Creation of a design methodology for devices that improve human mobility

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How a Diverse Research Ecosystem Has Generated New Rehabilitation Technologies: Review of NIDILRR’s Rehabilitation Engineering Research Centers

Over 50 million United States citizens (1 in 6 people in the US) have a developmental, acquired, or degenerative disability. The average US citizen can expect to live 20% of his or her life with a disability. Rehabilitation technologies play a major role in improving the quality of life for people with a disability, yet widespread and highly challenging needs remain. Within the US, a major effort aimed at the creation and evaluation of rehabilitation technology has been the Rehabilitation Engineering Research Centers (RERCs) sponsored by the National Institute on Disability, Independent Living, and Rehabilitation Research. As envisioned at their conception by a panel of the National Academy of Science in 1970, these centers were intended to take a “total approach to rehabilitation”, combining medicine, engineering, and related science, to improve the quality of life of individuals with a disability. Here, we review the scope, achievements, and ongoing projects of an unbiased sample of 19 currently active or recently terminated RERCs. Specifically, for each center, we briefly explain the needs it targets, summarize key historical advances, identify emerging innovations, and consider future directions. Our assessment from this review is that the RERC program indeed involves a multidisciplinary approach, with 36 professional fields involved, although 70% of research and development staff are in engineering fields, 23% in clinical fields, and only 7% in basic science fields; significantly, 11% of the professional staff have a disability related to their research. We observe that the RERC program has substantially diversified the scope of its work since the 1970’s, addressing more types of disabilities using more technologies, and, in particular, often now focusing on information technologies. RERC work also now often views users as integrated into an interdependent society through technologies that both people with and without disabilities co-use (such as the internet, wireless communication, and architecture). In addition, RERC research has evolved to view users as able at improving outcomes through learning, exercise, and plasticity (rather than being static), which can be optimally timed. We provide examples of rehabilitation technology innovation produced by the RERCs that illustrate this increasingly diversifying scope and evolving perspective. We conclude by discussing growth opportunities and possible future directions of the RERC program

Challenges in primary focal segmental glomerulosclerosis diagnosis: from the diagnostic algorithm to novel biomarkers

Biomarcadors; Algoritme de diagnosi; Glomerulosclerosi segmentària focalBiomarcadores; Algoritmo de diagnóstico; Glomeruloesclerosis segmentaria focalBiomarkers; Diagnosis algorithm; Focal segmental glomerulosclerosisPrimary or idiopathic focal segmental glomerulosclerosis (FSGS) is a kidney entity that involves the podocytes, leading to heavy proteinuria and in many cases progresses to end-stage renal disease. Idiopathic FSGS has a bad prognosis, as it involves young individuals who, in a considerably high proportion (∼15%), are resistant to corticosteroids and other immunosuppressive treatments as well. Moreover, the disease recurs in 30–50% of patients after kidney transplantation, leading to graft function impairment. It is suspected that this relapsing disease is caused by a circulating factor(s) that would permeabilize the glomerular filtration barrier. However, the exact pathologic mechanism is an unsettled issue. Besides its poor outcome, a major concern of primary FSGS is the complexity to confirm the diagnosis, as it can be confused with other variants or secondary forms of FSGS and also with other glomerular diseases, such as minimal change disease. New efforts to optimize the diagnostic approach are arising to improve knowledge in well-defined primary FSGS cohorts of patients. Follow-up of properly classified primary FSGS patients will allow risk stratification for predicting the response to different treatments. In this review we will focus on the diagnostic algorithm used in idiopathic FSGS both in native kidneys and in disease recurrence after kidney transplantation. We will emphasize those potential confusing factors as well as their detection and prevention. In addition, we will also provide an overview of ongoing studies that recruit large cohorts of glomerulopathy patients (Nephrotic Syndrome Study Network and Cure Glomerulonephropathy, among others) and the experimental studies performed to find novel reliable biomarkers to detect primary FSGS.The authors are current recipients of research grants from the Fondo de Investigación Sanitaria-Feder, Instituto de Salud Carlos III (PI17/00257, PI18/01704 and PI18/01832) and REDinREN (RD16/0009/0030)

Temporal Segmentation of Surgical Sub-tasks through Deep Learning with Multiple Data Sources

Many tasks in robot-assisted surgeries (RAS) can be represented by finite-state machines (FSMs), where each state represents either an action (such as picking up a needle) or an observation (such as bleeding). A crucial step towards the automation of such surgical tasks is the temporal perception of the current surgical scene, which requires a real-time estimation of the states in the FSMs. The objective of this work is to estimate the current state of the surgical task based on the actions performed or events occurred as the task progresses. We propose Fusion-KVE, a unified surgical state estimation model that incorporates multiple data sources including the Kinematics, Vision, and system Events. Additionally, we examine the strengths and weaknesses of different state estimation models in segmenting states with different representative features or levels of granularity. We evaluate our model on the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS), as well as a more complex dataset involving robotic intra-operative ultrasound (RIOUS) imaging, created using the da Vinci® Xi surgical system. Our model achieves a superior frame-wise state estimation accuracy up to 89.4%, which improves the state-of-the-art surgical state estimation models in both JIGSAWS suturing dataset and our RIOUS dataset

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 159

This bibliography lists 257 reports, articles, and other documents introduced into the NASA scientific and technical information system in September 1976

Modeling and segmentation of audio descriptor profiles with segmental models

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Software tools for comparing genomic sequence

We describe three software tools related to research in comparative genomics, a growing research area that explores the variation within and between organisms. We developed a set of tools that explore sequence similarity and differences in genomes. Two of these tools are specifically aimed at examining DNA sequence data from two or more genomes: (1) The Magenta\u27s OPUS tool compares genomic sequences to identify shared or unique segments between closely related species. This tool looks for functional similarities and differences in genomic data by classifying sequences into groups based on genomic categories: Orthologs, Paralogs, and Unique Sequence. (2) The DSNP tool looks at the nucleotide level to find single nucleotide polymorphisms (SNPs) within an individual. This program is a collection of existing and custom built tools to discover and analyze SNPs within the Daphnia pulex genome. (3) The third tool supports a user evaluation of two different visualization techniques for comparing nucleotide or protein sequences