Background: Zika virus (ZIKV) infection has significantly affected Latin America in 2015–2017. Most studies have been reported from Brazil and Colombia, and only a few from Central America. For these reasons we analyzed the incidence, incidence rates and evolution of cases in Honduras from 2016–2017. Methods: Using epidemiological weeks (EW) surveillance data on the ZIKV epidemics in Honduras, we estimated incidence rates (cases/100,000 population), and developed maps at national, departmental and municipal levels. Results: From 1 January 2016 to 31 December 2017, a total of 32,607 cases of ZIKV were reported (98.5% in 2016 for an incidence rate of 36.85 cases/100,000 pop; 1% confirmed by RT-PCR). The highest peak was reached on the EW 6°, 2016 (2,559 cases; 29.34 cases/100,000 pop). The department with the highest number of cases and incidence rate was Cortés (13,128 cases, 791.08 cases/100,000 pop in 2016). Discussion: The pattern and evolution of ZIKV infection in Honduras has been similar to that which occurred for chikungunya in 2015. As previously reported, infection with chikungunya involved predominantly the central and capital area of the country, reaching incidences there >750 cases/100,000 pop. Studies using geographical information systems linked with clinical disease characteristics are necessary to attain accurate epidemiological data for public health systems. Such information is also useful for assessment of risk for travelers who visit specific areas in a destination country.

Background: After serious epidemics of chikungunya (CHIKV) and Zika (ZIKV) in the Americas, dengue (DENV) have reemerged in most countries. We analyzed the incidence, incidence rates, and evolution of DENV cases in Honduras from 2015-2018 and the ongoing 2019 epidemic. Methods: Using epidemiological weeks (EW) surveillance data on the DENV in Honduras, we estimated incidence rates (cases/100,000 population), and developed maps at national, departmental, and municipal levels. Results: From 1 January 2016 to 21 July 2019, a total of 109,557 cases of DENV were reported, 28,603 in 2019, for an incidence rate of 312.32 cases/100,000 pop this year; 0.13% laboratory-confirmed. The highest peak was reached on the EW 28°, 2019 (5,299 cases; 57.89 cases/100,000 pop). The department with the highest number of cases and incidence rate was Cortes (8,404 cases, 479.68 cases/100,000 pop in 2019). Discussion: The pattern and evolution of DENV epidemic in 2019 in Honduras has been similar to that which occurred for in 2015. As previously reported, this epidemic involved the north and central areas of the country predominantly, reaching municipality incidences there >1,000 cases/100,000 pop (1%). Studies using geographical information systems linked with clinical disease characteristics are necessary to attain accurate epidemiological data for public health systems. Such information is also useful for assessment of risk for travelers who visit specific areas in a destination country.

The epidemic of Coronavirus Disease 2019 (COVID-19) have affected all the regions of the world, nevertheless, in some countries there is a lack of studies on its main clinical and epidemiological features. We analyzed the incidence, incidence rates, and evolution of COVID-19 cases in Honduras from February 18-April 24, 2020.Methods: Using daily epidemiological data from surveillance about COVID-19 in Honduras, we calculated the rates of incidence (cases/100,000 population), and developed at national, departmental, and municipal levels GIS-based maps.Results: February 18 - April 24, 2020, a sum of 3,169 suspected COVID-19 cases have been assessed by RT-PCR, 533 (16.8%) of them were positive, for an incidence rate of 5.73 cases/100,000 pop. The highest peak was reached on March 31 (48 cases). The department with the highest number of cases and incidence rate was Cortes (383 cases, 71.9% of the total, 21.45 cases/100,000 pop). Discussion: The pattern and evolution of COVID-19 epidemic in Honduras has been particularly focused in the major urban areas, San Pedro Sula and Tegucigalpa, the capital city. Studies using geographical information systems linked with clinical disease characteristics are necessary to attain accurate epidemiological data for public health systems. Such information is also useful for assessment of the evolution of the pandemic and monitoring interventions.

Feedback is a type of formative evaluation linked to student learning and academic achievement. However, it is not known how students perceive this process since there are no instruments to measure attitudes towards feedback. The objective of this work was to develop and validate an attitude scale towards academic feedback through an Exploratory Factor Analysis (EFA), using the principal components method and an internal consistency analysis using Cronbach's Alpha. The sample consisted of 274 students from 2015, 2016, and 2017 cohorts of pedagogy careers from five Chilean universities. The results showed the consolidation of the instrument and its final conformation in four components (Negative experience towards feedback, Effectiveness of feedback, Feedback as an opportunity, and Resistance towards feedback) with a total of 15 items, which allow measuring the students' attitude towards this written formative evaluation.

Non-homologous end-joining (NHEJ) is a major DNA repair pathway in mammalian cells that recognizes, processes and fixes DNA damages throughout the cell cycle, and is specifically important for homeostasis of post-mitotic neurons and developing lymphocytes. Neuronal apoptosis increases in the mice lacking NHEJ factors Ku70 and Ku80. Inactivation of other NHEJ genes, either Xrcc4 or Lig4, leads to massive neuronal apoptosis in the central nervous system (CNS) that correlates with embryonic lethality in mice. Inactivation of either Paxx, Mri or Dna-pkcs NHEJ gene results in normal CNS development due to compensatory effects of Xlf. Combined inactivation of Xlf/Paxx, Xlf/Mri and Xlf/Dna-pkcs, however, results in late embryonic lethality and high levels of apoptosis in CNS. To determine the impact of NHEJ factors on early stages of neurodevelopment, we isolated neural stem and progenitor cells from mouse embryos and investigated proliferation, self-renewal and differentiation capacity of these cells lacking either Xlf, Paxx, Dna-pkcs, Xlf/Paxx or Xlf/Dna-pkcs. We found that XLF, DNA-PKcs and PAXX maintain the neural stem and progenitor cell populations and neurodevelopment in mammals, which is particularly evident in the double knockout models.

In the former mining district of La Carolina (southern Spain), waste materials were accumulated without first waterproofing the substrate. Additionally, these mining dams were abandoned without any type of restoration, conditions that present a significant risk of contaminating the surrounding soils and waters. Prior to carry out the necessary works for isolating these residues, morphological characterization of the contact with the base substrate is needed. For that purpose, in recent years, different indirect study techniques have been used, among which electrical to-mography stands out. In this work, a less common geophysical technique, magnetometry, both in its magnetic and gra-diometric modalities, is used as an alternative method to analyse the geometry and structure of a mining dam. The results obtained with this methodology are compared with the results provided by electrical techniques, as well as with direct data from mechanical surveys and aerial photo-graphs prior to waste deposition. The magnetic anomaly curves obtained with geometric models generated with the Mag2dc program were also analysed. This comparative study shows the use-fulness of magnetic prospecting in characterizing the base geometry of these mining structures and therefore the interest in its application to environmental characterization studies.

Antarctica has attracted increasing interest in understanding its microbial communities, metabolic potential, and as a source of microbial hydrolytic enzymes with industrial applications, for which advances in next-generation sequencing technologies have greatly facilitated the study of unculturable microorganisms. In this work, soils from seven sub-Antarctic islands and Union Glacier were studied using a whole-genome shotgun metagenomic approach. The main findings were that the microbial community at all sites was predominantly composed of the bacterial phyla Actinobacteria and Cyanobacteria, and the families Streptomycetaceae and Pseudonocardiaceae. Regarding the xenobiotic biodegradation and metabolism pathway, genes associated with benzoate, chloroalkane, chloroalkene, and styrene degradation were predominant. In addition, putative genes encoding industrial enzymes with predicted structural properties associated with improved activity at low temperatures were found, with catalases and malto-oligosyltrehalose trehalohydrolase being the most abundant. Overall, our results show similarities between soils from different Antarctic sites with respect to more abundant bacteria and metabolic pathways, especially at higher classification levels, regardless of their geographic location. Furthermore, our results strengthen the potential of Antarctic soils as a source of industrially relevant enzymes.

With the increasing use of data-driven modeling methods new approaches to complex problems in the field of wind energy can be addressed. Topics reviewed through literature include wake modeling, performance monitoring and controls applications, condition monitoring and fault detection, and other data-driven research. Literature shows the advantages of data-driven methods reduce computational expense or complexity, particularly in the cases of wake modeling and controls, as well as various data-driven methodologies’ aptitude for predictive modeling and classification, as in the cases of fault detection and diagnosis. Significant work exists for fault detection while less work is found for controls applications. A methodology for creating data-driven wind turbine models for arbitrary performance parameters is proposed. Results are presented utilizing the methodology to create wind turbine models relating active adaptive twist to steady-state rotor thrust as a performance parameter of interest. Resulting models are evaluated by comparing root-mean-square-error (RMSE) on both the training and validation data sets, with Gaussian Process Regression (GPR), deemed an accurate model for this application. The resulting model undergoes particle swarm optimization to determine the optimal aerostructure twist shape at a given wind speed with respect to the modeled performance parameter, aerodynamic thrust load. The optimization process shows an improvement of 3.15% in thrust loading for the 10 MW reference turbine, and 2.66% for the 15 MW reference turbine.

Chlamydia trachomatis (Ct) infections are the most common sexually-transmitted infection (STI). Despite effective antibiotics for Ct, undetected infections or delayed treatment can lead to infertility, ectopic pregnancies, and chronic pelvic pain. Besides humans, chlamydia poses similar health challenges in animals such as C. suis (Cs) in pigs. Based on the similarities between humans and pigs as well as their chlamydia species, we use pigs as a large biomedical animal model for chlamydia research. In this study, we use the pig model to develop a vaccine candidate against Ct. The vaccine candidate consists of TriAdj-adjuvanted chlamydial protease-like activity factor (CPAF) protein. We tested two weekly administration options – twice intranasal (IN) followed by twice intramuscular (IM), or twice IM followed by twice IN. We assessed the humoral immune response not only in serum by CPAF-specific IgG including antibody avidity determination but also in cervical and rectal swabs by CPAF-specific IgG and IgA ELISAs. The systemic T-cell response was analyzed upon in vitro CPAF-restimulation via IFN-γ and IL-17 ELISpots as well as intracellular cytokine staining flow cytometry. Our data demonstrate that while the IN/IM vaccination mainly led to non-significant systemic immune responses, the vaccine candidate is highly immunogenic if administered IM/IN. This vaccination strategy induced high serum anti-CPAF IgG levels with strong avidity as well as high IgA and IgG levels in vaginal and rectal swabs and in uterine horn flushes. In addition, this vaccination strategy prompted a pronounced cellular immune response: Besides inducing IL-17 production, the vaccine candidate induced a strong IFN-γ response by CD4 T cells. In the IM/IN vaccinated pigs, these cells also significantly downregulated their CCR7 expression – a sign for differentiation into peripheral tissue homing effector/ memory cells. Conclusively, this study demonstrates strong immunogenicity of the IM/IN administered TriAdj-adjuvanted Ct CPAF vaccine candidate. Future studies will test vaccine efficacy of this promising Ct vaccine candidate. In addition, this project demonstrates suitability of the Cs pre-exposed outbred pig model for Ct vaccine development. Thereby, we aim to open the bottleneck of large animal models to facilitate the progression of Ct vaccine candidates into clinical trials.

Dementia, primarily caused by neurodegenerative diseases like Alzheimer’s disease (AD), affects millions worldwide, making detection and monitoring crucial. To enable these tasks, we propose encoding in-text pauses and filler words (i.e., “uh” and “um”) in text-based language models, and thoroughly evaluate their effect in performance. Additionally, we suggest using contrastive learning to improve performance in a multi-task framework. Our results demonstrated the effectiveness of our approaches in enhancing the model’s performance, achieving 87% accuracy and an 86% F1-score. Compared to the state-of-the-art, our approach has similar performance despite having significantly fewer parameters. This highlights the importance of pause and filler word encoding on the detection of dementia.

With opioid overdose rates on the rise, we aim to develop a county-level risk stratification, specifically focusing on access to Medications for Opioid Use Disorder (MOUD) and high overdose rates. We examined over 15 million records from the South Carolina Prescription Tracking System (SCRIPTS) across 46 counties. Additionally, we incorporated data from opioid treatment programs, healthcare professionals prescribing naltrexone, clinicians with buprenorphine waivers, and county-level overdose fatality statistics. To assess the risk of opioid misuse, we classified counties into high-risk and low-risk categories based on prescription rates, overdose fatalities, and treatment service availability. Utilizing statistical methods, we compared prescription patterns and outcomes between these groups. Our study shows that between 2017 and 2021, opioid prescriptions decreased from 64,223 to 41,214 per 100,000 residents, while fentanyl-related overdose deaths increased by 312%. High-risk counties have significantly higher rates of fentanyl prescriptions and rely more on out-of-state doctors. They also exhibit higher instances of doctor shopping and had fewer medical doctors per capita, with limited access to MOUD. To effectively combat the opioid crisis, we advocate for improved local healthcare infrastructure, broader treatment access, stricter management of out-of-state prescriptions, and vigilant tracking of prescription patterns. Tailored local strategies are essential for mitigating the opioid epidemic in these communities.

Intramammary infections (mastitis) of dairy cows, along with other periparturient diseases, have become problematic within the dairy industry as they lead to loss of milk production. The main objective of this study was to determine whether elevated somatic cell counts (SCC) in cows prior to drying off are related to the incidence of other periparturient diseases. Additionally, we de-termined whether milk composition and milk yield are affected by a high SCC prior to drying off. Somatic cell counts of milk samples were determined prior to dry off (n = 140) and were used to classify cows in the study as high (>200,000 cells/mL) or low (<200,000 cells/mL) SCC. The com-position of milk was analyzed before drying off and at 1 and 2 weeks after calving. The results showed that an elevated SCC before drying off was related to the incidence of ketosis. Cows with a high SCC at drying off also showed an increased likelihood of retained placenta, metritis, and lameness postpartum; however, it was not statistically significant. Milk lactose was lower in cows with high SCC, while protein content was lower after parturition. Milk production was lower for cows with pre-drying elevated SCC, particularly for cows with retained placenta, keto-sis, and mastitis. In conclusion, cows with pre-drying elevated SCC were more likely to develop disease after parturition and produce less milk and with lower lactose and protein content.

The use of Retrieval-augmented generation (RAG) using large language models (LLMs) has shown potential for addressing issues such as hallucinations and inadequately contextualized responses. A pivotal stage in the RAG process involves a retriever for retrieving chunks based on semantic similarity with the query. In this study, we advocate for and provide experimental evidence supporting integrating and maintaining questions and answers (QA) formatted databases to improve retrieved-context representations and response quality. Our experiments evaluate our approach on benchmark RAG datasets using standard evaluation metrics and provide comparative analyses against state-of-the-art retrieval methods, showing the potential of our approach.

This work (in two parts) will review the recently developed predictive modeling methodology called “4th-BERRU-PM” and its applicability to energy systems as exemplified by an illustrative application to the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark. The acronym 4th-BERRU-PM designates the “Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling” methodology, which yields best-estimate results with reduced uncertainties for the first fourth-order moments (mean values, covariance, skewness, and kurtosis) of the optimally predicted posterior distribution of model results and calibrated model parameters. The 4th-BERRU-PM uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses to model parameters, thus incorporating, as particular cases, the results previously predicted by the second-order predictive modeling methodology 2nd-BERRU-PM, and vastly generalizing the results produced by extant data assimilation and data adjustment procedures. The 4th-BERRU-PM methodology encompasses the scopes of high-order sensitivity analysis (SA), uncertainty quantification (UQ), data assimilation (DA) and model calibration (MC). The application of the 4th-BERRU-PM methodology to energy systems is illustrated by means of the above-mentioned OECD/NEA reactor physics benchmark, which is modeled using the neutron transport Boltzmann equation involving 21976 imprecisely known parameters, the solution of which is representative of “large-scale computations.” The model result (“response”) of interest is the leakage of neutrons through the outer surface of this spherical benchmark, which can be computed numerically and measured experimentally. Part 1 of this work illustrates the impact of high-order sensitivities, in conjunction with parameter standard deviations of various magnitudes, on the determination of the expected value and variance of the computed response in terms of the first four moments of the distribution of the uncertain model parameters. Part 2 of this work will illustrate the capabilities of the 4th-BERRU-PM methodology for combining computational and experimental information, up to and including forth-order sensitivities and distributional moments, for producing best-estimate values for the predicted responses and model parameters while reducing their accompanying uncertainties.

Cancer is one of the leading causes of death, making timely diagnosis and prognosis very important. Utilization of AI (artificial intelligence) enables providers to organize and process patient data in a way that can lead to better overall outcomes. This review paper aims to look at the varying uses of AI for diagnosis and prognosis and clinical utility. PubMed and EBSCO databases were utilized for finding publications from January 1, 2013, to December 22, 2023. Articles were collected using key search terms such as “artificial intelligence” and “machine learning.” Included in the collection were studies of the application of AI in determining cancer diagnosis and prognosis using multi-omics data, radiomics, pathomics, clinical and laboratory data. The resulting 89 studies were categorized into eight sections based on the type of data utilized and then further subdivided into two subsections focusing on cancer diagnosis and prognosis, respectively. 8 studies integrated more than one form of omics, namely genomics, transcriptomics, epigenomics, and proteomics. Incorporating AI into cancer diagnosis and prognosis alongside omics and clinical data represents a significant advancement. Given the considerable potential of AI in this domain, ongoing prospective studies are essential to enhance algorithm interpretability and to ensure safe clinical integration.

The G-protein-coupled estrogen receptor (GPER; G protein-coupled estrogen receptor 30, also known as GPR30) is a novel estrogen receptor and has emerged as a promising target for ovarian cancer. GPER, a seven-transmembrane receptor, suppresses cellular viability and migration in studied ovarian cancer cells. However, its impact on the fallopian tube, which is the potential origin of high-grade serous (HGSC) ovarian cancer, has not been addressed. This study was conducted to evaluate the relationship of GPER, ovarian cancer subtypes, i.e., high-grade serous cell lines (OV90 and OVCAR420), as well as potential HGSC ovarian cancer origin (i.e., the fallopian tube cell line FT190). The selective ligand assessed here is the agonist G-1, which was utilized in an in vitro study to characterize its effects on cellular viability and migration. As a result, this study has addressed the effect of a specific GPER antagonist in combination with an agonist on cell viability, providing a better understanding of the effects of these compounds on our diverse group of studied cell lines. Strikingly, attenuated cell proliferation and migration behaviors were observed in the presence of G-1. Thus, our in vitro study reveals the impact of the origin of HGSC ovarian cancers and highlights the GPER agonist G-1 as a potential therapy for ovarian cancer.

Mn-promoted bulk iron catalysts with high specific surface area (82-211 m2.g-1) were synthesized by coprecipitation followed by drying under supercritical conditions. The catalysts were tested in the CO2 hydrogenation reaction. The Mn-promoted iron catalysts showed better textural properties than the bare Fe2O3 catalyst, allowing better dispersion of the active phase, easier reduction of iron oxide, better carburization of iron oxides and higher catalytic activity than the bare Fe2O3 catalyst. The best activity results were obtained by catalyst promotion with a very low amount of Mn (Mn/Fe ratio of 0.05). Upon steady-state conditions (T=340 ºC, total pressure of 20 bar and H2/CO2=3), this catalyst showed high CO2 conversion (44.2%) and selectivity to C2-C4 hydrocarbons (68%, olefin to paraffin ratio of 0.54), while the selectivity to C5+ hydrocarbons, CH4 and CO was about 3.2, 38.5 and 5%, respectively. A close correlation was found between catalyst textural properties and CO2 conversion. The most active MnFe-0.05 catalyst shows high stability during 72 h of reaction related to low amount of soft coke formation and catalyst activation through the formation of χ-Fe5C2 phase during the on-stream reaction.

Wastewater treatment plants (WWTPs) are thought to be potential incubators of antibiotic resistance. Persistence of commonly used antibiotics in wastewater may increase the potential for selection of resistance genes transferred between bacterial populations, some of which may pose a threat to human health. In this study, we measured the concentrations of ten antibiotics in wastewater plant influents and effluents, and in surface waters up- and downstream from two Charlotte area treatment facilities. We performed Illumina shotgun sequencing to assay the microbial community and resistome compositions at each site across four time points from late winter to mid-summer of 2016. Antibiotics are present throughout wastewater treatment, and elevated concentrations of multiple antibiotics are maintained in moving stream water downstream of effluent release. While some human gut and activated sludge associated taxa are detectable downstream, these seem to attenuate with distance while the core microbial community of the stream remains fairly consistent. We observe slight suppression of functional pathways in the downstream microbial communities, including amino acid, carbohydrate and nucleic acid metabolism as well as nucleotide and amino acid scavenging. Nearly all antibiotic resistance genes (ARGs) and potentially pathogenic taxa are removed in the treatment process, though a few ARG markers are elevated downstream of effluent release. Taken together, these results represent baseline measurements which future studies can utilize to help to determine which factors control the movement of antibiotics and resistance genes through aquatic urban ecosystems before, during and after wastewater treatment.

Quantum squeezing, an intriguing phenomenon that amplifies the uncertainty of one variable while diminishing that of its conjugate, may be studied as a time-dependent process, with exact solutions frequently derived from frameworks grounded in adiabatic invariants. Remarkably, we reveal that exact solutions can be ascertained in the presence of time-variant elastic forces, eschewing dependence on invariants or frozen eigenstates formalism. Delving into these solutions as an inverse problem unveils their direct connection to the design of elastic fields, responsible for inducing squeezing transformations onto canonical variables. Of particular note, the dynamic transformations under investigation belong to a class of gentle quantum operations, distinguished by their delicate manipulation of particles, thereby circumventing the abrupt energy surges commonplace in conventional control protocols.

This article is a theoretical contribution to mental model research, which currently has different threads. Whereas some researchers focus on the perceived causal structure, others also include decision policies and decisions. We focus on the link between recognized causal structure (“mental models of dynamic systems”) and policies, proposing Johnson-Laird’s theory of mental models as the link. The resulting framework hypothesizes two types of systematic mental model errors: (1) misrepresentation of the system’s structure and (2) failure to deploy relevant mental models of possibilities. Examination of three experiments through this lens reveals errors of both types. Therefore, we propose that the cognitive theory of mental models opens a path to better understand how people construct their decision policies and develop interventions to reduce such mental model errors. The article closes by raising several questions for empirical studies of the reasoning process leading from mental models of dynamic systems to decision policies.

Additive manufacturing, commonly referred to as 3D printing, is an exciting and versatile manufacturing technology that is gaining traction and interest in both academic and industrial settings. Polymeric materials are essential components in a majority of the “inks” used across the various 3D printing technologies. As the environmental ramifications of sole or primary reliance on petrochemicals as a resource for industrial polymers continue to manifest themselves at a global scale, a transition to more sustainable bioderived alternatives could offer solutions. In particular, cellulose is promising due to its global abundance, biodegradability, excellent thermal and mechanical properties, and ability to be chemically modified to suit various applications. Thus far, cellulose has typically been incorporated in additive manufacturing applications as a substrate, filler, or reinforcement for other materials. However, due to its versatility and desirable properties, the exploration of all-cellulose 3D printing materials and associated methodologies is increasing. This review will discuss the progress and status of all-cellulose 3D printing approaches, associated challenges, and their potential as a key player in a more sustainable industrial future.

Abstract: Herbicide flashback is an undesirable response that results in damage or mortality of non-target plants near chemically treated plants. In severe instances, crop trees can be injured resulting in financial loss or inability to capture management objectives. Land managers need to know the potential limitations of using triclopyr acid (50% solution in water) and what quantity can cause damage to non-target trees (trees not aimed to control via herbicide). Three naturally regenerated (previous clearcut) bottomland hardwood sites, ranging from 14–20 years of age, received partial overstory deadening using Trycera® herbicide. Residual “leave” trees experienced indirect herbicide uptake resulting in some trees either experiencing mortality or partial mortality with top die-back/epicormic branching. An increased probability of damage occurred as tree diameter decreased and the number of treated stems around non-target stems increased. American sycamore, green ash, and sweetgum were most susceptible to flashback. This study examines the unanticipated chemical effect of deadening adjacent stems surrounding residual leave trees in poorly drained soils.

Exposure to extreme heat is a known risk factor that is associated with increased heat-related illness (HRI) outcomes. The relevance of heat wave definitions could change across the health conditions and geographies due to the heterogenous climate profile. This study compared the sensitivity of 28 heat wave definitions associated with HRI emergency department visits over five summer seasons (2011-2016), stratified by two physiographic regions (Coastal and Piedmont) in North Carolina. The HRI rate ratios associated with heat waves were estimated using the generalized linear regression framework assuming a negative binomial distribution. We compared the Akaike Information Criterion (AIC) values across the heat wave definitions to identify an optimal heat wave definition. In the Coastal region, heat wave definition based on daily maximum temperature with a threshold >90^th percentile for two or more consecutive days had the optimal model fit. In the Piedmont region, heat wave definition based on the daily minimum temperature with a threshold value >90^th percentile for two or more consecutive days was optimal. Additionally, we observed that the optimal heat wave definitions from this study captured moderate and frequent heat episodes than the national weather service (NWS) heat products that worked best for extreme heat episodes. This study compared the HRI morbidity risk associated with epidemiologic-based heat wave definitions and with NWS heat products. Our findings could be used for public health education and suggest recalibrating NWS heat products.

Predicting anomalies in manufacturing assembly lines is crucial for reducing time and labor costs and improving processes. For instance, in rocket assembly, premature part failures can lead to significant financial losses and labor inefficiencies. With the abundance of sensor data in the Industry 4.0 era, machine learning (ML) offers potential for early anomaly detection. However, current ML methods for anomaly prediction have limitations, with F1-measure scores of only 50% and 66% for prediction and detection, respectively. This is due to challenges like the rarity of anomalous events, scarcity of high-fidelity simulation data (actual data is expensive), and the complex relationships between anomalies not easily captured by traditional ML approaches. Specifically, these challenges relate to two dimensions of anomaly prediction: predicting when anomalies will occur and understanding the dependencies between them. This paper introduces a new method called Robust and Interpretable 2D Anomaly Prediction (RI2AP) designed to address both dimensions effectively. RI2AP is demonstrated on a rocket assembly simulation, showing up to a 30-point improvement in F1 measure compared to current ML methods. This highlights its potential to enhance automated anomaly prediction in manufacturing. Additionally, RI2AP includes a novel interpretation mechanism inspired by a causal-influence framework, providing domain experts with valuable insights into sensor readings and their impact on predictions. Finally, the RI2AP model is deployed in a real manufacturing setting for assembling rocket parts. Results and insights from this deployment demonstrate the promise of RI2AP for anomaly prediction in manufacturing assembly pipelines.

Harmful algal blooms (HABs) are natural events produced by massive concentration of toxic phytoplankton that can color red, ocher, brown or yellow large extensions of water, its intensity depends on the different species of phytoplankton involved in the proliferation. The spreading of these formations involves an interaction of biological, chemical, meteorological and anthropogenic factors. Several species with potential toxicity have been reported along Mexican coasts, such as Gymnodinium catenatum, Karenia brevis, Pyrodinium bahamense var. compressum. The toxic bloom not only causes an impact during the event, it produces negative effects afterward, such as accumulating deposits of organic matter, alterations of benthic community structure and composition, species presence/absence, and bioaccumulation of toxins in organisms such as bivalve molluscs mainly. Poisoning may occur by consuming contaminated seafood or by direct exposure to aerosols of the toxins, which can provoke diarrhea or even death. Due to the impact of this type of event on the economy, environment and public health, strategies for monitoring, prevention, and systematic mitigation have been implemented for the evaluation of HAB effects. The aim of this review was to determine the state-of-the art of HAB, their reports and effects on the environment and public health in Mexico.

This work continues the review and illustrative application to energy systems of the “Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling” (4th-BERRU-PM) methodology. The 4th-BERRU-PM methodology uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, in-cluding fourth (and higher) order sensitivities of computed model responses with respect to model parameters. The 4th-BERRU-PM methodology yields the fourth-order MaxEnt posterior distribution of experimentally measured and computed model responses and parameters, in the combined phase-space of model responses and parameters. The 4th-BERRU-PM methodology encompasses fourth-order sensitivity analysis (SA) and uncertainty quantification (UQ), which were reviewed in the accompanying work (Part 1), as well as fourth-order data assimilation (DA) and model calibration (MC) capabilities, which will be reviewed and illustrated in this work (Part 2). The applicability of the 4th-BERRU-PM methodology to energy systems is illustrated by using the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark, which is modeled using the linear neutron transport Boltzmann equation, involving 21976 im-precisely known parameters. This benchmark is representative of “large-scale computations” such as those involved in the modeling of energy systems. The result (“response”) of interest for the PERP benchmark is the leakage of neutrons through the outer surface of this spherical benchmark, which can be computed numerically and measured experimentally. The impact of the high-order sensitivities of the response with respect to the PERP model parameters is quantified for “high-precision” parameters (2% standard deviations) and “medium-precision” parameters (5% standard deviations). Analyzing the best-estimate results with reduced uncertainties for the 1st- through 4th-order moments (mean values, covariance, skewness, and kurtosis) produced by the 4th-BERRU-PM methodology for the PERP benchmark indicates that, even for systems mod-eled by linear equations (e.g., the PERP benchmark), retaining only first-order sensitivities is insufficient for reliable predictive modeling (including SA, UQ, DA, MC). At least second-order sensitivities should be retained in order to obtain reliable predictions.

This paper characterizes the influence of synoptic-scale air mass conditions on spatial and temporal patterns of precipitation in North Carolina over a 16-year period (2003-2018). National Center for Environmental Prediction Stage IV multi-sensor precipitation estimates were used to describe seasonal variations in precipitation in the context of prevailing air mass conditions classified using the spatial synoptic classification system. Spatial analyses identified significant clustering of high daily precipitation amounts distributed along the east side of the Appalachian Mountains and along the coastal plains. Significant and heterogeneous clustering was prevalent in summer months and tended to coincide with land cover boundaries and complex terrain. The summer months were dominated by maritime tropical air mass conditions whereas dry moderate air mass conditions prevailed in the winter, spring, and fall. Between the three geographic regions of North Carolina, highest precipitation amounts were received in western North Carolina during the winter and spring, and in eastern North Carolina in the summer and fall. Central North Carolina received the least amount of precipitation; however, there was substantial variability between regions due to prevailing air mass conditions. There was an observed shift toward warmer and more humid air mass conditions in the winter, spring, and fall months throughout the study period (2003-2018), indicating a shift toward air mass conditions conducive to higher daily average rain rates in North Carolina.

Measurements of systems taken along a continuous functional dimension, such as time or space, are ubiquitous in many fields, from the physical and biological sciences to economics and engineering. Such measurements can be viewed as realisations of an underlying smooth process sampled over the continuum. However, traditional methods for independence testing and causal learning are not directly applicable to such data, as they do not take into account the dependence along the functional dimension. By using specifically designed kernels, we introduce statistical tests for bivariate, joint, and conditional independence for functional variables. Our method not only extends the applicability to functional data of the Hilbert-Schmidt independence criterion (HSIC) and its d-variate version (d-HSIC), but also allows us to introduce a test for conditional independence by defining a novel statistic for the conditional permutation test (CPT) based on the Hilbert-Schmidt conditional independence criterion (HSCIC), with optimised regularisation strength estimated through an evaluation rejection rate. Our empirical results of the size and power of these tests on synthetic functional data show good performance, and we then exemplify their application to several constraint- and regression-based causal structure learning problems, including both synthetic examples and real socio-economic data.

Natural language Processing (NLP) has significantly transformed in the last decade, especially in the field of Language Modeling. Large Language Models (LLMs) have achieved SOTA performances on Natural Language Understanding (NLU) and Natural Language Generation (NLG) tasks by learning language representation in self-supervised ways. This paper provides a comprehensive survey to capture the progression of advances in Language Models. In this paper, we examine the different aspects of Language Models, which started with a few million parameters but have reached the size of a trillion in a very short time. We also look at how these LLMs transitioned from task-specific to task-independent to task-and-language-independent architectures. This paper extensively discusses different pre-training objectives, benchmarks, and transfer learning methods used in LLMs. It also examines different fine-tuning and In-Context learning techniques used in downstream tasks. It also explores how LLMs can perform well across many domains and datasets if sufficiently trained on a large and diverse dataset. Next, it discusses how, over time, the availability of cheap computational power and large datasets have improved LLM’s capabilities and raised new challenges. As part of our study, we also inspect LLMs from the lens of scalability to see how their performance is affected by the model’s depth, width, and data size. Lastly, we provide an empirical comparison of existing trends and techniques and a comprehensive analysis of where the field of LLM currently stand.

We present an open-source Star Tracker (ST) for Attitude Determination. Our implementation makes use of open source software of common usage in astronomy running on a Raspberry Pi 2 platform. The developed platform is open and available for parties interested in further development. Evaluation of the system showed that the ST is suitable for Cubesats of 2U or larger. The ST platform is capable of solving the Lost-In-Space (LIS) problem. Currently, the average accuracy reached of the algorithm is close to 5 seconds of arc with an average processing time of 75 seconds. However, a average accuracy of about 3 minutes of arc can be reached with 35 seconds in average processing time. We also describe the evaluation procedure, the found conclusions of this procedure, in particular the section of the algorithm where most of the processing time is spent and other possible source of errors not included in this study.

Background and objectives: Main aim of the study is to analyze the outcomes of NSTEMI admissions and testing the relevance of TIMI as a risk score in a real-world setting. Also looking at any potential social or health care disparities involved with outcomes of NSTEMI admissions. The study also investigates factors associated with mortality in NSTEMI admissions and correlation between in-hospital mortality and heart catheterization. . Materials and Methods: NSTEMI admissions were analyzed using the National Inpatient Sample. TIMI scores were calculated in the admissions and association between all-cause mortality and calculated TIMI score >=2 was analyzed. Difference in outcomes based on heart catheterization was studied in the subgroup with TIMI score >=2. Correlation between time to heart catheterization and all-cause mortality in NSTEMI admissions were analyzed. Results: No significant social or healthcare disparities were noted amongst outcomes for NSTEMI admissions. NSTEMI admissions with a calculated TIMI score >=2 had a statistically significant association with all-cause mortality during the admission. Prevalence of mortality among NSTEMI admissions with calculated TIMI score >2, who did not undergo heart catheterization was higher with statistical significance when compared to admissions that underwent heart catheterization. NSTEMI admissions with a calculated TIMI score >2 who underwent catheterization on or beyond the 4th day of hospitalization had statistically significant association with all-cause mortality during the admission. Age at admission, presence of CKD and CHF were associated with mortality in NSTEMI admissions, with statistical significance. Keywords: NSTEMI; TMI score; heart catheterization

Composites of Ag and TiO2 nanoparticles on cotton fabrics were synthetized in-situ by sono-chemical and hydrothermal methods achieving the successive formation of Ag-NPs and Ti-NPs directly on the fabric. The impregnated fabrics were characterized by ATR-FTIR spectroscopy, high resolution microscopy (HREM), scanning electron microscopy coupled with Ener-gy-dispersive X-ray spectroscopy (SEM-EDS), Raman, photoluminescence, UV-vis and DRS spectroscopies and by tension tests. Results showed the successful formation and impregnation of NPs on the cotton fabric, with a negligible leaching of NPs after several washing cycles. The photocatalytic activity of supported NPs was assessed by the degradation of methyl blue dye (MB) under solar and UV irradiation revealing improved photocatalytic activity of the Ag-TiO2/cotton composites due to a synergy of both Ag and TiO2 nanoparticles. This behavior is attributed to a diminished electron-hole recombination effect in the Ag-TiO2 cotton samples. The biocide activity of these composites on the growth inhibition of Staphylococcus aureus (Gram+) and Escherichia coli (Gram-) was confirmed, revealing interesting possibilities for the utilization of the functionalized cotton fabric as protective cloth for medical applications.

Online speech enhancement is of great interest, because of its diverse areas of application. The Demucs architecture has been recently shown to achieve a high level of performance, even with small audio segments, while requiring a relatively small amount of computational resources. However, an issue that it bears, as well as most of state-of-the-art speech enhancement techniques, is that of target speech selection: it is assumed that only one speech source is present. However, in real-life scenarios, more than one speech source may be present, in it is uncertain which speech source within the fed mixture should be enhanced. Thus, it is of interest to complement speech enhancement techniques (such as Demucs-based) with a target selection scheme, so as to ensure that only the source of interest is enhanced. In this work, two target selection strategies are explored: 1) an embedding-based strategy, using a codified sample of the target speech, and 2) a location-based strategy, using a beamforming-based pre-filter to select the target that is in front of a 2-microphone array. It is shown that while both strategies improve the performance of the Demucs-based technique when one or more speech interferences are present, they both have their pros and cons. Specifically, while the beamforming-based strategy achieves overall a better performance compared to the embedding-based strategy, it is sensitive against the location variation of the target speech source which the embedding-based strategy does not suffer from.

Tooth extraction is the most common procedure in dental practice. However, in the long term, it may cause alveolar ridge atrophy. This systematic review aimed to evaluate the role of platelet-rich fibrin (PRF) in post-exodontic alveolar ridge preservation in terms of its effectiveness in the regeneration of bone tissue as assessed by imaging and its efficacy compared to physiological bone healing. The study is presented in accordance with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. This systematic review was conducted using electronic databases such as PubMed, Scopus, Web of Science, and Science Direct. The grey literature search was conducted in the New York Academy of Medicine Grey Literature Report. All the studies in this systematic review were randomized controlled trials (RCTs). The risk of bias was performed according to the Cochrane Handbook for Systematic Reviews of Interventions 6.2 (RevMan 6.2). Considering the inclusion and exclusion criteria, we included 17 randomized clinical trials, published up to 2022 investigating the efficacy of PRF in post-exodontic bone regeneration. Based on the results of clinical studies, it can be stated that despite not being statistically significant, PRF promotes neoformation and prevents bone loss between three and four months post-extraction.

The rise of multi-sided digital platforms has transformed the global economy, enabling individuals and companies to create, deliver, and capture value through innovative connections and network effects. While scholars have studied the core strategy concepts and proposed platform architectures, less attention has been paid to the long-term sustainability of these platforms. This paper addresses this gap by examining the relationship between sustainability and platform architecture. Specifically, it asks what mechanisms characterize digital platforms for sustainability, and whether there are design choices that optimize the business model for sustainability. Using a mixed-methods approach that draws on platform literature, business models, and sustainable innovation, the paper develops an integrative framework to identify elements and design choices in platform businesses which support sustainability outcomes. The framework is then used to classify the business models of the identified platforms, revealing specific design choices for platform architecture that can optimize their business models for sustainability. This contribution to existing literature provides an artifact that allocates key decisions on value creation, delivery, and capture that are key to platforms and includes an additional layer to optimize them for long-term sustainability. As digital platforms continue to drive the economy, this research is relevant not only to platform practitioners, but also to policy makers and society at large.

Mexico is an exceptional setting for ethnomycology since human cultures have interacted with fungi for thousands of years; the state of Michoacán is particularly important since nearly 11% of the fungi species recorded in Mexico occur there, 139 species being edible. This study aimed to analyze the taxonomic diversity and use forms of fungi, their position in worldview of people, and the management forms practiced on edible fungi in the Purépecha communities of Cherán and Pichátaro and the environmental problems in relationship with fungi management. We conducted semi-structured interviews during visits to regional markets, participant observations in harvesting areas, workshops, and presentation of results to the communities and communal authorities. We recorded ethnoecological information for 21 edible fungi species. The words jongo and terekua correspond to useful fungi in Purépecha, while jeramba refers to not edible fungi. In Cherán people identify different vegetation types where mushrooms occur; they recognize the “pinadas forest” dominated by Pinus leiophylla, “encineras” (dominated by Quercus crassipes), “tepamu forest” (dominated by Alnus acuminata), “sharhari forest” (Quercus aff. laeta), and grasslands, all of them sites where mushrooms grow. Fungi handlers identified environmental problems like land use changes, illegal extraction of forest resources, deforestation, unplanned urban growth, uncontrolled fires, livestock raising, and agricultural intensification, which affect fungi communities. In turn, these factors have secondary consequences like soil erosion, reduction of native vegetation and reduction of rainwater retention that directly disturb diversity, distribution, and abundance of fungi. Information from this study and workshops conducted with the community helped to design strategies for conservation of both forests and fungi.

Neurology is a quickly evolving specialty that requires clinicians to make precise and prompt diagnoses and clinical decisions based on the latest evidence-based medicine practices. In all Neurology subspecialties, Stroke and Epilepsy in particular, clinical decisions affecting patient outcomes depend on neurologists accurately assessing patient disability. Artificial intelligence (AI) can predict the expected neurological impairment from an AIS (Acute Ischemic Stroke), the possibility of ICH (IntraCranial Hemorrhage) expansion, and the clinical outcomes of comatose patients. This review article informs readers of artificial intelligence principles and methods. The article introduces the basic terminology of artificial intelligence before reviewing current and developing AI applications in neurology practice. AI holds promise as a tool to ease a neurologist’s daily workflow and supply unique diagnostic insights by analyzing data simultaneously from several sources, including neurological history and examination, blood and CSF laboratory testing, CNS electrophysiologic evaluations, and CNS imaging studies. AI-based methods are poised to complement the other tools neurologists use to make prompt and precise decisions that lead to favorable patient outcomes.

Studies of the fundamental origins of friction have undergone a rapid acceleration in recent years by providing valuable information on the nanoscale mechanisms responsible for the friction at the macroscopic level. Significant efforts have been directed into developing composite nanofluids and nanoparticle additives to unlock new tribological properties unattainable by traditional lub-ricants. The studies are now further evolving by developing methods to achieve active control of the nano- and/or mesoscale friction by application of magnetic and electric fields external to the contact. These methods constitute an area of rapidly growing interest, and they also are illumi-nating how the performance of conventional lubricants could be enhanced through synergistic addition of nanoparticles (NPs). This mini-review highlights 25 publications that collectively re-veal significant progress, as well as important outstanding challenges, to a fundamental under-standing of how the addition of NPs impacts the lubricant performance. The first two sections focus on the use of Quartz Crystal Microbalance (QCM), a technique that spans atomic to mac-roscale length and time scales. Subsequent sections expand to complimentary methods comparing results at the macro and atomic scale for similar materials. These studies highlight the pivotal role of the nanoparticle charge and surface treatment, while also indicating that rolling of nano-particles is ineffective and/or detrimental.

Daily maximum surface air temperature (Tamax) is a crucial factor for understanding complex land surface processes under rapid climate change. Remote detection of Tamax has widely relied on the empirical relationship between air temperature and land surface temperature (LST), a product derived from remote sensing. However, little is known about how such a relationship is affected by the high heterogeneity in landscapes and dynamics in seasonality. This study aims to advance our understanding of the roles of land cover and seasonal variation in the estimation of Tamax using the MODIS (Moderate Resolution Imaging Spectroradiometer) LST product. We developed statistical models to link Tamax and LST in the middle and lower reaches of the Yangtze River in China for five major land-cover types (i.e., forest, shrub, water, impervious surface, cropland, and grassland) and two seasons (i.e., growing season and non-growing season). Results show that the performance of modeling the Tamax-LST relationship was highly dependent on land cover and seasonal variation. Estimating Tamax over grasslands and water bodies achieved superior performance; while uncertainties were high over forested lands that contained extensive heterogeneity in species types, plant structure, and topography. We further found that all the land-cover specific models developed for the plant non-growing season outperformed the corresponding models developed for the growing season. Discrepancies in model performance mainly occurred in the vegetated areas (forest, cropland, and shrub), suggesting an important role of plant phenology in defining the statistical relationship between Tamax and LST. For impervious surfaces, the challenge of capturing the high spatial heterogeneity in urban settings using the low-resolution MODIS data made Tamax estimation a difficult task, which was especially true in the growing season.

A new sustainable polypropylene terephthalate (PPT) coating was synthesized from recycled polyethylene terephthalate (PET) and applied on hydraulic concrete substrate to improve its durability. First step, PET bottles wastes were grinded and depolymerized by glycolysis using propylene glycol (PG), in a Vessel-type reactor (20–180 °C) to synthesize bis(2-hydroxypropyl)-terephthalate (BHPT), which was applied as coating to one-three layers on hydraulic concrete substrate, by brushing technique and polymerized (150 °C for 15 h) to obtain PPT. PET, BHPT and PPT were characterized by FT-IR, PET and PPT by TGA, and the PPT coatings by SEM (thickness), by ASTM-D3359-17 (adhesion) and by water contact angle (wettability). The durability of hydraulic concrete coated with PPT was studied by resist chloride ion penetration (ASTM-C1202-17), carbonation depth at 28 days (RILEM-CPC-18), and absorption water ratio (ASTM-C1585-20). The results demonstrated that the BHPT and PPT were synthetized (FT-IR), PPT had a similar thermal behaviour to PET (TGA); the PPT coatings had good adhesion to the substrate, with thicknesses of micrometric units; hydrophilic (similar to PET coatings), and the durability of hydraulic concrete coated with PPT (2-3 layers) improved (migration of chloride ions decreased, carbonation depth was negligible, and the absorption water ratio decreased).

The extent of correlation or co-movement among the returns of developed and/or emerging stock markets remains pivotal for efficiently diversifying global portfolios. This correlation is prone to variation over time as a consequence of escalating economic interdependence fostered by international trade and financial markets. In this study, we analyzed the time-varying correlation and co-movement between the JSE.JO stock market of South Africa and its developed and developing stock market partners. The Dynamic Conditional Correlation - Exponential Generalized Autoregressive Conditional Heteroscedasticity (DCC-EGARCH) methodology is employed with different multivariate distributions to explore the time-varying correlation and volatilities between the JSE.JO stock market and its partners. Based on the conditional correlation results, the JSE.JO stock market is integrated and co-moves with its partners, and the conditional correlation for all markets exhibit time-variant behavior. The conditional volatility results show that the JSE.JO stock market behaves differently from other markets, especially after 2015, indicating a positive sign for investors to diversify between JSE.JO and its partners. The highest value of conditional volatility for markets was in 2020 during the COVID-19 pandemic, representing the riskiest period that investors should avoid due to the lack of diversification opportunities during crises.

Abstract: Background: With the growing trends in recreational marijuana use, our study aims at analyzing the association between Acute coronary syndromes (ACS) and Ventricular Fibrillation (Vfib) and cannabis use disorder in young adults (18 – 45) Methods: National In-patient Sample was used for population analysis. Probit logistic regression was used for analyzing the associations between diagnosis of ACS/ Vfib and documented cannabis use disorder. Propensity Score matched samples were made from the ACS/Vfib admissions with Cannabis use disorder to analyze the in-hospital outcomes. Results: Statistically significant association was found between admission diagnosis of ACS/ vfib, and documented cannabis use disorder in the study population. The association remained sig-nificant after accounting for potential confounders. Documented race as black, documented di-agnosis of uncontrolled hypertension and diabetes were found to have significant association with admissions for ACS among the population with documented Cannabis use disorder. No difference in mortality were found between ACS admissions with and without documented cannabis use disorder in the propensity matched cohorts. Conclusions: Documented diagnosis of Cannabis use disorder has statistically significant associ-ation with admission diagnosis of ACS/Vfib even after accounting for confounders. Documented race as black, and co existing diagnosis of uncontrolled hypertension and diabetes had significant association with admission diagnosis of ACS in population with documented cannabis use dis-order. Keywords: Cannabis use disorder; ACS; Ventricular fibrillation

While the field of personality neuroscience has extensively focused on humans and, in a few cases, primates and rodents, a wide range of research on fish personality has emerged in the last decades. This research is focused mainly on the ecological and evolutionary causes of individual differences, but also aimed less extensively at proximal mechanisms (e.g., neurochemistry or genetics). We argue that, if consistent and intentional work is made to solve some of the meta-theoretical issues of personality research both on fish and mammals, fish personality research can lead to important advances in personality neuroscience as a whole. The five dimensions of personality in fish (shyness-boldness, exploration-avoidance, activity, aggressiveness, and sociability) need to be translated into models that explicitly recognize the impacts of personality in psychopathology, synergizing research on fish as model organisms in experimental psychopathology, personality neuroscience, and ecological-ethological approaches to the evolutionary underpinnings of personality to produce a powerful framework to understand individual differences.

Cardiovascular disease is the most common cause of death. Oxidative stress and inflammation are pathophysiological processes involved in the development of cardiovascular diseases, so anti-inflammatory and antioxidant agents that modulate redox balance have become the targets of research to evaluate their molecular mechanisms and therapeutic properties. Astaxanthin, a carotenoid of the xanthophyll group, has potent antioxidant effects due to its molecular structure and its arrangement in the plasma membrane, factors that favor the neutralization of reactive oxygen and nitrogen species. This carotenoid also stands out for its anti-inflammatory activity, possibly interrelated with its antioxidant effect, as well as for its modulation of lipid and glucose metabolism. Considering the potential positive effects of astaxanthin on cardiovascular health evidenced by preclinical and clinical studies, this paper describes the molecular and cellular mechanisms related to the antioxidant and anti-inflammatory properties of this carotenoid in cardiovascular diseases, especially atherosclerosis.

Background: Bariatric surgery (BS) has a significant impact on body composition (BC) and con-sequently may affect established sarcopenic obesity (SO) in candidate patients. The aim of this study is to assess the utility of muscle ultrasound (MUS) of rectus femoris thickness (RFT) for the evalua-tion of BC and skeletal muscle function in patients undergoing BS compared to bioimpedance analysis (BIA), dual-energy X-ray absorptiometry (DEXA) and dynamometry. On the other hand, we aim to demonstrate how MUS of RFT correlates better with the quality of life (Qol) in this population, likely because it enables the detection of regional quadriceps muscle sarcopenia, in comparison to the other mentioned methods. Methods: This is a prospective pilot study including 77 subjects (64.9% female, mean age: 53.2 ± 8.67 years) who underwent BS. Handgrip strength was measured using a dynamometer, fat-free mass index (iFFM) was assessed by BIA, appendicular muscle index (AMI) was calculated using DEXA and RFT was measured by MUS. Moreover, Homeostasis Model Assessment of Insulin Resintance (HOMA-IR) was calculated. All these measurements were conducted 1 month prior to BS and at the 12-month follow-up. QoL was as-sessed using the Moorehead-Ardelt questionnaire. Results: The mean BMI decreased by 12.95 ± 3.56 kg/m2 (p=0.001). Firstly, we observed positive correlation pre-surgery between HOMA and: RFT (r=0.27, p=0.02), iFFM (r=0.36, p=0.001), AMI (r=0.31, p=0.01) and dynamometer (r=0.26, p=0.02). In addition, we found correlation between RFT and iFFM (pre-surgical: r=0.31, p=0.01; post-surgical: r=0.25, p=0.05) and between RFT and lower extremities AMI post-surgical (r=0.27, p=0.04). Secondly, we observed significant reductions in iFFM, AMI and RFT (p=0.001), but not with dynamometer (p=0.94). Finally, a tendency to a positive correlation between QoL question-naire and RFT post-surgery (r=0.23, p=0.079) was observed. Conclusions: Our results suggest that RFT measured by MUS for the evaluation of SO and the follow-up after BS of these patients. Moreover, RFT can provide relevant information about regional sarcopenia and probably has an accurate correlation with QoL in comparison with the other methods.

Background: Galectins are innate immune system regulators, associated with disease progression in cancer. This paper aims to investigate the correlation between mutated cancer-critical genes and galectin levels in breast cancer patients to determine whether galectins and genetic profiles can be used as biomarkers for disease and potential therapy targets. Materials and Methodology: Prisma Health Cancer Institute’s Biorepository provided seventy-one breast cancer patient samples, including all four stages as well as major molecular subtypes and histologies of breast cancer. Hotspot mutation statuses of cancer-critical genes were determined using multiplex PCR in tumor samples from the same patients by Precision Genetics and the University of South Carolina Functional Genomics Core Facility. The galectin levels of patients’ sera were analyzed using enzyme-linked immunosorbent assay (ELISA) to measure the concentrations of galectins-1, -3, and -9. Analysis was performed using JMP software to compare mean and median serum galectin levels between samples with and without specific cancer-critical genes including pooled t-test, Wilcoxon test, ANOVA, Tukey’s range test, and Steel Dwass Method (α=0.05). Results: Our analysis indicates that KIT mutations correlate with elevated serum levels of galectin-1 and -9 in patients with breast cancer. In patients with Luminal A subtype, FLT3 mutation correlates with lower serum galectin-1 and -9 levels and TP53 mutations correlate with higher serum galectin-3 levels. Patients with invasive ductal carcinoma had significantly higher serum galectin-3 levels than patients with ductal carcinoma in situ. Patients with both TP53 and PIK3CA mutations exhibit elevated serum galectin-3 levels, while patients with one or neither mutation show no significant difference in serum galectin-3 levels. In addition, metastatic breast cancer samples were more likely to have KIT or PIK3CA mutation compared to primary breast cancer samples. Conclusion: The relationship between genetic mutations and galectin levels has the potential to identify appropriate candidates for combined therapy, targeting genetic mutations and galectins. Further understanding of the effect of genetic mutations and galectin levels on cancer progression and metastasis could aid in the search for biomarkers for breast cancer diagnosis, disease progression, and prognosis.

In the present work, we evaluate the prediction capability of six Pedotransfer functions (PTFs), reported in the literature, for the saturated hydraulic conductivity estimations (Ks). We used a database with 900 measured samples obtained from the Irrigation District 023, in San Juan del Rio, Queretaro, Mexico. Additionally, six new PTFs were construct for Ks from clay percentage, bulk density and saturation water content data. The results show, for the evaluated models, that one model present an overestimation for Ks>0.5 cm h-1 values, three models have a underestimation for Ks>1.0 cm h-1 and two models have a good correlation (R2>0.98) but are necessary more than three parameters. Nevertheless, the last two models requires from three to four parameters in order to get the optimization. By other hand, the models proposed in this work have a similar correlation with a less number of parameters: the fit is seen to be much better than using the existing ones, achieving a correlation of R2 = 0.9822 with only one variable and a R2 = 0.9901 when we use two.

Different types of gold nanoparticles have been synthesized that great potential in medical applications such as medical imaging, bio-analytical sensing and photothermal therapy. However, their stability, polydispersity and biocompatibility are major issues of concern. For example, the synthesis of gold nanorods, obtained through the elongated micelle process, produce them with a high positive surface charge that is cytotoxic. While gold nanoshells are unstable and within a few weeks they decompose due to Ostwald ripening. In this work, we report the self-assembly of the capsid protein of cowpea chlorotic mottle virus (CCMV) around spherical gold nanoparticles, gold nanorods and gold nanoshells to form virus-like particles (VLPs). All gold nanoparticles were synthesized or treated to give them a negative surface charge, so they can interact with the positive N-terminus of the capsid protein leading to the formation of the VLPs. To induce the protein self-assembly around the negative gold nanoparticles, we use different pH and ionic strength conditions that were determined from the capsid protein phase diagram. The encapsidation with the viral capsid protein confers them better biocompatibility, stability, monodispersity and a new biological substrate on which one can introduce specific ligands towards particular cells, broadening the possibilities of medical application.

(1) Background: Interparental conflict is a phenomenon that poses a serious threat not only to the quality of life for the couple but also to the father-child relationship, mother-child relationship, and well-being of adolescents. This study examined the influence of marital conflict on father-child and mother-child attachment and the psychosocial adjustment (2) Methods: 67 adolescents involved in Child Welfare Services (CWS) in disadvantaged neighbourhoods in southern Spain were examined. The sample was split into two groups according to the exposure to marital conflict. Subsequently, differences between groups were analysed on father attachment, mother attachment, aggression, antisocial behaviour, and emotional intelligence. (3) Results: Primarily, the data showed significantly worse attachment with the father among conflict-exposed versus non-conflict-exposed adolescents. These results were not found for the attachment with the mother. Additionally, greater anger and worse stress management were found in conflict-exposed adolescents. (4) Conclusions: The results partially confirmed the spillover and compensatory hypothesis. Practical implications point out that developing preventive interventions that protect the father-adolescent attachment in situations of family conflict is recommended.

Environmental factors, such as drought-stress, significantly impact maize growth and productivity worldwide. To improve yield and quality, effective strategies for early detection and mitigation of drought-stress in maize are essential. This paper presents a detailed analysis of three imaging trials conducted to detect drought-stress in maize plants using an existing, custom-developed, low cost, high throughput phenotyping platform. We propose a pipeline for early detection of water stress in maize plants using a Vision Transformer classifier and analysis of distributions of near-infrared (NIR) reflectance from the plants. We also explored suitable regions on the plant that are more sensitive to drought-stress and show that the region surrounding the youngest expanding leaf (YEL) and the stem can be used as a more consistent alternative to analysis involving just the YEL. Our results show good separation between well-watered and drought-stressed trials for two out of the three imaging trials both in terms of classification accuracy from data-driven features as well as through analysis of histograms of NIR reflectance.

Most existing coal-fired power plants were designed for sustained operation at full load to maximize efficiency, reliability, and revenue, as well as to operate air pollution control devices at design conditions. Depending on plant type and design, these plants can adjust output within a fixed range in response to the plant operating or market conditions. The need for flexibility driven by increased penetration of variable and non-dispatchable power generation such as wind and solar is shifting the traditional mission profile of thermoelectric power plants in three ways: more frequent shutdowns when market or grid conditions warrant, more aggressive load ramp rates (rate of output change), and lower minimum sustainable load, which provides a wider operating range and helps avoid costly plant shutdowns. Recent studies have shown that flexibility of a coal-fired power plant can be improved by energy storage. The objective of this work was to analyze a set of energy storage options and determine their impact on flexibility and economics of a representative coal-fired power plant. The effect of three energy storage systems integrated with a coal power plant on plant flexibility and economics was investigated. The results obtained in this project show that Energy Storage Systems integrated with a thermal power plant improves plant flexibility and participation in the Energy and Ancillary Services markets which improves plant financial performance. The study was funded by the U.S. Department Office of Fossil Energy FE-1 under Award Number DE-FE0031903.

The kynurenine pathway (KP) and the endocannabinoid system (ECS) are known to be deregulated in depression and obesity; however, it has been recognized that acute physical exercise has an important modulating role inducing changes in the mobilization of their respective metabolites -endocannabinoids (eCB) and kynurenines (KYN)- which overlap at some points, acting as im-portant antidepressant, anti-nociceptive, anti-inflammatory, and antioxidant biomarkers. There-fore, the aim of this review is to analyze and discuss some studies performed recently to investigate the potential interactions between both systems, particularly, those related with exercise-derived endocannabinoidome and kynurenine mechanisms and to elucidate how, the prescription of physical exercise could represent a new approach for the clinical manage of these two conditions.

Self-assembly is a spontaneous process through which macroscopic structures are formed from basic microscopic constituents (e.g. molecules or colloids). By contrast, the formation of large biological molecules inside the cell (such as proteins or nucleic acids) is a process more akin to self-organization than to self-assembly, as it requires a constant supply of external energy. Recent studies have tried to merge self-assembly with self-organization by analyzing the assembly of self-propelled (or active) colloid-like particles whose motion is driven by a permanent source of energy. Here we present evidence that points to the fact that self-propulsion considerably enhances the assembly of polymers: self-propelled molecules are found to assemble into polymer-like structures, the average length of which increases towards a maximum as the self-propulsion force increases. Beyond this maximum, the average polymer length decreases due to the competition between bonding energy and disruptive forces that result from collisions. The assembly of active molecules might have promoted the formation of large pre-biotic polymers that could be the precursors of the informational polymers we observe nowadays.

Pterygium is a common ocular disease caused by abnormal cellular proliferation leading to abnormal fibrovascular growth of the conjunctiva. The main treatment is surgical removal; however, despite the improvement of surgical techniques and development of adjuvant therapies, postoperative recurrence, which can be as high as 89%, remains a challenge. Currently, pterygium excision with conjunctival autograft remains the preferred surgical technique to decrease recurrence, especially when combined with ciclosporin eye drops. Nevertheless, there is no gold standard surgical technique to prevent pterygium recurrence. Therefore, this review aims to assess the safety, effectiveness and, applicability of platelet-rich fibrin membrane for primary pterygium surgery and assess its possible benefits in resource-limited settings.

The use of active video games or exergames has been booming recently, as they are motivating and promote exercise and healthy lifestyle habits for their users, and these immersive virtual reality (IVR) exercise programs could be compared to traditional physical exercise programs. A starting point for this would be to explore the physiological responses that can be generated by its use. Therefore, the main objective of this study was to evaluate the response across certain physiological parameters (heart rate, blood pressure and stress) after an IVR exergame exposure in a sample of healthy adults, and secondarily to explore its feasibility and usefulness as a tool to facilitate physical exercise. 37 healthy adults (22-54 years, 54.1% women) participated in the study. They carried out one exergaming session with the HTC Vive ProTM hardware. The primary outcome was evaluated using the concentration of cortisol, heart rate and blood pressure. Secondary outcomes were evaluated with safety using the Simulator Sickness Questionnaire (SSQ); usability evaluated with the System Usability Scale (SUS); personal experiences evaluated with Game Experience Questionnaire (GEQ) and perceived exertion through the Borg perceived effort scale. All participants completed the IVR session without significant adverse effects, and all physiological parameters analysed increased significantly in relation to pre-intervention levels. Perceived exertion corresponded to a moderate to intense exercise (6.30±0.50/10 on the modified Borg scale). All the sample considered the experience as good or very good and would recommend it. These findings support that our IVR session can be compared to a moderate to intense physical activity, as it involved similar perceived exertion with significant increases in heart rate, blood pressure and salivary cortisol levels.

The visual system is regulated by the nervous through neurotransmitters, which play an important role in visual and ocular functions. One of those neurotransmitters is acetylcholine, a key molecule that plays a diversity of biological functions. On the other hand, acetylcholinesterase, the enzyme responsible for the hydrolysis of acetylcholine, is implicated in cholinergic function. However, several studies showed that in addition to their enzymatic functions, Acetylcholinesterase exerts non-catalytic functions. In recent years, the importance of evaluating all possible functions of acetylcholine-acetylcholinesterase has been evidenced. Nevertheless, there is evidence that suggests cholinesterase activity in the eye can regulate some biological events both in structures of the anterior and posterior segment of the eye and therefore in the visual information that is processed in the visual cortex. Hence, the evaluation of cholinesterase activity could be a possible marker of alterations in cholinergic activity not only in ocular disease but also in systemic diseases.

Energy transitions are reshaping the global energy system. Such shift has taken the power system to become a critical infrastructure for achieving economic development of every nation in the planet, therefore, guaranteeing its security is crucial, not only for energy purposes but as a part of a national security strategy. This paper presents a multi-dimensional index developed to assess energy security of electrical systems in the long term. This tool, named Power System Security Index (PSIx), and which has been previously used for the evaluation of a country in two different time frames, is applied to evaluate the member countries of the Latin American Energy Organization, located within the Latin America and the Caribbean region, in order to measure their performance on energy security. Mixed results were obtained from the analysis, with clear top performers in the region such as Argentina, while there are others with broad areas of opportunity, as it is the case of Haiti.

Galectin-3 is considered a cancer biomarker and bioindicator of fibrosis and cardiac remodeling, and, therefore, it is desirable to develop convenient methods for its detection. Herein, an approach based on the development of multivalent electrochemical probes with high galectin-3 sensing abilities is reported. The probes consist of multivalent presentations of lactose–ferrocene conjugates scaffolded on poly(amido amine) (PAMAM) dendrimers and gold nanoparticles. Such multivalent lactose–ferrocene conjugates are synthesized by coupling of azidomethylferrocene-lactose building blocks on alkyne-functionalized PAMAM, for the case of the glycodendrimers, and to disulfide‐functionalized linkers that are then used for the surface modification of citrate-stabilized gold nanoparticles. The binding and sensing abilities towards galectin 3 of both ferrocene-containing lactose dendrimers and gold nanoparticles have been evaluated by means of isothermal titration calorimetry, UV-vis spectroscopy, and differential pulse voltammetry. The highest sensitivity by electrochemical methods to galectin-3 was shown by lactosylferrocenylated gold nanoparticles, which are able to detect the lectin in nanomolar concentrations.

Cocoa is an important commodity crop not only to produce one of the most complex products such as chocolate from the sensory perspective, but one that commonly grows in developing countries close to the tropics. This paper presents novel techniques applied using cover photography and a novel computer application (VitiCanopy) to assess the canopy architecture of cocoa trees in a commercial plantation in Queensland, Australia. From the cocoa trees monitored, pod samples were collected, fermented, dried and grinded to obtain the aroma profile per tree using gas chromatography. The canopy architecture data were used as inputs in an artificial neural network (ANN) algorithm and the aroma profile considering six main aromas as targets. The ANN model rendered high accuracy (R = 0.82; MSE = 0.09) with no overfitting. The model was then applied to a satellite image from the whole cocoa field studied to produce canopy vigor and aroma profile maps up to the tree-by-tree scale. The tool developed could aid significantly the canopy management practices in cocoa trees that have a direct effect on cocoa quality.

Background: COVID-19 is currently one of the most important medical challenges as it affects the entire population with children being infected as easily as adults. Objective: The study evaluated the mortality of under 17-year-olds and compare the frequency with that of adults in a cohort of 8986 COVID-19 positive patients hospitalized in a single care center. Design: retrospective clinical trial, all hospitalizations of COVID-19 positive patients as diagnosed by RT-qPCR were analyzed by revisiting their medical records. Setting: 8986 COVID-19 positive patients hospitalized in a Hospital de Base and the Infant and Maternal Hospital of São Jose do Rio Preto, SP, Brazil (HB/HCM) being these totals of 383 COVID-19 positive under 17-patients. Patients: patients COVID-19 positive under 17-year-olds were grouped by age and evaluated by analyzing their medical records. They were categorized as Group I, children, and adolescents under 19 years old and Group II, adults over 19 years. Results: Overall mortality in under 19-year-olds was 12 deaths, that is, 3.13% of the patients in this age group and 0.55% of the overall mortality from COVID-19. Mortality in the group of adults was 2197 equivalent to 24.45% of the hospitalized adults and 99.45% of overall deaths. Mortality was significantly higher for adults than for under 19-year olds (p-value < 0.0001). Regarding the different ages of children and adolescents, the number of deaths of under 1-year olds was 2/123 (1.62%), of 1-4 years it was 4/95 (2.11%), of 5-9 years it was 1/47 (2.13%), of 10-14 years it was 1/40 (2.5%) and of 15-19 years it was 4/78 (5.13%). There were no statistical differences between the different age groups ( p-value = 0.3). Conclusion: Mortality from COVID-19 in children and adolescents was lower than in adults and generally associated with other comorbidities with the main association being with heart disease.

The burnout syndrome, which affects so many healthcare workers, has recently awakened wide interest due to the severe repercussions related to its appearance. Even though job factors are determinant to its development, not all individuals exposed to the same work conditions show burnout, which demonstrates the importance of individual variables such as personality. The purpose of this study was to determine personality characteristics of a sample of nursing professionals based on the Big Five model, and then, having determined the personality profiles, analyze the differences in burnout and engagement based on those profiles. The sample was made up of 1236 nurses. An ad hoc questionnaire was prepared to collect the sociodemographic data, and the Brief Burnout Questionnaire, the Utrecht Work Engagement Scale and the Big Five Inventory-10 were used. The results showed that the existence of burnout in this group of workers, is associated negatively with extraversion, agreeableness, conscientiousness and openness to experience, and positively with the neuroticism personality trait. These personality factors showed the opposite pattern with regard to engagement. Three different personality profiles were also found in nursing personnel, in which professionals who had a profile marked by strong neuroticism and low scores on the rest of the personality traits where those who were most affected by burnout.

The purpose of this study was to identify different adolescent profiles identified by their use tobacco/alcohol and violent behavior repertoires as well as to analyze the extent to which they show impulsivity traits. Participants were selected by cluster random sampling. There was a total of 822 high school students in the sample aged 13 to 18 years with a mean of 14.84 (SD=.87). A cluster analysis with the following variables was done to form the groups: Use of tobacco, Use of alcohol, Physical aggression, Verbal aggression, Anger and Hostility. Three groups of adolescents resulted from these five variables. The multivariate comparison demonstrated the existence of significant between-group differences, and individual analysis for each of the dependent variables (impulsivity dimensions) showed that the relationship was statistically significant in all cases. In conclusion, analysis of factors possibly associated with adolescent’s risk behavior makes possible and orients intervention in different stages of development for sustainable consumption in adolescents.

The role that Colombia will play in terms of food supply in the coming years has generated a discussion on the agricultural practices implemented, specifically in the use of pesticides. Therefore, this article aims to provide a comprehensive and complete review of the status of pesticide use in the agricultural sector, highlighting the agri-food industry in Colombia. Currently, the use of pesticides has intensified throughout the world, generating a call for attention by governmental entities in terms of regulation. Colombia for the year 2019 presented a rate of use of 7.8 kg/ha of pesticides, placing it among the twenty countries with the highest rate. On the other hand, the quantities and economic benefits of exports and imports are growing steadily. Several organizations have focused their efforts on determining the health consequences that people exposed to chronic and/or acute intoxication may suffer. This has led to cases of cytogenetic, neurocognitive and physical effects, prompting governmental organizations to regulate the use of pesticides. Finally, the need to design pesticide management and handling systems that respond to the needs of producers and the sector, integrating international food safety guidelines, is identified.

The cancelation factor (CF) is a model for the ratio between gravity wave perturbations in the airglow intensity to that in the ambient temperature. The CF model allows to estimate the momentum and energy flux of gravity waves seen in nightglow images as well as the divergence of these fluxes due to waves propagating through the mesosphere and lower thermosphere region, where the nightglow and the Na layers are located. This study uses a set of T/W Na Lidar data and zenith nightglow image observations of the OH and O(1S) emissions to test and validate the CF model from the experimental perspective. The dataset analyzed was obtained during campaigns carried out at the Andes Lidar Observatory (ALO), Chile in 2015, 2016, and 2017. The CF modeled function was compared with observed points from an empirical method for vertically propagating waves that calculates directly the ratio of the gravity wave amplitude seen in nightglow images to the wave amplitude seen in lidar temperatures. We show that the CF analytical relationship underestimates the observed results generally. However, the O(1S) emission line has better agreement respect to the theoretical value due to simpler nightglow photochemistry. In contrast, the observed CF ratio from the OH emission deviates by a factor of two from the modeled asymptotic value.

(1) Background: The purpose of this study was therefore to analyze the predictive capacity of the three variables (impulsivity, sensation-seeking and drug use) in aggressors and victims of violence. (2) Methods: The design is cross-sectional quasi-experimental. A sample of 822 students aged 13 to 18 who had filled in an ad hoc questionnaire, the State Impulsivity Scale and the Sensation-Seeking Scale, was used for this. (3) Results: The results show that aggressors had high levels of gratification, automatism, attentional factor, disinhibition and susceptibility to boredom, and use alcohol and/or tobacco. The variables that could predict involvement as an aggressor in peer conflict are use of alcohol, smoking, high levels of gratification, automatism and attentional factors and a high degree of disinhibition and susceptibility to boredom. The disinhibition is the best predictor of aggressor. (4) Conclusions: Thus having available empirical evidence that facilitates detection of predictive variables for participation in violence is going to favor the design of effective education intervention for reducing risk behavior.

This study examined how practice distribution schedules influenced accuracy and efficacy in the tennis serve. 24 recreational players were randomized into two experimental groups. Both groups carried out 4 series of 10 services per session during 12 sessions. Massed practice group did not rested between services and rested 10 seconds between series. Distributed practice group rested 10 seconds between services and 30 seconds between series. The accuracy and efficacy were registered and computed based on the 2D digitalization. The learning and retention were tested in a post-test and retention test. The learning program increased the efficacy in the post-test in both groups (p =.001 massed group; p =.015 distributed group). On the retest, the group of massed practice decreased efficacy (p =.021). In accuracy, the group of massed practice reduced its performance in the post-test (p =.015). The effects of the program on the accuracy in the retention test were not observed. It is concluded that this type of sports learning programs, applied at a recreational level, can provide a rigorous approach that contributes with guarantees to the learning of tennis.

In graduate programs such as social work, field education is the signature pedagogy of education. As such, student placement with an appropriate field education agency is critical to ensure academic success and career readiness. A variety of Learning Management System (LMS) and Integrated Planning and Advising Service (IPAS) technologies have been developed to fully integrate technology into the educational system and streamline and improve the learning experience for students, educators, and administrators. Few (if any) of the existing solutions have capabilities to match students with field educators on the basis of an individual student’s completed coursework and area of specialization, as well as field educator needs and opportunities. This paper describes our experience developing a custom LMS/IPAS system—the School of Social Work information System (SSWiS)—that was designed specifically for student learning, faculty advising, and academic administration within our social work graduate program. We present the challenges that motivated the design of the SSWiS before describing the architecture and functionality of our solution. We then discuss our preliminary evaluation results. We conclude with a discussion of the benefits and limitations of our system in the context of today’s technical needs in graduate education in social work and other fields.

Precise HDV-RNA detection and quantification are pivotal for accurate diagnosis and monitoring of response to newly approved treatment. We evaluate the performance of three assays: Hepatitis Delta RT-PCR system, EurobioPlex HDV assay, and the RoboGene HDV RNA Quantification kit 2.0, testing 151 HBsAg-positive samples, 90 HDV-RNA negative and 61 HDV-RNA positive. We also evaluated serial dilutions of the WHO international standard for HDV, PEI 7657/12. Finally, all HDV-RNA positive samples were genotyped using a next-generation sequencing strategy. Qualitative results indicated a 100% concordance between tests. Quantitative results correlated well between tests, r2=0.703 (Vircell-vs-Eurobio), r2=0.833 (Vircell-vs-RoboGene), r2=0.835 (Robogene-vs-Eurobio). Bias index was 2.083 (Vircell-vs-Eurobio), -1.283 (Vircell-vs-RoboGene), and -3.36 (Robogene-vs-Eurobio). Using the WHO IS, Vircell overestimated the viral load by 0.98 log IU/mL, Eurobio by 1.46 log IU/mL, and RoboGene underestimated it by 0.98 log IU/mL. Fifty-nine samples were successfully genotyped (Genotype 1, n=52; Genotype 5, n=7; Genotype 6, n=1), with similar results in terms of correlation and bias. The three assays demonstrate promising performance. Their accuracy, reflected in concordance rates and quantitative analysis, makes them suitable for clinical practice. However, in the absence of assay harmonization, quantitative HDV RNA monitoring should be performed in the same laboratory and assay to avoid variability.

Aggressive behavior in adolescence is influenced by a diversity of individual, family and social variables. The purpose of this study was to analyze the relationship between family functioning, emotional intelligence and values for development of different types of aggression, as well as to establish profiles according to the predictor variables of aggression. To do this, a sample of 317 high school students aged 13 to 18 were administered the Peer Conflict Scale, the Family Functionality Scale, the Brief Emotional Intelligence Inventory for Senior Citizens and the Values for Adolescent Development Scales. The study showed that stress management, positive adolescent development and family functioning predominated in nonaggressive subjects with higher scores than aggressors. There was also a negative relationship between the different types of aggression and emotional intelligence, positive values and family functioning. In addition, two different profiles were found. The first had low scores on all the variables, while the second profile had higher scores on all the variables except family functioning which was higher.

Insect meal as a protein source has been considered a sustainable way to feed animals. H. illucens and T. molitor larvae meal are considered high protein sources for poultry, also presenting considerable amounts of fatty acids, vitamins, and minerals. However, other potential components in insect meal and insect oil have been more studied nowadays. Chitin, lauric acid, and antimicrobial peptides can present antimicrobial and prebiotic functions, indicating that low inclusions of insect meal can beneficially affect broilers` health and immune responses. This systematic review was developed to study the impact of insect products on health parameters of broilers, and a metanalysis was conducted to evaluate the effects on performance. A database was obtained based on a selection of manuscripts from 2016 to January 2023, following the mentioned parameters. There were positive effects of both H. illucens and T. molitor meal or oil products on poultry health status, especially in microbiota population, immune responses, and antimicrobial properties. The average daily gain was greater in broilers fed T. molitor meal compared to H. illucens meal (p = 0.002). The results suggest that low levels of insect meal were suitable for broilers, without result in negative effects on performance, while the insect oil can totally replace soybean oil without negative impacts.

Helical (frontier) molecular orbitals [Chem. Sci. 2013, 4, 4278] have been reported almost a decade ago. Yet, helical molecular spin densities [Mol. Phys. 2022, e2157774] have been observed by us only very recently. In this work, we have observed that some chiral boron clusters (B–16, B–20, B–24, and B–28) can simultaneously have helical molecular orbitals and helical spin densities. To our best knowledge, it is the first time we have discovered that inorganic boron clusters assume such an unprecedented property. We have unambiguously unraveled that it is chiral Jahn-Teller distortion of quasi-planar boron clusters that drives the formation of helical molecular spin densities. More interestingly, we have shown that elongation/enhancement of helical molecular orbitals can be achieved by simply adding more building blocks via a linker. Aromaticity properties of these boron clusters are also discussed. Implication of this work is straightforward that boron clusters may find potential applications in spintronics, such as molecular magnets.

The broad bean plant contains L-DOPA, a compound that is essential for patients with Parkin-son’s disease. However, little has been reported on other broad bean compounds that have bene-ficial effects on health. The objective was to evaluate plants of four Mexican broad bean varieties to determine the content and yield of total phenolic compounds (TPC), total flavonoids (TF), and L-DOPA, as well as to analyze the flavonoid profile and antioxidant (AA) and anti-inflammatory (AANTI) activity in vitro. Broad bean seeds were sown in the field and plants were harvested 20 days after emergence. The analyses were performed with visible UV spectrophotometry and HPLC. The variety José María produced the highest yield of TPC (9.30 g m-2), TF (8.08 g m-2) and L-DOPA (5.64 g m-2) per unit of area. The highest yields per plant were obtained with the Rojita variety: TPC (0.25 g plant-1), TF (0.21 g plant-1) and L-DOPA (0.17 g plant-1). This variety also had the highest antioxidant (CI50= 87.68 µg mL-¹) and anti-inflammatory (CI50= 74.40 mg mL-1) activi-ty, which was attributed to the L-DOPA compounds and to rutin and isoorientins, respectively. The flavonoid profile revealed the presence of rutin and isoorientins, which had not been previ-ously detected in the broad bean plant.

Chikungunya virus (CHIKV) is a mosquito borne RNA virus that poses an emerging threat to humans. As other RNA viruses, CHIKV encodes an error-prone RNA polymerase which, in addition to full-length genomes, gives rise to truncated, non-functional genomes coined defective viral genomes (DVGs). DVGs have been intensively studied in the context of therapy, as they can inhibit viral replication and dissemination in their hosts. In this work, we interrogate the influence of viral RNA secondary structures on the production of CHIKV DVGs. We experimentally map RNA secondary structures of CHIKV genome using selective 2′-hydroxyl acylation analyzed by primer extension and mutational profiling (SHAPE-MaP), which couples chemical labelling with next-generation sequencing. We correlate the inferred secondary structure with preferred deletion sites of CHIKV DVGs. We document an increased probability of DVG generation with truncations at unpaired nucleotides within the secondary structure. We then generated a CHIKV mutant bearing synonymous changes at the nucleotide level to disrupt existing RNA secondary structure (CHIKV-D2S). We show that CHIKV-D2S presents altered DVG generation compared to wild-type virus, correlating with its change in RNA secondary structure obtained by SHAPE-MaP. Our work thus demonstrates that RNA secondary structure impacts CHIKV DVG production during replication.

Tumor cells display abnormal growth and division, avoiding the natural process of cell death. These cells can be benign (non-cancerous growth) or malignant (cancerous growth). Over the past few decades, numerous in vitro or in vivo tumor models have been employed to understand the molecular mechanisms associated with tumorigenesis in diverse aspects. However, our comprehension of how non-tumor cells transform into tumor cells at molecular and cellular levels remains incomplete. The nematode C. elegans has emerged as an excellent model organism for exploring various phenomena, including tumorigenesis. Although C. elegans does not naturally develop cancer, it serves as a valuable platform for identifying oncogenes and the underlying mechanisms within a live organism. In this review, we describe three distinct germline tumor models in C. elegans, highlighting their associated mechanisms and related regulators. Given the conservation of many of the regulators implicated in C. elegans tumorigenesis, it is proposed that these unique models hold significant potential for enhancing our comprehension of the broader control mechanisms governing tumorigenesis.

Portugal Mainland has hundreds of thousands of people living in the Atlantic coastal zone, with numerous high economic value activities and a high number of infrastructures that must be protected from natural coastal hazard, namely extreme storms and sea level rise (SLR). In the context of climate change adaptation strategies, a reliable and accurate assessment of the physical vulnerability to SLR is crucial. This study is a contribution to the implementation of flooding standards imposed by the European Directive 2007/60/EC, which requires each member state to assess the risk associated to SLR and floods caused by extreme events. Therefore, coastal hazard in the Continental Atlantic coast of Portugal Mainland was evaluated for 2025, 2050 and 2100 in the whole coastal extension with different sea level scenarios for different extreme event return periods and due to SLR. A coastal flooding probabilistic map was produced based on the developed methodology using Geographic Information Systems (GIS) technology. The Extreme Flood Hazard Index (EFHI) was determined on flood probabilistic bases through five probability intervals of 20% of amplitude. For a given SLR scenario, the EFHI is expressed, on the probabilistic flooding maps for an extreme tidal maximum level, by five hazard classes ranging from 1 (Very Low) to 5 (Extreme).

The increasing complexity of vehicular traffic and the prevalence of accidents, particularly rear-end collisions, necessitate enhancements in automotive safety technologies. Adilights, an innovative automotive lighting system, addresses this need by introducing a dynamic visual communication method between vehicles. This system features a novel design that employs variable-intensity brake lights configured in an upside-down "U" shape to indicate braking severity. The intensity and pattern of the lights change in response to the pressure applied to the brake pedal, providing clear and immediate feedback to following drivers. This design not only improves the perceptibility of braking signals, particularly in dense traffic and poor visibility conditions but also aims to reduce the frequency of rear-end collisions significantly. The paper details the development and testing of Adilights, discusses its integration with current vehicular systems, and evaluates its effectiveness through simulated scenarios. The introduction of Adilights marks a step forward in the use of adaptive lighting systems for enhanced vehicular communication and road safety.\cite{Lee2007RearEnd}

The discovery of a growing number of exoplanets and even extrasolar systems supports the scientific consensus that it is possible to find other signs of life in the universe. The present work proposes for the first time, an explicit mechanism inspired by the dynamics of biological dispersion, widely used in ecology and epidemiology, to study the dispersion of biogenic units, interpreted as complex organic molecules, between rocky or water exoplanets (habitats) located inside star clusters. The results of the dynamic simulation suggest that for clusters with populations lower than 4 M+/ly³ it is not possible to obtain biogenic worlds after 5 Gyr. Above this population size, biogenic dispersion seems to follow a power law, the larger the density of worlds lesser will be the impact rate (β ) value to obtain at least one viable biogenic Carrier habitat after 5 Gyr. Finally, when we investigate scenarios by varying β, a well-defined set of density intervals can be defined in accordance to its characteristic β value, suggesting that biogenic dispersion has a behavior of “minimal infective dose” of “minimal biogenic effective” events by interval i.e. once this dose has been achieved, doesn’t matter if additional biogenic impact events occur on the habitat.

Recently, coastal swamps have been acknowledged for their capability to alleviate shorelines and defend coastal communities. Mangroves play a prominent role in obstructing water currents in riverbanks, shorelines, and coastal areas. Mangrove roots have the significant contribution to the resiliency of the vegetation structure. Yet, mangrove model has lately been called into question by lab experimental evidence. In this paper, the flow characteristics past root models are reviewed. coastal swamps are among the most fruitful and carbon‐rich ecosystems on the planet. Long‐term carbon putting away in coastal wetlands happens mostly below ground as soil organic matter. Mangrove servs as a carbon sink, impacts wetland ecosystem configuration, purpose, and firmness. To expect and ease the properties of climate change, there is a necessity to advance considerate of environmental controls on wetland. The impact of four soil formation factors are reviewed. Across the shorelines, soil organic matter was highest in mangrove forests and it was lower areas.

Abstract: An attractive application of crude glycerol is in the generation of biomethane by means of anaerobic co-digestion, involving the decomposition of the organic matter in two or more substrates by bacteria and archaea, in the absence of oxygen. Thus, the objective of this work was to evaluate the potential of neural networks and fuzzy logic to predict the production of biomethane from the anaerobic co-digestion of glycerol and/or sugarcane molasses. Firstly, a reactor model was implemented using Scilab, with Monod kinetics involving two substrates and an intermediate (M2SI model), to generate a database for subsequent fitting and evaluation of neural and fuzzy models. The neural network package of Matlab was used. Fuzzy modeling was applied using the Takagi-Sugeno approach available in the ANFIS package of Matlab. The biomethane production results simulated by M2SI were used in neural network modeling, firstly employing a “generic” network applicable to all 8 scenarios. A very good fit was obtained (R²>0.99). Excellent performance was also observed for specific artificial neural networks (one for each condition). The parameters of the M2SI model for the 8 different conditions were also mapped using a neural network, as a function of the organic material composition. A fit with R²>0.99 was obtained using 25 neurons. In the case of the fuzzy logic, RMSE of 18.88 mL of methane was obtained with 216 rules, which was a value lower than 0.5% of the order of magnitude of the accumulated methane. It could be concluded from the results that fuzzy logic and artificial neural networks offer excellent ability to predict methane production, as well as to parameterize the M2SI kinetic model (using neural networks).

Local food has continued to become more popular in both the home and as featured ingredients in all segments of food service. Numerous studies have explored the motivating factors for consumer purchasing motivators. However, no known study has explored if there is a correlation between ego and intent to buy local foods. This study adopted a modified version of the Theory of Trying and surveyed a sample of 237 food service buying agents in South Carolina to examine their perceptions of buying local. Results suggest that ego is a predictor of trying to purchase local foods for restaurants. It was also found that the modified model is an acceptable model for predicting buyer intent. These findings support the majority of the studies looking at ego-centric behaviors with green purchases and could potentially help local food suppliers to better market their products to foodservice establishments. This study could also act as the foundation for future studies to explore the full extent of ego’s motivational effect on purchasing intentions in the food service industry.

Abstract: Rhizobia are soil proteobacteria able to engage in a nitrogen-fixing symbiotic interaction with legumes which involves root rhizobial infection and bacterial invasion of new organs formed by the plant in response to the presence of appropriate bacterial partners. This interaction relies on a complex molecular dialogue between both symbionts. Bacterial N-acetyl-glucosamine oligomers called Nod factors are indispensable in most cases for early steps of the symbiotic interaction. In addition, different rhizobial surface polysaccharides, such as exopolysaccharides (EPS), may also be symbiotically relevant. EPS are acidic polysaccharides located out of the cell with little or no cell association that carry out important roles both in free-life and in symbiosis. EPS production is very complexly modulated and, frequently, co-regulated with that of Nod factors, but the type of co-regulation varies depending on the rhizobial strain. Many studies point out a signalling role of EPS-derived oligosaccharides in root infection and nodule invasion but, in certain symbiotic couples, EPS can be dispensable for a successful interaction. In summary, the complex regulation of the production of rhizobial EPS varies in different rhizobia and the relevance of this polysaccharide in symbiosis with legumes depends on the specific interacting couple.

The aim of this paper is to explore the watertightness behaviour for high pressure applications using Multijetfusion technology and polyamide 12 as a material. It reports an efficient solution for manufacturing functional prototypes and final parts for water pressure applications. It provides manufacturing rules to engineers in the pressurized product development process up to 10 MPa of nominal pressure. The research findings show manufacturers the possibility of using additive manufacturing as an alternative to traditional manufacturing. Water leakage was studied using different printing orientations and wall thickness for a range of pressure values. An industrial ball valve was printed and validated with the ISO 9393 standard also meeting tolerance requirements. This paper is a pioneering approach to the additive manufacturing of high performance fluid handling components. This approach solves the problem of leakage caused by porosity in additive manufacturing technologies

This review article conducts an in-depth analysis of the role of next-generation technologies in soilless vegetable production, highlighting their groundbreaking potential to revolutionize yield, efficiency, and sustainability. These technologies, such as AI-driven monitoring systems and precision farming methods, offer unparalleled accuracy in monitoring critical variables like nutrient concentrations and pH levels. However, the paper also addresses the multifaceted challenges that hinder the widespread adoption of these technologies. The high initial investment costs pose a significant barrier, particularly for small and medium-scale farmers, thereby risking the creation of a technological divide in the industry. Additionally, the technical complexity of these systems demands specialized expertise, potentially exacerbating knowledge gaps among farmers. Ethical considerations are scrutinized, including data privacy concerns and potential job displacement due to automation. Regulatory challenges, such as international trade regulations and policy frameworks, are discussed as they may need revision to accommodate these new technologies. The paper concludes by emphasizing that while these sustainable technologies offer transformative benefits, their potential for broad adoption is constrained by a complex interplay of financial, technical, ethical, regulatory, and social factors. Comprehensive, multi-faceted solutions are therefore essential for their ethical and equitable implementation.

In the present study, Electrocoagulated Metal Hydroxide Sludge (EMHS) was analyzed as adsorbent material to remove both fluoride ion (F-) and arsenic V (As(V)) from aqueous effluents. This material was generated during an electrocoagulation process using Aluminum anode. It was characterized by using specific surface areas and the surface morphology was studied by scanning electron microscopy (SEM). Adsorbent fixed-beds are generally studied to remove different class of contaminants. EMHS was evaluated using a continuous flow rate column test with an experimental design. The effect of initial concentration of F- (2.5–10 mg L⁻¹) and the Empty Bed Contact Time (EBCT (0.4–0.8 min)) was studied following a central composite design methodology. The experimented parameters had a significant influence on saturation time, breakthrough volume, and breakthrough time. A response surface analysis was a tool for analyzing the adsorption study, showing interactions that are complicated to identify by others methods. The results, here reported, revealed that EMHS is an efficient and promising adsorbent material in order to remove F- and As(V) from water contaminated by these pollutants.

The aim of this work is to evaluate the impact of proposed modifications to conventional capturing and discharging effluents devices in Subsurface Flow Wetland (SSFW) systems. Main modifications proposed consist on extending the influence of the capture and discharge device in such a way that the SSFW width and height are fully covered. Additionally an SSFW prototype was built as pilot including the proposed modifications and the impact of the innovative device is measured based on the efficiency of the Chemical Oxygen Demand (COD) removal as compared to a traditionally built SSFW. The results show that for the innovative device, the COD removal was 10% higher than for the conventional device.

The objective of this work is to evaluate the impact of the innovative modifications made to the conventional effluent capture and discharge devices used in subsurface flow wetlands (SSFW). The main modifications developed consist on extending the influence of the capture and discharge device in such a way that the SSFW width and height are fully covered. This improved innovative device was applied and evaluated in two subsurface flow wetlands, one on a pilot scale and one on a real scale. To evaluate the impact of the innovative device with respect to conventional one in the operational functioning of subsurface flow wetlands, the elimination of chemical oxygen demand was measured and compared. The results show that for the innovative device, the COD removal was 10% higher than for the conventional device, confirming the validity and effectiveness of the modifications implemented in the effluent capture and discharge devices used in SSFW.

In the field of visualization, understanding users’ analytical process is important to determine the effectiveness of a designed visualization application. To understand the users’ analytical process, numerous studies have been performed to capture and analyze user interactions. Although many studies have emphasized the importance of analyzing user interactions to understand users’ analytical reasoning processes, few have successfully linked these interactions to users’ reasoning processes. This paper introduces an approach that bridges this gap by correlating semantic user interactions with analysis decisions through an interactive wire transaction analysis system and a visual state transition matrix. With the designed analysis system, interactive analysis can be performed to evaluate financial fraud in wire transactions. It also allows the mapping of captured user interactions and analytical decisions back onto the visualization, revealing users’ distinct results. The visual state transition matrix further aids to help understanding users’ analytical flows, revealing their decision-making processes. Classification machine learning algorithms are applied to assess the effectiveness of our approach in understanding analysts’ strategies by connecting them to their decisions. From the study, we observed an average of 72% accuracy in clearly classifying the semantic user interactions. For classifying individual decisions, we observed an average of 70% classification accuracy. This emphasizes the importance of capturing semantic user interactions to understand users’ analytical processes. Overall, it is determined that the proposed approach not only enhances the understanding of analytical behaviors but also offers a robust approach for evaluating user interactions in visualization tools.

Cancer remains as a worldwide cause of death. Investigational research efforts have included the administration of tumors-derived extracts to healthy animals. Having previously demonstrated that administration of non-transmissible, human cancers-derived homogenates induced malignant tumors in mice; we examined here the consequences of administering 50 or 100 µg of protein of crude homogenates from samples of mammary carcinoma, pancreatic adenocarcinoma, and melanoma. The protocol conceived six inoculations per week during three months. Concurrent control group received homogenates of healthy donor skin cosmetic surgery fragments. Mammary carcinoma homogenate administration did not provoke animals’ deterioration or mortality. Multiple foci of lung adenocarcinomas with broad expression of malignity histomarkers, coexisting with small cell-like carcinomas were found. Disseminated cells, positive to classic epithelial markers were detected in lymphoid nodes. Pancreas tumor and melanoma homogenates administrations severely deteriorated animals’ health. Pancreas tumor induced lung poorly-differentiated adenocarcinomas, as a remarkable pancreatic islets hyperplasia. Melanoma affected lungs with atypical adenomatous hyperplasia and solid pseudopapillary adenocarcinoma. Giant atypical hepatocytes with variegated nuclei were also observed. Kidney parenchyma exhibited dispersed foci of neoplastic cells within a desmoplastic matrix. A noticeable nuclear overlapping with hyperchromatic nuclei, mitotic figures, and prominent nuclear atypia was identified in epidermal cells. None of these changes were ever detected in any mouse of the control groups. This study confirms and extends our hypothesis that tumor homogenates contain and may act as a vector for “malignancy drivers”, which ultimately implement a carcinogenesis process in otherwise healthy mice.

More than 20 lines of independent evidence from clinical observations, studies in laboratory animal models, pharmacokinetic considerations, and numerous temporal and spatial associations previously indicate that numerous genetic and environmental factors leading to inflammation and oxidative stress confer vulnerability to aberrant metabolism of acetaminophen during early development, leading to autism spectrum disorder (ASD). Contrary to this conclusion, multivariate analyses of cohort data adjusting for inflammation-associated factors have tended to show little to no risk of acetaminophen use for neurodevelopment. To resolve this discrepancy, here we use in-silico methods to create an ideal (virtual) population of 120000 individuals in which 50% of all cases of virtual ASD are induced by oxidative stress-associated cofactors and acetaminophen use. We demonstrate that Cox regression analysis of this ideal data set shows little to no risk of acetaminophen use if cofactors which create aberrant metabolism of acetaminophen are adjusted for in the analysis. Further, under-reporting of acetaminophen use is shown to be a considerable problem for this analysis, leading to large and erroneously low calculated risks of acetaminophen use. In addition, we argue that factors which impart susceptibility to acetaminophen-induced injury, and propensity for acetaminophen use itself, can be shared between the prepartum, peripartum, and postpartum periods, creating additional difficulty in analysis of existing datasets to determine risks of acetaminophen exposure for neurodevelopment during a specific time frame. It is concluded that risks of acetaminophen use for neurodevelopment obtained from multivariate analysis of cohort data depend on underlying assumptions in the analyses, and that other evidence, both abundant and robust, demonstrate the critical role of acetaminophen in the etiology of ASD.

Although the jellyfish species that inhabit the Mediterranean coastal waters are not lethal, their sting can cause severe pain and systemic effects that pose a health risk to humans. Despite the frequent occurrence of jellyfish stings, currently no consensus exists regarding the most appropriate first aid protocol among the scientific community. Over the years, several different rinse solutions have been proposed of which vinegar, or acetic acid, is one of the most noticeable as a rinse solution with efficacy data published. We investigated the effect of vinegar and seawater on the nematocyst discharge process in species representative of the Mediterranean region such as Pelagia noctiluca (Scyphozoa) and Carybdea marsupialis (Cubozoa) by means of: (1) direct observation on the nematocyst discharge by light microscope (Tentacle solution assay) and (2) the quantification of hemolytic area (Tentacle skin blood agarose assay). The nematocyst discharge of both species was not stimulated by seawater, and it was classified as a neutral solution. In P. noctiluca, vinegar produced nematocyst discharge per ser while inhibiting the nematocyst discharge of C. marsupilais. These results suggest that the use of vinegar cannot be universally recommended. While in case of a cubozoan C. marsupialis sting, the inhibitory effect of vinegar makes it the best rinse solution par excellence, in case of a scyphozoan P. noctiluca sting, its application can be counterproductive, worsening the pain and discomfort of the stung area.

Enterococcus faecalis is a bacterium that can develop a multidrug resistance profile associated with the community as well as nosocomial-acquired infections. Among the treatment options for these infections are aminoglycosides combined with bacterial cell wall inhibitors such as beta-lactams, since E. faecalis is intrinsically resistant to aminoglycosides. One of its most representative resistance mechanisms is the expression of aminoglycoside-modifying enzymes, such as the aminoglycoside phosphotransferase type IIIa of E. faecalis (EfAPH(3')-IIIa). This enzyme acts by phosphorylating aminoglycosides in an ATP-dependent reaction, modifying the 3' position of hydroxyl groups of these antibiotics. Considering this scenario, 3,092 natural products obtained from the ZINC22 database were analyzed to select molecules with the highest affinity for the nucleotide-binding pocket of EfAPH(3')-IIIa, which could be potential aminoglycoside adjuvants. The molecules that showed the best-score results obtained from ensemble docking-based virtual screening were ZINC000000952700 (BS-1), ZINC000014793040 (BS-2) and ZINC000015498603 (BS-3). The most promising results were for BS-2, a flavone derivative, due to its improved stability profile in molecular dynamics simulation (average values of RMSD of 0.23 nm, and Rg of 1.94 nm), binding free energy calculations (average ΔG total of -35.3 nm), as well as better toxicological profile (lower probability of hepatotoxicity, carcinogenic, immunotoxicity, mutagenicity, and cytotoxicity effects), compared to BS-1 and BS-3. These results allow us to propose that a flavone derivative may act as an adjuvant to aminoglycosides in the treatment of E. faecalis infections, acting as an inhibitor in the nucleotide-binding pocket of EfAPH(3')-IIIa.

Severe COVID-19 is associated with viraemia and multiple organ disease. Similar clinicopathological features have been previously seen in SARS and MERS. Clinically, the severity of SARS, MERS and COVID-19 has been associated with the presence of SARS-CoV, MERS-CoV or SARS-CoV2 viraemia in affected patients. In vitro work has looked at the pattern of viral entry and release from polarised epithelial cells infected by coronaviruses. This work has demonstrated a correlation between the severity of a coronavirus infection and the ability of the virus to reach and infect the basal surface of host cells. It has been postulated that this ability helps the virus invade the bloodstream of the host, resulting in a systemic infection with multiple organ involvement. Here we propose that basal surface release and entry of COVID-19 into and out of cells at epithelial-endothelial interface plays a key pathogenic role in severe COVID-19 disease.

In this paper, the fatigue response of Fused Filament Fabrication (FFF) Acrylonitrile butadiene styrene (ABS) parts is studied. Different building parameters (layer height, nozzle diameter, infill density, and printing speed) were chosen to study their influence on the lifespan of cylindrical specimens according to a design of experiments (DOE) using the Taguchi methodology. The same DOE was applied on two different specimen sets using two different infill patterns: rectilinear and honeycomb. The results show that infill density is the most important parameter for both studied patterns. The specimens manufactured with the honeycomb pattern show longer lifespans. The best parameter set associated to that infill was chosen for a second experimental phase, in which the specimens were tested under different maximum bending stresses to construct the Wöhler curve associated to this 3D printing configuration. The results of this study are useful to design and manufacture ABS end-use parts that are expected to work under oscillating periodic loads.

The use of alcohol and tobacco is related to several variables which act as risk or protective factors, depending on the circumstances. The objectives of this study were to analyze the relationship between emotional intelligence, resilience and family functioning in adolescent use of alcohol and tobacco and to find emotional profiles for their use considering self-concept. The sample was made up of 317 high school students aged 13 to 18 who filled out the Brief Emotional Intelligence Inventory, the Resilience Scale for Adolescents, the APGAR Scale, the Alcohol Expectancy Questionnaire - Adolescents and the Five-Factor Self-Concept Questionnaire. The results revealed that emotional intelligence and resilience, specifically, stress management and family cohesion were significant in the group of nonusers. Family functioning acts as a predictor factor for onset of use of tobacco and alcohol. Positive expectancies about drinking alcohol were found to be a risk factor and the intrapersonal factor to be protective. Both stress management and family cohesion were protective factors against smoking. Furthermore, cluster analysis revealed emotional profiles for users of both substances based on self-concept. Finally, the importance of the direction of the relationship between the variables studied for intervention in this problem should be mentioned. Responsible use by improving adolescent decision-making is one of the results expected from this type of intervention.

The high prevalence of thyroid nodules and increased availability of neck ultrasound have led to an increased incidence of diagnostic thyroid fine needle aspirations, with approximately 20% yielding indeterminate results. The recent availability of molecular tests has helped guide the clinical management of these cases. This paper aims to review and compare three main commercially available molecular cytology platforms in the U.S. – Afirma GSC, Thyroseq GC, and ThyGeNEXT+ThyraMIR. Sequential improvements of the Afirma GSC and Thyroseq GC tests have led to higher positive and negative predictive values, sensitivity, and specificity. Comparative studies revealed similar diagnostic performance between these tests, with considerations for factors such as cost and processing time. Thyroseq GC provides detailed genomic information and specific management recommendations. ThyGeNEXT+ThyraMIR, though less studied, presents promising results, particularly in miRNA analysis for weak driver mutations. Challenges in interpreting results include variations in reporting and the evolving nature of testing platforms. Questions persist regarding cost-effectiveness and the utility of ultrasound characteristics in selecting candidates for molecular testing. While molecular testing has primarily served diagnostic purposes, advancements in understanding genetic alterations now offer therapeutic implications. FDA-approved options target specific genetic alterations, signaling a promising future for tailored treatments.

We present a general approach to define reproduction ratios or numbers to monitor the outbreak of epidemics that are modeled by mathematical evolution equations. This provides a solution to an important topic that has not been completely settled in the literature, especially in the case of complex epidemiological models. We illustrate our procedure with a full implementation of a standard deterministic SEIR model that is applied to examine the Covid-19 outbreaks and the effects of intervention measures in several countries in America (Argentina, Brazil, Mexico, USA) and Europe (France, Italy, Spain and UK) in 2020. Our code is also used to investigate herd immunity levels for Covid-19, indicating values between 85% and 90%.

(1) Background: Learning and training in variable conditions favors adapting to unstable or changing environments. The aim of the study was to test the effect of variable practice on the accuracy of the forehand net approach shot in tennis.; (2) Methods: Thirty (n=30) first-class national players (12.9 ± 1.1 years old) participated, divided into three groups: i) induced variability training (n=10) (varying court surfaces and balls), ii) specific training (n=10) and iii) usual training -control group- (n=10). All groups trained for a month: 12 sessions of 20 minutes -3 per week-. The accuracy of the shots were analyzed through a 2D capture and digitization process of the ball bounce on the court; (3) Results: The variability group presented better accuracy values after the period without practice than the stable training group (p = 0.041; ES= 0.51).; (4) Conclusions: the application of variability in the game conditions during tennis training seems to have a favorable effect on the retention of accuracy in the forehand down the line approach to the net.

Abstract: Commercialization of extra virgin olive oil (EVOO) requires a best before date recom-mended at up to 24 months after bottling, stored under specific conditions. Thus, it is expected that the product retains its chemical properties and preserves its ‘extra virgin’ category. However, in-adequate storage conditions could alter the properties of EVOO. In this study, Arbequina EVOO was exposed to five storage conditions for up to one year to study the effects on the quality of the oil and the compounds responsible for flavor. Every 15 or 30 days, samples from each storage condition were analyzed determining physicochemical parameters, the profiles of phenols, volatile compounds, α-tocopherol and antioxidant capacity. Principal component analysis was utilized to better elucidate the relationships between composition of EVOOs and the storage conditions. EVOOs stored at -23 and 23 °C in darkness and 23 °C with light, differed from the oils stored at 30 and 40 °C in darkness. The former were associated with higher quantity of non-oxidized phenolic compounds and the latter with higher elenolic acid, oxidized oleuropein and ligstroside derivatives, which also increased with storage time. E-2-Nonenal (detected at trace levels in fresh oil) was selected as a marker of the degradation of Arbequina EVOO quality over time, with significant linear regressions identified for the storage conditions at 30 and 40 °C. Therefore, early oxidation in EVOO could be monitored by measuring E2-Nonenal levels.

Collagen, the most abundant structural protein found in mammals, plays a vital role as a constituent of the extracellular matrix (ECM) that surrounds cells. Collagen fibrils are strengthened through the formation of covalent cross-links, which involve complex enzymatic and non-enzymatic reactions. Lysyl oxidase (LOX) is responsible for catalyzing the oxidative deamination of lysine and hydroxylysine residues, resulting in the production of aldehydes, allysine, and hydroxyallysine. These intermediates undergo spontaneous condensation reactions, leading to the formation of immature cross-links, which are the initial step in the development of mature covalent cross-links. Additionally, non-enzymatic glycation contributes to the formation of abnormal cross-linking in collagen fibrils. During glycation, specific lysine and arginine residues in collagen are modified by reducing sugars, leading to the creation of Advanced Glycation End-products (AGEs). These AGEs have been associated with changes in the mechanical properties of collagen fibers. Interestingly, various studies have reported that plant polyphenols possess amine oxidase-like activity and can act as potent inhibitors of protein glycation. This review article focuses on compiling literature describing polyphenols with amine oxidase-like activity and antiglycation properties. Specifically, we explore the molecular mechanisms by which specific flavonoids impact or protect the normal collagen cross-linking process. Furthermore, we discuss how these dual activities can be harnessed to generate properly cross-linked collagen molecules, thereby promoting the stabilization of highly organized collagen fibrils.

1) Background: Studies have shown that there is a higher risk of burnout among employees in the healthcare sector. Therefore, this study focused on “Certified Nursing Aides” (CNAs) who have shown a high prevalence of burnout, and are therefore considered an especially vulnerable group. The objective of this study was to identify the relationships between some organizational, personal and sociodemographic factors and burnout; 2) Methods: The final study sample was made up of 278 working CNAs with a mean age of 40.88 (SD=9.41). To compile the data, an ad hoc questionnaire was used to collect sociodemographic information, and to collect professional and employment information, the Brief Emotional Intelligence Inventory for Adults, the Brief Questionnaire on Perceived Social Support, and The General Self-Efficacy Scale.; 3) Results: The results show that the Burnout Syndrome is significantly related negatively to all the emotional intelligence factors, self-efficacy and perceived social support. The risk of burnout is higher in younger persons and in permanently employed professionals. General self-efficacy and stress management act as protective factors against the likelihood of burnout.; and 4) Conclusions: This study suggests that organizations should urge coaching and transformational leadership training programs to promote the wellbeing and organizational commitment of workers.