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As part of an investigation to detect asymmetries in gait patterns in persons with shoulder injuries, the goal of the present study was to design and validate a Kinect-based motion capture system that would enable the extraction of joint kinematics curves during gait and to compare them with the data obtained through a commercial motion capture system. The study included eight male and two female participants, all diagnosed with anterolateral shoulder pain syndrome in their right upper extremity with a minimum 18 months of disorder evolution. The participants had an average age of 31.8 ± 9.8 years, a height of 173 ± 18 cm, and a weight of 81 ± 15 kg. The gait kinematics were sampled simultaneously with the new system and the Clinical 3DMA system. Shoulder, elbow, hip, and knee kinematics were compared between systems for the pathological and non-pathological sides using repeated measures ANOVA and 1D statistical parametric mapping. For most variables, no significant difference was found between systems. Evidence of a significant difference between the newly developed system and the commercial system was found for knee flexion-extension (p < 0.004, between 60 and 80% of the gait cycle), and for shoulder abduction-adduction. The good concurrent validity of the new Kinect-based motion analysis system found in this study opens promising perspectives for clinical motion tracking using an affordable and simple system.

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Instruments used to assess the mental well-being of young athletes in Brazil are scarce. Therefore, the present study aimed to translate, cross-culturally adapt for young athletes, and gather evidence of validity for the Sport Mental Health-Short Form (S-MHC) for use in Brazilian Portuguese. The research was conducted in five stages: translation, synthesis, back-translation, expert review, and validation of the psychometric properties. For validation, 246 young athletes of both genders (88 females, 35.8%), aged between 12 and 18 years (14.5 ± 1.9 years), were recruited. Psychometric methods were employed to confirm and validate the translated and adapted versions of the S-MHC for young athletes, including internal consistency using Cronbach's alpha and McDonald's omega, composite reliability, Item Characteristic Curve (ICC) using Item Response Theory (IRT), and Confirmatory Factor Analysis (CFA). Two structures were tested, with Model 1 loading the 14 items of the translated version of the S-MHC into a single latent factor and Model 2 loading the items into three factors related to emotional, social, and psychological sport well-being. Both models showed good validity, consistency, and reliability measures and can be used to investigate the sport well-being of young athletes. It was concluded that the translated version of the S-MHC in Brazilian Portuguese can be used to assess the sport well-being of young athletes in Brazil. Model 2 structure is recommended to observe the different nuances of emotional, social, and psychological well-being.

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Conventionally, the optimization of glucose biosensors is achieved by varying the concentrations of the individual reagents used to immobilize the enzyme. In this work, the effect and interaction between glucose oxidase enzyme (GOx), ferrocene methanol (Fc), and multi-walled carbon nanotubes (MWCNTs) at different concentrations were investigated by a design of experiments (DoE). For this analysis, a factorial design with three factors and two levels each was used with the software RStudio for statistical analysis. The data were obtained by electrochemical experiments on the immobilization of GOx-Fc/MWCNT at different concentrations. The results showed that the factorial DoE method was confirmed by the non-normality of the residuals and the outliers of the experiment. When examining the effects of the variables, analyzing the half-normal distribution and the effects and contrasts for GOx-Fc/MWCNT, the factors that showed the greatest influence on the electrochemical response were GOx, MWCNT, Fc, and MWCNT:Fc, and there is a high correlation between the factors GOx, MWCNT, Fc, and MWCNT:Fc, as shown by the analysis of homoscedasticity and multicollinearity. With these statistical analyses and experimental designs, it was possible to find the optimal conditions for different factors: 10 mM mL-1 GOx, 2 mg mL-1 Fc, and 15 mg mL-1 MWCNT show a greater amperometric response in the glucose oxidation. This work contributes to advancing enzyme immobilization strategies for glucose biosensor applications. Systematic investigation of DoE leads to optimized immobilization for GOx, enables better performance as a glucose biosensor, and allows the prediction of some outcomes.

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Multi-omics data integration is a term that refers to the process of combining and analyzing data from different omic experimental sources, such as genomics, transcriptomics, methylation assays, and microRNA sequencing, among others. Such data integration approaches have the potential to provide a more comprehensive functional understanding of biological systems and has numerous applications in areas such as disease diagnosis, prognosis and therapy. However, quantitative integration of multi-omic data is a complex task that requires the use of highly specialized methods and approaches. Here, we discuss a number of data integration methods that have been developed with multi-omics data in view, including statistical methods, machine learning approaches, and network-based approaches. We also discuss the challenges and limitations of such methods and provide examples of their applications in the literature. Overall, this review aims to provide an overview of the current state of the field and highlight potential directions for future research.

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OBJECTIVES: The control chart is a classic statistical technique in epidemiology for identifying trends, patterns, or alerts. One meaningful use is monitoring and tracking Infant Mortality Rates, which is a priority both domestically and for the World Health Organization, as it reflects the effectiveness of public policies and the progress of nations. This study aims to evaluate the applicability and performance of this technique in Brazilian cities with different population sizes using infant mortality data. RESULTS: In this article, we evaluate the effectiveness of the statistical process control chart in the context of Brazilian cities. We present three categories of city groups, divided based on population size and classified according to the quality of the analyses when subjected to the control method: consistent, interpretable, and inconsistent. In cities with a large population, the data in these contexts show a lower noise level and reliable results. However, in intermediate and small-sized cities, the technique becomes limited in detecting deviations from expected behaviors, resulting in reduced reliability of the generated patterns and alerts.

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BACKGROUND: Moral injury is prevalent among health care professionals, especially nurses. It can have negative personal consequences for clinicians, and indirectly impact the quality of patient care. Although nurses around the world experienced moral injury during the pandemic, it will continue to be a professional challenge. Thus, this study aimed to determine the psychometric properties of a scale measuring moral injury translated into Spanish. METHODS: A methodological study with a cross-sectional approach was conducted. After translating the Moral Injury Symptom Scale for Healthcare Professionals (MISS-HP) into Peruvian Spanish (MISS-HP-S) using International Test Commission methods, data were collected using online survey methods from a sample of 720 Peruvian nurses. Analytical methods included exploratory and confirmatory factor analysis, and invariance by age were examined. The corrected homogeneity index, ordinal alpha, and McDonald's omega allowed the evaluation of internal reliability. RESULTS: Findings from this sample of nurses who were mostly female (92%), from coastal Peru (57%), and averaged 39 (± 11) years of age, provided support for the validity and reliability of the MISS-HP-S. Structural validity was endorsed by findings indicating consistent factorial structure and adequate invariance among different age groups. In this study, three factors were observed: guilt/shame, condemnation, and spiritual strength. Internal consistency values included an ordinal alpha of 0.795 and McDonald's omega of 0.835. CONCLUSION: These findings differ from those reported from previous studies in other cultural contexts, suggesting the influence of cultural and sample-specific factors in the perception of moral injury among Peruvian nurses. Because this evidence supports the validity of the MISS-HP-S, it can be used in professional practice and in future research to identify and address situations that contribute to nurse moral injury.

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Visceral leishmaniasis (VL) is an urgent public health concern in Brazil. We evaluated the spatiotemporal distribution of VL to better understand the effects of economic activities related to agriculture, livestock, and deforestation on its incidence in the Brazilian Legal Amazon (BLA). The data on newly confirmed cases of VL in Brazilian municipalities from 2007 to 2020 were extracted from the Brazilian Notifiable Diseases Information System (SINAN) and analyzed. The data on agricultural production (planted area in hectares) and livestock (total number of cattle) were obtained from the Brazilian Institute of Geography and Statistics (IBGE), whereas deforestation data (in hectares) were obtained from the Amazon Deforestation Estimation Project (PRODES). SatScan and the local indicators of spatial association (LISA) were used to identify the spatial and temporal patterns of VL and its relationships with economic and environmental variables. The cumulative incidence rate was found to be 4.5 cases per 100,000 inhabitants. Based on the LISA results, areas with a high incidence of VL and deforestation were identified in the states of Roraima, Pará, and Maranhão. Strengthening deforestation monitoring programs and environmental enforcement actions can help implement public policies to control illegal deforestation and mitigate the socio-environmental vulnerability in the BLA. Therefore, areas identified in this study should be prioritized for controlling VL.

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Resumo A autoeficácia dos docentes e seu acesso a recursos podem influenciar a transição do ensino presencial para o ensino remoto emergencial. Este estudo objetivou construir e verificar evidências de validade das escalas de Autoeficácia e Acesso a Recursos de Docentes do Ensino Superior que passaram pela transição para aulas remotas. As escalas foram construídas e submetidas à validação semântica e por juízes. A coleta de dados foi realizada a distância com 135 professores. Foram realizadas análises descritivas, exploratórias e confirmatórias. Ambas as escalas apresentaram cargas fatoriais acima de 0,4. A escala de Autoeficácia docente apresentou-se unifatorial com bom índice de consistência interna (α = 0,95) e bons indicadores de ajuste (x 2 : 256,794; df: 131; CMIN/DF: 1,96; GFI: 0,89; RMSR: 0,05; CFI: 0,93; TLI: 0,91; RMSEA: 0,07). A escala de acesso a recursos apresentou-se com dois fatores e obteve bons índices de consistência interna (α entre 0,80 e 0,88) e bons indicadores de ajuste (x 2 : 67,99; df: 31; CMIN/DF: 2,19; GFI: 0,90; RMSR: 0,12; CFI: 0,94; TLI: 0,92; RMSEA: 0,094). Este estudo amplia o conhecimento a respeito da transição do ensino presencial para o ensino remoto, e traz implicações práticas que poderão auxiliar no planejamento e desenvolvimento das ações educacionais.

Abstract Teachers' self-efficacy and access to resources can influence the transition to emergency remote teaching. This study aimed to build and verify validity evidences of the Self-Efficacy and Access to Resources scales of Higher Education Teachers who underwent the transition to remote classes. The scales were constructed and submitted to semantic and expert validation. Data collection was carried out remotely with 135 teachers. Descriptive, exploratory and confirmatory analyzes were carried out. Both scales presented factor loadings above 0.4. The Teacher Self-Efficacy scale was unifactorial with a good internal consistency index (α = 0.95) and good adjustment indicators (x2: 256.794; df: 131; CMIN/DF: 1.96; GFI: 0.89; RMSR: 0.05; CFI: 0.93; TLI: 0.91; RMSEA: 0.07). The access to resources scale had two factors and obtained good internal consistency indexes (α between 0.80 and 0.88) and good adjustment indicators 67.99; df: 31; CMIN/DF: 2.19; GFI: 0.90; RMSR: 0.12; CFI: 0.94; TLI: 0.92; RMSEA: 0.094). This study expands knowledge regarding the transition from face-to-face teaching to remote teaching, and brings practical implications that may help in the planning and development of educational actions.

Resumen La autoeficacia de los docentes y el acceso a los recursos pueden influir en la transición a la enseñanza remota de emergencia. Este estudio tuvo como objetivo construir y verificar evidencias de validez de las escalas de Autoeficacia y Acceso a Recursos de Profesores de Educación Superior que atravesaron la transición a clases remotas. Las escalas fueron sometidas a validación semántica y de expertos. La recolección de datos se realizó de forma remota con 135 docentes. Se realizaron análisis descriptivos, exploratorios y confirmatorios. Ambas escalas presentaron cargas factoriales superiores α 0,4. La escala de Autoeficacia Docente fue unifactorial, con buen índice de consistencia interna (α = 0,95) y buenos indicadores de ajuste (x2: 256,794; gl: 131; CMIN/DF: 1,96; GFI: 0,89; RMSR: 0,05; CFI: 0,93; TLI: 0,91; RMSEA: 0,07). La escala de Acceso a Recursos tuvo dos factores y obtuvo buenos índices de consistencia interna (α entre 0,80 y 0,88) y buenos indicadores de ajuste X 67,99; df: 31; CMIN/DF: 2,19; GFI: 0,90; RMSR: 0,12; CFI: 0,94; TLI: 0,92; RMSEA: 0,094). Este estudio amplía el conocimiento sobre la transición de la enseñanza presencial a la enseñanza remota, y aporta implicaciones que pueden ayudar en la planificación y desarrollo de acciones educativas.

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This study evaluates the feasibility of using enzymatic technology to produce novel nanostructures of cellulose nanomaterials, specifically cellulose nanospheres (CNS), through enzymatic hydrolysis with endoglucanase and xylanase of pre-treated cellulose fibers. A statistical experimental design facilitated a comprehensive understanding of the process parameters, which enabled high yields of up to 82.7 %, while maintaining a uniform diameter of 54 nm and slightly improved crystallinity and thermal stability. Atomic force microscopy analyses revealed a distinct CNS formation mechanism, where initial fragmentation of rod-like nanoparticles and subsequent self-assembly of shorter rod-shaped nanoparticles led to CNS formation. Additionally, adjustments in process parameters allowed precise control over the CNS diameter, ranging from 20 to 100 nm, highlighting the potential for customization in high-performance applications. Furthermore, this study demonstrates how the process framework, originally developed for cellulose nanocrystals (CNC) production, was successfully adapted and optimized for CNS production, ensuring scalability and efficiency. In conclusion, this study emphasizes the versatility and efficiency of the enzyme-based platform for producing high-quality CNS, providing valuable insights into energy consumption for large-scale economic and environmental assessments.

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Novel foods especially formulated and targeted for the elderly population should provide sufficient nutrients and bioactive ingredients to counteract the natural age-related deterioration of various organs and tissues. Dietary protein and phenolic compounds achieve this goal; however, older adults have alterations in their gastrointestinal system that may impact their bioavailability and few studies have been aimed at this population. Since phenolic compounds are the subject of multiple biotransformations by host and microbiome enzymes during the digestion process, identification of their bioavailable forms in human plasma or tissues represents a considerable analytical challenge. In this study, UHPLC-ESI-QTOF/MS-MS, chemometrics, and multivariate statistical methods were used to identify the amino acids and phenolic compounds that were increased in the plasma of elderly adults after a 30-day intervention in which they had consumed an especially formulated muffin and beverage containing Brosimum alicastrum Sw. seed flour. A large interindividual variation was observed regarding the amino acids and phenolic metabolites identified in the plasma samples, before and after the intervention. Three phenolic metabolites were significantly increased in the population after the intervention: protocatechuic acid, 5-(methoxy-4'-hydroxyphenyl) valerolactone, and phloretic acid. These metabolites, as well as others that were not significantly increased (although they did increase in several individuals), are probably the product of the microbiota metabolism of the major phenolic compounds present in the B. alicastrum Sw. seed flour and other food ingredients. A significant decrease in 4-ethyl-phenol, a biomarker of stress, was observed in the samples. Results showed that the incorporation of foods rich in phenolic compounds into the regular diet of older adults contributes to the increase in bioactive compounds in plasma, that could substantially benefit their mental, cardiovascular, and digestive health.

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Corrosion deterioration of materials is a major problem affecting economic, safety, and logistical issues, especially in the aeronautical sector. Detecting the correct corrosion type in metal alloys is very important to know how to mitigate the corrosion problem. Electrochemical noise (EN) is a corrosion technique used to characterize the behavior of different alloys and determine the type of corrosion in a system. The objective of this research is to characterize by EN technique different aeronautical alloys (Al, Ti, steels, and superalloys) using different analysis methods such as time domain (visual analysis, statistical), frequency domain (power spectral density (PSD)), and frequency-time domain (wavelet decomposition, Hilbert Huang analysis, and recurrence plots (RP)) related to the corrosion process. Optical microscopy (OM) is used to observe the surface of the tested samples. The alloys were exposed to 3.5 wt.% NaCl and H2SO4 solutions at room temperature. The results indicate that HHT and recurrence plots are the best options for determining the corrosion type compared with the other methods due to their ability to analyze dynamic and chaotic systems, such as corrosion. Corrosion processes such as passivation and localized corrosion can be differentiated when analyzed using HHT and RP methods when a passive system presents values of determinism between 0.5 and 0.8. Also, to differentiate the passive system from the localized system, it is necessary to see the recurrence plot due to the similarity of the determinism value. Noise impedance (Zn) is one of the best options for determining the corrosion kinetics of one system, showing that Ti CP2 and Ti-6Al-4V presented 742,824 and 939,575 Ω·cm2, while Rn presented 271,851 and 325,751 Ω·cm2, being the highest when exposed to H2SO4.

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In 2023, cholera affected approximately 1 million people and caused more than 5000 deaths globally, predominantly in low-income and conflict settings. In recent years, the number of new cholera outbreaks has grown rapidly. Further, ongoing cholera outbreaks have been exacerbated by conflict, climate change, and poor infrastructure, resulting in prolonged crises. As a result, the demand for treatment and intervention is quickly outpacing existing resource availability. Prior to improved water and sanitation systems, cholera, a disease primarily transmitted via contaminated water sources, also routinely ravaged high-income countries. Crumbling infrastructure and climate change are now putting new locations at risk - even in high-income countries. Thus, understanding the transmission and prevention of cholera is critical. Combating cholera requires multiple interventions, the two most common being behavioral education and water treatment. Two-dose oral cholera vaccination (OCV) is often used as a complement to these interventions. Due to limited supply, countries have recently switched to single-dose vaccines (OCV1). One challenge lies in understanding where to allocate OCV1 in a timely manner, especially in settings lacking well-resourced public health surveillance systems. As cholera occurs and propagates in such locations, timely, accurate, and openly accessible outbreak data are typically inaccessible for disease modeling and subsequent decision-making. In this study, we demonstrated the value of open-access data to rapidly estimate cholera transmission and vaccine effectiveness. Specifically, we obtained non-machine readable (NMR) epidemic curves for recent cholera outbreaks in two countries, Haiti and Cameroon, from figures published in situation and disease outbreak news reports. We used computational digitization techniques to derive weekly counts of cholera cases, resulting in nominal differences when compared against the reported cumulative case counts (i.e., a relative error rate of 5.67% in Haiti and 0.54% in Cameroon). Given these digitized time series, we leveraged EpiEstim-an open-source modeling platform-to derive rapid estimates of time-varying disease transmission via the effective reproduction number ( R t ). To compare OCV1 effectiveness in the two considered countries, we additionally used VaxEstim, a recent extension of EpiEstim that facilitates the estimation of vaccine effectiveness via the relation among three inputs: the basic reproduction number ( R 0 ), R t , and vaccine coverage. Here, with Haiti and Cameroon as case studies, we demonstrated the first implementation of VaxEstim in low-resource settings. Importantly, we are the first to use VaxEstim with digitized data rather than traditional epidemic surveillance data. In the initial phase of the outbreak, weekly rolling average estimates of R t were elevated in both countries: 2.60 in Haiti [95% credible interval: 2.42-2.79] and 1.90 in Cameroon [1.14-2.95]. These values are largely consistent with previous estimates of R 0 in Haiti, where average values have ranged from 1.06 to 3.72, and in Cameroon, where average values have ranged from 1.10 to 3.50. In both Haiti and Cameroon, this initial period of high transmission preceded a longer period during which R t oscillated around the critical threshold of 1. Our results derived from VaxEstim suggest that Haiti had higher OCV1 effectiveness than Cameroon (75.32% effective [54.00-86.39%] vs. 54.88% [18.94-84.90%]). These estimates of OCV1 effectiveness are generally aligned with those derived from field studies conducted in other countries. Thus, our case study reinforces the validity of VaxEstim as an alternative to costly, time-consuming field studies of OCV1 effectiveness. Indeed, prior work in South Sudan, Bangladesh, and the Democratic Republic of the Congo reported OCV1 effectiveness ranging from approximately 40% to 80%. This work underscores the value of combining NMR sources of outbreak case data with computational techniques and the utility of VaxEstim for rapid, inexpensive estimation of vaccine effectiveness in data-poor outbreak settings.

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Coffee Breeding programs have traditionally relied on observing plant characteristics over years, a slow and costly process. Genomic selection (GS) offers a DNA-based alternative for faster selection of superior cultivars. Stacking Ensemble Learning (SEL) combines multiple models for potentially even more accurate selection. This study explores SEL potential in coffee breeding, aiming to improve prediction accuracy for important traits [yield (YL), total number of the fruits (NF), leaf miner infestation (LM), and cercosporiosis incidence (Cer)] in Coffea Arabica. We analyzed data from 195 individuals genotyped for 21,211 single-nucleotide polymorphism (SNP) markers. To comprehensively assess model performance, we employed a cross-validation (CV) scheme. Genomic Best Linear Unbiased Prediction (GBLUP), multivariate adaptive regression splines (MARS), Quantile Random Forest (QRF), and Random Forest (RF) served as base learners. For the meta-learner within the SEL framework, various options were explored, including Ridge Regression, RF, GBLUP, and Single Average. The SEL method was able to predict the predictive ability (PA) of important traits in Coffea Arabica. SEL presented higher PA compared with those obtained for all base learner methods. The gains in PA in relation to GBLUP were 87.44% (the ratio between the PA obtained from best Stacking model and the GBLUP), 37.83%, 199.82%, and 14.59% for YL, NF, LM and Cer, respectively. Overall, SEL presents a promising approach for GS. By combining predictions from multiple models, SEL can potentially enhance the PA of GS for complex traits.

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Respiratory diseases represent one of the most significant economic burdens on healthcare systems worldwide. The variation in the increasing number of cases depends greatly on climatic seasonal effects, socioeconomic factors, and pollution. Therefore, understanding these variations and obtaining precise forecasts allows health authorities to make correct decisions regarding the allocation of limited economic and human resources. We aimed to model and forecast weekly hospitalizations due to respiratory conditions in seven regional hospitals in Costa Rica using four statistical learning techniques (Random Forest, XGboost, Facebook's Prophet forecasting model, and an ensemble method combining the above methods), along with 22 climate change indices and aerosol optical depth as an indicator of pollution. Models were trained using data from 2000 to 2018 and were evaluated using data from 2019 as testing data. During the training period, we set up 2-year sliding windows and a 1-year assessment period, along with the grid search method to optimize hyperparameters for each model. The best model for each region was selected using testing data, based on predictive precision and to prevent overfitting. Prediction intervals were then computed using conformal inference. The relative importance of all climatic variables was computed for the best model, and similar patterns in some of the seven regions were observed based on the selected model. Finally, reliable predictions were obtained for each of the seven regional hospitals.

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Low-cost sensors integrated with the Internet of Things can enable real-time environmental monitoring networks and provide valuable water quality information to the public. However, the accuracy and precision of the values measured by the sensors are critical for widespread adoption. In this study, 19 different low-cost sensors, commonly found in the literature, from four different manufacturers are tested for measuring five water quality parameters: pH, dissolved oxygen, oxidation-reduction potential, turbidity, and temperature. The low-cost sensors are evaluated for each parameter by calculating the error and precision compared to a typical multiparameter probe assumed as a reference. The comparison was performed in a controlled environment with simultaneous measurements of real water samples. The relative error ranged from - 0.33 to 33.77%, and most of them were ≤ 5%. The pH and temperature were the ones with the most accurate results. In conclusion, low-cost sensors are a complementary alternative to quickly detect changes in water quality parameters. Further studies are necessary to establish a guideline for the operation and maintenance of low-cost sensors.

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The first step in comprehending the properties of Au10 clusters is understanding the lowest energy structure at low and high temperatures. Functional materials operate at finite temperatures; however, energy computations employing density functional theory (DFT) methodology are typically carried out at zero temperature, leaving many properties unexplored. This study explored the potential and free energy surface of the neutral Au10 nanocluster at a finite temperature, employing a genetic algorithm coupled with DFT and nanothermodynamics. Furthermore, we computed the thermal population and infrared Boltzmann spectrum at a finite temperature and compared it with the validated experimental data. Moreover, we performed the chemical bonding analysis using the quantum theory of atoms in molecules (QTAIM) approach and the adaptive natural density partitioning method (AdNDP) to shed light on the bonding of Au atoms in the low-energy structures. In the calculations, we take into consideration the relativistic effects through the zero-order regular approximation (ZORA), the dispersion through Grimme's dispersion with Becke-Johnson damping (D3BJ), and we employed nanothermodynamics to consider temperature contributions. Small Au clusters prefer the planar shape, and the transition from 2D to 3D could take place at atomic clusters consisting of ten atoms, which could be affected by temperature, relativistic effects, and dispersion. We analyzed the energetic ordering of structures calculated using DFT with ZORA and single-point energy calculation employing the DLPNO-CCSD(T) methodology. Our findings indicate that the planar lowest energy structure computed with DFT is not the lowest energy structure computed at the DLPN0-CCSD(T) level of theory. The computed thermal population indicates that the 2D elongated hexagon configuration strongly dominates at a temperature range of 50-800 K. Based on the thermal population, at a temperature of 100 K, the computed IR Boltzmann spectrum agrees with the experimental IR spectrum. The chemical bonding analysis on the lowest energy structure indicates that the cluster bond is due only to the electrons of the 6 s orbital, and the Au d orbitals do not participate in the bonding of this system.

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OBJECT: Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) are well-known and powerful imaging techniques for MRI. Although DTI evaluation has evolved continually in recent years, there are still struggles regarding quantitative measurements that can benefit brain areas that are consistently difficult to measure via diffusion-based methods, e.g., gray matter (GM). The present study proposes a new image processing technique based on diffusion distribution evaluation of López-Ruiz, Mancini and Calbet (LMC) complexity called diffusion complexity (DC). MATERIALS AND METHODS: The OASIS-3 and TractoInferno open-science databases for healthy individuals were used, and all the codes are provided as open-source materials. RESULTS: The DC map showed relevant signal characterization in brain tissues and structures, achieving contrast-to-noise ratio (CNR) gains of approximately 39% and 93%, respectively, compared to those of the FA and ADC maps. DISCUSSION: In the special case of GM tissue, the DC map obtains its maximum signal level, showing the possibility of studying cortical and subcortical structures challenging for classical DTI quantitative formalism. The ability to apply the DC technique, which requires the same imaging acquisition for DTI and its potential to provide complementary information to study the brain's GM structures, can be a rich source of information for further neuroscience research and clinical practice.

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Infectious disease (ID) cohorts are key to advancing public health surveillance, public policies, and pandemic responses. Unfortunately, ID cohorts often lack funding to store and share clinical-epidemiological (CE) data and high-dimensional laboratory (HDL) data long term, which is evident when the link between these data elements is not kept up to date. This becomes particularly apparent when smaller cohorts fail to successfully address the initial scientific objectives due to limited case numbers, which also limits the potential to pool these studies to monitor long-term cross-disease interactions within and across populations. CE data from 9 arbovirus (arthropod-borne viruses) cohorts in Latin America were retrospectively harmonized using the Maelstrom Research methodology and standardized to Clinical Data Interchange Standards Consortium (CDISC). We created a harmonized and standardized meta-cohort that contains CE and HDL data from 9 arbovirus studies from Latin America. To facilitate advancements in cross-population inference and reuse of cohort data, the Reconciliation of Cohort Data for Infectious Diseases (ReCoDID) Consortium harmonized and standardized CE and HDL from 9 arbovirus cohorts into 1 meta-cohort. Interested parties will be able to access data dictionaries that include information on variables across the data sets via Bio Studies. After consultation with each cohort, linked harmonized and curated human cohort data (CE and HDL) will be made accessible through the European Genome-phenome Archive platform to data users after their requests are evaluated by the ReCoDID Data Access Committee. This meta-cohort can facilitate various joint research projects (eg, on immunological interactions between sequential flavivirus infections and for the evaluation of potential biomarkers for severe arboviral disease).

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The presence of phenazopyridine in water is an environmental problem that can cause damage to human health and the environment. However, few studies have reported the adsorption of this emerging contaminant from aqueous matrices. Furthermore, existing research explored only conventional modeling to describe the adsorption phenomenon without understanding the behavior at the molecular level. Herein, the statistical physical modeling of phenazopyridine adsorption into graphene oxide is reported. Steric, energetic, and thermodynamic interpretations were used to describe the phenomenon that controls drug adsorption. The equilibrium data were fitted by mono, double, and multi-layer models, considering factors such as the numbers of phenazopyridine molecules by adsorption sites, density of receptor sites, and half saturation concentration. Furthermore, the statistical physical approach also calculated the thermodynamic parameters (free enthalpy, internal energy, Gibbs free energy, and entropy). The maximum adsorption capacity at the equilibrium was reached at 298 K (510.94 mg g-1). The results showed the physical meaning of adsorption, indicating that the adsorption occurs in multiple layers. The temperature affected the density of receptor sites and half saturation concentration. At the same time, the adsorbed species assumes different positions on the adsorbent surface as a function of the increase in the temperature. Meanwhile, the thermodynamic functions revealed increased entropy with the temperature and the equilibrium concentration.

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Important foraging and nesting habitats for Caribbean green sea turtles (Chelonia mydas) exist within the Mesoamerican Reef System in the Mexican Caribbean. During the last 25 years, urban development and touristic activities have drastically increased in Quintana Roo, Mexico. Moreover, in the last decade, massive pelagic sargasso blooms have also afflicted this region; however, information about the biochemical responses of Caribbean green turtles to these inputs is absent. This study aimed to assess if the oxidative stress indicators in the red blood cells of green turtles are valuable biomarkers of the extent of the anthropic impact in this region. Persistent organic pollutants (POPs) were also measured in the plasma of free-living green turtles during 2015-2018 to characterize these habitats further. As biochemical biomarkers, the production rate of superoxide radical (O2•-), carbonylated protein content, and lipid peroxidation (TBARS) levels, and the activities of superoxide dismutase, glutathione S-transferase (GST), catalase, glutathione peroxidase were measured in erythrocytes. A 15 % occurrence of fibropapillomatosis (FP) was revealed, with tumor size being positively correlated with CAT activity in the affected individuals. A multivariate analysis embracing all oxidative stress markers discriminated green turtles between years of capture (p < 0.001), with those sampled during 2015 presenting the highest production of O2•- (p = 0.001), activities of GST (p < 0.001), levels of TBARS (p < 0.001) and carbonylated proteins (p = 0.02). These local and temporal biochemical responses coincided with the first massive Sargassum spp. bloom reported in the region. The results of this study corroborate the utility of the oxidative stress indicators as biomarkers of environmental conditions (sargasso blooms and POPs) in the green turtle as sentinel species.

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