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The γ-aminobutyric acid (GABA) receptors play pivotal roles in the transmission of neuronal information in the nervous system of insects, which has led these proteins to be targeted by synthetic and natural products. Here, we assessed the insecticidal potential of the essential oil of Pectis brevipedunculata (Gardner) Sch. Bip., a neotropical Asteraceae plant used in traditional medicine, for controlling Drosophila suzukii (Matsumura) adults by feeding exposure. By using in silico approaches, we disentangle the contribution of GABA receptors and other potential neuronal targets (e.g., acetylcholinesterase, glutathione-S-transferases) in insects that may explain the essential oil differential activities against D. suzukii and two essential pollinator bees (Apis mellifera Linnaeus and Partamona helleri Friese). Neral (26.7%) and geranial (33.9%) were the main essential oil components which killed D. suzukii with an estimated median lethal concentration (LC50) of 2.25 µL/mL. Both pollinator forager bee species, which would likely contact this compound in the field, were more tolerant to the essential oil and did not have their diet consumptions affected by the essential oil. Based on the molecular predictions for the three potential targets and the essential oil main components, a higher affinity of interaction with the GABA receptors of D. suzukii (geranial -6.2 kcal/mol; neral -5.8 kcal/mol) in relation to A. mellifera (geranial -5.2 kcal/mol; neral -4.9 kcal/mol) would contribute to explaining the difference in toxicities observed in the bioassays. Collectively, our findings indicated the involvement of GABA receptors in the potential of P. brevipedunculata essential oil as an alternative tool for controlling D. suzukii.

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Photovoltaic (PV) power prediction plays a critical role amid the accelerating adoption of renewable energy sources. This paper introduces a bidirectional long short-term memory (BiLSTM) deep learning (DL) model designed for forecasting photovoltaic power one hour ahead. The dataset under examination originates from a small PV installation located at the Polytechnic School of the University of Alcala. To improve the quality of historical data and optimize model performance, a robust data preprocessing algorithm is implemented. The BiLSTM model is synergistically combined with a Bayesian optimization algorithm (BOA) to fine-tune its primary hyperparameters, thereby enhancing its predictive efficacy. The performance of the proposed model is evaluated across diverse meteorological and seasonal conditions. In deterministic forecasting, the findings indicate its superiority over alternative models employed in this research domain, specifically a multilayer perceptron (MLP) neural network model and a random forest (RF) ensemble model. Compared with the MLP and RF reference models, the proposed model achieves reductions in the normalized mean absolute error (nMAE) of 75.03% and 77.01%, respectively, demonstrating its effectiveness in this type of prediction. Moreover, interval prediction utilizing the bootstrap resampling method is conducted, with the acquired prediction intervals carefully adjusted to meet the desired confidence levels, thereby enhancing the robustness and flexibility of the predictions.

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ABSTRACT Introduction: Submaximal strength testing appears to be valid to prescribe the intensity for strength training protocols that reduce the risk of injuries and testing time. Objective: This study aimed to assess the predictive ability of body mass parameters to estimate 4-6 repetitions maximum (4-6 RM) of Leg press 45°, Chest press, and Pull-down exercises. Methods: Eleven male bodybuilders (age 38.27 ± 10.48 years) participated in this study. Participants completed an incremental external load up to find the load allowing them to perform 4 to 6 maximal repetitions for each exercise in random order. The starting load was 50% of body mass for chest press and pull-down exercises and 100% for leg press. The load increment after each set was 20 kg for lower limb exercises and 10 kg for upper body exercises. Results: Results revealed that body mass had good to optimal relationships with 4-6 RM for all three exercises. Results showed that body mass had a good prediction ability for all three criterion measures. Conclusion: The prediction equations suggested in this study may allow coaches to estimate the 4-6 RM of leg press 45°, chest press, and pull-down performances. Evidence Level IV; Case series.

RESUMEN Introducción: El test de fuerza submáxima parece ser válido para prescribir la intensidad en protocolos de entrenamiento de fuerza, reduciendo el riesgo de lesiones y la duración del test. Objetivo: Evaluar la capacidad predictiva de los parámetros de masa corporal para estimar 4-6 repeticiones máximas (4-6 RM) de ejercicios de Leg press 45°, Chest press y Pull-down realizados por fisicoculturistas. Métodos: Once fisicoculturistas masculinos (38,27 ± 10,48 años) participaron en el estudio. Completaron la carga externa incremental hasta encontrar la carga que les permitiera realizar de 4 a 6 repeticiones máximas para cada ejercicio, en orden aleatorio. La carga inicial se fijó en el 50% de la masa corporal para los ejercicios Chest press y Pull-down, y en el 100% para los ejercicios Leg press. El incremento de carga después de cada ronda fue de 20 kg para los miembros inferiores y 10 kg para los miembros superiores. Resultados: Los resultados revelaron que la masa corporal tiene relaciones satisfactorias con 4-6 RM para los tres ejercicios. Los resultados mostraron que la masa corporal tiene una buena capacidad predictiva en las tres medidas. Conclusión: Las ecuaciones de predicción sugeridas en este estudio pueden permitir a los entrenadores utilizar estos ejercicios para medir el rendimiento a 4-6 RM en ejercicios de Leg press 45°, Chest press y Pull-down. Nivel de Evidencia IV; serie de casos.

RESUMO Introdução: O teste de força submáxima parece ser válido para prescrever a intensidade nos protocolos de treinamento de força, reduzindo o risco de lesões e duração dos testes. Objetivo: Avaliar a capacidade preditiva dos parâmetros de massa corporal para estimar o exercício de 4-6 repetições máximas (4-6 RM) nos exercícios de Leg press 45°, Chest press e Pull-down efetuados por fisiculturistas. Métodos: Onze fisiculturistas masculinos (38,27 ± 10,48 anos) participaram do estudo. Eles completaram a carga externa incremental até encontrar a carga que lhes permitia realizar de 4 a 6 repetições máximas para cada exercício, em ordem aleatória. A carga inicial foi fixada em 50% da massa corporal para os exercícios de Chest press e Pull-down, e 100% para o de Leg press. O incremento de carga após cada rodada foi de 20 kg para o exercício de membros inferiores e 10 kg em membros superiores. Resultados: Os resultados revelaram que a massa corporal apresenta relações satisfatórias com 4-6 RM para todos os três exercícios. Os resultados mostraram que a massa corporal possui boa capacidade preditiva em todas as três medidas. Conclusão: As equações de previsão sugeridas nesse estudo podem permitir o uso desses exercícios pelos técnicos para medir a performance a 4-6 RM nos exercícios de Leg press 45°, Chest press, e Pull-down. Nível de evidência IV; série de casos.

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The COVID-19 pandemic erupted at the beginning of 2020 and proved fatal, causing many casualties worldwide. Immediate and precise screening of affected patients is critical for disease control. COVID-19 is often confused with various other respiratory disorders since the symptoms are similar. As of today, the reverse transcription-polymerase chain reaction (RT-PCR) test is utilized for diagnosing COVID-19. However, this approach is sometimes prone to producing erroneous and false negative results. Hence, finding a reliable diagnostic method that can validate the RT-PCR test results is crucial. Artificial intelligence (AI) and machine learning (ML) applications in COVID-19 diagnosis has proven to be beneficial. Hence, clinical markers have been utilized for COVID-19 diagnosis with the help of several classifiers in this study. Further, five different explainable artificial intelligence techniques have been utilized to interpret the predictions. Among all the algorithms, the k-nearest neighbor obtained the best performance with an accuracy, precision, recall and f1-score of 84%, 85%, 84% and 84%. According to this study, the combination of clinical markers such as eosinophils, lymphocytes, red blood cells and leukocytes was significant in differentiating COVID-19. The classifiers can be utilized synchronously with the standard RT-PCR procedure making diagnosis more reliable and efficient.

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In this paper, we propose a hierarchical Bayesian approach for modeling the evolution of the 7-day moving average for the number of deaths due to COVID-19 in a country, state or city. The proposed approach is based on a Gaussian process regression model. The main advantage of this model is that it assumes that a nonlinear function f used for modeling the observed data is an unknown random parameter in opposite to usual approaches that set up f as being a known mathematical function. This assumption allows the development of a Bayesian approach with a Gaussian process prior over f. In order to estimate the parameters of interest, we develop an MCMC algorithm based on the Metropolis-within-Gibbs sampling algorithm. We also present a procedure for making predictions. The proposed method is illustrated in a case study, in which, we model the 7-day moving average for the number of deaths recorded in the state of São Paulo, Brazil. Results obtained show that the proposed method is very effective in modeling and predicting the values of the 7-day moving average.

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Is there variation across cultures in what counts as a lie? Here we present evidence for a potentially unique conceptualization of lying in Shuar-Achuar communities in Ecuador, contrasting this conceptualization with people in twelve other countries and non-Shuar-Achuar Ecuadorians. In Shuar-Achuar communities, but not others, predictions of the future that turn out to be false are considered lies, even when the events that render them false are unforeseen. Failed commitments, on the other hand, are not seen as lies when unforeseen events prevent them from being kept. To explain this phenomenon, we suggest that there is an epistemic norm that regulates predictive speech acts in Shuar-Achuar communities, linked to the view that the future can be known under certain special circumstances. This norm holds that claiming knowledge of the future is a form of lying when events prove the prediction false. Commitments, on the other hand, do not imply certainty about the future and so are not considered lies when circumstances prevent them from being fulfilled. In addition, we found several other factors that influence whether speech acts are categorized as lies, including the speaker's expertise, group membership, and the nature of the outcome.

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Trypanosoma cruzi is a protozoan transmitted by the insect Triatoma infestans, popularly known as kissing bug. This protozoan causes the Chagas disease, a Neglected Tropical Disease. This study aimed to investigate, through DFT method and B3LYP hybrid functional, the physicochemical, pharmacokinetic, and pharmacodynamic properties of the alkaloids present in the leaves of the species Pilocarpus microphyllus (jaborandi) as a potential inhibitory activity on the protease sterol 14α-demethylase of T. cruzi associated with the techniques of molecular docking, molecular dynamics, MM-PBSA and ADMET predictions. The molecules of isopilosine, epiisopiloturine, epiisopilosine, and pilosine showed up the lowest binding energies by molecular docking, good human intestinal absorption, low penetration in the blood-brain barrier, antiprotozoal and anticarcinogenic activities in ADMET studies. It has been observed a better binding affinity of the sterol 14α-demethylase protease with isopilosine in molecular dynamics and MM-PBSA studies, which indicates it as a potential drug candidate for Chagas disease.Communicated by Ramaswamy H. Sarma.

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Rambouillet sheep are commonly raised in extensive grazing systems in the US, mainly for wool and meat production. Genomic evaluations in US sheep breeds, including Rambouillet, are still incipient. Therefore, we aimed to evaluate the feasibility of performing genomic prediction of breeding values for various traits in Rambouillet sheep based on single nucleotide polymorphisms (SNP) or haplotypes (fitted as pseudo-SNP) under a single-step GBLUP approach. A total of 28,834 records for birth weight (BWT), 23,306 for postweaning weight (PWT), 5,832 for yearling weight (YWT), 9,880 for yearling fibre diameter (YFD), 11,872 for yearling greasy fleece weight (YGFW), and 15,984 for number of lambs born (NLB) were used in this study. Seven hundred forty-one individuals were genotyped using a moderate (50 K; n = 677) or high (600 K; n = 64) density SNP panel, in which 32 K SNP in common between the two SNP panels (after genotypic quality control) were used for further analyses. Single-step genomic predictions using SNP (H-BLUP) or haplotypes (HAP-BLUP) from blocks with different linkage disequilibrium (LD) thresholds (0.15, 0.35, 0.50, 0.65, and 0.80) were evaluated. We also considered different blending parameters when constructing the genomic relationship matrix used to predict the genomic-enhanced estimated breeding values (GEBV), with alpha equal to 0.95 or 0.50. The GEBV were compared to the estimated breeding values (EBV) obtained from traditional pedigree-based evaluations (A-BLUP). The mean theoretical accuracy ranged from 0.499 (A-BLUP for PWT) to 0.795 (HAP-BLUP using haplotypes from blocks with LD threshold of 0.35 and alpha equal to 0.95 for YFD). The prediction accuracies ranged from 0.143 (A-BLUP for PWT) to 0.330 (A-BLUP for YGFW) while the prediction bias ranged from -0.104 (H-BLUP for PWT) to 0.087 (HAP-BLUP using haplotypes from blocks with LD threshold of 0.15 and alpha equal to 0.95 for YGFW). The GEBV dispersion ranged from 0.428 (A-BLUP for PWT) to 1.035 (A-BLUP for YGFW). Similar results were observed for H-BLUP or HAP-BLUP, independently of the LD threshold to create the haplotypes, alpha value, or trait analysed. Using genomic information (fitting individual SNP or haplotypes) provided similar or higher prediction and theoretical accuracies and reduced the dispersion of the GEBV for body weight, wool, and reproductive traits in Rambouillet sheep. However, there were no clear improvements in the prediction bias when compared to pedigree-based predictions. The next step will be to enlarge the training populations for this breed to increase the benefits of genomic predictions.

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The use of formulations containing botanical products for controlling insects that vector human and animal diseases has increased in recent years. Plant extracts seem to offer fewer risks to the environment and to human health without reducing the application strategy's efficacy when compared to synthetic and conventional insecticides and repellents. Here, we evaluated the potential of extracts obtained from caninana, Chiococca alba (L.) Hitchc. (Rubiaceae), plants as a tool to be integrated into the management of Aedes aegypti, one of the principal vectors for the transmission of arborviruses in humans. We assessed the larvicidal and repellence performance against adult mosquitoes and evaluated the potential undesired effects of the extracts on non-target organisms. We assessed the susceptibility and predatory abilities of the nymphs of Belostoma anurum, a naturally occurring mosquito larva predator, and evaluated the C. alba extract's cytotoxic effects in mammalian cell lines. Our chromatographic analysis revealed 18 compounds, including rutin, naringin, myricetin, morin, and quercetin. The methanolic extracts of C. alba showed larvicidal (LC50 = 82 (72-94) mg/mL) activity without killing or affecting the abilities of B. anurum to prey upon mosquito larvae. Our in silico predictions revealed the molecular interactions between rutin and the AeagOBP1 receptor to be one possible mechanism for the repellent potential recorded for formulations containing C. alba extracts. Low cytotoxicity against mammalian cell lines reinforces the selectivity of C. alba extracts. Collectively, our findings highlight the potential of C. alba and one of its constituents (rutin) as alternative tools to be integrated into the management of A. aegypti mosquitoes.

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BACKGROUND: Diabetes is associated to a high financial and disease burden, explaining a large proportion of expenditure of the health system in one year. The purpose of this study was to estimate long-term costs and health outcomes of recently diagnosed patients with type 2 diabetes in Chile. METHODS: Cost and consequence study based on mathematical discrete event simulation (DES) model. We modelled expected costs (USD) and quality-adjusted life-years (QALYs) from diagnosis to death (or the age of 95) of a hypothetical cohort of 100,000 incident cases, simulated based on the Chilean National Health Survey 2018. The incidence of twelve complications was estimated assuming the hazard functions provided by the United Kingdom Prospective Diabetes Study. We explore heterogeneity across patients based on their baseline risk covariates and their impact on costs and QALYs. RESULTS: The expected cost and QALY of a recently diagnosed type 2 diabetes patient in Chile were USD 8660 and 12.44 QALYs. Both costs and QALYs were independently determined by baseline risk and the patient's life expectancy from the diagnosis. Length of life since diagnosis showed the major impact on costs (5.2% increase for every additional year). Myocardial infarction was the most frequent complication (47.4%) and the most frequent cause of death. CONCLUSION: Diabetes type 2 determines a significant expenditure of the health system and substantial health losses. Although the control of cardiovascular risk factors and the metabolic control of the disease, both have an important impact on costs and outcomes, the main impact is achieved by postponing the age of onset of the disease.

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Cucurbita moschata D. seed oil contains approximately 75% unsaturated fatty acids, with high levels of monounsaturated fatty acids and antioxidant compounds such as vitamin E and carotenoids, constituting a promising food in nutritional terms. In addition, the Brazilian germplasm of C. moschata exhibits remarkable variability, representing an important source for the genetic breeding of this vegetable and other cucurbits. The present study evaluated the productivity and profile of the seed oil of 91C. moschata accessions from different regions of Brazil maintained in the Vegetable Germplasm Bank of the Federal University of Viçosa (BGH-UFV). A field experiment was conducted between January and July 2016. The accessions showed high genetic variability in terms of characteristics related to seed oil productivity (SOP), such as the weight of seeds per fruit and productivity of seeds, providing predicted selection gains of 29.39 g and 0.26 t ha-1, respectively. Based on the phenotypic and genotypic correlations, a greater SOP can be achieved while maintaining a high oleic acid concentration and low linoleic acid concentration, providing oil of better nutritional and chemical quality. In the variability analysis, the accessions were clustered into five groups, which had different averages for SOP and fatty acid concentration of seed oil, an approach that will guide the use of appropriate germplasm in programs aimed at genetic breeding for SOP and seed oil profile. Per se analysis identified BGH-4610, BGH-5485A, BGH-6590, BGH-5556A, BGH-5472A, and BGH-5544A as the most promising accessions in terms of SOP, with an average (µ + g) of approximately 0.20 t ha-1. The most promising accessions for a higher oleic acid concentration of seed oil were BGH-5456A, BGH-3333A, BGH-5361A, BGH-5472A, BGH-5544A, BGH-5453A, and BGH-1749, with an average (µ + g) of approximately 30%, almost all of which were also the most promising in terms of a lower linoleic acid concentration of the seed oil, with an average (µ + g) of approximately 45%. Part of the C. moschata accessions evaluated in the present study can serve as a promising resource in genetic breeding programs for SOP and fatty acid profile, aiming at the production of oil with better nutritional and physicochemical quality.

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Background: A one-third reduction in premature mortality (30-69 years) from chronic noncommunicable diseases is goal 3.4 of the United Nations Sustainable Development Goals (UN SDG). The burden of NCDs is expected to continue to increase in low- and middle-income countries, including Brazil. Objectives: The aim of this study was to assess geographical and temporal patterns in premature cancer mortality in Brazil between 2001 and 2015 and to predict this to 2030 in order to benchmark against the 3.4 SDG target. Methods: We used data on deaths from cancer in those aged 30-69, by age group, sex and cancer site, between 2001 and 2015 from the National Mortality Information System of Brazil (SIM). After correcting for ill-defined causes, crude and world age-standardised mortality rates per 100,000 inhabitants were calculated nationally and for the 5 regions. Predictions were calculated using NordPred, up to 2030. Results: The difference in observed (2011-2015) and predicted (2026-2030) mortality was compared against the SDG 3.4 target. Between 2011-2015 and 2026-2030 a 12.0% reduction in premature cancer age-standardised mortality rate among males and 4.6% reduction among females is predicted nationally. Across regions this varied from 2.8% among females in North region to 14.7% among males in South region. Lung cancer mortality rates are predicted to decrease among males but not among females nationally (men 28%, females 1.1% increase) and in all regions. Cervical cancer mortality rates are projected to remain very high in the North. Colorectal cancer mortality rates will increase for both sexes in all regions except the Southeast. Conclusions and recommendation: Cancer premature mortality is expected to decrease in Brazil, but the extent of the decrease will be far from the SDG 3.4 target. Nationally, only male lung cancer will be close to reaching the SDG 3.4 target, reflecting the government's long-term efforts to reduce tobacco consumption. Projected colorectal cancer mortality increases likely reflect the epidemiological transition. This and, cervical cancer control will continue to be major challenges. These results will help inform strategic planning for cancer primary prevention, early detection and treatment programs; such initiatives should take cognizance of the regional differences highlighted here.

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OBJECTIVE: We aimed to develop and test an algorithm for individual patient predictions of problem coping experiences (PCE) (i.e., patients' understanding and ability to deal with their problems) effects in cognitive-behavioral therapy. Method: In an outpatient sample with a variety of diagnoses (n=1010), we conducted Dynamic Structural Equation Modelling to estimate within-patient cross-lagged PCE effects on outcome during the first ten sessions. In a randomly selected training sample (2/3 of the cases), we tried different machine learning algorithms (i.e., ridge regression, LASSO, elastic net, and random forest) to predict PCE effects (i.e., the degree to which PCE was a time-lagged predictor of symptoms), using baseline demographic, diagnostic, and clinically-relevant patient features. Then, we validated the best algorithm on a test sample (1/3 of the cases). RESULTS: The random forest algorithm performed best, explaining 14.7% of PCE effects variance in the training set. The results remained stable in the test set, explaining 15.4% of PCE effects variance. CONCLUSIONS: The results show the suitability to perform individual predictions of process effects, based on patients' initial information. If the results are replicated, the algorithm might have the potential to be implemented in clinical practice by integrating it into monitoring and therapist feedback systems.

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In January 25, 2019, the B1 dam of Córrego do Feijão mine located in Brumadinho municipality (Minas Gerais, Brazil) collapsed and injected nearly 2.8 Mm3 of iron (Fe)- and manganese (Mn)-rich tailings in the Paraopeba River. This study assessed the contribution of tailings to the contamination of sediments and water by those metals. The dataset was built through daily to weekly samplings executed in the two years following the event, at 27 sites located along the Paraopeba plus 9 sites located at the confluence of main tributaries. The results evidenced a distinct contribution in the sectors "Anomalous" (8.6-63.3 km downstream from the dam) and "Natural" (115.8-341.6 km). The "Anomalous" sector presented large Fe/Al (12.2 ± 6.4) and Mn/Al (0.33 ± 0.19) ratios in sediments, thus being rich in tailings, while the "Natural" sector presented small ratios (2.4 ± 1.0; 0.06 ± 0.03) comparable to the natural sediments. A 500-700 m3/s stream flow discharge in the Paraopeba caused pronounced drops to the Fe/Al and Mn/Al ratios in the "Anomalous" sector, attributed to the mixture of contaminated sediments from the main water course with uncontaminated sediments injected by the tributaries during the event. Non-linear regressions showed Fe/Al and Mn/Al declines in the "Anomalous" sector, related with tailings mobilization downstream. The concentrations of Fe and Al in the sediments correlated positively with the corresponding concentrations in the Paraopeba water, conditioned by raising discharge rates and variations in the water pH. The contribution of tailings to the Fe correlation was demonstrated. No direct relation was established between the Mn concentrations in water and stream discharge, because manganese is associated with fine particles in the tailings that are mobilized to the water column even under low flows. The preliminary results of Seasonal Autoregressive Integrated Moving Average models predicted the return of Paraopeba to a pre-collapse condition in 7-11 years.

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Genomic prediction (GP) offers great opportunities for accelerated genetic gains by optimizing the breeding pipeline. One of the key factors to be considered is how the training populations (TP) are composed in terms of genetic improvement, kinship/origin, and their impacts on GP. Hydrogen cyanide content (HCN) is a determinant trait to guide cassava's products usage and processing. This work aimed to achieve the following objectives: (i) evaluate the feasibility of using cross-country (CC) GP between germplasm's of Embrapa Mandioca e Fruticultura (Embrapa, Brazil) and The International Institute of Tropical Agriculture (IITA, Nigeria) for HCN; (ii) provide an assessment of population structure for the joint dataset; (iii) estimate the genetic parameters based on single nucleotide polymorphisms (SNPs) and a haplotype-approach. Datasets of HCN from Embrapa and IITA breeding programs were analyzed, separately and jointly, with 1,230, 590, and 1,820 clones, respectively. After quality control, ∼14K SNPs were used for GP. The genomic estimated breeding values (GEBVs) were predicted based on SNP effects from analyses with TP composed of the following: (i) Embrapa genotypic and phenotypic data, (ii) IITA genotypic and phenotypic data, and (iii) the joint datasets. Comparisons on GEBVs' estimation were made considering the hypothetical situation of not having the phenotypic characterization for a set of clones for a certain research institute/country and might need to use the markers' effects that were trained with data from other research institutes/country's germplasm to estimate their clones' GEBV. Fixation index (FST) among the genetic groups identified within the joint dataset ranged from 0.002 to 0.091. The joint dataset provided an improved accuracy (0.8-0.85) compared to the prediction accuracy of either germplasm's sources individually (0.51-0.67). CC GP proved to have potential use under the present study's scenario, the correlation between GEBVs predicted with TP from Embrapa and IITA was 0.55 for Embrapa's germplasm, whereas for IITA's it was 0.1. This seems to be among the first attempts to evaluate the CC GP in plants. As such, a lot of useful new information was provided on the subject, which can guide new research on this very important and emerging field.

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Mucopolysaccharidosis type I (MPS I) is an autosomal recessive disease characterized by the deficiency of alpha-L-iduronidase (IDUA), an enzyme involved in glycosaminoglycan degradation. More than 200 disease-causing variants have been reported and characterized in the IDUA gene. It also has several variants of unknown significance (VUS) and literature conflicting interpretations of pathogenicity. This study evaluated 586 variants obtained from the literature review, five population databases, in addition to dbSNP, Human Genome Mutation Database (HGMD), and ClinVar. For the variants described in the literature, two datasets were created based on the strength of the criteria. The stricter criteria subset had 108 variants with expression study, analysis of healthy controls, and/or complete gene sequence. The less stringent criteria subset had additional 52 variants found in the literature review, HGMD or ClinVar, and dbSNP with an allele frequency higher than 0.001. The other 426 variants were considered VUS. The two strength criteria datasets were used to evaluate 33 programs plus a conservation score. BayesDel (addAF and noAF), PON-P2 (genome and protein), and ClinPred algorithms showed the best sensitivity, specificity, accuracy, and kappa value for both criteria subsets. The VUS were evaluated with these five algorithms. Based on the results, 122 variants had total consensus among the five predictors, with 57 classified as predicted deleterious and 65 as predicted neutral. For variants not included in PON-P2, 88 variants were considered deleterious and 92 neutral by all other predictors. The remaining 124 did not obtain a consensus among predictors.

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Contaminants of emerging concern (CECs) have been a focus of study for years, with investigations revealing the contamination of different environmental matrices (surface water, soil, air, and sediment) by diverse classes of microcontaminants. Understanding the contamination profiles requires identification and risk assessment of the microcontaminants. In the present work, analysis was made of the presence of 3250 compounds in 27 samples from the Conceição River (Rio Grande do Sul State, Brazil), using an SPE-LC-QTOF MS method. In total, 150 microcontaminants (confirmed and suspected) of different classes, especially pesticides and pharmaceuticals, were identified by an initial qualitative analysis. Subsequently, in silico predictions of eight endpoints, using quantitative structure-activity relationship ((Q)SAR) models, were employed to determine the risk of each previously screened microcontaminant. This large amount of (Q)SAR data, frequently with conflicting information in relation to the responses of the different endpoints, makes it difficult to define which microcontaminants should be prioritized for analysis. Therefore, in order to rank the identified microcontaminants by risk assessment, two multi-criteria decision-making (MCDM) ranking techniques (ToxPi and TOPSIS), associated with a weighting method, were performed to establish the order of priority for further quantitative analysis of the most hazardous microcontaminants. The two rankings were statistically similar, especially for the 20 highest priority microcontaminants. Nonetheless, sensitivity tests carried out for the ToxPi and TOPSIS outputs showed higher performance robustness of TOPSIS, compared to ToxPi. This is the first time that such an approach (screening/(Q)SAR/MCDM methods) has been performed in the context of microcontaminant environmental risk evaluation and demonstrated to be an available strategy to help rank the most concern microcontaminants identified in aqueous environment samples.

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In this study, we defined the target population of environments (TPE) for wheat breeding in India, the largest wheat producer in South Asia, and estimated the correlated response to the selection and prediction ability of five selection environments (SEs) in Mexico. We also estimated grain yield (GY) gains in each TPE. Our analysis used meteorological, soil, and GY data from the international Elite Spring Wheat Yield Trials (ESWYT) distributed by the International Maize and Wheat Improvement Center (CIMMYT) from 2001 to 2016. We identified three TPEs: TPE 1, the optimally irrigated Northwestern Plain Zone; TPE 2, the optimally irrigated, heat-stressed North Eastern Plains Zone; and TPE 3, the drought-stressed Central-Peninsular Zone. The correlated response to selection ranged from 0.4 to 0.9 within each TPE. The highest prediction accuracies for GY per TPE were derived using models that included genotype-by-environment interaction and/or meteorological information and their interaction with the lines. The highest prediction accuracies for TPEs 1, 2, and 3 were 0.37, 0.46, and 0.51, respectively, and the respective GY gains were 118, 46, and 123 kg/ha/year. These results can help fine-tune the breeding of elite wheat germplasm with stable yields to reduce farmers' risk from year-to-year environmental variation in India's wheat lands, which cover 30 million ha, account for 100 million tons of grain or more each year, and provide food and livelihoods for hundreds of millions of farmers and consumers in South Asia.

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Este artículo presenta una historia general de las epidemias históricas y de las nuevas enfermedades emergentes, señalando sus factores desencadenantes. Se afirma que las epidemias son inevitables, y que su riesgo aumenta en proporción al tamaño, la complejidad y el poder tecnológico de nuestras sociedades. La historia enseña que las epidemias han sido casi siempre desencadenadas por cambios en el ambiente ocasionados por las propias actividades humanas. Las enfermedades infecciosas son manifestación de una interacción ecológica entre la especie humana y otra especie de microorganismos. Y las epidemias son resultado del cambio en algún factor ambiental capaz de influir en esa interacción. Las catástrofes epidémicas son inevitables: en primer lugar, porque no podemos evitar formar parte de cadenas tróficas en las que comemos y somos comidos por los microbios; en segundo lugar, porque las infecciones son mecanismos evolutivos y factores reguladores del equilibrio ecológico, que regulan sobre todo el tamaño de las poblaciones; y, en tercer lugar, porque las intervenciones técnicas humanas, al modificar los equilibrios previos, crean equilibrios nuevos que son más vulnerables. De este modo las sociedades humanas son más vulnerables cuanto más complejas. Y los éxitos humanos en la modificación de condiciones ambientales conservan, o más bien aumentan, el riesgo de catástrofes epidémicas. Todas las necesarias medidas de vigilancia y control epidemiológico imaginables pueden disminuir los daños que producen las epidemias, pero nunca podrán evitarlas.

This article presents a general history of historical epidemics, and new emerging diseases, pointing out their triggers. It is claimed that epidemics are inevitable, and that their risk increases in proportion to the size, complexity, and technological power of our societies. History teaches that epidemics have almost always been triggered by changes in the environment caused by human activities themselves. Infectious diseases are manifestations of an ecological interaction between the human species and another species of microorganisms. And epidemics are the result of a change in some environmental factor capable of influencing that interaction. Epidemic catastrophes are inevitable: firstly, because we cannot help but be part of trophic chains in which we eat and are eaten by microbes; secondly, because infections are evolutionary mechanisms and regulatory factors of ecological balance, which regulate especially the size of populations; and thirdly, because human technical interventions, in changing previous balances, create new balances that are more vulnerable. In this way human societies are more vulnerable the more complex. And human successes in modifying environmental conditions retain, or rather increase, the risk of epidemic catastrophes. All necessary epidemiological surveillance and control measures imaginable can lessen the damage caused by epidemics, but they can never prevent them.

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RESUMO

Genotyping-by-sequencing (GBS) provides the marker density required for genomic predictions (GP). However, GBS gives a high proportion of missing SNP data which, for species without a chromosome-level genome assembly, must be imputed without knowing the SNP physical positions. Here, we compared GP accuracy with seven map-independent and two map-dependent imputation approaches, and when using all SNPs against the subset of genetically mapped SNPs. We used two rubber tree (Hevea brasiliensis) datasets with three traits. The results showed that the best imputation approaches were LinkImputeR, Beagle and FImpute. Using the genetically mapped SNPs increased GP accuracy by 4.3%. Using LinkImputeR on all the markers allowed avoiding genetic mapping, with a slight decrease in GP accuracy. LinkImputeR gave the highest level of correctly imputed genotypes and its performances were further improved by its ability to define a subset of SNPs imputed optimally. These results will contribute to the efficient implementation of genomic selection with GBS. For Hevea, GBS is promising for rubber yield improvement, with GP accuracies reaching 0.52.

Assuntos