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Background Apraxia and action disorganization syndrome (AADS) after stroke can disrupt activities of daily living (ADL). Occupational therapy has been effective in improving ADL performance, however, inclusion of multiple tasks means it is unclear which therapy elements contribute to improvement. We evaluated the efficacy of a task model approach to ADL rehabilitation, comparing training in making a cup of tea with a stepping training control condition. Methods Of the 29 stroke survivors with AADS who participated in this cross-over randomized controlled feasibility trial, 25 were included in analysis [44% females; mean(SD) age = 71.1(7.8) years; years post-stroke = 4.6(3.3)]. Participants attended five 1-hour weekly tea making training sessions in which progress was monitored and feedback given using a computer-based system which implemented a Markov Decision Process (MDP) task model. In a control condition, participants received five 1-hour weekly stepping sessions. Results Compared to stepping training, tea making training reduced errors across 4 different tea types. The time taken to make a cup of tea was reduced so the improvement in accuracy was not due to a speed-accuracy trade-off. No improvement linked to tea making training was evident in a complex tea preparation task (making two different cups of tea simultaneously), indicating a lack of generalisation in the training. Conclusions The clearly specified but flexible training protocol, together with information on the distribution of errors, provide pointers for further refinement of task model approaches to ADL rehabilitation. It is recommended that the approach be tested under errorless learning conditions with more impaired patients in future research. Trial registration Retrospectively registered at ClinicalTrials.gov on 5th August 2019 [NCT04044911] https://clinicaltrials.gov/ct2/show/NCT04044911?term=Cogwatch&rank=1

To drive progress in the field of data science, we propose 10 challenge areas for the research community to pursue. Since data science is broad, with methods drawing from computer science, statistics, and other disciplines, and with applications appearing in all sectors, these challenge areas speak to the breadth of issues spanning science, technology, and society. We preface our enumeration with meta-questions about whether data science is a discipline. We then describe each of the 10 challenge areas. The goal of this article is to start a discussion on what could constitute a basis for a research agenda in data science, while recognizing that the field of data science is still evolving.

Experimental evolution allows the observation of change over time as laboratory populations evolve in response to novel, controlled environments. Microbial evolution experiments take advantage of cryopreservation to archive experimental populations in glycerol media, creating a frozen, living “fossil” record. Prior research with Escherichia coli has shown that cryopreservation conditions can affect cell viability and that allele frequencies across the genome can change in response to a freeze–thaw event. We expand on these observations by characterizing fitness and genomic consequences of multiple freeze−thaw cycles in diploid yeast populations. Our study system is a highly recombinant Saccharomyces cerevisiae population (SGRP-4X) that harbors standing genetic variation that cryopreservation may threaten. We also investigate the four parental isogenic strains crossed to create the SGRP-4X. We measure cell viability over five consecutive freeze−thaw cycles; whereas we find that viability increases over time in the evolved recombinant populations, we observe no such viability improvements in the parental strains. We also collect genome-wide sequence data from experimental populations initially, after one freeze−thaw, and after five freeze−thaw cycles. In the recombinant evolved populations, we find a region of significant allele frequency change on chromosome 15 containing the ALR1 gene. In the parental strains, we find little evidence for new mutations. We conclude that cryopreserving yeast populations with standing genetic variation may have both phenotypic and genomic consequences, though the same cryopreservation practices may have only small impacts on populations with little or no initial variation.

Oak woodlands are dependent on frequent fire to maintain the low stem density and diverse understories that typify these ecosystems. Without this recurrent disturbance, fire-sensitive conifer competitors encroach on oaks, reducing their vigor, and diminishing habitat quality. In fire-excluded oak woodlands, stand-replacing wildfire can trigger shifts in canopy dominance from seed-generated conifers to oaks, which are capable of vigorous sprouting following topkill. We examined the occurrence and sprouting dynamics of California black oak (Quercus kelloggii Newb.) following repeated wildfires in the Lassen National Forest, California. We found that following reburn, changes in oak relative stand dominance, as well as oak sprout basal area and height, were each positively related to fire severity (P

The primary lipid source in feeds used at Pacific salmon ( Oncorhynchus spp.) and steelhead (anadromous O. mykiss ) conservation hatcheries in California and the Pacific Northwest United States is marine fish oil which is characterized by high levels of long-chain polyunsaturated fatty acids (LC-PUFAs). As these fatty acids are prone to peroxidation, the high marine fish oil content of salmonid feeds is likely a contributing factor in a disease called steatitis which is an ongoing issue at some hatcheries. Dermal lesions associated with steatitis have been observed when affected fish were transferred from enclosed hatchery buildings to outdoor rearing units exposed to sunlight. Therefore, a two-factor study examining possible interactive effects of dietary lipid source and ultraviolet (UV) radiation on growth responses, histology and tissue fatty acid profiles of juvenile steelhead was conducted. For 10 weeks, fish were fed diets containing canola oil (CO), fish oil (FO) or oxidized fish oil (OFO) while exposed to fluorescent or UV light. Each treatment was randomly assigned to triplicate groups of 50 fish initially weighing 1.6 g/fish. The main effects of lipid and light source on growth responses and survival were not statistically significant, but percent weight gain was significantly affected by interaction of the main effects. Whole body histopathology revealed significantly higher (more severe) steatitis scores in fish fed FO or OFO compared with fish fed CO and in fish exposed to UV light compared with fish exposed to fluorescent light. Whole body lipid concentration and tissue fatty acid profile were significantly affected by lipid and light source but not their interaction. These results suggest reducing dietary LC-PUFAs and minimizing UV light exposure may reduce steatitis in steelhead.

Measuring post-fire effects at landscape scales is critical to an ecological understanding of wildfire effects. Predominantly this is accomplished with either multi-spectral remote sensing data or through ground-based field sampling plots. While these methods are important, field data is usually limited to opportunistic post-fire observations, and spectral data often lacks validation with specific variables of change. Additional uncertainty remains regarding how best to account for environmental variables influencing fire effects (e.g., weather) for which observational data cannot easily be acquired, and whether pre-fire agents of change such as bark beetle and timber harvest impact model accuracy. This study quantifies wildfire effects by correlating changes in forest structure derived from multi-temporal Light Detection and Ranging (LiDAR) acquisitions to multi-temporal spectral changes captured by the Landsat Thematic Mapper and Operational Land Imager for the 2012 Pole Creek Fire in central Oregon. Spatial regression modeling was assessed as a methodology to account for spatial autocorrelation, and model consistency was quantified across areas impacted by pre-fire mountain pine beetle and timber harvest. The strongest relationship (pseudo-r2 = 0.86, p

On March 26, 2018, the Data Science Institute at Columbia University hosted the inaugural Data Science Leadership Summit. It drew together a cross-section of academic researchers and administrators to discuss how university practices could best adapt to the emerging field of data science. The summit was co-funded by the National Science Foundation, the Gordon and Betty Moore Foundation, and the Alfred P. Sloan Foundation. This interview with Jeannette Wing, the Avanessians Director of the Data Science Institute at Columbia University and the author of The Data Life Cycle , discusses the motivation of the summit and summarizes its key findings. The interview was conducted by another experienced leader in data science, David Banks, the Director of the Statistical and Applied Mathematical Sciences Institute (SAMSI). Keywords: data science institute, education units, inter-disciplinary, multi-disciplinary, university administration

To put data science in context, we present phases of the data life cycle, from data generation to data interpretation. These phases transform raw bits into value for the end user. Data science is thus much more than data analysis, e.g., using techniques from machine learning and statistics; extracting this value takes a lot of work, before and after data analysis. Moreover, data privacy and data ethics need to be considered at each phase of the life cycle.Keywordsanalysis, collection, data life cycle, ethics, generation, interpretation, management, privacy, storage, story-telling, visualization

Accurate estimates of growth and structural changes are key for forest management tasks such as determination of optimal rotation times, optimal rotation times, site indices and for identifying areas experiencing difficulties to regenerate. Estimation of structural changes, especially for biomass, is also key to quantify greenhouse gas (GHG) emissions/sequestration. We compared two different modeling strategies to estimate changes in V, BA and B, at three different spatial aggregation levels using auxiliary information from two light detection and ranging (LiDAR) flights. The study area is Blacks Mountains Experimental Forest, a ponderosa pine dominated forest in Northern California for which two LiDAR acquisitions separated by six years were available. Analyzed strategies consisted of (1) directly modeling the observed changes as a function of the LiDAR auxiliary information ( δ -modeling method) and (2) modeling V, BA and B at two different points in time, including a term to account for the temporal correlation, and then computing the changes as the difference between the predicted values of V, BA and B for time two and time one. We analyzed predictions and measures of uncertainty at three different level of aggregation (i.e., pixels, stands or compartments and the entire study area). Results showed that changes were very weakly correlated with the LiDAR auxiliary information. Both modeling alternatives provided similar results with a better performance of the δ -modeling for the entire study area; however, this method also showed some inconsistencies and seemed to be very prone to extrapolation problems. The y -modeling method, which seems to be less prone to extrapolation problems, allows obtaining more outputs that are flexible and can outperform the δ -modeling method at the stand level. The weak correlation between changes in structural attributes and LiDAR auxiliary information indicates that pixel-level maps have very large uncertainties and estimation of change clearly requires some degree of spatial aggregation; additionally, in similar environments, it might be necessary to increase the time lapse between LiDAR acquisitions to obtain reliable estimates of change.

The explosion of interest in KDD and other Data Science/Machine Learning/AI conferences is just one of the many signs that these technologies are no longer confined to the realms of academia and a hand-full of tech companies. As our daily lives seamlessly integrate more and more data-driven applications, people's excitement is tempered by worry about the technologies' potential to disrupt their existence. Having worked for almost 30 years to design and develop these technologies, the KDD community now should examine and debate the impact of Machine Learning & AI on the broader world. Beyond the hype, where do we stand with respect to the dangers? What role can our community play to alleviate concerns around AI taking jobs, or taking over? How can the value derived from data be distributed fairly? Are concerns about inequity well-founded or rather largely problems of perception? What can be done to bring data hunger and data sharing concerns to a level of equilibrium? How do we prepare people to interact with intelligent systems at scale? Can we unleash the incredible responsiveness of the KDD community toward longer-term more impactful projects across sectors that are essential for social good, such as Health, Environmental Sustainability, and Public Welfare.

I use the tagline "Data for Good" to state paronomastically how we as a community should be promoting data science, especially in training future generations of data scientists. First, we should use data science for the good of humanity and society. Data science should be used to better people's lives. Data science should be used to improve relationships among people, organizations, and institutions. Data science, in collaboration with other disciplines, should be used to help tackle societal grand challenges such as climate change, education, energy, environment, healthcare, inequality, and social justice. Second, we should use data in a good manner. The acronym FATES suggests what "good" means. Fairness means that the models we build are used to make unbiased decisions or predictions. Accountability means to determine and assign responsibility-to someone or to something-for a judgment made by a machine. Transparency means being open and clear to the end user about how an outcome, e.g., a classification, a decision, or a prediction, is made. Ethics for data science means paying attention to both the ethical and privacy-preserving collection and use of data as well as the ethical decisions that the automated systems we build will make. Safety and security (yes, two words for one "S") means ensuring that the systems we build are safe (do no harm) and secure (guard against malicious behavior).

Asymmetry in weight-bearing is a common feature in poststroke hemiparesis and is related to temporal asymmetry during walking. The aim of this study was to investigate the effect of an auditory cue for stepping in place on measures of temporal and weight-bearing asymmetry. A total of 10 community-dwelling adults (6 males and 4 females) with chronic poststroke hemiparesis performed 5 un-cued stepping trials and 5 stepping trials cued by an auditory metronome cue. A Vicon system was used to collect full body kinematic trajectories. Two force platforms were used to measure ground reaction forces. Step, swing, and stance times were used to calculate temporal symmetry ratios. Weight-bearing was assessed using the vertical component of the ground reaction force and center of mass–center of pressure separation at mid-stance. Weight-bearing asymmetry was significantly reduced during stepping with an auditory cue. Asymmetry values for step, swing, and stance times were also significantly reduced with auditory cueing. These findings show that auditory cueing when stepping in place produces immediate reductions in measures of temporal asymmetry and dynamic weight-bearing asymmetry.

Governments and developers are pursuing offshore wind energy to address climate change, but multiple wind farms may cumulatively affect wildlife populations. Assessments of cumulative effects must first calculate the cumulative exposure of a wildlife population to a hazard and then estimate how the exposure will affect the population. Our research responds to the first need by developing a model designed to assess how different wind farm siting scenarios cumulatively expose wildlife. The model assesses cumulative exposure by identifying all locations where development could occur, placing wind farms within this suitability layer, and then overlaying wind engineering and biological data sets. The first model output is a graphical representation of how offshore wind farm siting decisions affect wildlife cumulative exposure. The second output is an index that ranks which offshore wind farm siting decisions will have the greatest influence on wildlife cumulative exposure. Together these outputs provide stakeholders with valuable information that could be used to guide siting and management decisions.

Background: Variation in physiological deficits underlying upper limb paresis after stroke could influence how people recover and to which physical therapy they best respond. Objectives: To determine whether functional strength training (FST) improves upper limb recovery more than movement performance therapy (MPT). To identify: (a) neural correlates of response and (b) whether pre-intervention neural characteristics predict response. Design: Explanatory investigations within a randomised, controlled, observer-blind, and multicentre trial. Randomisation was computer-generated and concealed by an independent facility until baseline measures were completed. Primary time point was outcome, after the 6-week intervention phase. Follow-up was at 6 months after stroke. Participants: With some voluntary muscle contraction in the paretic upper limb, not full dexterity, when recruited up to 60 days after an anterior cerebral circulation territory stroke. Interventions: Conventional physical therapy (CPT) plus either MPT or FST for up to 90 min-a-day, 5 days-a-week for 6 weeks. FST was "hands-off" progressive resistive exercise cemented into functional task training. MPT was "hands-on" sensory/facilitation techniques for smooth and accurate movement. Outcomes: The primary efficacy measure was the Action Research Arm Test (ARAT). Neural measures: fractional anisotropy (FA) corpus callosum midline; asymmetry of corticospinal tracts FA; and resting motor threshold (RMT) of motor-evoked potentials. Analysis: Covariance models tested ARAT change from baseline. At outcome: correlation coefficients assessed relationship between change in ARAT and neural measures; an interaction term assessed whether baseline neural characteristics predicted response. Results: 288 Participants had: mean age of 72.2 (SD 12.5) years and mean ARAT 25.5 (18.2). For 240 participants with ARAT at baseline and outcome the mean change was 9.70 (11.72) for FST + CPT and 7.90 (9.18) for MPT + CPT, which did not differ statistically (p = 0.298). Correlations between ARAT change scores and baseline neural values were between 0.199, p = 0.320 for MPT + CPT RMT (n = 27) and -0.147, p = 0.385 for asymmetry of corticospinal tracts FA (n = 37). Interaction effects between neural values and ARAT change between baseline and outcome were not statistically significant. Conclusions: There was no significant difference in upper limb improvement between FST and MPT. Baseline neural measures did not correlate with upper limb recovery or predict therapy response. Trial registration: Current Controlled Trials: ISRCT 19090862, http://www.controlled-trials.com.

Accurate timing of movement in the hundreds of milliseconds range is a hallmark of human activities such as music and dance. Its study requires accurate measurement of the times of events (often called responses) based on the movement or acoustic record. This chapter provides a comprehensive over - view of methods developed to capture, process, analyse, and model individual and group timing [...] This chapter is structured in five main sections, as follows. We start with a review of data capture methods, working, in turn, through a low cost system to research simple tapping, complex movements, use of video, inertial measurement units, and dedicated sensorimotor synchronisation software. This is followed by a section on music performance, which includes topics on the selection of music materials, sound recording, and system latency. The identification of events in the data stream can be challenging and this topic is treated in the next section, first for movement then for music. Finally, we cover methods of analysis, including alignment of the channels, computation of between channel asynchrony errors and modelling of the data set.

The way an object is released by the passer to a partner is fundamental for the success of the handover and for the experienced fluency and quality of the interaction. Nonetheless, although its apparent simplicity, object handover involves a complex combination of predictive and reactive control mechanisms that were not fully investigated so far. Here, we show that passers use visual-feedback based anticipatory control to trigger the beginning of the release, to launch the appropriate motor program, and adapt such predictions to different speeds of the receiver's reaching out movements. In particular, the passer starts releasing the object in synchrony with the collision with the receiver, regardless of the receiver's speed, but the passer's speed of grip force release is correlated with receiver speed. When visual feedback is removed, the beginning of the passer's release is delayed proportionally with the receiver's reaching out speed; however, the correlation between the passer's peak rate of change of grip force is maintained. In a second study with 11 participants receiving an object from a robotic hand programmed to release following stereotypical biomimetic profiles, we found that handovers are experienced as more fluent when they exhibit more reactive release behaviours, shorter release durations, and shorter handover durations. The outcomes from the two studies contribute understanding of the roles of sensory input in the strategy that empower humans to perform smooth and safe handovers, and they suggest methods for programming controllers that would enable artificial hands to hand over objects with humans in an easy, natural and efficient way.

Eating disorders are becoming increasingly prevalent and can be associated with negative outcomes and chronic mental health disorders. The two most-common eating disorders worldwide are anorexia nervosa, and bulimia nervosa. Given the increasing prevalence of these two disorders, it is crucial for GPs to be able to assess and then apply diagnostic criteria, arrange initial investigations and advocate for the correct management of patients who present with eating disorders. This article aims to outline a best practice approach.

The ability to estimate and monitor standing dead trees (snags) has been difficult due to their irregular and sparse distribution, often requiring intensive sampling methods to obtain statistically significant estimates. This study presents a new method for estimating and monitoring snags using neighborhood attribute filtered airborne discrete-return lidar data. The method first develops and then applies an automated filtering algorithm that utilizes three dimensional neighborhood lidar point-based intensity and density statistics to remove lidar points associated with live trees and retain lidar points associated with snags. A traditional airborne lidar individual-tree detection procedure is then applied to the snag-filtered lidar point cloud, resulting in stem map of identified snags with height estimates. The filtering algorithm was developed using training datasets comprised of four different forest types in wide range of stand conditions, and then applied to independent data to determine successful snag detection rates. Detection rates ranged from 43 to 100%, increasing as the size of snags increased. The overall detection rate for snags with DBH ≥ 25 cm was 56% (± 2.9%) with low commission error rates. The method provides the ability to estimate snag density and stem map a large proportion of snags across the landscape. The resulting information can be used to analyze the spatial distribution of snags, provide a better understanding of wildlife snag use dynamics, assess achievement of stocking standard requirements, and bring more clarity to snag stocking standards.

Light fingertip touch between partners swaying rhythmically side by side evokes interpersonal synchrony. In non-dancers and dancers swaying to a metronome, we examined the effects of frequency scaling and touch between the partners on both postural (ankle-hip) and inter-personal coordination. In both groups, touch did not interfere with the ankle-hip coordination. In non-dancers but not dancers, increasing frequency resulted in a loss of the ankle-hip coupling that was accompanied by a reduction of the touch mediated interpersonal synchrony. It is suggested that the effect of touch on interpersonal synchrony depends on the reliability of the haptic information sensed at the fingertip and assumes an in phase ankle-hip coupling. These findings have implications in clinical practice when using touch to help balance impaired individuals.

Measurements of the cross sections for charged current deep inelastic scattering in e+p collisions with a longitudinally polarised positron beam are presented. The measurements are based on a data sample with an integrated luminosity of 132 pb^-1 collected with the ZEUS detector at HERA at a centre-of-mass energy of 318 GeV. The total cross section is presented at positive and negative values of the longitudinal polarisation of the positron beams. The single-differential cross-sections ds/dQ^2, ds/dx and ds/dy are presented for Q^2 > 200 GeV^2. The reduced cross-section is presented in the kinematic range 200 < Q^2 < 60,000 GeV^2 and 0.006 < x < 0.562. The measurements agree well with the predictions of the Standard Model. The results are used to determine a lower limit on the mass of a hypothetical right-handed W boson., 33 pages, 11 figures, 7 tables

The first observation of (anti)deuterons in deep inelastic scattering at HERA has been made with the ZEUS detector at a centre-of-mass energy of 300--318 GeV using an integrated luminosity of 120 pb-1. The measurement was performed in the central rapidity region for transverse momentum per unit of mass in the range 0.3, Comment: 26 pages, 9 figures, 5 tables, submitted to Nucl. Phys. B

Single- and double-differential inclusive dijet cross sections in neutral current deep inelastic ep scattering have been measured with the ZEUS detector using an integrated luminosity of 374 pb^-1. The measurement was performed at large values of the photon virtuality, Q^2, between 125 and 20000 GeV^2. The jets were reconstructed with the k_T cluster algorithm in the Breit reference frame and selected by requiring their transverse energies in the Breit frame, E_T,B^jet, to be larger than 8 GeV. In addition, the invariant mass of the dijet system, M_jj, was required to be greater than 20 GeV. The cross sections are described by the predictions of next-to-leading-order QCD., 33 pages, 14 figures, 8 tables

Mercury is a potent contaminant that can disrupt an organism's behavior and physiology, ultimately affecting reproductive success. Over the last 100 years, environmental deposition of anthropogenic sourced mercury has increased globally, particularly in the U.S. Northeast region. Marine birds are considered effective bioindicators of ecosystem health, including persistent marine contaminants. Goodale et al. (2008) found that mercury exposure exceeded adverse effects levels in some marine bird species breeding across the Gulf of Maine. We re-examined mercury contamination in four species identified as effective bioindicators. Compared with the previous sampling effort, inshore-feeding species showed significant increases in mercury exposure, while one pelagic-feeding species remained stable. This suggests that a major shift may have occurred in methylmercury availability in inshore waters of the Gulf of Maine. Understanding environmental mercury trends in the Gulf of Maine, and its significance to marine birds and other taxa will require a dedicated, standardized, long-term monitoring scheme.

We explore the ways in which animate objects can be used to cue actions as part of coaching in Activities of Daily Living (ADL). In this case, changing the appearance or behavior of a physical object is intended to cue actions which are appropriate for a given context. The context is defined by the intention of the users, the state of the objects and the tasks for which these objects can be used. We present initial design prototypes and simple user trials which explore the impact of different cues on activity. It is shown that raising the handle of a jug, for example, not only cues the act of picking up the jug but also encourages use of the hand adjacent to the handle; that combinations of lights (on the objects) and auditory cues influence activity through reducing uncertainty; and that cueing can challenge pre-learned action sequences. We interpret these results in terms of the idea that the animate objects can be used to create affording situations, and discuss implications of this work to support relearning of ADL following brain damage or injury, such as might arise following a stroke.

Computer science has produced, at an astonishing and breathtaking pace, amazing technology that has transformed our lives with profound economic and societal impact. In the course of the past ten years, we have come to realize that computer science offers not just useful software and hardware artifacts, but also an intellectual framework for thinking, what I call “computational thinking”. Everyone can benefit from thinking computationally. My grand vision is that computational thinking will be a fundamental skill—just like reading, writing, and arithmetic—used by everyone by the middle of the 21st Century.

We explore the ways in which smart objects can be used to cue actions as part of coaching for Activities of Daily Living (ADL) following brain damage or injury, such as might arise following a stroke. In this case, appropriate actions are cued for a given context. The context is defined by the intention of the users, the state of the objects and the tasks for which these objects can be used. This requires objects to be instrumented so that they can recognize the actions that users perform. In order to provide appropriate cues, the objects also need to be able to display information to users, e.g., by changing their physical appearance or by providing auditory output. We discuss the ways in which information can be displayed to cue user action.