereditary probands). Although the frequency of ASD was similar in deletion and duplication proband carriers (16.0% and 20.0%, respectively), the FSIQ was significantly lower (by 26.3 points) in the duplication probands with ASD. There also were lower head circumference and BMI measurements among duplication carriers, which is consistent with the findings of previous studies. CONCLUSIONS AND RELEVANCE The mean effect of the duplication on cognition is similar to that of the reciprocal deletion, but the variance in the duplication is significantly higher, with severe and mild subgroups not observed with the deletion. These results suggest that additional genetic and familial factors contribute to this variability. Additional studies will be necessary to characterize the predictors of cognitive deficits