ugh use along with other diagnostic methods. Emergency department (ED) revisits increase overcrowding and predicting which patients may need to revisit could increase patient safety. This study aimed to identify clinical variables that could be used to predict the probability of revisiting ED within 48 hours of discharge. A retrospective case-control study was conducted between July 2018 and January 2019 at the Emergency Medicine Department in Ramathibodi Hospital, Bangkok, Thailand. Patients who revisited the ED within 48 hours of discharge (case group) and patients who did not (control group) participated. The predictive factors for ED revisit were identified through multivariate logistic regression analysis. The case group consisted of 372 patients, who revisited the ED within 48 hours, and the control group consisted of 1488 patients. The most common reason for revisiting the ED was recurring gastrointestinal illness, in 107 patients (28.76%). According to the multivariate data analysis , five factors influenced the probability of revisiting the ED age of more than 60 years (p < 0.001, OR = 2.04, 95%CI 1.51-2.77), initial Emergency Severity Index (ESI) triage level of 2 (p = 0.007, OR = 1.20, 95%CI 0.93-1.56), ED stay duration of 4 hours or longer (p = 0.013, OR = 1.12, 95%CI 0.87-1.44), body temperature of ≥37.5ºC on discharge (p = 0.034, OR = 1.34, 95%CI 1.00-1.80), and pulse rate of less than 60 (OR = 1.55, 95%CI 0.87-2.77) or more than 100 beats/minute (OR = 1.53, 95%CI 1.10-2.11) (p = 0.011). According to the findings, the most important and independent predictive factor of ED revisit within 48 hours of discharge were, age ≥ 60 years, ESI triage level 2, ED length of stay ≥ 4 hours, temperature ≥ 37.5 C, and 60 > pulse rate ≥ 100 beats/minute. pulse rate ≥ 100 beats/minute.Selection of sugar beet (Beta vulgaris L.) cultivars that are resistant to Cercospora Leaf Spot (CLS) disease is critical to increase yield. Such selection requires an automatic, fast, and objective method to assess CLS severity on thousands of cultivars in the field. For this purpose, we compare the use of submillimeter scale RGB imagery acquired from an Unmanned Ground Vehicle (UGV) under active illumination and centimeter scale multispectral imagery acquired from an Unmanned Aerial Vehicle (UAV) under passive illumination. https://www.selleckchem.com/products/chir-124.html Several variables are extracted from the images (spot density and spot size for UGV, green fraction for UGV and UAV) and related to visual scores assessed by an expert. Results show that spot density and green fraction are critical variables to assess low and high CLS severities, respectively, which emphasizes the importance of having submillimeter images to early detect CLS in field conditions. Genotype sensitivity to CLS can then be accurately retrieved based on time integrals of UGV- and UAV-derived scores. While UGV shows the best estimation performance, UAV can show accurate estimates of cultivar sensitivity if the data are properly acquired. Advantages and limitations of UGV, UAV, and visual scoring methods are finally discussed in the perspective of high-throughput phenotyping.Early detection of plant diseases, prior to symptom development, can allow for targeted and more proactive disease management. The objective of this study was to evaluate the use of near-infrared (NIR) spectroscopy combined with machine learning for early detection of rice sheath blight (ShB), caused by the fungus Rhizoctonia solani. We collected NIR spectra from leaves of ShB-susceptible rice (Oryza sativa L.) cultivar, Lemont, growing in a growth chamber one day following inoculation with R. solani, and prior to the development of any disease symptoms. Support vector machine (SVM) and random forest, two machine learning algorithms, were used to build and evaluate the accuracy of supervised classification-based disease predictive models. Sparse partial least squares discriminant analysis was used to confirm the results. The most accurate model comparing mock-inoculated and inoculated plants was SVM-based and had an overall testing accuracy of 86.1% (N = 72), while when control, mock-inoculated, and inoculated plants were compared the most accurate SVM model had an overall testing accuracy of 73.3% (N = 105). These results suggest that machine learning models could be developed into tools to diagnose infected but asymptomatic plants based on spectral profiles at the early stages of disease development. While testing and validation in field trials are still needed, this technique holds promise for application in the field for disease diagnosis and management.Highly repeatable, nondestructive, and high-throughput measures of above-ground biomass (AGB) and crop growth rate (CGR) are important for wheat improvement programs. This study evaluates the repeatability of destructive AGB and CGR measurements in comparison to two previously described methods for the estimation of AGB from LiDAR 3D voxel index (3DVI) and 3D profile index (3DPI). Across three field experiments, contrasting in available water supply and comprising up to 98 wheat genotypes varying for canopy architecture, several concurrent measurements of LiDAR and AGB were made from jointing to anthesis. Phenotypic correlations at discrete events between AGB and the LiDAR-derived biomass indices were significant, ranging from 0.31 (P less then 0.05) to 0.86 (P less then 0.0001), providing confidence in the LiDAR indices as effective surrogates for AGB. The repeatability of the LiDAR biomass indices at discrete events was at least similar to and often higher than AGB, particularly under water limitation. The correlations between calculated CGR for AGB and the LiDAR indices were moderate to high and varied between experiments. However, across all experiments, the repeatabilities of the CGR derived from the LiDAR indices were appreciably greater than those for AGB, except for the 3DPI in the water-limited environment. In our experiments, the repeatability of either LiDAR index was consistently higher than that of AGB, both at discrete time points and when CGR was calculated. These findings provide promising support for the reliable use of ground-based LiDAR, as a surrogate measure of AGB and CGR, for screening germplasm in research and wheat breeding.