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This study is focused on investigating the impact of the COVID-19 pandemic on women’s well-being and the connection between their level of engagement in dance and movement activities and self-reported emotional well-being. A total of 480 individuals completed an anonymous and confidential survey via Qualtrics XM. The data was analyzed using primarily nonparametric tests like Pearson’s Chi-Square Test of Independence, Kruskal–Wallis H, or Mann–Whitney U. Data analysis revealed significant differences between the pre- and post-pandemic levels of activity in this sample. Individuals reported that they committed significantly less time to dance and movement activities as a result of the pandemic. Intergroup analyses revealed that women who identified as White engaged more frequently in dance and movement activities during the pandemic, but they also had significantly more anxiety and sadness compared with women of color. Women who identified as divorced, widowed, or separated reported more of an impact on their level of engagement in movement and dance activities than women who identified as married. Women who identified as being in their 20s and 30s were more hopeful than women 60 years and older. Overall, participants who reported spending more time engaged in dance and movement activities during the pandemic were significantly less sad and more hopeful than those who did not.

Advances in real‐time data processing have enabled robot teams to continuously adapt their sampling locations as they monitor their environments, enabling them to build highly predictive models of complex, dynamic environments. These models, in turn, enable robots to make better plans and more quickly adapt to changing environmental conditions. However, continuously identifying high information content regions and assigning robots to these new locations requires addressing the long‐standing multi‐robot task allocation problem. Existing allocation methods use task‐specific planning and/or control strategies that lack the flexibility needed to monitor spatiotemporal environments. In contrast, biological collectives robustly handle a wide range of environment conditions by relying on resource selection mechanisms that are beneficial to the survival of the population. Taking inspiration from biology, we address the challenge of flexibly monitoring spatiotemporal environments by using a team‐wide macroscopic ensemble approach which naturally mimics biological selection techniques. Existing macroscopic allocation strategies enable robots to switch between sampling regions, but unlike biological counterparts cannot respond to changing environmental conditions and perform poorly when team sizes are small. In this work, we introduce an online adaptive macroscopic allocation strategy that leverages environmental feedback to enable adaptation to changing environmental conditions. Our approach results in the synthesis of single‐agent task selection policies that achieves flexible assignment of robots for a range of dynamic conditions that perform well even when team sizes are small. We propose an online, environment feedback‐driven macroscopic ensemble approach to adapt robot team task allocation in spatiotemporal environments by controlling robot populations rather than assigning individual robots, all while maintaining robust team performance even for small teams. Our simulation and experimental results show better or comparable monitoring performance across a range of spatiotemporal environments compared to standard adaptive sampling baselines.

Mountain lions (Puma concolor) are often difficult to monitor because of their low capture probabilities, extensive movements, and large territories. Methods for estimating the abundance of this species are needed to assess population status, determine harvest levels, evaluate the impacts of management actions on populations, and derive conservation and management strategies. Traditional mark—recapture methods do not explicitly account for differences in individual capture probabilities due to the spatial distribution of individuals in relation to survey effort (or trap locations). However, recent advances in the analysis of capture—recapture data have produced methods estimating abundance and density of animals from spatially explicit capture—recapture data that account for heterogeneity in capture probabilities due to the spatial organization of individuals and traps. We adapt recently developed spatial capture—recapture models to estimate density and abundance of mountain lions in western Montana. Volunteers and state agency personnel collected mountain lion DNA samples in portions of the Blackfoot drainage (7,908 km 2 ) in west-central Montana using 2 methods: snow back-tracking mountain lion tracks to collect hair samples and biopsy darting treed mountain lions to obtain tissue samples. Overall, we recorded 72 individual capture events, including captures both with and without tissue sample collection and hair samples resulting in the identification of 50 individual mountain lions (30 females, 19 males, and 1 unknown sex individual). We estimated lion densities from 8 models containing effects of distance, sex, and survey effort on detection probability. Our population density estimates ranged from a minimum of 3.7 mountain lions/100 km 2 (95% CI 2.3—5.7) under the distance only model (including only an effect of distance on detection probability) to 6.7 (95% CI 3.1—11.0) under the full model (including effects of distance, sex, survey effort, and distance × sex on detection probability). These numbers translate to a total estimate of 293 mountain lions (95% CI 182—451) to 529 (95% CI 245—870) within the Blackfoot drainage. Results from the distance model are similar to previous estimates of 3.6 mountain lions/100 km 2 for the study area; however, results from all other models indicated greater numbers of mountain lions. Our results indicate that unstructured spatial sampling combined with spatial capture—recapture analysis can be an effective method for estimating large carnivore densities. Published 2012. This article is a U.S. Government work and is in the public domain in the USA.

Understanding swarm pattern formation is of great interest because it occurs naturally in many physical and biological systems, and has artificial applications in robotics. In both natural and engineered swarms, agent communication is typically local and sparse. This is because, over a limited sensing or communication range, the number of interactions an agent has is much smaller than the total possible number. A central question for self-organizing swarms interacting through sparse networks is whether or not collective motion states can emerge where all agents have coherent and stable dynamics. In this work we introduce the phenomenon of swarm shedding in which weakly-connected agents are ejected from stable milling patterns in self-propelled swarming networks with finite-range interactions. We show that swarm shedding can be localized around a few agents, or delocalized, and entail a simultaneous ejection of all agents in a network. Despite the complexity of milling motion in complex networks, we successfully build mean-field theory that accurately predicts both milling state dynamics and shedding transitions. The latter are described in terms of saddle-node bifurcations that depend on the range of communication, the inter-agent interaction strength, and the network topology.

Effective priming and activation of tumor-specific CD8+ cytotoxic T lymphocytes (CTLs) is crucial for realizing the potential of therapeutic cancer vaccination. This requires cytosolic antigens that feed into the MHC class I presentation pathway, which is not efficiently achieved with most current vaccination technologies. Photochemical internalization (PCI) provides an emerging technology to route endocytosed material to the cytosol of cells, based on light-induced disruption of endosomal membranes using a photosensitizing compound. Here, we investigated the potential of PCI as a novel, minimally invasive, and well-tolerated vaccination technology to induce priming of cancer-specific CTL responses to peptide antigens. We show that PCI effectively promotes delivery of peptide antigens to the cytosol of antigen-presenting cells (APCs) . This resulted in a 30-fold increase in MHC class I/peptide complex formation and surface presentation, and a subsequent 30- to 100-fold more efficient activation of antigen-specific CTLs compared to using the peptide alone. The effect was found to be highly dependent on the dose of the PCI treatment, where optimal doses promoted maturation of immature dendritic cells, thus also providing an adjuvant effect. The effect of PCI was confirmed by the successful induction of antigen-specific CTL responses to cancer antigens in C57BL/6 mice following peptide vaccination using PCI technology. We thus show new and strong evidence that PCI technology holds great potential as a novel strategy for improving the outcome of peptide vaccines aimed at triggering cancer-specific CD8+ CTL responses.

Short-term medical trips (STMTs) from high-resource countries frequently provide care in low and middle-income countries. Little existing literature objectively tracks the long-term outcomes of these interventions on the receiving populations over time to assess potential benefits and to ensure no harm is being done. The purpose of this study was to objectively analyze the outcomes of a biannual STMT to Santo Domingo, Dominican Republic on hypertension (HTN), diabetes mellitus type 2 (DM2), and cardiovascular disease (CVD) risk over a five-year period (2015-2019). Data from 1655 patients was extracted from the electronic medical record. In patients who received treatment and had more than one visit, a linear mixed model was used to analyze effects on systolic blood pressure (SBP) and hemoglobin A1C (HbA1C) values over time. In patients with high CVD risk based on a non-laboratory-based assessment, provider compliance with prescribing an aspirin and statin was calculated and tracked over time. In patients with HTN who received treatment, average SBP was 148.83 mmHg (SD = 23.96) at initial visit and demonstrated no change over time (Estimate: 0.68 mmHg/year increase, p = 0.46). HbA1C data was insufficient for analysis. Treatment for patients with high CVD risk with an aspirin and statin improved from 41.46% in 2015 to 70.51% in 2019. SBP in patients with HTN treated by this STMT demonstrated no significant change over time. Possible contributing factors included patient education, access and adherence to medications, and documentation of data. Provider compliance with appropriate prescribing was high for patients with HTN and DM2 and improved over time for patients with high CVD risk, serving as an indirect measure for potential long-term benefits on these populations. All STMTs should objectively track outcomes of their interventions to assess risks and benefits to the communities being served.

The blood coagulation response to decompression stress in humans has yet to be fully investigated. Here we utilised calibrated automated thrombogram (CAT) on samples from healthy volunteers exposed to decompression stress to investigate real-time thrombin generation. To induce decompression stress, fifteen apparently healthy males (age 20–50 yr) were exposed to two consecutive ascents to 25,000 ft for 60 min (1st ascent) and then 90 min (2nd ascent) while breathing 100% oxygen. Citrated blood samples were taken prior to exposure (T0), following the 2nd ascent (T8) and at 24 h (T24). Thrombin generation curves were obtained using ThrombinoscopeTM. Parameters determined were lag time (LAG), time to peak (TTP), peak thrombin (PEAK), endogenous thrombin potential (ETP) and velocity index (VEL). Of the 15 subjects, 12 had validated coagulation profiles. TTP and ETP showed no significant differences. However, there was a significant increase in VEL from T0 to T8 (p = 0.025) and from T8 to T24 (p = 0.043). A non-significant trend of an overall increase in PEAK was also observed from T0 to T8 (p = 0.069) and from T8 to T24 (p = 0.098). PEAK and VEL were found to be correlated. Taken together, these two parameters suggest an overall shift towards a more procoagulant profile following hypobaric stress.

Background Monitoring is a crucial part of trial conduct and ensures that participants’ data is fairly represented, and future healthcare information is enhanced. This project aims to improve trial monitoring by creating a trial monitoring plan (TMP) template with input from individuals experienced in monitoring clinical trials. Methods A review of monitoring plans received from UK Clinical Research Collaboration (UKCRC) registered clinical trials units (CTU)s created the basis for a preliminary TMP template and a Delphi survey. The TMP template was finalised after 2 rounds of a Delphi survey and a two-part consensus meeting including trialists with experience and expertise in monitoring clinical trials. Results We received 31 monitoring plans from UKCRC-registered CTUs and reviewed over 800 monitoring items for inclusion in a TMP template, selecting items based on guidelines such as Good Clinical Practice (GCP) and our monitoring experiences. For certain items, further expert input was required. A total of 66 items were chosen for a Delphi survey involving 47 participants from 25 UK CTUs and industry. After the first round, all 66 items were retained, and six additional items were suggested by Delphi participants. In the second round, 37 items reached consensus for inclusion in the TMP template. The Delphi process was followed by a consensus meeting with 9 participants from 9 UK CTUs and industry. Participants in the consensus meeting voted on the 32 further items that had not reached the definition of consensus within the Delphi, regarding each item’s inclusion in or exclusion from the TMP template. The voting resulted in 18 items being excluded, leaving 14 items to be included in the TMP template. The process overall resulted in a standardised TMP template with input from many individuals with interest, experience, or expertise in monitoring clinical trials. Conclusion A TMP template was developed building on the currently used monitoring plans and with input from those experienced in clinical trial monitoring. Using a centrally developed good quality TMP template should contribute towards maintaining consistency in monitoring standards across all CTUs, resulting in higher research quality and improved quality assurance. Its use should provide reassurance to participants that their participation is carefully monitored to ensure that their data or any samples provided are treated with confidentiality, integrity, and respect and that their rights and well-being are protected.

Good decision-making is essential to conserving wildlife populations. Although there may be multiple ways to address a problem, perfect solutions rarely exist. Managers are therefore tasked with identifying decisions that will best achieve desired outcomes. Structured decision making (SDM) is a method of decision analysis used to identify the most effective, efficient, and realistic decisions while accounting for values and priorities of the decision maker. The stepwise process includes identifying the management problem, defining objectives for solving the problem, developing alternative approaches to achieve the objectives, and formally evaluating which alternative is most likely to accomplish the objectives. The SDM process can be more effective than informal decision-making because it provides a transparent way to quantitatively evaluate decisions for addressing multiple management objectives while incorporating science, uncertainty, and risk tolerance. To illustrate the application of this process to a management need, we present an SDM-based decision tool developed to identify optimal decisions for proactively managing risk of pneumonia epizootics in bighorn sheep (Ovis canadensis) in Montana. Pneumonia epizootics are a major challenge for managers due to long-term impacts to herds, epistemic uncertainty in timing and location of future epizootics, and consequent difficulty knowing how or when to manage risk. The decision tool facilitates analysis of alternative decisions for how to manage herds based on predictions from a risk model, herd-specific objectives, and predicted costs and benefits of each alternative. Decision analyses for 2 example herds revealed that meeting management objectives necessitates specific approaches unique to each herd. The analyses showed how and under what circumstances the alternatives are optimal compared to other approaches and current management. Managers can be confident that these decisions are effective, efficient, and realistic because they explicitly account for important considerations managers implicitly weigh when making decisions, including competing management objectives, uncertainty in potential outcomes, and risk tolerance.

Background Despite the US Food and Drug Administration (FDA) and European Medicines Agency (EMA) encouraging the use of risk-based monitoring for trials in 2013, there remains a lack of evidence-based guidelines on how to monitor. We surveyed the academic United Kingdom Clinical Research Collaboration (UKCRC) registered clinical trials units (CTUs) to find out their policy on monitoring of phase III randomised clinical trials of an investigational medicinal product (CTIMPs). Methods An online survey of monitoring policy with sections on the CTU, central monitoring and on-site monitoring was sent to all 50 UKCRC registered CTUs in November 2018. Descriptive data analysis and tabulations are reported using the total number answering each question. Results A total of 43/50 (86%) of CTUs responded with 38 conducting phase III randomised CTIMP trials. Of these 38 CTUs, 34 finished the survey. Most CTUs (36/37, 97%) use a central monitoring process to guide, target or supplement site visits. More than half (19/36, 53%) of CTUs do not use an automated monitoring report when centrally monitoring trials and all units use trial team knowledge to make a final decision on whether an on-site visit is required. A total of 31/34 (91%) CTUs used triggers to decide whether or not to conduct an on-site monitoring visit. On-site, a mixture of source data verification and checking of processes was carried out. The CTUs overwhelmingly (27/34, 79%) selected optimising central monitoring as their most pressing concern. Conclusion The survey showed a wide variation in phase III randomised CTIMP trial monitoring practices by academic clinical trials units within a single research-active country. We urgently need to develop evidence-based regulator-agreed guidance for CTUs on best practice for both central and on-site monitoring and to develop tools for all CTUs to use.