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Remote or online proctoring (invigilating) is a technology primarily used to improve the integrity of online examinations. The use of remote proctoring increased significantly as the world switched to online assessment during the COVID-19 pandemic. Remote proctoring received negative media attention, including concerns about user privacy, discrimination and the accuracy of automated systems for detecting and reporting cheating. However, it is unclear whether these media concerns fully reflect the experiences of students. Online assessment offers a number of potential advantages to learners and education providers, and it seems likely that it is here to stay. It is essential to fully understand the learner experience of remote proctoring, with a view to ensuring it is as effective as possible while meeting the needs of all stakeholders, especially those being proctored. We undertook a scoping review of research into the student experience of online proctoring, with a pragmatic focus, aimed at developing guidance for higher education providers, based on the student experience. We reviewed primary research studies which evaluated the student experience of the use of remote proctoring for summative assessment in Higher Education. We used the Education Research Information Center database (ERIC) and Google Scholar. 21 papers were identified, from which the positives and negatives of the student experience were extracted, along with the main recommendations from the research. These were then synthesised into a series of summary recommendations by thematic analysis, by a team of researchers that included students and academic staff. We found that student experience was largely negative, influenced by concerns over privacy, technological challenges, fairness and stress. Recommendations were to include the student voice in decisions about how and why to use remote proctoring and limiting the use of remote proctoring. Working with students as partners and limiting the use of remote proctoring where possible, are key to ensuring a positive student experience.

IntroductionHeart failure (HF) is a frequent cause of readmissions. Despite caring for underresourced patients and dependence on government funding, safety net hospitals frequently incur penalties for failing to meet pay-for-performance readmission metrics. Limited research exists on the causes of HF readmissions in safety net hospitals. Therefore, we sought to investigate predictors of 30-day all-cause readmission in HF patients in the safety net setting.MethodsWe performed a retrospective chart review of patients admitted for HF from October 2018 to April 2019. We extracted data on demographics and medical comorbidities and performed patient-specific review of social determinants and mental health in 4 domains: race/ethnicity, housing status, substance use, and mental illness. Multivariable Poisson regression modeling was employed to evaluate associations with 30-day all-cause readmission.ResultsThe study population included 290 patients, among whom the mean age was 59 years and 71% (n = 207) were male; 42% (120) were Black/African American (AA), 22% (64) were Hispanic/Latino, and 96% (278) had public insurance; 28% (79) were not housed, 19% (56) had a diagnosis of mental illness, and active substance use was common. The 30-day readmission rate was 25.5% (n = 88). Factors that were associated with increased risk of readmission included self-identifying as Black/AA (relative risk 2.28, 95% confidence interval 1.00-5.20) or Hispanic/Latino (2.53, 1.07-6.00), experiencing homelessness (2.07, 1.21-3.56), living in a shelter (3.20, 1.27-8.02), or intravenous drug use (IVDU) (2.00, 1.08-3.70).ConclusionRace/ethnicity, housing status, and substance use were associated with increased risk of 30-day all-cause readmission in HF patients in a safety net hospital. In contrast to prior studies, medical comorbidities were not associated with increased risk of readmission.

Although children display strong individual differences in emotion expression, they also engage in emotional synchrony or reciprocity with interaction partners. To understand this paradox between trait-like and dyadic influences, the goal of the current study was to investigate children's emotion expression using a Social Relations Model (SRM) approach. Playgroups consisting typically of four same-sex unfamiliar nine-year-old children (N = 202) interacted in a round-robin format (6 dyads per group). Each dyad completed two 5-minute tasks, a challenging frustration task and a cooperative planning task. Observers coded children's emotions during the tasks (happy, sad, angry, anxious, neutral) on a second-by-second basis. SRM analyses provided substantial evidence of both the trait-like nature of children's emotion expression (through significant effects for actor variance, multivariate actor-actor correlations, and multivariate intrapersonal correlations) and the dyadic nature of their emotion expression (through significant effects for partner variance, relationship variance, dyadic reciprocity correlations, and multivariate interpersonal correlations).

Rice cultivation is crucial in providing global food needs, particularly in highland areas with distinct environmental conditions. Adapting promising rice lines suitable for highland ecosystems is essential to ensure sustainable rice production in such regions. This study assesses the agronomic performance, heritability, and correlation of traits in new promising rice lines adapted to highland ecosystems. The latest promising rice lines are designed with the unique environmental conditions of highland regions in mind. This research was conducted in Buntu Barana’ Village, Tikala Sub-district, with an elevation of 858.4 m above sea level. The study was carried out in rain-fed semi-irrigated paddy fields. The research was conducted from May 2023 to January 2024. The research methodology involves testing various agronomic traits for yield production components of the new type of promising rice lines at different highland locations. The research procedures included the selection of viable seed strains, seeding, fertilization, balanced irrigation, control of plant pest organisms, harvesting, and post-harvesting. Evaluated agronomic traits encompass plant height, days to maturity, number of productive tillers, length of panicles, number of filled grains per panicle, and grain yield. Additionally, heritability analysis is performed to assess the extent to which observed trait variations are attributed to genetic factors. Research findings reveal significant variations in agronomic traits among the new type of promising rice lines. The highest grain yield is achieved in genotype UKIT102-2-056, which is 8.3 tons per hectare of paddy rice. These lines exhibit objective adaptation to the highland ecosystem, with some lines demonstrating superior performance in specific parameters, especially early maturity, namely 97 days after planting. Heritability analysis indicates that certain traits have high heritability levels, suggesting a significant role of genetic factors in controlling these traits. Furthermore, this study unveils correlation patterns among the observed agronomic traits. Significant positive and negative correlations among several traits indicate complex interrelationships among these agronomic characteristics. These findings offer further insights into the relationships between these traits and can be utilized to develop more adaptive rice lines in highland environments.

The integration of multidisciplinary tumor boards (MTBs) is fundamental in delivering state-of-the-art cancer treatment, facilitating collaborative diagnosis and management by a diverse team of specialists. Despite the clear benefits in personalized patient care and improved outcomes, the increasing burden on MTBs due to rising cancer incidence and financial constraints necessitates innovative solutions. The advent of artificial intelligence (AI) in the medical field offers a promising avenue to support clinical decision-making. This review explores the perspectives of clinicians dedicated to the care of cancer patients—surgeons, medical oncologists, and radiation oncologists—on the application of AI within MTBs. Additionally, it examines the role of AI across various clinical specialties involved in cancer diagnosis and treatment. By analyzing both the potential and the challenges, this study underscores how AI can enhance multidisciplinary discussions and optimize treatment plans. The findings highlight the transformative role that AI may play in refining oncology care and sustaining the efficacy of MTBs amidst growing clinical demands.

Sex-related differences in psychopharmacology present unique challenges in both clinical and research settings. Recognition of sex differences in psychopharmacological treatment has increased in recent years, but a significant research gap regarding variations between men and women still exists. Biological factors, including hormonal fluctuations, genetic factors, and brain structure differences, contribute significantly to differential drug responses. Moreover, social determinants can influence the differential burden of psychiatric disorders between the sexes and may impact treatment plans. Incorporating sex as a key variable in personalized treatment programs and plans holds the potential to optimize efficacy and minimize adverse effects in psychopharmacology. Sex-related challenges in psychopharmacology necessitate a nuanced approach to treatment. Further research is needed to fully understand these differences and to develop guidelines for personalized medication management. By addressing these challenges, clinicians can improve treatment outcomes and enhance the quality of life of patients with psychiatric disorders.

The aim of these evidence-based guidelines is to present a consensus position from members of the Italian Unitary Society of Colon-Proctology (Società Italiana Unitaria di Colon-Proctologia, SIUCP) on the diagnosis and management of hemorrhoidal disease, with the goal of guiding physicians in the choice of the best treatment option. A panel of experts was charged by the Board of the SIUCP to develop key questions on the main topics related to the management of hemorrhoidal disease and to perform an accurate and comprehensive literature search on each topic, in order to provide evidence-based answers to the questions and to summarize them in statements. All the clinical questions were discussed by the expert panel in multiple rounds through the Delphi approach and, for each statement, a consensus among the experts was reached. The questions were created according to PICO (patients, intervention, comparison, and outcomes) criteria, and the statements were developed adopting the GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) methodology. In cases of grade 1 hemorrhoidal prolapse, outpatient procedures including hemorrhoidal laser procedure and sclerotherapy may be considered the preferred surgical options. For grade 2 prolapse, nonexcisional procedures including outpatient treatments, hemorrhoidal artery ligation and mucopexy, laser hemorrhoidoplasty, the Rafaelo procedure, and stapled hemorrhoidopexy may represent the first-line treatment options, whereas excisional surgery may be considered in selected cases. In cases of grades 3 and 4, stapled hemorrhoidopexy and hemorrhoidectomy may represent the most effective procedures, even if, in the expert panel opinion, stapled hemorrhoidopexy represents the gold-standard treatment for grade 3 hemorrhoidal prolapse.

Abstract Pervasive SARS-CoV-2 infections in humans have led to multiple transmission events to animals. While SARS-CoV-2 has a potential broad wildlife host range, most documented infections have been in captive animals and a single wildlife species, the white-tailed deer. The full extent of SARS-CoV-2 exposure among wildlife communities and the factors that influence wildlife transmission risk remain unknown. We sampled 23 species of wildlife for SARS-CoV-2 and examined the effects of urbanization and human use on seropositivity. Here, we document positive detections of SARS-CoV-2 RNA in six species, including the deer mouse, Virginia opossum, raccoon, groundhog, Eastern cottontail, and Eastern red bat between May 2022–September 2023 across Virginia and Washington, D.C., USA. In addition, we found that sites with high human activity had three times higher seroprevalence than low human-use areas. We obtained SARS-CoV-2 genomic sequences from nine individuals of six species which were assigned to seven Pango lineages of the Omicron variant. The close match to variants circulating in humans at the time suggests at least seven recent human-to-animal transmission events. Our data support that exposure to SARS-CoV-2 has been widespread in wildlife communities and suggests that areas with high human activity may serve as points of contact for cross-species transmission.

Abstract Background Mobile upright PET devices have the potential to enable previously impossible neuroimaging studies. Currently available options are imagers with deep brain coverage that severely limit head/body movements or imagers with upright/motion enabling properties that are limited to only covering the brain surface. Methods In this study, we test the feasibility of an upright, motion-compatible brain imager, our Ambulatory Motion-enabling Positron Emission Tomography (AMPET) helmet prototype, for use as a neuroscience tool by replicating a variant of a published PET/fMRI study of the neurocorrelates of human walking. We validate our AMPET prototype by conducting a walking movement paradigm to determine motion tolerance and assess for appropriate task related activity in motor-related brain regions. Human participants (n = 11 patients) performed a walking-in-place task with simultaneous AMPET imaging, receiving a bolus delivery of F18-Fluorodeoxyglucose. Results Here we validate three pre-determined measure criteria, including brain alignment motion artifact of less than

This paper presents advances in the data management strategy applied to 4D multimodal mechanics of material sample microstructures. Guidelines to build a data platform allowing for complex workflows, involving several high-throughput experimental and numerical techniques, and complying with FAIR data management principles, are discussed. Next, their implementation within the open-source Python package Pymicro is presented, offering a high-level interface to build complex datasets through multimodal methodologies. Its capability to enable and automate complex workflows by building a digital twin of a commercially pure titanium sample under tension are then demonstrated. The digital twin contains microstructural and mechanical data for thousands of grains gathered on the same sample through synchrotron DCT and in-situ SEM experiments, as well as full-field numerical simulation. Finally, a local and statistical comparison between simulation and measurements of plastic slip and crystal rotation in hundreds of grains is shown, as an example of the contribution of this platform to multimodal data convergence and its importance for the development of a new generation of material models.