Categorization of compensatory motions in transradial myoelectric prosthesis users

Background: Prosthesis users perform various compensatory motions to accommodate for the loss of the hand and wrist as well as the reduced functionality of a prosthetic hand. Objectives: Investigate different compensation strategies that are performed by prosthesis users. Study Design: Comparative analysis Methods: 20 able-bodied subjects and 4 prosthesis users performed a set of bimanual activities. Movements of the trunk and head were recorded using a motion capture system, and a digital video recorder. Clinical motion angles were calculated to assess the compensatory motions made by the prosthesis users. The video recording also assisted in visually identifying the compensations. Results: Compensatory motions by the prosthesis users were evident in the tasks performed (slicing and stirring activities) as compared to the benchmark of able-bodied subjects. Compensations took the form of a measured increase in range of motion, an observed adoption of a new posture during task execution, and pre-positioning of items in the workspace prior to initiating a given task. Conclusion: Compensatory motions were performed by prosthesis users during the selected tasks. These can be categorized into three different types of compensations

Automated Discovery of Food Webs from Ecological Data Using Logic-Based Machine Learning

Networks of trophic links (food webs) are used to describe and understand mechanistic routes for translocation of energy (biomass) between species. However, a relatively low proportion of ecosystems have been studied using food web approaches due to difficulties in making observations on large numbers of species. In this paper we demonstrate that Machine Learning of food webs, using a logic-based approach called A/ILP, can generate plausible and testable food webs from field sample data. Our example data come from a national-scale Vortis suction sampling of invertebrates from arable fields in Great Britain. We found that 45 invertebrate species or taxa, representing approximately 25% of the sample and about 74% of the invertebrate individuals included in the learning, were hypothesized to be linked. As might be expected, detritivore Collembola were consistently the most important prey. Generalist and omnivorous carabid beetles were hypothesized to be the dominant predators of the system. We were, however, surprised by the importance of carabid larvae suggested by the machine learning as predators of a wide variety of prey. High probability links were hypothesized for widespread, potentially destabilizing, intra-guild predation; predictions that could be experimentally tested. Many of the high probability links in the model have already been observed or suggested for this system, supporting our contention that A/ILP learning can produce plausible food webs from sample data, independent of our preconceptions about “who eats whom.” Well-characterised links in the literature correspond with links ascribed with high probability through A/ILP. We believe that this very general Machine Learning approach has great power and could be used to extend and test our current theories of agricultural ecosystem dynamics and function. In particular, we believe it could be used to support the development of a wider theory of ecosystem responses to environmental change

An objective spinal motion imaging assessment (OSMIA): reliability, accuracy and exposure data

BACKGROUND: Minimally-invasive measurement of continuous inter-vertebral motion in clinical settings is difficult to achieve. This paper describes the reliability, validity and radiation exposure levels in a new Objective Spinal Motion Imaging Assessment system (OSMIA) based on low-dose fluoroscopy and image processing. METHODS: Fluoroscopic sequences in coronal and sagittal planes were obtained from 2 calibration models using dry lumbar vertebrae, plus the lumbar spines of 30 asymptomatic volunteers. Calibration model 1 (mobile) was screened upright, in 7 inter-vertebral positions. The volunteers and calibration model 2 (fixed) were screened on a motorised table comprising 2 horizontal sections, one of which moved through 80 degrees. Model 2 was screened during motion 5 times and the L2-S1 levels of the volunteers twice. Images were digitised at 5fps. Inter-vertebral motion from model 1 was compared to its pre-settings to investigate accuracy. For volunteers and model 2, the first digitised image in each sequence was marked with templates. Vertebrae were tracked throughout the motion using automated frame-to-frame registration. For each frame, vertebral angles were subtracted giving inter-vertebral motion graphs. Volunteer data were acquired twice on the same day and analysed by two blinded observers. The root-mean-square (RMS) differences between paired data were used as the measure of reliability. RESULTS: RMS difference between reference and computed inter-vertebral angles in model 1 was 0.32 degrees for side-bending and 0.52 degrees for flexion-extension. For model 2, X-ray positioning contributed more to the variance of range measurement than did automated registration. For volunteer image sequences, RMS inter-observer variation in intervertebral motion range in the coronal plane was 1.86 degreesand intra-subject biological variation was between 2.75 degrees and 2.91 degrees. RMS inter-observer variation in the sagittal plane was 1.94 degrees. Radiation dosages in each view were below the levels recommended for a plain film. CONCLUSION: OSMIA can measure inter-vertebral angular motion patterns in routine clinical settings if modern image intensifier systems are used. It requires skilful radiography to achieve optimal positioning and dose limitation. Reliability in individual subjects can be judged from the variance of their averaged inter-vertebral angles and by observing automated image registration

I read it on reddit: Exploring the role of online communities in the 2016 US elections news cycle

Reddit has developed into a significant platform for political discussion among Millennials. In this exploratory study, we examine subscription trends on three political sub-forums on Reddit during the 2016 US presidential elections: /The_Donald, /SandersForPresident, and /HillaryClinton. As a theoretical framework, we draw from work on online communities’ group identity and cohesion. Concretely, we investigate how subscription dynamics relate to positive, negative and neutral news events occurring during the election cycle. We classify news events using a sentiment analysis of event-related news headlines. We observe that users who supported Sanders displayed no consolidation of support for Clinton after she won the Democratic Party’s presidential nomination. Secondly, we show that negative news events affected Sanders and Clintons subscription trends negatively, while showing no effect for Donald Trump. This gives empirical credence to Trump’s controversial claim that he could “stand in the middle of 5th Avenue and shoot somebody and not lose any voters”. We offer a number of explanations for the observed phenomena: the nature of the content of the three subreddits, their cultural dynamics, and changing dynamics of partisanship. We posit that the ‘death of expertise’ expresses itself on Reddit as a switch in persuasion tactics from a policy-based to an emotions-based approach, and that group members’ agreement on policy proved a weak marker for online communities’ group identity and cohesion. We also claim that strong partisanship coupled with weak party affiliation among Millennials contributed to the low levels of Democratic support consolidation after Clinton won the nomination

Refined clothespin relocation test and assessment of motion

Background: Advancements in upper limb prosthesis design have focused on providing increased degrees of freedom for the end effector through multiple articulations of a prosthetic hand, wrist and elbow. Measuring improvement in patient function with these devices requires development of appropriate assessment tools. Objectives: This study presents a refined clothespin relocation test for measuring performance and assessing compensatory motion between able-bodied subjects and subjects with upper limb impairments. Study Design: Comparative analysis Methods: Trunk and head motions of 13 able-bodied subjects who performed the refined clothespin relocation test were compared to the motion of a transradial prosthesis user with a single degree of freedom hand. Results: There were observable differences between the prosthesis user and the able-bodied group. The assessment used provided a clear indication of the differences in motion through analysis of compensatory motion. Conclusion: The refined clothespin relocation test provides additional benefits over the standard clothespin assessment and makes identification of compensatory motions easily identifiable to the researcher. While this paper establishes the method for the new assessment, further validation will need to be performed with more users

Neural Correlate of Filtering of Irrelevant Information from Visual Working Memory

In a dynamic environment stimulus task relevancy could be altered through time and it is not always possible to dissociate relevant and irrelevant objects from the very first moment they come to our sight. In such conditions, subjects need to retain maximum possible information in their WM until it is clear which items should be eliminated from WM to free attention and memory resources. Here, we examined the neural basis of irrelevant information filtering from WM by recording human ERP during a visual change detection task in which the stimulus irrelevancy was revealed in a later stage of the task forcing the subjects to keep all of the information in WM until test object set was presented. Assessing subjects' behaviour we found that subjects' RT was highly correlated with the number of irrelevant objects and not the relevant one, pointing to the notion that filtering, and not selection, process was used to handle the distracting effect of irrelevant objects. In addition we found that frontal N150 and parietal N200 peak latencies increased systematically as the amount of irrelevancy load increased. Interestingly, the peak latency of parietal N200, and not frontal N150, better correlated with subjects' RT. The difference between frontal N150 and parietal N200 peak latencies varied with the amount of irrelevancy load suggesting that functional connectivity between modules underlying fronto-parietal potentials vary concomitant with the irrelevancy load. These findings suggest the existence of two neural modules, responsible for irrelevant objects elimination, whose activity latency and functional connectivity depend on the number of irrelevant object

Automating Genomic Data Mining via a Sequence-based Matrix Format and Associative Rule Set

There is an enormous amount of information encoded in each genome – enough to create living, responsive and adaptive organisms. Raw sequence data alone is not enough to understand function, mechanisms or interactions. Changes in a single base pair can lead to disease, such as sickle-cell anemia, while some large megabase deletions have no apparent phenotypic effect. Genomic features are varied in their data types and annotation of these features is spread across multiple databases. Herein, we develop a method to automate exploration of genomes by iteratively exploring sequence data for correlations and building upon them. First, to integrate and compare different annotation sources, a sequence matrix (SM) is developed to contain position-dependant information. Second, a classification tree is developed for matrix row types, specifying how each data type is to be treated with respect to other data types for analysis purposes. Third, correlative analyses are developed to analyze features of each matrix row in terms of the other rows, guided by the classification tree as to which analyses are appropriate. A prototype was developed and successful in detecting coinciding genomic features among genes, exons, repetitive elements and CpG islands

Work-related physical and psychosocial risk factors for sick leave in patients with neck or upper extremity complaints

Objectives: To study work-related physical and psychosocial risk factors for sick leave among patients who have visited their general practitioner for neck or upper extremity complaints. Methods: Three hundred and forty two patients with neck or upper extremity complaints completed self-report questionnaires at baseline and after 3 months. Cox regression models were used to investigate the association between work-related risk factors and sick leave (i.e., lost days from work due to neck or upper extremity complaints in 3 months). Effect modification by sick leave at baseline, sex, worrying and musculoskeletal co-morbidity was evaluated by adding product terms to the regression models. Results: In the subgroup of patients who scored high on the pain copying scale "worrying" the hazard ratio of sick leave was 1.32 (95% CI 1.07-1.62) per 10% increase in heavy physical work. The subgroup of patients who were sitting for long periods of time had a reduced risk of sick leave as compared to patients who did not spend a lot of time sitting, again only in patients who scored high on the pain coping scale "worrying" (adjusted HR = 0.17, 95%-CI 0.04-0.72). Other work-related risk factors were not significantly related to sick leave. Conclusions: Heavy physical work increased the risk of sick leave and prolonged sitting reduced the risk of sick leave in a subgroup of patients who worried much about their pain. Additional large longitudinal studies of sufficiently large size among employees with neck or upper extremity complaints are needed to confirm our results. © Springer-Verlag 2007

Prediction of leak flow rate in plastic water distribution pipes using vibro-acoustic measurements

Leakage from water distribution systems is a worldwide issue with consequences including loss of revenue, health and environmental concerns. Leaks have typically been found through leak noise correlation by placing sensors either side of the leak and recording and analysing its vibro-acoustic emission. While this method is widely used to identify the location of the leak, the sensors also record data that could be related to the leak’s flow rate, yet no reliable method exists to predict leak flow rate in water distribution pipes using vibro-acoustic emission. The aim of this research is to predict leak flow rate in medium-density polyethylene pipe using vibro-acoustic emission signals. A novel experimental methodology is presented whereby circular holes of four sizes are tested at several leak flow rates. Following the derivation of a number of features, least squares support vector machines are used in order to predict leak flow rate. The results show a strong correlation highlighting the potential of this technique as a rapid and practical tool for water companies to assess and prioritise leak repair