Spatial Analysis of the Ecogeographic Diversity of Wild Creeping Cucumber (Melothria pendula L.) for In Situ and Ex Situ Conservation in Mexico.

Melothria pendula L., a wild relative of cucurbit crops, is also used for food and as a medicinal plant in Mexico. The objective of this study was to ecogeographically characterize the known populations of M. pendula in Mexico, determining its adaptive range and possible sites for in situ and ex situ conservation. To achieve this goal, we compiled a dataset of 1270 occurrences of M. pendula from herbarium and botanical databases and individual observations. Adaptive scenarios were generated through the development of an ecogeographic land characterization (ELC) map, preceded by the identification of abiotic variables influencing the species' distribution. Eleven bioclimatic, edaphic, and geophysical variables were found to be important for the species' distribution. The ELC map obtained contained 21 ecogeographic categories, with 14 exhibiting the presence of M. pendula. By analyzing ecogeographic representativeness, 111 sites of high interest were selected for the efficient collection of M. pendula in Mexico. Eight high-priority hotspots for future in situ conservation of M. pendula were also identified based on their high ecogeographic diversity, with only three of these hotspots located within protected natural areas. In this study, ecogeographic approaches show their potential utility in conservation prioritization when genetic data are scarce, a very common condition in crop wild relatives.

Deep learning for discriminating non-trivial conformational changes in molecular dynamics simulations of SARS-CoV-2 spike-ACE2.

Molecular dynamics (MD) simulations produce a substantial volume of high-dimensional data, and traditional methods for analyzing these data pose significant computational demands. Advances in MD simulation analysis combined with deep learning-based approaches have led to the understanding of specific structural changes observed in MD trajectories, including those induced by mutations. In this study, we model the trajectories resulting from MD simulations of the SARS-CoV-2 spike protein-ACE2, specifically the receptor-binding domain (RBD), as interresidue distance maps, and use deep convolutional neural networks to predict the functional impact of point mutations, related to the virus's infectivity and immunogenicity. Our model was successful in predicting mutant types that increase the affinity of the S protein for human receptors and reduce its immunogenicity, both based on MD trajectories (precision = 0.718; recall = 0.800; [Formula: see text] = 0.757; MCC = 0.488; AUC = 0.800) and their centroids. In an additional analysis, we also obtained a strong positive Pearson's correlation coefficient equal to 0.776, indicating a significant relationship between the average sigmoid probability for the MD trajectories and binding free energy (BFE) changes. Furthermore, we obtained a coefficient of determination of 0.602. Our 2D-RMSD analysis also corroborated predictions for more infectious and immune-evading mutants and revealed fluctuating regions within the receptor-binding motif (RBM), especially in the [Formula: see text] loop. This region presented a significant standard deviation for mutations that enable SARS-CoV-2 to evade the immune response, with RMSD values of 5Å in the simulation. This methodology offers an efficient alternative to identify potential strains of SARS-CoV-2, which may be potentially linked to more infectious and immune-evading mutations. Using clustering and deep learning techniques, our approach leverages information from the ensemble of MD trajectories to recognize a broad spectrum of multiple conformational patterns characteristic of mutant types. This represents a strategic advantage in identifying emerging variants, bypassing the need for long MD simulations. Furthermore, the present work tends to contribute substantially to the field of computational biology and virology, particularly to accelerate the design and optimization of new therapeutic agents and vaccines, offering a proactive stance against the constantly evolving threat of COVID-19 and potential future pandemics.

Diffusion on PCA-UMAP Manifold: The Impact of Data Structure Preservation to Denoise High-Dimensional Single-Cell RNA Sequencing Data.

Single-cell transcriptomics (scRNA-seq) is revolutionizing biological research, yet it faces challenges such as inefficient transcript capture and noise. To address these challenges, methods like neighbor averaging or graph diffusion are used. These methods often rely on k-nearest neighbor graphs from low-dimensional manifolds. However, scRNA-seq data suffer from the 'curse of dimensionality', leading to the over-smoothing of data when using imputation methods. To overcome this, sc-PHENIX employs a PCA-UMAP diffusion method, which enhances the preservation of data structures and allows for a refined use of PCA dimensions and diffusion parameters (e.g., k-nearest neighbors, exponentiation of the Markov matrix) to minimize noise introduction. This approach enables a more accurate construction of the exponentiated Markov matrix (cell neighborhood graph), surpassing methods like MAGIC. sc-PHENIX significantly mitigates over-smoothing, as validated through various scRNA-seq datasets, demonstrating improved cell phenotype representation. Applied to a multicellular tumor spheroid dataset, sc-PHENIX identified known extreme phenotype states, showcasing its effectiveness. sc-PHENIX is open-source and available for use and modification.

Geospatial analysis of cardiovascular mortality before and during the COVID-19 pandemic in Peru: analysis of the national death registry to support emergency management in Peru.

Background: COVID-19 has led to significant global mortality, with Peru being among the countries most affected. While pre-existing comorbidities have been linked to most cases, the exact distribution of fatalities within the country remains unclear. We aimed to assess deaths attributed to cardiovascular diseases (CVD) before and during the COVID-19 pandemic across various regions and provinces in Peru. Methods: An observational georeferencing study was designed. Peru faced four waves of COVID-19 over three years, with variable impacts across its three regions (Coast, Highlands, and Jungle). Deaths related to cardiovascular diseases, such as heart failure (HF), arrhythmia, acute myocardial infarction (AMI), strokes, and acute coronary syndrome, were examined as primary variables. The study period spanned pre-pandemic years (2017-2019) and pandemic years (2020-2021), utilizing death data from the National Death Information System (SINADEF). The georeferencing analysis was conducted using ArcGIS v10.3. Results: A total of 28,197 deaths were recorded during the study period, with significant increases during the pandemic (2020-2021). Cardiovascular deaths were disproportionately higher during the pandemic, totaling 19,376 compared to 8,821 in the pre-pandemic period (p < 0.001). AMI and HF were the leading causes of mortality, showing significant increases from the pre-pandemic (5,573 and 2,584 deaths) to the pandemic period (12,579 and 5,628 deaths), respectively. Deaths due to CVD predominantly affected individuals aged over 60, with significant increases between the two study periods (7,245 vs. 16,497 deaths, p = 0.002). Geospatial analysis revealed regional disparities in CVD mortality, highlighting provinces like Lima and Callao as COVID-19 critical areas. The substantial increase in cardiovascular deaths during the COVID-19 pandemic in Peru showed distinctive patterns across regions and provinces. Conclusions: Geospatial analysis identified higher-risk areas and can guide specific interventions to mitigate the impact of future health crises. Understanding the dynamic relationship between pandemics and cardiovascular health is crucial for effective public health strategies.

Optimization of resolution as a simultaneous function of solvent composition, pH and temperature in reversed-phase liquid chromatography.

BACKGROUND: The latest chromatographic retention models are capable of accurately describe the dependencies of retention over a wide range of experimental conditions. By using a suitable conversion, these models can be transformed into equations expressing the optimization criteria as function of multiples variables. Even though that theoretical models significantly reduce the experimental requirements for optimizations, these models have been barely used. Instead, most optimizations rely on empirical exploration of the relationships between criterions and variables. There is a need for a strategy to reduce the required number of experiments in multivariated optimization of separations, and Fundamental Models offer a clear opportunity for addressing it. RESULTS: A Fundamental Model is used to give the simultaneous dependence of chromatographic retention of seven ionizable pesticides on the three variables: solvent composition, temperature and pH (w, T, pH). Based on few experiments, the 10 parameters required to predict the chromatographic retention of those compounds, taken as model analytes, can be obtained. Two mathematical treatments to convert retentions into resolutions between pairs are used: one considering extracolumn dispersions and other neglecting these contributions. Using the Overlapped Resolutions Maps, extended to four dimensions, two optimal conditions can be found for the two different mathematical conversions. Chromatographic conditions were empirically evaluated obtaining the best results for the optimization considering extracolumn dispersions, proving that this condition is a true optimal. It was demonstrated that any small shift in any of the variables from this true optimal leads to a loss in resolution. SIGNIFICANCE: Fundamental Models describing chromatographic retention as a simultaneous function of multiple variables are nowadays very accurate. In this work is demonstrated that these models are useful not only to predict retentions, but also to optimize separations, even in the more challenging mode: isocratic, isothermal and iso-pH. However, the success in the optimization procedure depends also on the proper definition of the mathematical conversion of the Fundamental Models into optimization criteria.

Reactivity of amino acids and short peptide sequences: identifying bioactive compounds via DFT calculations.

Bioactive peptides are short amino acid sequences that play important roles in various physiological processes, including antioxidant and protective effects. These compounds can be obtained through protein hydrolysis and have a wide range of potential applications in a variety of areas. However, despite the potential of these compounds, more in-depth knowledge is still necessary to better understand details regarding their chemical reactivity and electronic properties. In this study, we used molecular modeling techniques to investigate the electronic structure of isolated amino acids (AA) and short peptide sequences. Details on the relative alignments between the frontier electronic levels, local chemical reactivity and donor-acceptor properties of the 20 primary amino acids and some di- and tripeptides were evaluated in the framework of the density functional theory (DFT). Our results suggest that the electronic properties of isolated amino acids can be used to interpret the reactivity of short sequences. We found that aromatic and charged amino acids, as well as Methionine, play a key role in determining the local reactivity of peptides, in agreement with experimental data. Our analyses also allowed us to identify the influence of the relative position of AA and terminations on the local reactivity of the sequences, which can guide experimental studies and help to propose/evaluate possible mechanisms of action. In summary, our data indicate that the position of active sites of polypeptides can be predicted from short sequences, providing a promising strategy for the synthesis and bioprospection of new optimized compounds.

Update of the geographic range of Humboldt's squirrel monkeys (Saimiri cassiquiarensis cassiquiarensis Lesson, 1840) using a model-based approach.

Saimiri cassiquiarensis cassiquiarensis (Cebidae) is a primate subspecies with a wide distribution in the Amazonian region of Brazil, Colombia, and Venezuela. However, the boundaries of its geographic range remain poorly defined. This study presents new occurrence localities for this subspecies and updates its distribution using a compiled data set of 140 occurrence records based on literature, specimens vouchered in scientific collections, and new field data to produce model-based range maps. After cleaning our data set, we updated the subspecies' extent of occurrence, which was used in model calibration. We then modeled the subspecies' range using a maximum entropy algorithm (MaxEnt). The final model was adjusted using a fixed threshold, and we revised this polygon based on known geographic barriers and parapatric congeneric ranges. Our findings indicate that this subspecies is strongly associated with lowland areas, with consistently high daily temperatures. We propose modifications to all range boundaries and estimate that 3% of the area of occupancy (AOO, as defined by IUCN) has already been lost due to deforestation, resulting in a current range of 224,469 km2. We also found that 54% of their AOO is currently covered by protected areas (PAs). Based on these results, we consider that this subspecies is currently properly classified as Least Concern, because it occupies an extensive range, which is relatively well covered by PAs, and is currently experiencing low rates of deforestation.

Saimiri cassiquiarensis cassiquiarensis (Cebidae) é uma subespécie de primata com ampla distribuição na região amazônica do Brasil, Colômbia e Venezuela. No entanto, os limites de sua distribuição geográfica permanecem mal definidos. Este estudo apresenta novas localidades de ocorrência para essa subespécie e atualiza sua distribuição usando 140 registros de ocorrência compilados com base na literatura, espécimes depositados em coleções científicas e novos registros de campo para produzir mapas de distribuição baseados em modelos. Após a limpeza do nosso banco de dados, atualizamos a extensão de ocorrência da subespécie, que foi usada na calibração do modelo. Em seguida, modelamos a área de distribuição da subespécie usando um algoritmo de entropia máxima (MaxEnt). O modelo final foi ajustado usando um limiar fixo e revisamos esse polígono com base em barreiras geográficas conhecidas e na distribuição de congêneres parapátricas. Nosso modelo sugere que a espécie é fortemente associada a áreas planas, com temperaturas diárias consistentemente altas. Propomos modificações em todos os limites da área de distribuição e estimamos que 3% da área de ocupação (AOO, conforme definida pela IUCN) da subespécie já foi perdida devido ao desmatamento, resultando em uma área de distribuição atual de 224,469 km2. Também estimamos que 54% de sua AOO encontra­se atualmente coberta por áreas protegidas. Com base nesses resultados, consideramos que a subespécie está apropriadamente classificada como Pouco Preocupante, pois ocupa uma área extensa, que é relativamente bem coberta por áreas protegidas e atualmente apresenta baixas taxas de desmatamento.

Using Computational Simulations Based on Fuzzy Cognitive Maps to Detect Dengue Complications.

Dengue remains a globally prevalent and potentially fatal disease, affecting millions of people worldwide each year. Early and accurate detection of dengue complications is crucial to improving clinical outcomes and reducing the burden on healthcare systems. In this study, we explore the use of computational simulations based on fuzzy cognitive maps (FCMs) to improve the detection of dengue complications. We propose an innovative approach that integrates clinical data into a computational model that mimics the decision-making process of a medical expert. Our method uses FCMs to model complexity and uncertainty in dengue. The model was evaluated in simulated scenarios with each of the dengue classifications. These maps allow us to represent and process vague and fuzzy information effectively, capturing relationships that often go unnoticed in conventional approaches. The results of the simulations show the potential of our approach to detecting dengue complications. This innovative strategy has the potential to transform the way clinical management of dengue is approached. This research is a starting point for further development of complication detection approaches for events of public health concern, such as dengue.

Artificial neural networks optimize the establishment of a Brazilian germplasm core collection of winter squash (Cucurbita moschata D.).

With widespread cultivation, Cucurbita moschata stands out for the carotenoid content of its fruits such as ß and α-carotene, components with pronounced provitamin A function and antioxidant activity. C. moschata seed oil has a high monounsaturated fatty acid content and vitamin E, constituting a lipid source of high chemical-nutritional quality. The present study evaluates the agronomic and chemical-nutritional aspects of 91 accessions of C. moschata kept at the BGH-UFV and propose the establishment of a core collection based on multivariate approaches and on the implementation of Artificial Neural Networks (ANNs). ANNs was more efficient in identifying similarity patterns and in organizing the distance between the genotypes in the groups. The averages and variances of traits in the CC formed using a 15% sampling of accessions, were closer to those of the complete collection, particularly for accumulated degree days for flowering, the mass of seeds per fruit, and seed and oil productivity. Establishing the 15% CC, based on the broad characterization of this germplasm, will be crucial to optimize the evaluation and use of promising accessions from this collection in C. moschata breeding programs, especially for traits of high chemical-nutritional importance such as the carotenoid content and the fatty acid profile.

A bibliometric analysis of Community Dentistry and Oral Epidemiology: Fifty years of publications.

OBJECTIVES: In celebration of the journal's 50th anniversary, the aim of the study was to review the whole collection of Community Dentistry and Oral Epidemiology (CDOE) publications from 1973 to 2022 and provide a complete overview of the main publication characteristics. METHODS: The study used bibliometric techniques such as performance and science mapping analysis of 3428 articles extracted from the Scopus database. The data were analysed using the 'Bibliometrix' package in R. The journal's scientific production was examined, along with the yearly citation count, the distribution of publications based on authors, the corresponding author's country and affiliation and citation count, citing source and keywords. Bibliometric network maps were constructed to determine the conceptual, intellectual and social collaborative structure over the past 50 years. The trending research topics and themes were identified. RESULTS: The total number of articles and average citations has increased over the years. D Locker, AJ Spencer, A Sheiham and WM Thomson were the most frequently published authors, and PE Petersen, GD Slade and AI Ismail published papers with the highest citations. The most published countries were the United States, United Kingdom, Brazil and Canada, frequently engaging in collaborative efforts. The most common keywords used were 'dental caries', 'oral epidemiology' and 'oral health'. The trending topics were healthcare and health disparities, social determinants of health, systematic review and health inequalities. Epidemiology, oral health and disparities were highly researched areas. CONCLUSION: This bibliometric study reviews CDOE's significant contribution to dental public health by identifying key research trends, themes, influential authors and collaborations. The findings provide insights into the need to increase publications from developing countries, improve gender diversity in authorship and broaden the scope of research themes.

O ensino de ciências na educação básica: desvendando o sistema nervoso através de cartilhas e mapas mentais / Science teaching in basic education: unveiling the nervous system through primers and mental maps / Enseñanza de ciencias en educación básica: desvelando el sistema nervioso a través de primers y mapas mentales

Objetivo: o estudo visou um relato de experiências entre os autores sobre a tutoria do módulo três durante o curso EAD no ano de 2022. Método: Este curso com atividades síncronas e assíncronas, para professores da educação básica e estudantes de graduação, foi realizado em outubro e novembro, do ano de 2022 e culminou na construção de uma cartilha com mapas mentais, temas e estratégias trabalhados durante o curso comomateriais pedagógicos para o ensino fundamental II. Resultados:A cartilha intitulada: as consequências do consumo de álcool ao sistema nervoso, teve como parceria professores de duas escolas básicas. Esta apresenta informações anatomofisiológicas a respeito do funcionamento do sistema nervoso e o álcool. A temática explica como o funcionamento do sistema nervoso pode ser afetado pelo uso de bebidas alcoólicas; compreensão das alterações causadas ao funcionamento do sistema nervoso pela ingestão de álcool e instrumentalização dos professores com mais um recurso pedagógico. Conclusão:Dessa forma, foi possível a promoção da sensibilização dos estudantes quanto aos aspectos negativos do uso de bebidas alcoólicas. Assim como, prevenção nos jovens quanto ao seuuso indiscriminado, colaborando com a popularização da ciência.

Objective:the study aimed to report experiences between the authors regarding the tutoring of module three during the EAD course in the year 2022. Method:This course with synchronous and asynchronous activities, for basic education teachers and undergraduate students, was carried out in October and November, 2022 and culminated in the construction of a booklet with mental maps, themes and strategies workedon during the course as teaching materials for elementary school II. Results:The booklet entitled: the consequences of alcohol consumption on the nervous system, was partnered with teachers from two basic schools. This presents anatomophysiological information regarding the functioning of the nervous system and alcohol. The theme explains how the functioning of the nervous system can be affected by the use of alcoholic beverages; understanding the changes caused to the functioning of the nervous system byalcohol intake and providing teachers with yet another pedagogical resource. Conclusion:In this way, it was possible to promote student awareness regarding the negative aspects of the use of alcoholic beverages. As well as prevention among young people regarding its indiscriminate use, collaborating with the popularization of science.

Objetivo: el estudio tuvo como objetivo relatar experiencias entre los autores respecto a la tutoría del módulo tres durante el curso EAD en el año 2022. Método: Este curso con actividades sincrónicas y asincrónicas, para docentes de educación básica y estudiantes de pregrado, se realizó en los meses de octubre y noviembre de 2022 y culminó con la construcción de una cartilla con mapas mentales, temáticas y estrategias trabajadas durante el curso como material didáctico para la escuela primaria II. Resultados:El cuadernillo titulado: las consecuencias del consumo de alcohol en el sistema nervioso, fue elaborado en colaboración con docentes de dos escuelas básicas. Presenta información anatomofisiológica sobre el funcionamiento del sistema nervioso y el alcohol. El tema explica cómo el funcionamiento del sistema nervioso puede verse afectado por el uso de bebidas alcohólicas; comprender los cambios que provoca en el funcionamiento del sistema nervioso la ingesta de alcohol y dotar a los docentes de un recurso pedagógico más. Conclusión: De esta manera, fue posible sensibilizar a los estudiantes sobre los aspectos negativos del consumo de alcohol. Así como la prevención entre los jóvenes sobre su uso indiscriminado, contribuyendo a la popularización de la ciencia.

Climate change-induced degradation of expert range maps drawn for kissing bugs (Hemiptera: Reduviidae) and long-standing current and future sampling gaps across the Americas

BACKGROUND Kissing bugs are the vectors of Trypanosoma cruzi, the etiological agent of Chagas disease (CD). Despite their epidemiological relevance, kissing bug species are under sampled in terms of their diversity and it is unclear what biases exist in available kissing bug data. Under climate change, range maps for kissing bugs may become less accurate as species shift their ranges to track climatic tolerance. OBJECTIVES Quantify inventory completeness in available kissing bug data. Assess how well range maps are at conveying information about current distributions and potential future distributions subject to shift under climate change. Intersect forecasted changes in kissing bug distributions with contemporary sampling gaps to identify regions for future sampling of the group. Identify whether a phylogenetic signal is present in expert range knowledge as more closely related species may be similarly well or lesser understood. METHODS We used species distribution models (SDM), specifically constructed from Bayesian additive regression trees, with Bioclim variables, to forecast kissing bug distributions into 2100 and intersect these with current sampling gaps to identify priority regions for sampling. Expert range maps were assessed by the agreement between the expert map and SDM generated occurrence probability. We used classical hypothesis testing methods as well as tests of phylogenetic signal to meet our objectives. FINDINGS Expert range maps vary in their quality of depicting current kissing bug distributions. Most expert range maps decline in their ability to convey information about kissing bug occurrence over time, especially in under sampled areas. We found limited evidence for a phylogenetic signal in expert range map performance. MAIN CONCLUSIONS Expert range maps are not a perfect account of species distributions and may degrade in their ability to accurately convey distribution knowledge under future climates. We identify regions where future sampling of kissing bugs will be crucial for completing biodiversity inventories.

Is it possible to automate the discovery of process maps for the time-driven activity-based costing method? A systematic review.

OBJECTIVES: The main objective of this manuscript was to identify the methods used to create process maps for care pathways that utilized the time-driven activity-based costing method. METHODS: This is a systematic mapping review. Searches were performed in the Embase, PubMed, CINAHL, Scopus, and Web of Science electronic literature databases from 2004 to September 25, 2022. The included studies reported practical cases from healthcare institutions in all medical fields as long as the time-driven activity-based costing method was employed. We used the time-driven activity-based costing method and analyzed the created process maps and a qualitative approach to identify the main fields. RESULTS: A total of 412 studies were retrieved, and 70 articles were included. Most of the articles are related to the fields of orthopedics and childbirth-related to hospital surgical procedures. We also identified various studies in the field of oncology and telemedicine services. The main methods for creating the process maps were direct observational practices, complemented by the involvement of multidisciplinary teams through surveys and interviews. Only 33% of the studies used hospital documents or healthcare data records to integrate with the process maps, and in 67% of the studies, the created maps were not validated by specialists. CONCLUSIONS: The application of process mining techniques effectively automates models generated through clinical pathways. They are applied to the time-driven activity-based costing method, making the process more agile and contributing to the visualization of high degrees of variations encountered in processes, thereby making it possible to enhance and achieve continual improvements in processes.

Basin Entropy and Shearless Barrier Breakup in Open Non-Twist Hamiltonian Systems.

We consider open non-twist Hamiltonian systems represented by an area-preserving two-dimensional map describing incompressible planar flows in the reference frame of a propagating wave, and possessing exits through which map orbits can escape. The corresponding escape basins have a fractal nature that can be revealed by the so-called basin entropy, a novel concept developed to quantify final-state uncertainty in dynamical systems. Since the map considered violates locally the twist condition, there is a shearless barrier that prevents global chaotic transport. In this paper, we show that it is possible to determine the shearless barrier breakup by considering the variation in the escape basin entropy with a tunable parameter.

Exponentially Long Transient Time to Synchronization of Coupled Chaotic Circle Maps in Dense Random Networks.

We study the transition to synchronization in large, dense networks of chaotic circle maps, where an exact solution of the mean-field dynamics in the infinite network and all-to-all coupling limit is known. In dense networks of finite size and link probability of smaller than one, the incoherent state is meta-stable for coupling strengths that are larger than the mean-field critical coupling. We observe chaotic transients with exponentially distributed escape times and study the scaling behavior of the mean time to synchronization.

Evaluating natural and anthropogenic inputs on the distribution of potentially toxic elements in urban soil of Valdivia, Chile.

The increasing population in urban areas in the last decades requires an effort to understand the geochemistry of contaminant elements in urban soil. Topsoil plays a crucial role in the exposure of Potentially Toxic Elements (PTEs) to humans through ingestion, dermal contact, and inhalation. In Chile, the last census revealed that 88.6% of people live in cities or towns and only 11.4% in rural areas. This study presents the first systematic geochemical survey of urban soil in the city of Valdivia, in the South of Chile. Topsoil samples (0-10 cm depth) were collected in less disturbed locations within the city at 130 sampling sites using a grid of 0.25 km2 squares covering a total area of approximately 30 km2. The concentrations of Al, Fe, Na, Ca, Mg, K, Ti, Be, V, Cr, Mn, Co, Ni, Cu, Zn, As, Mo, Sn, Cd, Se, Pb and Hg were measured. The results showed that high concentrations of Cu, V, Zn and Pb are located mainly in the city's northern area and exceed international soil quality legislation for agricultural use. Data processing comprised plotting of individual spatial distribution maps and the use of a combination of multivariate statistical methods. Hierarchical cluster analysis and principal component analysis identified three element associations. The two element groups V-Al-Ti-Fe-Cr-Co-Mn-Be-Ni and Ca-Na-K-As-Mg are interpreted as a dominant lithological origin related to the most pristine soil conditions in less populated areas. By contrast, the Sn-Pb-Zn-Mo-(Cu-Hg) association presents a significant correlation with urbanization indicators, including vehicular traffic and industrial activities developed since the end of the nineteenth century in Valdivia.

Os ciclos de engajamento no trabalho de servidores públicos federais / Los ciclos de compromiso laboral de los servidores públicos federales / The work engagement cycles of federal civil servants

Resumo Servidores públicos têm diferentes níveis de engajamento no trabalho ao longo da vida laboral. Essas variações, denominadas "ciclos de engajamento no trabalho", ocorrem graças aos recursos disponíveis e às demandas do ambiente de trabalho. Diante disso, o presente artigo busca descrever os ciclos de engajamento no trabalho de servidores públicos federais com base em suas histórias de vida profissional, evidenciando demandas e recursos relevantes do ambiente ocupacional na trajetória dessas pessoas. Usando o modelo job demands-resources (JD-R) e a metodologia de mapas cognitivos, foi possível identificar um ciclo positivo do engajamento no trabalho, um ciclo reforçador - relacionado com oportunidades e valorização - e dois ciclos de desequilíbrio, um ligado à produtividade disfuncional e, outro, à descontinuidade administrativa. A análise dos ciclos de engajamento permitiu identificar recursos do ambiente de trabalho que interferem de diferentes maneiras no engajamento de servidores públicos. Por fim, foi utilizado o conceito de "ciclo de enfrentamento" como subsídio de políticas para servidores desengajados.

Resumen Los servidores públicos tienen diferentes niveles de compromiso en el trabajo a lo largo de su vida laboral. Estas variaciones, llamadas ciclos de compromiso laboral, ocurren debido a los recursos disponibles y las demandas del entorno laboral. Frente a eso, el presente trabajo buscó describir los ciclos de compromiso en el trabajo de los servidores públicos federales a partir de sus historias de vida profesional, destacando demandas y recursos del ambiente laboral relevantes en la trayectoria laboral de esas personas. Utilizando el modelo job demands-resources (JD-R) y la metodología de mapas cognitivos, fue posible identificar un ciclo positivo de compromiso en el trabajo, un ciclo de refuerzo ‒ relacionado con oportunidades y apreciación ‒ y dos ciclos de desequilibrio, uno relacionado con la productividad disfuncional y el otro con la discontinuidad administrativa. El análisis de los ciclos de compromiso permitió identificar los recursos del ambiente de trabajo que interfieren de diferentes formas en el compromiso de los servidores públicos. Finalmente, se utilizó el concepto de "ciclo de afrontamiento" como base de las políticas para los servidores desinteresados.

Abstract Civil servants have different levels of work engagement throughout their working lives. These variations are the work engagement cycles, which occur based on available resources and work environment demands. This study describes the work engagement cycles of federal civil servants based on their professional life histories, highlighting the demands and resources of the work environment in their professional trajectory. Using the job demands-resources (JD-R) model and the cognitive maps methodology, it was possible to identify a positive cycle of work engagement, a reinforcing cycle (related to opportunities and appreciation), and two disequilibrium cycles, one related to dysfunctional productivity and the other to administrative discontinuity. The analysis of the engagement cycles allowed the identification of work environment resources that interfere in the engagement of public servants in different ways. Finally, the concept of "coping cycle" was used as a subsidy of policies for disengaged servants.

Cooperative Multiband Spectrum Sensing Using Radio Environment Maps and Neural Networks.

Cogitive radio networks (CRNs) require high capacity and accuracy to detect the presence of licensed or primary users (PUs) in the sensed spectrum. In addition, they must correctly locate the spectral opportunities (holes) in order to be available to nonlicensed or secondary users (SUs). In this research, a centralized network of cognitive radios for monitoring a multiband spectrum in real time is proposed and implemented in a real wireless communication environment through generic communication devices such as software-defined radios (SDRs). Locally, each SU uses a monitoring technique based on sample entropy to determine spectrum occupancy. The determined features (power, bandwidth, and central frequency) of detected PUs are uploaded to a database. The uploaded data are then processed by a central entity. The objective of this work was to determine the number of PUs, their carrier frequency, bandwidth, and the spectral gaps in the sensed spectrum in a specific area through the construction of radioelectric environment maps (REMs). To this end, we compared the results of classical digital signal processing methods and neural networks performed by the central entity. Results show that both proposed cognitive networks (one working with a central entity using typical signal processing and one performing with neural networks) accurately locate PUs and give information to SUs to transmit, avoiding the hidden terminal problem. However, the best-performing cognitive radio network was the one working with neural networks to accurately detect PUs on both carrier frequency and bandwidth.

Convolutional Neural Network Classification of Topographic Electroencephalographic Maps on Alcoholism.

Alcohol use is a leading risk factor for substantial health loss, disability, and death. Thus, there is a general interest in developing computational tools to classify electroencephalographic (EEG) signals in alcoholism, but there are a limited number of studies on convolutional neural network (CNN) classification of alcoholism using topographic EEG signals. We produced an original dataset recorded from Brazilian subjects performing a language recognition task. Then, we transformed the Event-Related Potentials (ERPs) into topographic maps by using the ERP's statistical parameters across time, and used a CNN network to classify the topographic dataset. We tested the effect of the size of the dataset in the accuracy of the CNNs and proposed a data augmentation approach to increase the size of the topographic dataset to improve the accuracies. Our results encourage the use of CNNs to classify abnormal topographic EEG patterns associated with alcohol abuse.

Hydropower Plants as Dispersal Barriers in Freshwater Species Distribution Models: Using Restrictions through Asymmetrical Dispersal Predictors.

Hydropower plants represent one of the greatest threats for freshwater fish by fragmenting the habitat and avoiding the species dispersal. This type of dispersal barrier is often disregarded when predicting freshwater species distribution due to the complexity in inserting the species dispersal routes, and thus the barriers, into the models. Here, we evaluate the impact of including hydroelectric dams into species distribution models through asymmetrical dispersal predictors on the predicted geographic distribution of freshwater fish species. For this, we used asymmetrical dispersal (i.e., AEM) as predictors for modeling the distribution of 29 native fish species of Tocantins-Araguaia River basin. After that, we included the hydropower power plant (HPP) location into the asymmetrical binary matrix for the AEM construction by removing the connections where the HPP is located, representing the downstream disconnection a dam causes in the fish species dispersal route. Besides having higher predicted accuracy, the models using the HPP information generated more realistic predictions, avoiding overpredictions to areas suitable but limited to the species dispersal due to an anthropic barrier. Furthermore, the predictions including HPPs showed higher loss of species richness and nestedness (i.e., loss of species instead of replacement), especially for the southeastern area which concentrates most planned and built HPPs. Therefore, using dispersal constraints in species distribution models increases the reliability of the predictions by avoiding overpredictions based on premise of complete access by the species to any area that is climatically suitable regardless of dispersal barriers or capacity. In conclusion, in this study, we use a novel method of including dispersal constraints into distribution models through a priori insertion of their location within the asymmetrical dispersal predictors, avoiding a posteriori adjustment of the predicted distribution.