The development of wearable technology, which enables motion tracking analysis for human movement outside the laboratory, can improve awareness of personal health and performance. This study used a wearable smart sock prototype to track foot–ankle kinematics during gait movement. Multivariable linear regression and two deep learning models, including long short-term memory (LSTM) and convolutional neural networks, were trained to estimate the joint angles in sagittal and frontal planes measured by an optical motion capture system. Participant-specific models were established for ten healthy subjects walking on a treadmill. The prototype was tested at various walking speeds to assess its ability to track movements for multiple speeds and generalize models for estimating joint angles in sagittal and frontal planes. LSTM outperformed other models with lower mean absolute error (MAE), lower root mean squared error, and higher R-squared values. The average MAE score was less than 1.138° and 0.939° in sagittal and frontal planes, respectively, when training models for each speed and 2.15° and 1.14° when trained and evaluated for all speeds. These results indicate wearable smart socks to generalize foot–ankle kinematics over various walking speeds with relatively low error and could consequently be used to measure gait parameters without the need for a lab-constricted motion capture system.

Monoclonal antibody therapeutics to treat coronavirus disease (COVID-19) have been authorized by the US Food and Drug Administration under Emergency Use Authorization (EUA). Many barriers exist when deploying a novel therapeutic during an ongoing pandemic, and it is critical to assess the needs of incorporating monoclonal antibody infusions into pandemic response activities. We examined the monoclonal antibody infusion site process during the COVID-19 pandemic and conducted a descriptive analysis using data from 3 sites at medical centers in the United States supported by the National Disaster Medical System. Monoclonal antibody implementation success factors included engagement with local medical providers, therapy batch preparation, placing the infusion center in proximity to emergency services, and creating procedures resilient to EUA changes. Infusion process challenges included confirming patient severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positivity, strained staff, scheduling, and pharmacy coordination. Infusion sites are effective when integrated into pre-existing pandemic response ecosystems and can be implemented with limited staff and physical resources.

Political event data are widely used in studies of political violence. Recent years have seen notable advances in the automated coding of political event data from international news sources. Yet, the validity of machine-coded event data remains disputed, especially in the context of event geolocation. We analyze the frequencies of human- and machine-geocoded event data agreement in relation to an independent (ground truth) source. The events are human rights violations in Colombia. We perform our evaluation for a key, 8-year period of the Colombian conflict and in three 2-year subperiods as well as for a selected set of (non)journalistically remote municipalities. As a complement to this analysis, we estimate spatial probit models based on the three datasets. These models assume Gaussian Markov Random Field error processes; they are constructed using a stochastic partial differential equation and estimated with integrated nested Laplacian approximation. The estimated models tell us whether the three datasets produce comparable predictions, underreport events in relation to the same covariates, and have similar patterns of prediction error. Together the two analyses show that, for this subnational conflict, the machine- and human-geocoded datasets are comparable in terms of external validity but, according to the geostatistical models, produce prediction errors that differ in important respects.

Implementation of clinically useful research discoveries in the academic environment is challenged by limited funding for early phase proof-of-concept studies and inadequate expertise in product development and commercialization. To address these limitations, the National Institutes of Health (NIH) established the National Centers for Accelerated Innovations (NCAI) program in 2013. Three centers competed successfully for awards through this mechanism. Here, we present the experience of one such center, the Boston Biomedical Innovation Center (B-BIC), and demonstrate its remarkable success at the translation of innovations to clinical application and commercialization, as well as skills development and education.

The diurnal feeding patterns of dairy cows affects the 24 h robot utilisation of pasture-based automatic milking systems (AMS). A decline in robot utilisation between 2400 and 0600 h currently occurs in pasture-based AMS, as cow feeding activity is greatly reduced during this time. Here, we investigate the effect of a temporal variation in feed quality and quantity on cow feeding behaviour between 2400 and 0600 h as a potential tool to increase voluntary cow trafficking in an AMS at night. The day was allocated into four equal feeding periods (0600 to 1200, 1200 to 1800, 1800 to 2400 and 2400 to 0600 h). Lucerne hay cubes (CP = 19.1%, water soluble carbohydrate = 3.8%) and oat, ryegrass and clover hay cubes with 20% molasses (CP = 11.8%, water soluble carbohydrate = 10.7%) were offered as the ‘standard’ and ‘preferred’ (preference determined previously) feed types, respectively. The four treatments were (1) standard feed offered ad libitum (AL) throughout 24 h; (2) as per AL, with preferred feed replacing standard feed between 2400 and 0600 h (AL + P); (3) standard feed offered at a restricted rate, with quantity varying between each feeding period (20:10:30:60%, respectively) as a proportion of the (previously) measured daily ad libitum intake (VA); (4) as per VA, with preferred feed replacing standard feed between 2400 and 0600 h (VA + P). Eight non-lactating dairy cows were used in a 4 × 4 Latin square design. During each experimental period, treatment cows were fed for 7 days, including 3 days habituation and 4 days data collection. Total daily intake was approximately 8% greater (P < 0.001) for the AL and AL + P treatments (23.1 and 22.9 kg DM/cow) as compared with the VA and VA + P treatments (21.6 and 20.9 kg DM/cow). The AL + P and VA treatments had 21% and 90% greater (P < 0.001) dry matter intake (DMI) between 2400 and 0600 h, respectively, compared with the AL treatment. In contrast, the VA + P treatment had similar DMI to the VA treatment. Our experiment shows ability to increase cow feeding activity at night by varying feed type and quantity, though it is possible that a penalty to total DMI may occur using VA. Further research is required to determine if the implementation of variable feed allocation on pasture-based AMS farms is likely to improve milking robot utilisation by increasing cow feeding activity at night.

Achieving a consistent level of robot utilisation throughout 24 h maximises automatic milking system (AMS) utilisation. However, levels of robot utilisation in the early morning hours are typically low, caused by the diurnal feeding behaviour of cows, limiting the inherent capacity and total production of pasture-based AMS. Our objective was to determine robot utilisation throughout 24 h by dairy cows, based on milking frequency (MF; milking events per animal per day) in a pasture-based AMS. Milking data were collected from January and February 2013 across 56 days, from a single herd of 186 animals (Bos taurus) utilising three Lely A3 robotic milking units, located in Tasmania, Australia. The dairy herd was categorised into three equal sized groups (n=62 per group) according to the cow’s mean daily MF over the duration of the study. Robot utilisation was characterised by an interaction (P< 0.001) between the three MF groups and time of day, with peak milking time for high MF cows within one h of a fresh pasture allocation becoming available, followed by the medium MF and low MF cows 2 and 4 h later, respectively. Cows in the high MF group also presented for milking between 2400 and 0600 h more frequently (77% of nights), compared to the medium MF group (57%) and low MF group (50%). This study has shown the formation of three distinct groups of cows within a herd, based on their MF levels. Further work is required to determine if this finding is replicated across other pasture-based AMS farms.

Textual data are plagued by underreporting bias. For example, news sources often fail to report human rights violations. Cook et al. propose a multi-source estimator to gauge, and to account for, the underreporting of state repression events within human codings of news texts produced by the Agence France-Presse and Associated Press. We evaluate this estimator with Monte Carlo experiments, and then use it to compare the prevalence and seriousness of underreporting when comparable texts are machine coded and recorded in the World-Integrated Crisis Early Warning System dataset. We replicate Cook et al.’s investigation of human-coded state repression events with our machine-coded events, and validate both models against an external measure of human rights protections in Africa. We then use the Cook et al. estimator to gauge the seriousness and prevalence of underreporting in machine and human-coded event data on human rights violations in Colombia. We find in both applications that machine-coded data are as valid as human-coded data.

Background: Our view is that sleep disturbance may be a contributory causal factor in the development and maintenance of psychotic experiences. A recent series of randomized controlled intervention studies has shown that cognitive-behavioural approaches can improve sleep in people with psychotic experiences. However, the effects of psychological intervention for improving sleep have not been evaluated in young people at ultra-high risk of psychosis. Improving sleep might prevent later transition to a mental health disorder. Aims: To assess the feasibility and acceptability of an intervention targeting sleep disturbance in young people at ultra-high risk of psychosis. Method: Patients were sought from NHS mental health services. Twelve young people at ultra-high risk of psychosis with sleep problems were offered an eight-session adapted CBT intervention for sleep problems. The core treatment techniques were stimulus control, circadian realignment, and regulating day-time activity. Participants were assessed before and after treatment and at a one month follow-up. Results: All eligible patients referred to the study agreed to take part. Eleven patients completed the intervention, and one patient withdrew after two sessions. Of those who completed treatment, the attendance rate was 89% and an average of 7.6 sessions (SD = 0.5) were attended. There were large effect size improvements in sleep. Post-treatment, six patients fell below the recommended cut-off for clinical insomnia. There were also improvements in negative affect and psychotic experiences. Conclusion: This uncontrolled feasibility study indicates that treating sleep problems in young people at ultra-high of psychosis is feasible, acceptable, and may be associated with clinical benefits.

Scanning electron micrographs of spray droplet residual on wheat, sunflower, and kochia indicated that salts and various adjuvants applied with glyphosate affected deposit crystal content, thickness, and contact with the leaf surface. Spray deposits of glyphosate applied with diammonium sulfate contained distinct crystals, which related to enhanced toxicity of glyphosate applied alone, or to overcoming antagonism of glyphosate toxicity by calcium chloride, when applied to wheat. In general, glyphosate applied with antagonistic calcium chloride salt formed spray deposits that were amorphous, thick, and without crystals. Glyphosate sprayed with nonantagonistic diammonium sulfate and ammonium chloride salts had deposits with crystals and an evenly spread residue beneath the crystals. The various micrographs indicate that the antagonism of glyphosate phytotoxicity by salts may be in part from physical entrapment of glyphosate in the spray deposit. Glyphosate spray droplet residue contact with wheat, sunflower, and kochia surfaces related to observed differences in glyphosate toxicity to these species.

Automatic milking systems (AMS), one of the earliest precision livestock farming developments, have revolutionized dairy farming around the world. While robots control the milking process, there have also been numerous changes to how the whole farm system is managed. Milking is no longer performed in defined sessions; rather, the cow can now choose when to be milked in AMS, allowing milking to be distributed throughout a 24 h period. Despite this ability, there has been little attention given to milking robot utilization across 24 h. In order to formulate relevant research questions and improve farm AMS management there is a need to determine the current knowledge gaps regarding the distribution of robot utilization. Feed, animal and management factors and their interplay on levels of milking robot utilization across 24 h for both indoor and pasture-based systems are here reviewed. The impact of the timing, type and quantity of feed offered and their interaction with the distance of feed from the parlour; herd social dynamics, climate and various other management factors on robot utilization through 24 h are provided. This novel review draws together both the opportunities and challenges that exist for farm management to use these factors to improved system efficiency and those that exist for further research.

The purpose of this study was to analyze the reliability and validity evidence of scores on the Spanish version of EQ-i: YV-S in Spanish adolescents. The total sample was comprised of 508 participants from Grades 7 to 12, 241 males (47.4%) and 267 females (52.6%), each of whom completed the questionnaires on two separate occasions. Three [intrapersonal (α = .83, CR = .86, and McDonald Omega = .86), stress management (α = .83, CR = .86, and McDonald Omega = .85) and adaptability (α = .82, CR = .85, and McDonald Omega = .85)] of the four scales had acceptable internal consistency. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were used with FACTOR and EQS version 6.1 software to examine validity evidence based on internal structure drawn from the scores on the EQ-i: YV-S, supporting the multidimensionality of the questionnaire. Three models were tested; the best fit to the data was the hierarchical model (S-Bχ2/ df = 2.11, CFI = .93 and RMSEA = .047), which hypothesized that the four specific factors (interpersonal, intrapersonal, stress management, and adaptability) were explained with a second-order factor, Emotional-Social-Intelligence (ESI). Finally, significant positive correlations were found between general self-concept and EQ-i: YV-S [interpersonal (r = .153, p < .001), intrapersonal (r = .235, p < .001), stress management (r = .145, p < .001), adaptability (r = .311, p < .001) and ESI (r = .360, p < .001)]; ESI showed significant direct power prediction of the general self-concept (.52) as demonstrated through structural equation modeling.

Cointegration was introduced to our discipline by Renée Smith and Charles Ostrom Jr. and by Robert Durr more than two decades ago at political methodology meetings in Washington University�St. Louis and Florida State University. Their articles, along with comments by Neal Beck and John T. Williams, were published in a symposium like this one in the fourth volume of Political Analysis. Keele, Lin, and Webb (2016; hereafter KLW) and Grant and Lebo (2016; hereafter GL) show how, in the years that followed, cointegration was further evaluated by political scientists, and the related idea of error correction subsequently was applied.

Have the last twenty-plus years witnessed significant progress in modeling nonstationary political time series? In some respects, the answer is yes. The present symposium represents progress in understanding equation balance, analyzing bounded variables, and decomposing short- and longterm causal effects. In these respects KLW's and GL's articles deserve wide dissemination. But KLW and GL leave important methodological issues unresolved. They do not address some critical methodological challenges. From a historical perspective, the present symposium shows that we have made relatively little progress in modeling nonstationary political time series.