Spatiotemporal variability of lake surface water temperature and water quality parameters and its interrelationship with water hyacinth biomass in Lake Tana, Ethiopia.

Urbanization, agriculture, and climate change affect water quality and water hyacinth growth in lakes. This study examines the spatiotemporal variability of lake surface water temperature, turbidity, and chlorophyll-a (Chl-a) and their association with water hyacinth biomass in Lake Tana. MODIS Land/ Lake surface water temperature (LSWT), Sentinel 2 MSI Imagery, and in-situ water quality data were used. Validation results revealed strong positive correlations between MODIS LSWT and on-site measured water temperature (R = 0.90), in-situ turbidity and normalized difference turbidity index (NDTI) (R = 0.92), and in-situ Chl-a and normalized difference chlorophyll index (NDCI) (R = 0.84). LSWT trends varied across the lake, with increasing trends in the northeastern, northwestern, and southwestern regions and decreasing trends in the western, southern, and central areas (2001-2022). The spatial average LSWT trend decreased significantly in pre-rainy (0.01 ℃/year), rainy (0.02 ℃/year), and post-rainy seasons (0.01℃/year) but increased non-significantly in the dry season (0.00 ℃/year) (2001-2022, P < 0.05). Spatial average turbidity decreased significantly in all seasons, except in the pre-rainy season (2016-2022). Likewise, spatial average Chl-a decreased significantly in pre-rainy and rainy seasons, whereas it showed a non-significant increasing trend in the dry and post-rainy seasons (2016-2022). Water hyacinth biomass was positively correlated with LSWT (R = 0.18) but negatively with turbidity (R = -0.33) and Chl-a (R = -0.35). High spatiotemporal variability was observed in LSWT, turbidity, and Chl-a, along with overall decreasing trends. The findings suggest integrated management strategies to balance water hyacinth eradication and its role in water purification. The results will be vital in decision support systems and preparing strategic plans for sustainable water resource management, environmental protection, and pollution prevention.

Monitoring agricultural drought using different indices based on remote sensing data in the Brazilian biomes of Cerrado and Atlantic Forest.

Several remote sensing indices have been used to monitor droughts, mainly in semi-arid regions with limited coverage by meteorological stations. The objective of this study was to estimate and monitor agricultural drought conditions in the Jequitinhonha Valley region, located in the Brazilian biomes of the Cerrado and Atlantic Forest, from 2001 to 2021, using vegetation indices and the meteorological drought index from remote sensing data. Linear regression was applied to analyze drought trends and Pearson's correlation coefficient was applied to evaluate the relationship between vegetation indices and climatic conditions in agricultural areas using the Standardized Precipitation Index. The results revealed divergences in the occurrences of regional droughts, predominantly covering mild to moderate drought conditions. Analysis spatial of drought trends revealed a decreasing pattern, indicating an increase in drought in the Middle and Low Jequitinhonha sub-regions. On the other hand, a reduction in drought was observed in the High Jequitinhonha region. Notably, the Vegetation Condition Index demonstrated the most robust correlation with the Standardized Precipitation Index, with R values ​​greater than 0.5 in all subregions of the study area. This index showed a strong association with precipitation, proving its suitability for monitoring agricultural drought in heterogeneous areas and with different climatic attributes. The use of remote sensing technology made it possible to detect regional variations in the spatio-temporal patterns of drought in the Jequitinhonha Valley. This vision helps in the implementation of personalized strategies and public policies, taking into account the particularities of each area, in order to mitigate the negative impacts of drought on agricultural activities in the region.

A biobehavioral observational study to understand the multilevel determinants of cardiovascular health in Black women: the BLOOM Study protocol.

BACKGROUND: The racial/ethnic and gender disparities in cardiovascular disease (CVD) morbidity and mortality in the United States are evident. Across nearly every metric, non-Hispanic Black women have poorer overall cardiovascular health. Emerging evidence shows a disproportionately high burden of increased CVD risk factors in Black women of childbearing age, which has a far-reaching impact on both maternal and child outcomes, resulting in premature onset of CVD and further widens the racial disparities in CVD. There is growing recognition that the fundamental driver of persistent racial/ethnic disparities in CVD, as well as disparities in behavioral risk factors such as physical activity and sleep, is structural racism. Further, the lived personal experience of racial discrimination not only has a negative impact on health behaviors, but also links to various physiological pathways to CVD risks, such as internalized stress resulting in a pro-inflammatory state. Limited research, however, has examined the interaction between daily experience and health behaviors, which are influenced by upstream social determinants of health, and the downstream effect on biological/physiological indicators of cardiovascular health in non-pregnant Black women of childbearing age. METHODS/DESIGN: The BLOOM Study is an observational study that combines real-time ambulatory assessments over a 10-day monitoring period with in-depth cross-sectional lab-based physiological and biological assessments. We will use a wrist-worn actigraphy device to capture 24-h movement behaviors and electronic ecological momentary assessment to capture perceived discrimination, microaggression, and stress. Blood pressure will be captured continuously through a wristband. Saliva samples will be self-collected to assess cortisol level as a biomarker of psychological stress. Lab assessments include a fasting venous blood sample, and assessment of various indices of peripheral and cerebral vascular function/health. Participants' address or primary residence will be used to obtain neighborhood-level built environmental and social environmental characteristics. We plan to enroll 80 healthy Black women who are between 18 and 49 years old for this study. DISCUSSION: Results from this study will inform the development of multilevel (i.e., individual, interpersonal, and social-environmental levels) lifestyle interventions tailored to Black women based on their lived experiences with the goal of reducing CVD risk. GOV IDENTIFIER: NCT06150989.

Physical education teachers' experiences regarding online physical education lessons for children with autism spectrum disorder.

Background: The purpose of this qualitative study was to investigate the experiences of physical education (PE) teachers regarding online PE lessons for children with ASD during the COVID-19 pandemic. Method: Participants in this study were 16 PE teachers who took part in one-on-one semi-structured phone interviews. Interview data were analyzed using Braun and Clarke's recipe for thematic analysis. Result: Four overarching themes were found: (1) we were unprepared for online lessons, (2) challenges of online lessons, (3) parental support, and (4) solution offers. Conclusion: The results revealed that PE teachers were unprepared for the sudden transition to online lessons due to the COVID-19 pandemic. PE teachers started the online teaching with parent support despite the challenges, but they were not satisfied with the online PE model.

Dynamic shifts of functional diversity through climate-Resilient strategies and farmland restoration in a mountain protected area.

Land-use land-cover (LULC) change contributes to major ecological impacts, particularly in areas undergoing land abandonment, inducing modifications on habitat structure and species distributions. Alternative land-use policies are potential solutions to alleviate the negative impacts of contemporary tendencies of LULC change on biodiversity. This work analyzes these tendencies in the Montesinho Natural Park (Portugal), an area representative of European abandoned mountain rural areas. We built ecological niche models for 226 species of vertebrates (amphibians, reptiles, birds, and mammals) and vascular plants, using a consensus modelling approach available in the R package 'biomod2'. We projected the models to contemporary (2018) and future (2050) LULC scenarios, under four scenarios aiming to secure relevant ecosystem services and biodiversity conservation for 2050: an afforestation and a rewilding scenario, focused on climate-smart management strategies, and a farmland and an agroforestry recovery scenario, based on re-establishing human traditional activities. We quantified the influences of these scenarios on biodiversity through species habitat suitability changes for 2018-2050. We analyzed how these management strategies could influence indices of functional diversity (functional richness, functional evenness and functional dispersion) within the park. Habitat suitability changes revealed complementary patterns among scenarios. Afforestation and rewilding scenarios benefited more species adapted to habitats with low human influence, such as forests and open woodlands. The highest functional richness and dispersion was predicted for rewilding scenarios, which could improve landscape restoration and provide opportunities for the expansion and recolonization of forest areas by native species. The recovery of traditional farming and agroforestry activities results in the lowest values of functional richness, but these strategies contribute to complex landscape matrices with diversified habitats and resources. Moreover, this strategy could offer opportunities for fire suppression and increase landscape fire resistance. An integrative approach reconciling rewilding initiatives with the recovery of extensive agricultural and agroforestry activities is potentially an harmonious strategy for supporting the provision of ecosystem services while securing biodiversity conservation and functional diversity within the natural park.

An improved remote sensing reference evapotranspiration estimation by simple satellite data and machine learning.

Reference evapotranspiration (ET0) estimation is crucial for efficient irrigation planning, optimized water management and ecosystem modeling, yet it presents significant challenges, particularly when meteorological data availability is limited. This study utilized remote sensing data of land surface temperature (LST), day of year, and latitude, and employed a machine learning approach (e.g., random forest) to develop an improved remote sensing ET0 model. The model performed excellently in 567 meteorological stations in China with an R2 of 0.97, RMSE of 0.40, MBE of 0.00, and MAPE of 0.11 compared to the FAO-PM ET0; it also performed well globally, yielding an average R2 of 0.97 and RMSE of 0.43 across 120 sites in mid-latitude (20°-50°) regions. This model demonstrates simplicity, accuracy, robust and generalization, holding great potential for widespread application, especially in the large-scale, high-resolution estimation of ET0. This study will contribute to advancements in water resources management, agricultural planning, and climate change studies.

Oral glucose tolerance test-The imperfect gold standard for gestational diabetes screening: A qualitative study involving clinicians in regional, rural and remote areas of Western Australia.

ISSUE ADDRESSED: The oral glucose tolerance test is the 'gold standard' for detecting gestational diabetes in Australian and International guidelines. Test completion in regional, rural and remote regions may be as low as 50%. We explored challenges and enablers for regional, rural and remote antenatal clinicians providing gestational diabetes screening to better understand low oral glucose tolerance test completion. METHODS: We conducted a qualitative descriptive study using semi-structured interviews. Participants eligible for the study were doctors or midwives providing antenatal care in regional, rural and remote Western Australia, between August 2019 and November 2020. Interviews were recorded digitally and transcribed into a Word document. We conducted a thematic analysis after initial categorisation and deduction of themes through workshops involving the research team. RESULTS: We found a diversity of viewpoints on oral glucose tolerance test reliability for detecting gestational diabetes. Themes that emerged were; good collaboration between antenatal clinicians is required for successful screening; screening occurs throughout pregnancy using various tests; clinicians make significant efforts to address barriers; clinicians prioritise therapeutic relationships. CONCLUSIONS: Effective universal screening for gestational diabetes in regional, rural and remote Western Australia is difficult and more complex in practice than guidelines imply. Detecting gestational diabetes requires creative solutions, early identification of at risk women and trust and collaboration between clinicians and women. SO WHAT?: Detection of gestational diabetes in regional, rural and remote Western Australia remains poorly completed. New strategies are required to adequately identify women at risk of adverse birth outcomes relating to hyperglycaemia in pregnancy.

Association of systolic blood pressure variability with remote ischemic conditioning in acute ischemic stroke.

Systolic blood pressure variability (SBPV) is associated with outcome in acute ischemic stroke. Remote ischemic conditioning (RIC) has been demonstrated to be effective in stroke and may affect blood pressure. Relationship between SBPV and RIC treatment after stroke warrants investigation. A total of 1707 patients from per-protocol analysis set of RICAMIS study were included. The SBPV was calculated based on blood pressure measured at admission, Day 7, and Day 12. (I) To investigate the effect of SBPV on efficacy of RIC in stroke, patients were divided into High and Low categories in each SBPV parameter. Primary outcome was excellent functional outcome at 90 days. Compared with Control, efficacy of RIC in each category and interaction between categories were investigated. (II) To investigate the effect of RIC treatment on SBPV, SBPV parameters were compared between RIC and Control groups. Compared with Control, a higher likelihood of primary outcome in RIC was found in high category (max-min: adjusted risk difference [RD] = 7.2, 95% CI 1.2-13.1, P = 0.02; standard deviation: adjusted RD = 11.5, 95% CI 1.6-21.4, P = 0.02; coefficient of variation: adjusted RD = 11.2, 95% CI 1.4-21.0, P = 0.03). Significant interaction of RIC on outcomes were found between High and Low standard deviations (adjusted P < 0.05). No significant difference in SBPV parameters were found between treatment groups. This is the first report that Chinese patients with acute moderate ischemic stroke and presenting with higher SBPV, who were non-cardioemoblic stroke and not candidates for intravenous thrombolysis or endovascular therapy, would benefit more from RIC with respect to functional outcomes at 90 days, but 2-week RIC treatment has no effect on SBPV during hospital.

Hearing rehabilitation of adults with auditory processing disorder: a systematic review and meta-analysis of current evidence-based interventions.

Background: For adults with auditory processing disorder (APD), listening and communicating can be difficult, potentially leading to social isolation, depression, employment difficulties and certainly reducing the quality of life. Despite existing practice guidelines suggesting treatments, the efficacy of these interventions remains uncertain due to a lack of comprehensive reviews. This systematic review and meta-analysis aim to establish current evidence on the effectiveness of interventions for APD in adults, addressing the urgent need for clarity in the field. Methods: Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we conducted a systematic search across MEDLINE (Ovid), Embase (Ovid), Web of Science and Scopus, focusing on intervention studies involving adults with APD. Studies that met the inclusion criteria were grouped according to intervention with a meta-analysis only conducted where intervention, study design and outcome measure were comparable. Results: Out of 1,618 screened records, 13 studies were included, covering auditory training (AT), low-gain hearing aids (LGHA), and personal remote microphone systems (PRMS). Our analysis revealed: AT, Mixed results with some improvements in speech intelligibility and listening ability, indicating potential benefits but highlighting the need for standardized protocols; LGHA, The included studies demonstrated significant improvements in monaural low redundancy speech testing (p < 0.05), suggesting LGHA could enhance speech perception in noisy environments. However, limitations include small sample sizes and potential biases in study design. PRMS, Demonstrated the most consistent evidence of benefit, significantly improving speech testing results, with no additional benefit from combining PRMS with other interventions. Discussion: PRMS presents the most evidence-supported intervention for adults with APD, although further high-quality research is crucial for all intervention types. The establishment and implementation of standardized intervention protocols alongside rigorously validated outcome measures will enable a more evidence-based approach to managing APD in adults.

The Impact of the COVID-19 Pandemic on Older Adults Who Participate in Group Physical Exercise Program.

The COVID pandemic exposed the vulnerability of older adults in myriad ways and social service organizations faced unprecedented challenges in safely providing support for older adults. Since 2007, Hawai'i Healthy Aging Partnership (HHAP) has offered Enhance®Fitness, an evidence-based program to reduce the risk of falls and promote health among older adults. Due to the pandemic, all the Enhance®Fitness sites had to close and stop offering the program. The HHAP started to provide alternative activities remotely in May 2020. To explore the pandemic's impact, the feasibility of online exercise programs, and the support needed among older adults to stay physically active, HHAP surveyed existing Enhance®Fitness participants and received 291 responses (59% response rate). The study used frequency distributions, comparison of means, and chi-square to analyze the survey data. Findings showed that the shutdown of the group exercise program during the pandemic led to a health status decline, a reduction in physical activities, and a shift from group to individual physical activities among older adult participants. Most respondents tried the remote exercise opportunities during the pandemic and would consider joining the remote programs in the future. However, about one-fourth of the respondents did not participate in remote exercise activities due to the lack of electronic devices, internet access, or interest in remote activity formats. To ensure equitable access to physical exercise programs for older adults in the post-pandemic era, it is critical to address the access challenges and resources needed for providing multiple programming options.

Artificial intelligence-enhanced patient evaluation: bridging art and science.

The advent of digital health and artificial intelligence (AI) has promised to revolutionize clinical care, but real-world patient evaluation has yet to witness transformative changes. As history taking and physical examination continue to rely on long-established practices, a growing pipeline of AI-enhanced digital tools may soon augment the traditional clinical encounter into a data-driven process. This article presents an evidence-backed vision of how promising AI applications may enhance traditional practices, streamlining tedious tasks while elevating diverse data sources, including AI-enabled stethoscopes, cameras, and wearable sensors, to platforms for personalized medicine and efficient care delivery. Through the lens of traditional patient evaluation, we illustrate how digital technologies may soon be interwoven into routine clinical workflows, introducing a novel paradigm of longitudinal monitoring. Finally, we provide a skeptic's view on the practical, ethical, and regulatory challenges that limit the uptake of such technologies.

Exploring fine-scale urban landscapes using satellite data to predict the distribution of Aedes mosquito breeding sites.

BACKGROUND: The spread of mosquito-transmitted diseases such as dengue is a major public health issue worldwide. The Aedes aegypti mosquito, a primary vector for dengue, thrives in urban environments and breeds mainly in artificial or natural water containers. While the relationship between urban landscapes and potential breeding sites remains poorly understood, such a knowledge could help mitigate the risks associated with these diseases. This study aimed to analyze the relationships between urban landscape characteristics and potential breeding site abundance and type in cities of French Guiana (South America), and to evaluate the potential of such variables to be used in predictive models. METHODS: We use Multifactorial Analysis to explore the relationship between urban landscape characteristics derived from very high resolution satellite imagery, and potential breeding sites recorded from in-situ surveys. We then applied Random Forest models with different sets of urban variables to predict the number of potential breeding sites where entomological data are not available. RESULTS: Landscape analyses applied to satellite images showed that urban types can be clearly identified using texture indices. The Multiple Factor Analysis helped identify variables related to the distribution of potential breeding sites, such as buildings class area, landscape shape index, building number, and the first component of texture indices. Models predicting the number of potential breeding sites using the entire dataset provided an R² of 0.90, possibly influenced by overfitting, but allowing the prediction over all the study sites. Predictions of potential breeding sites varied highly depending on their type, with better results on breeding sites types commonly found in urban landscapes, such as containers of less than 200 L, large volumes and barrels. The study also outlined the limitation offered by the entomological data, whose sampling was not specifically designed for this study. Model outputs could be used as input to a mosquito dynamics model when no accurate field data are available. CONCLUSION: This study offers a first use of routinely collected data on potential breeding sites in a research study. It highlights the potential benefits of including satellite-based characterizations of the urban environment to improve vector control strategies.

Seasonal and trend variation of methane concentration over two provinces of South Africa using Sentinel-5p data.

South Africa faces the urgency to comprehensively understand and manage its methane (CH4) emissions. The primary aim of this study is to compare CH4 concentrations between Eastern Cape and Mpumalanga regions dominated by cattle farming and coal mining industries, respectively. CH4 concentration trends were analyzed for the period 2019 to 2023 using satellite data. Trend analysis revealed significant increasing trends in CH4 concentrations in both provinces, supported by Mann-Kendall tests that rejected the null hypothesis of no trend (Eastern Cape: p-value = 8.9018e-08 and Mpumalanga: p-value = 2.4650e-10). The Eastern Cape, a leading cattle farming province, exhibited cyclical patterns and increasing CH4 concentrations, while Mpumalanga, a major coal mining province, displayed similar increasing trends with sharper concentration points. The results show seasonal variations in CH4 concentrations in the Eastern Cape and Mpumalanga provinces. High CH4 concentrations are observed in the northwestern region during the December-January-February (DJF) season, while lower concentrations are observed in the March-April-May (MAM) and June-July-August (JJA) seasons in the Eastern Cape province. In the Mpumalanga province, there is a dominance of high CH4 concentrations in southwestern regions and moderately low concentrations in the northeastern regions, observed consistently across all seasons. The study also showed an increasing CH4 concentration trend from 2019 to 2023 for both provinces. The study highlights the urgent need to address CH4 emissions from both cattle farming and coal mining activities to mitigate environmental impacts and promote sustainable development. Utilizing geographic information system (GIS) and remote sensing technologies, policymakers and stakeholders can identify and address the sources of CH4 emissions more effectively, thereby contributing to environmental conservation and sustainable resource management.

A comprehensive health assessment approach using ensemble deep learning model for remote patient monitoring with IoT.

The goal of this research is to create an ensemble deep learning model for Internet of Things (IoT) applications that specifically target remote patient monitoring (RPM) by integrating long short-term memory (LSTM) networks and convolutional neural networks (CNN). The work tackles important RPM concerns such early health issue diagnosis and accurate real-time physiological data collection and analysis using wearable IoT devices. By assessing important health factors like heart rate, blood pressure, pulse, temperature, activity level, weight management, respiration rate, medication adherence, sleep patterns, and oxygen levels, the suggested Remote Patient Monitor Model (RPMM) attains a noteworthy accuracy of 97.23%. The model's capacity to identify spatial and temporal relationships in health data is improved by novel techniques such as the use of CNN for spatial analysis and feature extraction and LSTM for temporal sequence modeling. Early intervention is made easier by this synergistic approach, which enhances trend identification and anomaly detection in vital signs. A variety of datasets are used to validate the model's robustness, highlighting its efficacy in remote patient care. This study shows how using ensemble models' advantages might improve health monitoring's precision and promptness, which would eventually benefit patients and ease the burden on healthcare systems.

Proposal and validation of a new approach in tele-rehabilitation with 3D human posture estimation: a randomized controlled trial in older individuals with sarcopenia.

OBJECTIVE: Through a randomized controlled trial on older adults with sarcopenia, this study compared the training effects of an AI-based remote training group using deep learning-based 3D human pose estimation technology with those of a face-to-face traditional training group and a general remote training group. METHODS: Seventy five older adults with sarcopenia aged 60-75 from community organizations in Changchun city were randomly divided into a face-to-face traditional training group (TRHG), a general remote training group (GTHG), and an AI-based remote training group (AITHG). All groups underwent a 3-month program consisting of 24-form Taichi exercises, with a frequency of 3 sessions per week and each session lasting 40 min. The participants underwent Appendicular Skeletal Muscle Mass Index (ASMI), grip strength, 6-meter walking pace, Timed Up and Go test (TUGT), and quality of life score (QoL) tests before the experiment, during the mid-term, and after the experiment. This study used SPSS26.0 software to perform one-way ANOVA and repeated measures ANOVA tests to compare the differences among the three groups. A significance level of p < 0.05 was defined as having significant difference, while p < 0.01 was defined as having a highly significant difference. RESULTS: (1) The comparison between the mid-term and pre-term indicators showed that TRHG experienced significant improvements in ASMI, 6-meter walking pace, and QoL (p < 0.01), and a significant improvement in TUGT timing test (p < 0.05); GTHG experienced extremely significant improvements in 6-meter walking pace and QoL (p < 0.01); AITHG experienced extremely significant improvements in ASMI, 6-meter walking pace, and QoL (p < 0.01), and a significant improvement in TUGT timing test (p < 0.05). (2) The comparison between the post-term and pre-term indicators showed that TRHG experienced extremely significant improvements in TUGT timing test (p < 0.01); GTHG experienced significant improvements in ASMI and TUGT timing test (p < 0.05); and AITHG experienced extremely significant improvements in TUGT timing test (p < 0.01). (3) During the mid-term, there was no significant difference among the groups in all tests (p > 0.05). The same was in post-term tests (p > 0.05). CONCLUSION: Compared to the pre-experiment, there was no significant difference at the post- experiment in the recovery effects on the muscle quality, physical activity ability, and life quality of patients with sarcopenia between the AI-based remote training group and the face-to-face traditional training group. 3D pose estimation is equally as effective as traditional rehabilitation methods in enhancing muscle quality, functionality and life quality in older adults with sarcopenia. TRIAL REGISTRATION: The trial was registered in ClinicalTrials.gov (NCT05767710).

Evaluation of a Remote Patient Monitoring Program During the COVID-19 Pandemic: Retrospective Case Study With a Mixed Methods Explanatory Sequential Design.

BACKGROUND: Community health center (CHC) patients experience a disproportionately high prevalence of chronic conditions and barriers to accessing technologies that might support the management of these conditions. One such technology includes tools used for remote patient monitoring (RPM), the use of which surged during the COVID-19 pandemic. OBJECTIVE: The aim of this study was to assess how a CHC implemented an RPM program during the COVID-19 pandemic. METHODS: This retrospective case study used a mixed methods explanatory sequential design to evaluate a CHC's implementation of a suite of RPM tools during the COVID-19 pandemic. Analyses used electronic health record-extracted health outcomes data and semistructured interviews with the CHC's staff and patients participating in the RPM program. RESULTS: The CHC enrolled 147 patients in a hypertension RPM program. After 6 months of RPM use, mean systolic blood pressure (BP) was 13.4 mm Hg lower and mean diastolic BP 6.4 mm Hg lower, corresponding with an increase in hypertension control (BP<140/90 mm Hg) from 33.3% of patients to 81.5%. Considerable effort was dedicated to standing up the program, reinforced by organizational prioritization of chronic disease management, and by a clinician who championed program implementation. Noted barriers to implementation of the RPM program were limited initial training, lack of sustained support, and complexities related to the RPM device technology. CONCLUSIONS: While RPM technology holds promise for addressing chronic disease management, successful RPM program requires substantial investment in implementation support and technical assistance.

Multidisciplinary approach to diagnostic radiology education: a novel educational intervention for Turkish medical students.

Teleconferencing can facilitate a multidisciplinary approach to teaching radiology to medical students. This study aimed to determine whether an online learning approach enables students to appreciate the interrelated roles of radiology and other specialties during the management of different medical cases. Turkish medical students attended five 60-90-minute online lectures delivered by radiologists and other specialists from the United States and Canada through Zoom meetings between November 2020 and January 2021. Student ambassadors from their respective Turkish medical schools recruited their classmates with guidance from the course director. Students took a pretest and posttest to assess the knowledge imparted from each session and a final course survey to assess their confidence in radiology and the value of the course. A paired t-test was used to assess pretest and posttest score differences. A 4-point Likert-type scale was used to assess confidence rating differences before and after attending the course sessions. A total of 1,458 Turkish medical students registered for the course. An average of 437 completed both pre- and posttests when accounting for all five sessions. Posttest scores were significantly higher than pretest scores for each session (P < 0.001). A total of 546 medical students completed the final course survey evaluation. Students' rating of their confidence in their radiology knowledge increased after taking the course (P < 0.001). Students who took our course gained an appreciation for the interrelated roles of different specialties in approaching medical diagnoses and interpreting radiological findings. These students also reported an increased confidence in radiology topics and rated the course highly relevant and insightful. Overall, our findings indicated that multidisciplinary online education can be feasibly implemented for medical students by video teleconferencing.

Socio-economic differences in remote schoolwork during the COVID-19 pandemic: a trend analysis of the 2020 and 2021 school-closure periods using the UK Understanding Society data.

In the United Kingdom, the COVID-19 pandemic in 2020 and 2021 led to two extended periods of school closures. Research on inequality of learning opportunity as a result of these closures used a single indicator of socio-economic status, neglecting important determinants of remote learning. Using data from the Understanding Society (USoc) COVID-19 surveys we analysed the levels and differentials in the uptake of remote schoolwork using parental social class, information technology (IT) availability in the home and parental working patterns to capture the distinct resources that families needed to complete remote schoolwork. This is also the first study to assess the extent to which the differentials between socio-economic groups changed between the first and second school-closure periods caused by the pandemic. We found that each of the three factors showed an independent association with the volume of remote schoolwork and that their effect was magnified by their combination. Children in families where the main parent was in an upper-class occupation, where both parents worked from home and where the children had their own IT spent more time doing remote schoolwork than other groups, particularly compared to children of single parents who work from home, children in families where the main parent was in a working-class occupation, where the child had to share IT, and where the parents did not work regularly from home. The differentials between socio-economic groups in the uptake of schoolwork were found to be stable between the two school-closure periods.

Spectral enhancement of PlanetScope using Sentinal-2 images to estimate soybean yield and seed composition.

Soybean is an essential crop to fight global food insecurity and is of great economic importance around the world. Along with genetic improvements aimed at boosting yield, soybean seed composition also changed. Since conditions during crop growth and development influences nutrient accumulation in soybean seeds, remote sensing offers a unique opportunity to estimate seed traits from the standing crops. Capturing phenological developments that influence seed composition requires frequent satellite observations at higher spatial and spectral resolutions. This study introduces a novel spectral fusion technique called multiheaded kernel-based spectral fusion (MKSF) that combines the higher spatial resolution of PlanetScope (PS) and spectral bands from Sentinel 2 (S2) satellites. The study also focuses on using the additional spectral bands and different statistical machine learning models to estimate seed traits, e.g., protein, oil, sucrose, starch, ash, fiber, and yield. The MKSF was trained using PS and S2 image pairs from different growth stages and predicted the potential VNIR1 (705 nm), VNIR2 (740 nm), VNIR3 (783 nm), SWIR1 (1610 nm), and SWIR2 (2190 nm) bands from the PS images. Our results indicate that VNIR3 prediction performance was the highest followed by VNIR2, VNIR1, SWIR1, and SWIR2. Among the seed traits, sucrose yielded the highest predictive performance with RFR model. Finally, the feature importance analysis revealed the importance of MKSF-generated vegetation indices from fused images.

Prediction of heart failure events based on physiologic sensor data in HINODE defibrillator patients.

AIMS: Hospitalizations are common in patients with heart failure and are associated with high mortality, readmission and economic burden. Detecting early signs of worsening heart failure may enable earlier intervention and reduce hospitalizations. The HeartLogic algorithm is designed to predict worsening heart failure using diagnostic data from multiple device sensors. The main objective of this analysis was to evaluate the sensitivity of the HeartLogic alert calculation in predicting worsening heart failure events (HFEs). We also evaluated the false positive alert rate (FPR) and compared the incidence of HFEs occurring in a HeartLogic alert state to those occurring out of an alert state. METHODS: The HINODE study enrolled 144 patients (81 ICD and 63 CRT-D) with device sensor data transmitted via a remote monitoring system. HeartLogic alerts were then retrospectively simulated using relevant sensor data. Clinicians and patients were blinded to calculated alerts. Reported adverse events with HF symptoms were adjudicated and classified by an independent HFE committee. Sensitivity was defined as the ratio of the number of detected usable HFEs (true positives) to the total number of usable HFEs. A false positive alert was defined as an alert with no usable HFE between the alert onset date and the alert recovery date plus 30 days. The patient follow-up period was categorized as in alert state or out of alert state. The event rate ratio was the HFE rate calculated in alert to out of alert. RESULTS: The patient cohort was 79% male and had an average age of 68 ± 12 years. This analysis yielded 244 years of follow-up data with 73 HFEs from 37 patients. A total of 311 HeartLogic alerts at the nominal threshold (16) occurred across 106 patients providing an alert rate of 1.27 alerts per patient-year. The HFE rate was 8.4 times greater while in alert compared with out of alert (1.09 vs. 0.13 events per patient-year; P < 0.001). At the nominal alert threshold, 80.8% of HFEs were detected by a HeartLogic alert [95% confidence interval (CI): 69.9%-89.1%]. The median time from first true positive alert to an adjudicated clinical HFE was 53 days. The FPR was 1.16 (95% CI: 0.98-1.38) alerts per patient-year. CONCLUSIONS: Results suggest that signs of worsening HF can be detected successfully with remote patient follow-up. The use of HeartLogic may predict periods of increased risk for HF or clinically significant events, allowing for early intervention and reduction of hospitalization in a vulnerable patient population.