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Agrobiodiversity can promote food and nutritional security. Agrobiodiverse agricultural systems are frequently found on small family coffee farms with distinct economic, social, and environmental characteristics in Central and South America. Brazil is the world’s largest coffee producer, with the state of Minas Gerais, especially the Zona da Mata mesoregion, being the country’s leading producer, recognized for its small mountainous family coffee farms. The presence and composition of home gardens in Brazilian family coffee farms still lack objective and measurable assessments of species agrobiodiversity. Given the limited knowledge about agrobiodiversity at these properties, this study aimed to: (i) evaluate plant communities and agrobiodiversity in the home gardens of family coffee farms in Zona da Mata of Minas Gerais State, Brazil; and (ii) document the use of plant species in these home garden communities. Evaluations were conducted in the home gardens of five family coffee farms located in the Zona da Mata region of Minas Gerais State, Brazil, with identification, quantification, and determination of Simpson, Shannon–Wiener, Pielou’s equitability, Morisita, alpha diversity, species–individual curve, and species use indices across 0.1-ha plots per farm. Significant variations in the number of individuals were recorded with the presence of fruit-bearing and vegetable species in the evaluated home gardens. There were notable differences in species compositions and agrobiodiversity indices of the home gardens of family coffee farms. Considerable plant diversity and species richness were observed in all home gardens, primarily associated with food, commercial, medicinal, and ornamental uses, contributing to the social function of the land and the preservation of plant diversity.

Background: In the post-coronavirus disease 2019 (COVID-19) era, remote diagnosis and precision preventive medicine have emerged as pivotal clinical medicine applications. This study aims to develop a digital health-monitoring tool that utilizes electronic medical records (EMRs) as the foundation for performing a non-random correlation analysis among different comorbidity patterns for heart failure (HF). Methods: Novel similarity indices, including proportional Jaccard index (PJI), multiplication of the odds ratio proportional Jaccard index (OPJI), and alpha proportional Jaccard index (APJI), provide a fundamental framework for constructing machine learning models to predict the risk conditions associated with HF. Results: Our models were constructed for different age groups and sexes and yielded accurate predictions of high-risk HF across demographics. The results indicated that the optimal prediction model achieved a notable accuracy of 82.1% and an area under the curve (AUC) of 0.878. Conclusions: Our noninvasive HF risk prediction system is based on historical EMRs and provides a practical approach. The proposed indices provided simple and straightforward comparative indicators of comorbidity pattern matching within individual EMRs. All source codes developed for our noninvasive prediction models can be retrieved from GitHub.

Background: The optimal frequency of EEG biofeedback sessions for elite athletes remains unclear, despite growing adoption of neurofeedback in high-performance sport. Methods: This randomized, controlled study compared three EEG biofeedback protocols (daily, every-other-day, every-third-day) in 24 national-level male judo athletes stratified into three phenotypic groups. Each protocol comprised 15 standardized sessions. Pre- and post-intervention assessments included functional indices (strength, power) and neurophysiological measures (Frontal Alpha Index, EMG amplitude/RMS, corrected strength sum). Biosensor performance was validated via signal quality metrics. Results: Daily EEG biofeedback produced superior improvements in strength, FAI, and fatigue resistance. Although LRG showed the largest pre–post RMS increase (+17.44 μV vs. +16.54 μV in HRG), HRG maintained the highest post-intervention RMS values and best fatigue resistance (MF_drop = −2.15 Hz). Significant group × time interactions were observed for FAI (p = 0.027) and RMS (p = 0.019). Every-other-day protocols yielded moderate gains, while every-third-day protocols produced minimal or maladaptive EMG–load dynamics. A robust dose–response relationship was evident. Conclusions: Session frequency is critical for optimizing neurofeedback interventions in elite athletes. Daily EEG biofeedback confers superior adaptation compared to less frequent dosing.

Aims Congestive heart failure (CHF) is associated with increased mortality. Early identification of individuals at risk for CHF may improve the poor prognosis. Decreased baroreceptor sensitivity (BRS) has been related to higher mortality in CHF. The aim was therefore to explore whether decreased BRS could identify patients at risk for the development of CHF. Methods and Results The PIVUS (Prospective Investigation of the Vasculature in Uppsala Seniors) study (1016 individuals, all aged 70 years) was used for analysis of baroreceptor sensitivity, measured by sequence analysis (BRSseq) and spectral analysis (BRSαHF and BRSαLF). During 15 years of follow‐up, 98/844 individuals developed CHF. Both decreased and increased BRSseq were associated with incident CHF (p = 0.027) after multiple adjustments. A similar pattern was seen for BRSαHF (p = 0.017) in a sex‐adjusted model, but not after multiple adjustment, while BRSαLF was unrelated to incident CHF. Conclusion A decreased BRS was associated with incident CHF in an elderly population. An increased BRS was also found, although to a lesser degree, to be linked to CHF, which was a novel finding. If reproduced in further studies, BRS might prove to be useful for an early identification of CHF in clinical practice. The PIVUS study was used for analysis of baroreceptor sensitivity, measured by sequence analysis (BRSseq) and spectral analysis (BRSαHF and BRSαLF). During 15 years of follow‐up, 98/844 individuals developed CHF. Both decreased and, to a lesser degree, increased BRSseq and BRSαHF were associated with incident CHF, while BRSαLF was not.

Background Radon gas, a decay product of radium, is a significant environmental health risk due to its radioactive properties. Understanding the radium content and radon exhalation rates in soil is crucial for evaluating potential radiological hazards and ensuring environmental safety. Methods Soil samples were collected from twelve different locations in Wukro, Tigray, representing various soil types. The sealed can technique, using LR-115 Type-II plastic track detectors, was employed to measure radium concentrations and radon exhalation rates over a four-month exposure period. Radon mass exhalation rates, surface exhalation rates, and radium concentrations were calculated, and the Alpha Index was also determined to assess radiological risk. Results and Discussion The radon mass exhalation rates ranged from 0.18 × 10 −7 Bq.kg −1.d −1 to 0.82 × 10 −7 Bq.kg −1.d −1, with a mean of 0.48 × 10 −7 Bq.kg −1.d −1. Surface exhalation rates varied from 0.38 × 10 −6Bq.m −2.d −1 to 1.72 × 10 −6 Bq.m −2.d −1, averaging 1.02 × 10 −6 Bq.m −2.d −1. Radium concentrations ranged from 0.33 to 1.47 Bq.kg −1, with an average of 0.87 Bq.kg −1. A significant positive correlation between radium content and radon exhalation rates was observed, indicating a direct relationship between these variables. Clay soils exhibited the highest radium concentrations, while sandy soils had the lowest. All measured values were below the safety limit of 370 Bq.kg −1 recommended by OECD and UNSCEAR, suggesting no significant radiological risk in the study area. Conclusion This study highlights the importance of monitoring natural radiation levels for environmental safety. The findings provide a baseline for future studies and emphasize the need for continuous assessment to detect any long-term changes in soil radioactivity.

One hundred and ninety-six drinking water samples from the different regions of Tarragona province (Catalonia, Spain) were analysed to determine the gross alpha and beta activity. Individual alpha emitting isotope activities were also determined to evaluate a possible relationship between their radiological content and the lithological and hydrogeological formations present in the studied area. The results obtained showed that approximately 23% of the analysed samples, mainly from five of the evaluated regions, had a gross alpha index exceeding the parametric value of 0.1 Bq/L for waters intended for human consumption according to the current legislation. This could be related to the presence of natural radionuclides in these water samples. The differences between the radiological content in these samples could be related to the different lithological conditions of the areas included in this study. High activity levels of 234 U, 238 U, 224 Ra, 226 Ra and 228 Ra were detected in specific samples, mainly from granitic and carbonate areas. This research also focuses on evaluating the radiological risk associated with water ingestion. In this regard, consuming 95.5% of the drinking water samples analysed would not imply a health risk to the population as the annual effective doses calculated were below 0.1 mSv/year. There was only one sample that exceeded this level with a value of 0.33 mSv/year. 226 Ra activity concentration was the radionuclide that mainly contributed to this dose.

Recent reports of increased national estimates of pediatric psychiatric emergency department (ED) visits and psychiatric hospitalizations emphasize the need to research these utilization patterns. To assess the patient-provider continuity of care (CoC) and compare the risk of psychiatric ED visits or hospitalization according to the CoC level. A cohort design was applied to Medicaid administrative claims data (2007-2014) for 3-16-year olds with a first psychiatric diagnosis between 2009 and 2013 (n=38,825). Continuously enrolled youths with (1) ≥1 outpatient psychiatric visits and (2) ≥4 pediatric outpatient visits in the prior 24 months. The authors assessed CoC in the 24 months before the first psychiatric outpatient visit and quantified CoC using the Alpha Index. The authors assessed patient-provider CoC before first psychiatric diagnosis and the odds of psychiatric ED visits or psychiatric hospitalizations in the year after diagnosis. Of the 38,825 youths, 88.9% received a first psychiatric diagnosis by age 14. The odds of ED visits were significantly higher among youths with low CoC [6.63%, adjusted odds ratio (AOR), 1.27; 95% confidence interval (CI), 1.13-1.41] or moderate CoC (5.76%; AOR, 1.14; 95% CI, 1.02-1.27) compared with those with high CoC (4.96%). Greater odds of psychiatric hospitalization related to low (7.53%; AOR, 1.17; 95% CI, 1.06-1.29) or moderate CoC (7.01%; AOR, 1.15; 95% CI, 1.03-1.27) compared with high CoC (6.06%). The odds of potentially disruptive clinical management and costly psychiatric ED visits or hospitalizations were lower for youths with high CoC. The findings support the need to research the impact of CoC on long-term pediatric mental health service use.

A suite of numerical weather prediction (NWP) verification diagnostics applicable to both scalar and vector variables is developed, highlighting the normalization and successive decomposition of model errors. The normalized root‐mean square error (NRMSE) is broken down into contributions from the normalized bias (NBias) and the normalized pattern error (NPE). The square of NPE, or the normalized error varianceα, is further analyzed into phase and amplitude errors, measured respectively by the correlation and the variance similarity. The variance similarity diagnostic is introduced to verify variability e.g. under different climates. While centered RMSE can be reduced by under‐prediction of variability in the model,αpenalizes over‐ and under‐prediction of variability equally. The error decomposition diagram, the correlation‐similarity diagram and the anisotropy diagram are introduced. The correlation‐similarity diagram was compared with the Taylor diagram: it has the advantage of analyzing the normalized error variance geometrically into contributions from the correlation and variance similarity. Normalization of the error metrics removes the dependence on the inherent variability of a variable and allows comparison among quantities of different physical units and from different regions and seasons. This method was used to assess the Coupled Ocean/Atmospheric Mesoscale Prediction System (COAMPS). The NWP performance degrades progressively from the midlatitudes through the sub‐tropics to the tropics. But similar cold and moist biases are noted and position and timing errors are the main cause of pattern errors. Although the suite of metrics is applied to NWP verification here, it is generally applicable as diagnostics for differences between two data sets. Key Points Development of a set of error diagnostics for model verification Comparison of tropical and midlatitude NWP performance Comparison with the Taylor diagram

We propose a design-based strategy to exploit pellet group counts performed within plots of a prefixed size using the clearance count technique with the purposes of analyzing habitat selection, mapping the pellet group presence throughout the study area, and estimating the abundance of deer populations. As is customary in design-based inference, the strategy is free of model assumptions, and the precision and statistical consistency of the proposed estimators are determined by the probabilistic sampling scheme adopted to locate plots. The unique necessary assumptions are the absence of migratory movements during the survey period, the accurate recording of the number of pellet groups deposited within sample plots between the 2 visits, and a precise approximation of the daily defecation rate of the population. In addition to these assumptions, which can be attained by a suitable design of the survey, the statistical soundness of the strategy rests on the use of tessellation stratified sampling, a stratified sampling scheme that ensures an even distribution of plots throughout the study area. The scheme also allows for the estimation of the standard errors and the construction of confidence intervals without involving any other assumptions. We applied this strategy in summer 2019 in a protected area of a Mediterranean coastal region to estimate the density of a fallow deer (Dama dama) population. We estimated the corresponding standard error considering the uncertainty entailed by the estimation of the daily defecation rate, with the purpose of performing reliable monitoring. The proposed strategy provided precise estimates of deer abundance and is readily implementable in the field, standardized, and easily repeatable over time, thus allowing reliable monitoring and comparisons across time and space, which are fundamental attributes for management of deer populations.

Novel methods for assessing baroreflex sensitivity (BRS) using only pulse photoplethysmography (PPG) signals are presented. Proposed methods were evaluated with a data set containing electrocardiogram (ECG), blood pressure (BP), and PPG signals from 17 healthy subjects during a tilt table test. The methods are based on a surrogate of α index, which is defined as the power ratio of RR interval variability (RRV) and that of systolic arterial pressure series variability (SAPV). The proposed α index surrogates use pulse-to-pulse interval series variability (PPV) as a surrogate of RRV, and different morphological features of the PPG pulse which have been hypothesized to be related to BP, as series surrogates of SAPV. A time-frequency technique was used to assess BRS, taking into account the non-stationarity of the protocol. This technique identifies two time-varying frequency bands where RRV and SAPV (or their surrogates) are expected to be coupled: the low frequency (LF, inside 0.04-0.15 Hz range), and the high frequency (HF, inside 0.15-0.4 Hz range) bands. Furthermore, time-frequency coherence is used to identify the time intervals when the RRV and SAPV (or their surrogates) are coupled. Conventional α index based on RRV and SAPV was used as Gold Standard. Spearman correlation coefficients between conventional α index and its PPG-based surrogates were computed and the paired Wilcoxon statistical test was applied in order to assess whether the indices can find significant differences ( < 0.05) between different stages of the protocol. The highest correlations with the conventional α index were obtained by the α-index-surrogate based on PPV and pulse up-slope (PUS), with 0.74 for LF band, and 0.81 for HF band. Furthermore, this index found significant differences between rest stages and tilt stage in both LF and HF bands according to the paired Wilcoxon test, as the conventional α index also did. These results suggest that BRS changes induced by the tilt test can be assessed with high correlation by only a PPG signal using PPV as RRV surrogate, and PPG morphological features as SAPV surrogates, being PUS the most convenient SAPV surrogate among the studied ones.