Reduced diurnal activity and increased stopover duration by molting Swainson’s Thrushes

Migration consists of a sequence of small- to large-scale flights often separated by stopovers for refueling. Tradeoffs between minimizing migration time (more flights, shorter stopovers) and maximizing energy gain (fewer flights, longer stopovers) will affect overall migration timing. For example, some individuals make long-term stopovers in high-quality habitat that maximize energy gain (e.g., molt-migration), but movement to those habitats likely costs time. We used radio telemetry and blood plasma metabolite levels to examine physiological and behavioral tradeoffs between molt-migrant (birds molting at the molt stopover; n = 59) and post-molt (birds that presumably completed their molt elsewhere; n = 19) migrant Swainson’s Thrushes (Catharus ustulatus) near Montreal, Canada. Molt-migration was a large time investment as the average stopover duration for molt-migrants was of 47 ± 9 days (~13% of the entire annual cycle), almost twice as long as previously assumed from banding records, and far longer than stopovers of post-molting individuals (7 ± 2 days). Daily mortality rate during the molt stopover was similar to the average annual daily mortality rate. Molt-migrants’ circadian rhythms closely matched light levels, whereas post-molting birds had irregular rhythms and averaged 1 hr greater activity per day than molt-migrants. Despite being less active, molt-migrants had similar refueling rates based on metabolite profiles. As compared with migrants that completed molt earlier, molt-migrants at this stopover site had slower subsequent migration rates. Thus, birds using long-term stopovers appeared to tradeoff energy (efficient refueling) for time (slower subsequent migration).

Implementation of a program to support direct support professionals to promote a healthy lifestyle for people with moderate to profound intellectual disabilities

Background There is a lack of theory-based interventions for direct support professionals (DSPs) to support a healthy lifestyle for people with moderate to profound intellectual disabilities (ID) despite their major role in this. This study aims to evaluate the preparation, implementation, and preliminary outcomes of a theory-based training and education program for DSPs to learn how to support these individuals. Methods The program consisting of e-learning, three in-person sessions, and three assignments was implemented. The implementation process was evaluated with a mixed method design with the following components: preparation phase, implementation phase, and the outcomes. These components were measured with project notes, questionnaires, interviews, reflections, assignments, food diaries, Actigraph/Actiwatch, and an inventory of daily activities. Results Regarding the preparation phase, enough potential participants met the inclusion criteria and the time to recruit the participants was 9 months. The program was implemented in four (residential) facilities and involved individuals with moderate to profound ID (n = 24) and DSPs (n = 32). The e-learning was completed by 81% of the DSPs, 72–88% attended the in-person sessions, and 34–47% completed the assignments. Overall, the fidelity of the program was good. DSPs would recommend the program, although they were either negative or positive about the time investment. Mutual agreement on expectations were important for the acceptability and suitability of the program. For the outcomes, the goals of the program were achieved, and the attitudes of DSPs towards a healthy lifestyle were improved after 3 months of the program (nutrition: p = < 0.01; physical activity: p = 0.04). A statistically significant improvement was found for food intake of people with ID (p = 0.047); for physical activity, no statistically significant differences were determined. Conclusions The theory-based program consisting of a training and education section for DSPs to support a healthy lifestyle for people with moderate to profound ID was feasible to implement and, despite some barriers regarding time capacity and mutual expectations, it delivered positive changes in both persons with moderate to profound ID and DSPs. Thus, the program is a promising intervention to support DSPs.

Antibiotic stewardship for nurses: Using e-learning modules to bridge the education gap

Objective: The Centers for Disease Control and Prevention has called for an interdisciplinary approach to antibiotic stewardship implementation that includes front-line nurses. The literature to date has identified key factors preventing uptake by nurses: lack of education, poor communication among providers, and unit culture. Three e-learning modules were developed to address the nurses’ education regarding the roles nurses play in antibiotic stewardship, antibiotic resistance, allergy assessment, medication side effects and interactions, pharmacokinetics–pharmacodynamics, culture interpretation, specimen collection, and the antibiogram. A survey was used to assess whether nurses felt more prepared to participate after finishing the modules. Setting: Front-line staff nurses in acute care were assigned e-learning modules as part of their pharmacy’s introduction of an antibiotic stewardship program for nurses. Methods: Nurses viewed the modules and completed a survey designed to rank their usefulness and to assess their attitudes. Results: Overall, 81% of nurses felt that they should be part of the antibiotic stewardship team. After completing the modules, 72% felt more empowered to participate in stewardship discussions and an additional 23% requested more education. Also, 97% felt that the information they learned could be utilized in everyday work regardless of the new program. The most cited barriers to stewardship activities were lack of education (45%) and hospital and/or unit culture (13%). Conclusion: Education and culture need to be addressed to overcome the barriers to nurses’ involvement in antimicrobial stewardship. E-learning can provide a simple and effective first step to educate nurses, with minimal time investment.

Initial application of deep learning to borescope detection of endoscope working channel damage and residue

Background and study aims Outbreaks of endoscopy-related infections have prompted evaluation for potential contributing factors. We and others have demonstrated the utility of borescope inspection of endoscope working channels to identify occult damage that may impact the adequacy of endoscope reprocessing. The time investment and training necessary for borescope inspection have been cited as barriers preventing implementation. We investigated the utility of artificial intelligence (AI) for streamlining and enhancing the value of borescope inspection of endoscope working channels. Methods We applied a deep learning AI approach to borescope inspection videos of the working channels of 20 endoscopes in use at our academic institution. We evaluated the sensitivity, accuracy, and reliability of this software for detection of endoscope working channel findings. Results Overall sensitivity for AI-based detection of borescope inspection findings identified by gold standard endoscopist inspection was 91.4 %. Labels were accurate for 67 % of these working channel findings and accuracy varied by endoscope segment. Read-to-read variability was noted to be minimal, with test-retest correlation value of 0.986. Endoscope type did not predict accuracy of the AI system (P = 0.26). Conclusions Harnessing the power of AI for detection of endoscope working channel damage and residue could enable sterile processing department technicians to feasibly assess endoscopes for working channel damage and perform endoscope reprocessing surveillance. Endoscopes that accumulate an unacceptable level of damage may be flagged for further manual evaluation and consideration for manufacturer evaluation/repair.

Applications of Machine learning in Computational Nanotechnology

Machine learning (ML) has gained extensive attentions in recent years due to its powerful data analysis capabilities. It has been successfully applied to many fields and helped the researchers to achieve several major theoretical and applied breakthroughs. Some of the notable applications in the field of computational nanotechnology are machine learning potentials, property prediction and material discovery. This review summarizes of the state-of-the-art research progress in these three fields. Machine learning potentials bridge the efficiency vs. accuracy gap between density functional calculations (DFT) and classical molecular dynamics (MD). For property predictions, machine learning provides a robust method that eliminate the needs of repetitive calculations for different simulation setup. Material design and drug discovery assisted by machine learning greatly reduces the capital and time investment by orders of magnitude. In this perspective, several common machine learning potentials and machine learning models are firstly introduced. Using these state-of-the-art models, developments in property predictions and material discovery are overviewed, respectively. Finally, this paper was concluded with an outlook on future directions of data-driven research activities in computational nanotechnology.

A learning based approach for designing extended unit cell metagratings

The possibility of arbitrary spatial control of incident wavefronts with the subwavelength resolution has driven research into dielectric optical metasurfaces in the last decade. The unit-cell based metasurface design approach that relies on a library of single element responses is known to result in reduced efficiency attributed to the inadequate accounting of the coupling effects between meta-atoms. Metasurfaces with extended unit-cells containing multiple resonators can improve design outcomes but their design requires extensive numerical computing and optimizations. We report a deep learning based design methodology for the inverse design of extended unit-cell metagratings. In contrast to previous reports, our approach learns the metagrating spectral response across its reflected and transmitted orders. Through systematic exploration, we discover network architectures and training dataset sampling strategies that allow such learning without requiring extensive ground-truth generation. The one-time investment of model creation can then be used to significantly accelerate numerical optimization of multiple functionalities as demonstrated by considering the inverse design of various spectral and polarization dependent splitters and filters. The proposed methodology is not limited to these proof-of-concept demonstrations and can be broadly applied to meta-atom-based nanophotonic system design and in realising the next generation of metasurface functionalities with improved performance.

Effective recruitment strategies for home-living vulnerable older adults with depression into a psychotherapy RCT

Objectives Vulnerable older adults, such as physically impaired or care-dependent individuals, are vastly underrepresented in psychotherapy research. Improving their inclusion in randomized controlled trials is necessary to determine the effectiveness of psychotherapy in this population. This study is the first to systematically evaluate strategies to recruit home-living vulnerable older adults with clinically significant depression into a large randomized controlled psychotherapy trial. Potential participants were approached directly (self-referral) or via cooperation with gatekeepers (gatekeeper-referral). Methods The initiator of the first contact with the study team and successful recruitment strategies were recorded. Referral strategies were compared with respect to number of inquiries and inclusion rates; study personnel’s time investment; and participant characteristics (sociodemographics, functional and cognitive status, depression and anxiety scores). Results Most of the N=197 participants were included via gatekeeper-referral (80.5%, 95%CI=[74.9%,86.1%], but time investment for gatekeeper-referrals was five times higher than for self-referral by media reports. Clinical psychologists and medical practitioners referred the largest proportion of participants (32.3% each) and referral by medical practitioners led to highest inclusion rates (55.6%; χ²(3)=8.964, p&lt;.05). Most participants were referred from a hospital setting (50.3%), whereas referral numbers by medical practices were low (15.9%). Participants who initiated the first contact themselves had higher inclusion rates and were less functionally and cognitively impaired. Conclusions Including home-living vulnerable older adults into psychotherapy trials requires simultaneous implementation of diverse recruitment strategies. Medical practitioners and psychologists, especially in hospitals, are the most effective recruitment strategy, but self-referral via media is most cost-efficient in terms of time investment.

Going digital: added value of electronic data collection in 2018 Afghanistan Health Survey

Background Through a nationally representative household survey in Afghanistan, we conducted an operational study in two relatively secure provinces comparing effectiveness of computer-aided personal interviewing (CAPI) with paper-and-pencil interviewing (PAPI). Methods In Panjshir and Parwan provinces, household survey data were collected using paper questionnaires in 15 clusters, and OpenDataKit (ODK) software on electronic tablets in 15 other clusters. Added value was evaluated from three perspectives: efficient implementation, data quality, and acceptability. Efficiency was measured through financial expenditures and time stamped data. Data quality was measured by examining completeness. Acceptability was studied through focus group discussions with survey staff. Results Survey costs were 68% more expensive in CAPI clusters compared to PAPI clusters, due primarily to the upfront one-time investment for survey programming. Enumerators spent significantly less time administering surveys in CAPI cluster households (248 min survey time) compared to PAPI (289 min), for an average savings of 41 min per household (95% CI 25–55). CAPI offered a savings of 87 days for data management over PAPI. Among 49 tracer variables (meaning responses were required from all respondents), small differences were observed between PAPI and CAPI. 2.2% of the cleaned dataset’s tracer data points were missing in CAPI surveys (1216/ 56,073 data points), compared to 3.2% in PAPI surveys (1953/ 60,675 data points). In pre-cleaned datasets, 3.9% of tracer data points were missing in CAPI surveys (2151/ 55,092 data points) compared to 3.2% in PAPI surveys (1924/ 60,113 data points). Enumerators from Panjsher and Parwan preferred CAPI over PAPI due to time savings, user-friendliness, improved data security, and less conspicuity when traveling; however approximately half of enumerators trained from all 34 provinces reported feeling unsafe due to Taliban presence. Community and household respondent skepticism could be resolved by enumerator reassurance. Enumerators shared that in the future, they prefer collecting data using CAPI when possible. Conclusions CAPI offers clear gains in efficiency over PAPI for data collection and management time, although costs are relatively comparable even without the programming investment. However, serious field staff concerns around Taliban threats and general insecurity mean that CAPI should only be conducted in relatively secure areas.

Transition to Online Education during the COVID-19 Pandemic: Impact of Changes in Alcohol Consumption and Experiencing Hangovers on Academic Functioning

In the Netherlands, the 2019 coronavirus (COVID-19) pandemic had a significant impact on daily life, with two extensive lockdowns enforced to combat the spread of the SARS-CoV-2 virus. These measures included the closure of bars and restaurants, and the transition from face-to-face to online education. A survey was conducted among Dutch pharmacy students and PhD-candidates to investigate the impact of COVID-19 lockdown on alcohol consumption, hangovers, and academic functioning. The analysis revealed a significant reduction in both quantity and frequency of alcohol consumption during the COVID-19 lockdown periods. This was accompanied with a significant reduction in hangover frequency and lower hangover severity during COVID-19 lockdown periods. The distribution of scores on academic performance showed great variability between respondents: while some participants reported impairment, others reported improved performance during the COVID-19 pandemic, or no change. Women reported that significantly more time investment was associated with maintaining these performance levels. Consistent among participants was the notion of reduced interactions with teachers and other students. Participants who reported more hangovers and most severe hangovers before COVID-19 benefited from the lockdown periods in terms of improved academic performance. Positive correlations were found between study grades/output and both the frequency and severity of hangovers experienced before COVID-19, suggesting that heavier drinkers, in particular, improved academic performance during the lockdown periods. In conclusion, COVID-19 lockdowns were associated with a significant reduction in both alcohol consumption and experiencing hangovers, which was, among heavier drinkers particularly, associated with significantly improved academic functioning.

COPUS, PORTAAL, or DART? Classroom Observation Tool Comparison From the Instructor User’s Perspective

Classroom observation tools are used to evaluate teaching and learning activities, and to provide constructive feedback to instructors. To help instructors with selecting a suitable tool based on their needs and available resources, in this study, a group of observers assessed lectures of an introductory biology course using three, broadly cited classroom assessment tools in the STEM field: the Classroom Observation Protocol for Undergraduate STEM (COPUS); the Practical Observation Rubric to Assess Active Learning (PORTAAL); and the Decibel Analysis for Research in Teaching (DART). From a user’s perspective, we evaluated 1) the type and extent of information each tool provides, and 2) the time investment and difficulty of working with each tool. The assessment result of each tool was compared, with a list of expected outcomes generated by surveying a group of college instructors and with the result of a self-teaching assessment tool, Teaching Practices Inventory (TPI). Our findings conclude that each tool provided valuable assessment with a broad range of outcomes and time investment: PORTAAL offered the most detailed information on the quality of teaching practices and students’ engagement, but it demanded the greatest time investment. DART provided a basic estimation of active learning proportion with the least effort. The level of assessment outcome and the time investment when using COPUS was found to be less than PORTAAL, and more than DART. The TPI self-assessment outcome was found to be slightly optimistic regarding the proportion of active learning practices used in the studied course. This comparative study can help instructors in selecting a tool that suits their needs and available resources for a better assessment of their classroom teaching and learning.