Developing biofortified foods such as lettuce is a frequent goal of breeding programs. One obstacle to the success of these efforts is the high temporal and financial cost of determining leaf constituents. Image phenotyping has been increasingly used in crop breeding, but not in lettuce breeding. Until now, the use of image phenotyping to indirectly select carotenoid-rich lettuce inbred lines has not been reported. Therefore, the goal of this study was to use image phenotyping to select lettuce inbred lines with different carotenoid levels. Twenty-two inbred lettuce lines, resulting from the hybridization of the cultivars Pira 72 and Uberlândia 10000 and six successive selfings were evaluated. ‘Grand Rapids’, ‘UFU-Biofort’ and ‘Uberlândia 10000’ were used as controls. The following variables were evaluated: agronomic potential, apparent genetic diversity and image phenotyping. The data were submitted to the ANOVA F test (p ≤ 0.05) and means compared by the Scott–Knott test (p ≤ 0.05). Genetic divergence was represented by dendrograms constructed by UPGMA and the Tocher optimization method. The relative contribution of characters was assessed to identify the most relevant response variable. The genetic diversity of the evaluated germplasm bank was the greatest regarding soil plant analysis development (SPAD)/carotenoid values. Image phenotyping was successfully used to detect different levels of SPAD/carotenoid levels and could be a useful tool for plant breeding. The results of this study can be used to predict the nutritional values of the carotenoid content in lettuce leaves before commercialization.

Sweet corn (Zea mays subsp. saccharata) is mainly intended for industrial processing. Optimizing time and costs during plant breeding is fundamental. An alternative is the use of high-throughput phenotyping (HTP) indirect associated with agronomic traits and chlorophyll contents. This study aimed to (i) verify whether HTP by digital images is useful for screening sweet corn genotypes and (ii) investigate the correlations between the traits evaluated by conventional methods and those obtained from images. Ten traits were evaluated in seven S3 populations of sweet corn and in two commercial hybrids, three traits by classical phenotyping and the others by HTP based on RGB (red, green, blue) and multispectral imaging analysis. The data were submitted to the analyses of variance and Scott-Knott test. In addition, a phenotypic correlation graph was plotted. The hybrids were more productive than the S3 populations, showing an efficient evaluation. The traits extracted using HTP and classical phenotyping showed a high degree of association. HTP was efficient in identifying sweet corn genotypes with higher and lower yield. The vegetative canopy area (VCA), normalized difference vegetation index (NDVI), and visible atmospherically resistant index (VARI) indices were strongly associated with grain yield.

The epidemiology of potentially inappropriate medications (PIMs) in critical care units remains limited, especially in terms of the factors associated with their use.

Background: Adequate nursing staff to meet the demands of patient care in intensive care units (ICUs) is a goal pursued by all health care systems in the world. Purpose: The purpose of this study was to describe the nursing workload, the quantity and composition of nursing staff, and the patient-to-nurse ratio in ICUs from different countries considering the application of the Nursing Activities Score (NAS) for the adequacy of the nursing staff to the nursing workload. Methods: This was an observational, descriptive study with data from 15 ICUs in 10 different countries, comprising 941 patients and 666 nursing professionals. NAS was used to measure the nursing workload. Data about nursing workload and nursing staff were assessed daily during November 2017. Descriptive statistics were used for the data analysis. Results: The majority of patients were male (61%) with a mean age of 61.9 (SD, 16.9) years and a risk of mortality of 26.2%. The mean age of the nursing staff was 41.3 (SD, 10.5) years with 10.8 (SD, 8.9) years of experience in the ICU. Almost 40% were critical care nurses, and 35.7% were registered nurses. The mean NAS (3691 measures) was 67.7 (SD, 27.7). The patient-to-nursing staff ratio varied from 0.8:1 to 1.9:1. There were exceeding nursing staff hours in relation to the nursing hours required by the patients measured by NAS in 70% of the countries. Conclusions: The majority of the ICUs presented a high nursing workload and experienced nursing staff and were exceeding nursing staff hours according to the nursing workload in most countries. However, the diversity of nursing care contexts must be considered for estimating the number of professionals required to provide nursing care according to NAS in the ICUs.

Large-scale phenotyping using unmanned aerial vehicles (UAVs) has been considered an important tool for plant selection. This study aimed to estimate the correlations between agronomic data and vegetation indices (VIs) obtained at different flight heights and to select prediction models to evaluate the potential use of aerial imaging in cassava breeding programs. Various VIs were obtained and analyzed using mixed models to derive the best linear unbiased predictors, heritability parameters, and correlations with various agronomic traits. The VIs were also used to build prediction models for agronomic traits. Aerial imaging showed high potential for estimating plant height, regardless of flight height (r = 0.99), although lower-altitude flights (20 m) resulted in less biased estimates of this trait. Multispectral sensors showed higher correlations compared to RGB, especially for vigor, shoot yield, and fresh root yield (−0.40 ≤ r ≤ 0.50). The heritability of VIs at different flight heights ranged from moderate to high (0.51 ≤ $H_{Cullis}^2$ ≤ 0.94), regardless of the sensor used. The best prediction models were observed for the traits of plant vigor and dry matter content, using the Generalized Linear Model with Stepwise Feature Selection (GLMSS) and the K-Nearest Neighbor (KNN) model. The predictive ability for dry matter content increased with flight height for the GLMSS model ($R^2$ = 0.26 at 20 m and $R^2$ = 0.44 at 60 m), while plant vigor ranged from $R^2$ = 0.50 at 20 m to $R^2$ = 0.47 at 40 m in the KNN model. Our results indicate the practical potential of implementing high-throughput phenotyping via aerial imaging for rapid and efficient selection in breeding programs.