Muscle Synergies Facilitate Computational Prediction of Subject-Specific Walking Motions.

Researchers have explored a variety of neurorehabilitation approaches to restore normal walking function following a stroke. However, there is currently no objective means for prescribing and implementing treatments that are likely to maximize recovery of walking function for any particular patient. As a first step toward optimizing neurorehabilitation effectiveness, this study develops and evaluates a patient-specific synergy-controlled neuromusculoskeletal simulation framework that can predict walking motions for an individual post-stroke. The main question we addressed was whether driving a subject-specific neuromusculoskeletal model with muscle synergy controls (5 per leg) facilitates generation of accurate walking predictions compared to a model driven by muscle activation controls (35 per leg) or joint torque controls (5 per leg). To explore this question, we developed a subject-specific neuromusculoskeletal model of a single high-functioning hemiparetic subject using instrumented treadmill walking data collected at the subject's self-selected speed of 0.5 m/s. The model included subject-specific representations of lower-body kinematic structure, foot-ground contact behavior, electromyography-driven muscle force generation, and neural control limitations and remaining capabilities. Using direct collocation optimal control and the subject-specific model, we evaluated the ability of the three control approaches to predict the subject's walking kinematics and kinetics at two speeds (0.5 and 0.8 m/s) for which experimental data were available from the subject. We also evaluated whether synergy controls could predict a physically realistic gait period at one speed (1.1 m/s) for which no experimental data were available. All three control approaches predicted the subject's walking kinematics and kinetics (including ground reaction forces) well for the model calibration speed of 0.5 m/s. However, only activation and synergy controls could predict the subject's walking kinematics and kinetics well for the faster non-calibration speed of 0.8 m/s, with synergy controls predicting the new gait period the most accurately. When used to predict how the subject would walk at 1.1 m/s, synergy controls predicted a gait period close to that estimated from the linear relationship between gait speed and stride length. These findings suggest that our neuromusculoskeletal simulation framework may be able to bridge the gap between patient-specific muscle synergy information and resulting functional capabilities and limitations

Nanoscale electrochemistry of sp2 carbon materials: from graphite and graphene to carbon nanotubes

Carbon materials have a long history of use as electrodes in electrochemistry, from (bio)electroanalysis to applications in energy technologies, such as batteries and fuel cells. With the advent of new forms of nanocarbon, particularly, carbon nanotubes and graphene, carbon electrode materials have taken on even greater significance for electrochemical studies, both in their own right and as components and supports in an array of functional composites. With the increasing prominence of carbon nanomaterials in electrochemistry comes a need to critically evaluate the experimental framework from which a microscopic understanding of electrochemical processes is best developed. This Account advocates the use of emerging electrochemical imaging techniques and confined electrochemical cell formats that have considerable potential to reveal major new perspectives on the intrinsic electrochemical activity of carbon materials, with unprecedented detail and spatial resolution. These techniques allow particular features on a surface to be targeted and models of structure–activity to be developed and tested on a wide range of length scales and time scales. When high resolution electrochemical imaging data are combined with information from other microscopy and spectroscopy techniques applied to the same area of an electrode surface, in a correlative-electrochemical microscopy approach, highly resolved and unambiguous pictures of electrode activity are revealed that provide new views of the electrochemical properties of carbon materials. With a focus on major sp2 carbon materials, graphite, graphene, and single walled carbon nanotubes (SWNTs), this Account summarizes recent advances that have changed understanding of interfacial electrochemistry at carbon electrodes including: (i) Unequivocal evidence for the high activity of the basal surface of highly oriented pyrolytic graphite (HOPG), which is at least as active as noble metal electrodes (e.g., platinum) for outer-sphere redox processes. (ii) Demonstration of the high activity of basal plane HOPG toward other reactions, with no requirement for catalysis by step edges or defects, as exemplified by studies of proton-coupled electron transfer, redox transformations of adsorbed molecules, surface functionalization via diazonium electrochemistry, and metal electrodeposition. (iii) Rationalization of the complex interplay of different factors that determine electrochemistry at graphene, including the source (mechanical exfoliation from graphite vs chemical vapor deposition), number of graphene layers, edges, electronic structure, redox couple, and electrode history effects. (iv) New methodologies that allow nanoscale electrochemistry of 1D materials (SWNTs) to be related to their electronic characteristics (metallic vs semiconductor SWNTs), size, and quality, with high resolution imaging revealing the high activity of SWNT sidewalls and the importance of defects for some electrocatalytic reactions (e.g., the oxygen reduction reaction). The experimental approaches highlighted for carbon electrodes are generally applicable to other electrode materials and set a new framework and course for the study of electrochemical and interfacial processes

Social Media Behaviors and Experiences During the COVID-19 Pandemic: Associations With Anxiety, Depression, and Stress

The majority of research concerning public health crises and social media platforms has focused on analyzing the accuracy of information within social media posts. The current exploratory study explored social media users’ specific social media behaviors and experiences during the early weeks of the COVID-19 pandemic and whether these behaviors and experiences related to anxiety, depression, and stress. Data were collected March 21–31, 2020 from adults in the United States (N = 564) through snowball sampling on social media sites and Prime Panels. Online surveys included questions regarding social media use during the pandemic and the Depression Anxiety and Stress Scales (DASS). Forward stepwise modeling procedures were used to build three models for anxiety, stress, and depression. Participants who actively engaged with COVID-19 social media content were more likely to experience higher anxiety. Those who had emotional experiences via social media and used social media to connect during the pandemic were susceptible to higher levels of stress and depression. The current study suggests that during the pandemic specific behaviors and experiences via social media were related to anxiety, stress, and depression. Thus, limiting time spent on social media during public health crises may protect the mental health of individuals

Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review.

Preserving cognition and mental capacity is critical to aging with autonomy. Early detection of pathological cognitive decline facilitates the greatest impact of restorative or preventative treatments. Artificial Intelligence (AI) in healthcare is the use of computational algorithms that mimic human cognitive functions to analyze complex medical data. AI technologies like machine learning (ML) support the integration of biological, psychological, and social factors when approaching diagnosis, prognosis, and treatment of disease. This paper serves to acquaint clinicians and other stakeholders with the use, benefits, and limitations of AI for predicting, diagnosing, and classifying mild and major neurocognitive impairments, by providing a conceptual overview of this topic with emphasis on the features explored and AI techniques employed. We present studies that fell into six categories of features used for these purposes: (1) sociodemographics; (2) clinical and psychometric assessments; (3) neuroimaging and neurophysiology; (4) electronic health records and claims; (5) novel assessments (e.g., sensors for digital data); and (6) genomics/other omics. For each category we provide examples of AI approaches, including supervised and unsupervised ML, deep learning, and natural language processing. AI technology, still nascent in healthcare, has great potential to transform the way we diagnose and treat patients with neurocognitive disorders

Combined roles for breeding synchrony, habitat and scale as predictors of extrapair paternity

publishedVersio

Mars and frame-dragging: study for a dedicated mission

In this paper we preliminarily explore the possibility of designing a dedicated satellite-based mission to measure the general relativistic gravitomagnetic Lense-Thirring effect in the gravitational field of Mars. The focus is on the systematic error induced by the multipolar expansion of the areopotential and on possible strategies to reduce it. It turns out that the major sources of bias are the Mars'equatorial radius R and the even zonal harmonics J_L, L = 2,4,6... of the areopotential. An optimal solution, in principle, consists of using two probes at high-altitudes (a\approx 9500-9600 km) and different inclinations, and suitably combining their nodes in order to entirely cancel out the bias due to \delta R. The remaining uncancelled mismodelled terms due to \delta J_L, L = 2,4,6,... would induce a bias \lesssim 1%, according to the present-day MGS95J gravity model, over a wide range of admissible values of the inclinations. The Lense-Thirring out-of-plane shifts of the two probes would amount to about 10 cm yr^-1.Comment: LaTex2e, 16 pages, 5 figures, no tables. To appear in General Relativity and Gravitatio

Submillimeter Wave Astronomy Satellite observations of comet 9P/Tempel 1 and Deep Impact

On 4 July 2005 at 5:52 UT the Deep Impact mission successfully completed its goal to hit the nucleus of 9P/Tempel 1 with an impactor, forming a crater on the nucleus and ejecting material into the coma of the comet. NASA's Submillimeter Wave Astronomy Satellite (SWAS) observed the 1(10)-1(01) ortho-water ground-state rotational transition in comet 9P/Tempel 1 before, during, and after the impact. No excess emission from the impact was detected by SWAS and we derive an upper limit of 1.8e7 kg on the water ice evaporated by the impact. However, the water production rate of the comet showed large natural variations of more than a factor of three during the weeks before and after the impact. Episodes of increased activity with Q(H2O)~1e28 molecule/s alternated with periods with low outgassing (Q(H2O)<~5e27 molecule/s). We estimate that 9P/Tempel 1 vaporized a total of N~4.5e34 water molecules (~1.3e9 kg) during June-September 2005. Our observations indicate that only a small fraction of the nucleus of Tempel 1 appears to be covered with active areas. Water vapor is expected to emanate predominantly from topographic features periodically facing the Sun as the comet rotates. We calculate that appreciable asymmetries of these features could lead to a spin-down or spin-up of the nucleus at observable rates.Comment: 38 pages, 2 tables, 7 figures; Icarus, in pres

Asymptomatic Thoracic Kidney

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66641/2/10.1177_000992286900800510.pd

The Utility of the Timed Up-and-Go Test in Predicting Cognitive Performance: A Cross-Sectional Study of Independent Living Adults in a Retirement Community.

Physical, emotional, and cognitive changes are well documented in aging populations. We administered a comprehensive battery of mental and physical health measures and the Montreal Cognitive Assessment (MoCA; a cognitive screening tool) to 93 independently living older adults (OAs) residing in a Continuing Care Senior Housing Community. Performance on the Timed Up-and-Go (TUG) test (a measure of functional mobility) correlated more strongly with the MoCA total score than did measures of aging, psychiatric symptoms, sleep, and both self-report and objective physical health. Furthermore, it was associated with MoCA Attention, Language, Memory, and Visuospatial/Executive subscales. The MoCA-TUG relationship remained significant after controlling for demographic and physical/mental health measures. Given that the TUG explained significantly more variance in broad cognitive performance than a comprehensive battery of additional physical and mental health tests, it may function as a multimodal measure of health in OAs, capturing physical changes and correlating with cognitive measures