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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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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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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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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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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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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.