Wound healing is rarely seen as a problem in healthy individuals; however, under certain pathophysiological conditions, this process can be impaired, leading to the emergence of chronic wounds, which are themselves a serious public health problem. This work aimed to review the most important and recent literature on the use of metallic nanoparticles, in this case those produced from plant extracts, and their application as healing agents. For this, we firstly provided an insight into the pathophysiology of the wound healing process and then the main routes for obtaining metallic nanoparticles. The methodology of synthesis, which is part of the so-called green synthesis, has been the subject of several studies reporting the use of medicinal plants as subtracts for the production of different nanoparticles (of silver, gold, and zinc). Additionally, their use as wound healing agents is close related with biological properties such as antimicrobial, anti-inflammatory, and healing. Finally, we addressed in vitro and in vivo studies reporting the efficiency of metallic nanoparticles synthesized with plant extracts and applied in different pharmaceutical forms on the wound healing process. Based on these satisfactory results, we verified that metallic nanoparticles are potential therapeutic alternatives when compared to the traditional synthetic healing agents, thus foreseeing the production of new drugs in the pharmaceutical market.

The objective of this work is to evaluate and compare the levels of bioactive compounds in basil leaves (Ocimum basilicum), fresh and after thermal drying processing freeze-dried, using methodological procedures that follow internationally recommended and accepted standards. Fresh basil leaves had 9.50 -63.3 mg/100g (vitamin C), 1.8 -3.9 mg EAG/g (total polyphenols), 0.73-1.78 mg/g (flavonoids), 2287.8-1003,8 μg/100g (chlorophyll a), 2606-2287 μg/100g (chlorophyll b), 16.71-20.6 (total carotenoids).Regarding color, there was variation in the parameters L*, a* and b* of the dry sample, but maintained the tendency towards green e (a+) and yellow (b-). Infrared analysis (FTIR) demonstrated the presence of functional groups related to cellulose, hemicellulose and lignin, confirmed by micrography (SEM) with visualization of plant parenchyma residues and fiber bundles. Thermogravimetry (TG/DTG) shows stability in the range of 234 °C, showing more intense mass loss at 294.6 °C. Given the data, it is possible to infer that basil is little affected by freeze-drying, with few changes in bioactive compounds, chemical groups and with good thermal stability. Thus, the application of the freeze-drying technique is a viable alternative to the commercialization of basil leaves, prolonging their useful life and increasing the forms of food applications.

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.

Preeclampsia is a complex pregnancy-related hypertensive disorder which poses significant risks for both maternal and fetal health. Preeclampsia affects 5-8% of the pregnancies in the United States, causing a significant public health and economic burden. Despite extensive research, the etiology and pathogenesis of preeclampsia remain elusive, but have been correlated with maternal conditions such as obesity. In the past decades, the incidence of preeclampsia increased along with the prevalence of obesity among women of reproductive age. Maternal obesity has been shown to negatively affect pregnancy in almost all aspects. However, the precise mechanisms by which obesity influences preeclampsia are unclear. Ankyrin repeat and SOCS Box Containing protein 4 (ASB4) is an E3 ubiquitin ligase that can promote the degradation of a wide range of target proteins. ASB4-null mice display a full spectrum of preeclampsia-like phenotypes during pregnancy including hypertension, proteinuria, and decreased litter size. Furthermore, maternal obesity induced by a high fat diet aggravates preeclampsia-like phenotypes in pregnant mice lacking ASB4. Variants in the ASB4 gene have been associated with obesity in humans, and a functional connection between the ASB4 gene and obesity has been established in mice. This review discusses the connections between preeclampsia, obesity, and ASB4.

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.

X-ray absorption fine structure (XAFS) spectroscopy, temperature-programmed reduction (TPR) and temperature-programmed hydride decomposition (TPHD) were employed to elucidate the structures of a series of PdRe/Al2O3 bimetallic catalysts for selective hydrogenation of furfural. TPR evidenced low-temperature Re reduction in the bimetallic catalysts consistent with migration of [ReO4]- (perrhenate) species to hydrogen-covered Pd nanoparticles on highly hydroxylated  Al2O3. TPHD revealed strong suppression of β-PdHx formation in the reduced catalysts prepared by (i) co-impregnation and (ii) [HReO4] impregnation of reduced Pd/Al2O3 indicating formation of Pd-rich alloy nanoparticles; however, reduced catalysts prepared by (iii) [Pd(NH3)4]2+ impregnation of calcined Re/Al2O3 and subsequent re-calcination did not. Re LIII X-ray absorption edge shifts were used to determine average Re oxidation states after reduction at 400°C. XAFS spectroscopy and high-angle annular dark field (HAADF)-scanning transmission electron microscopy (STEM) revealed that a reduced 5 wt.% Re/Al2O3 catalyst contained small Re clusters and nanoparticles comprising Re atoms in low positive oxidation states (~1.5+) and incompletely reduced Re species (primarily Re4+). XAFS spectroscopy of the bimetallic catalysts evidenced Pd-Re bonding consistent with Pd-rich alloy formation. Pd and Re total first-shell coordination numbers suggest that either Re is segregated to the surface (and Pd to the core) of alloy nanoparticles and/or segregated Pd nanoparticles are larger than Re nanoparticles (or clusters). The Cowley short-range order parameters are strongly positive indicating a high degree of heterogeneity (clustering or segregation of metal atoms) in these bimetallic catalysts. Catalysts prepared using the Pd(NH3)4[ReO4]2 double complex salt (DCS) exhibit greater Pd-Re intermixing but remain heterogeneous on the atomic scale.

Human development encompasses the integration of neuromotor, psychological, social-emotional, and cognitive processes across time. However, a better understanding of the interconnectedness of motor and cognitive development throughout the lifespan is needed. The integrative and dynamic nature of short-term motor skill acquisition and long-term developmental processes may foster executive functions. Unfortunately, assessments of motor competence have traditionally focused on skillfulness from a movement perspective, using restrictive task protocols that decontextualize performance and limit cognitive involvement. Additionally, traditional motor-cognitive dual-task assessments centered on cognitive involvement during specific movement protocols, have minimized impacts of task complexity and motor competence on cognitive performance. To gain a more ecologically valid understanding of the relation between motor and cognitive development, a novel assessment paradigm using an integrative approach to motor-cognitive dual-tasking is needed. The purpose of the present paper is to: 1) provide a conceptual bridge, based on multidisciplinary evidence, to effectively link the concurrent development of motor competence and executive functions via learning-related and exercise-related neurotrophic mechanisms, and 2) use this conceptual bridge to inform the development of novel motor-cognitive dual-task assessments that account for the role of task complexity, current levels of motor competence, and the continuous decision-making inherent in “real world” performance environments.

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.

High temperature Ni-superalloys are limited by reliance on critical elements and complex processing steps. High entropy alloys (HEAs) have demonstrated stable single/dual phase microstructures with strong ordering tendencies. A dual phase (FCC+BCC) Al0.5Co0.5CrFeNi1.5Ti0.25 alloy (developed using CALPHAD) was tested for room and high temperature compression behavior. The microstructure mainly comprises of coarse ordered FCC(L12) surrounding decomposed lamellae of ordered FCC(L12) and BCC(L21) phases, which coarsened upon annealing at high temperature (1100 °C) into a bi-continuous structure of disordered FCC+BCC(B2). Additionally, cold rollability of the cast alloy increased from <5% to ~40% after annealing, with a corresponding softening observed in room temperature compression mainly due to disordering of FCC and BCC phases. Peak flow stresses of 793.45MPa, 535.12MPa, and 324.40MPa were achieved at 800oC, 900oC, and 1000oC respectively for the high temperature annealed alloy.

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.

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.

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.

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.

In this work, we propose an approach for the autonomous navigation of mobile robots using fusion of sensor data by a Double Deep Q-Network with collision avoidance by detecting moving people via computer vision techniques. We evaluate two data fusion methods for the proposed autonomous navigation approach: Interactive and Late fusion strategy. Both are used to integrate mobile robot sensors through the following sensors: GPS, IMU and, an RGB-D camera. The proposed collision avoidance module is implemented along with the sensor fusion architecture in order to prevent the autonomous mobile robot from colliding with moving people. The simulation results indicate a significant impact on the success of completing the proposed mission by the mobile robot with the fusion of sensors, indicating a performance increase (success rate) of 27% in relation to navigation without sensor fusion. With the addition of moving people in the environment, deploying the people detection and collision avoidance security module has improved about 14% the success rate when compared to that of the autonomous navigation approach without the security module. Video was developed with robot navigation using the DDQN-Late Fusion https://www.loom.com/share/684afa6a5b0148afadc9a200ab9f3483.

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.

Acute myeloid leukemias are characterized by genetic alterations that lead to a complete or partial block at various stages of myeloid differentiation, leading to continual proliferation of myeloid progenitor cells. These alterations can include a well-documented chromosomal translocation that leads to the synthesis of the PML-retinoic acid receptor fusion protein that leads to a block in differentiation and the ability to induce differentiation with all-trans retinoic acid and other small molecules. In essence, in large part, the disease is due to stem cell-like cells that are blocked in their ability to undergo terminal differentiation to more mature, non-proliferative cell types. We discovered a long non-coding RNA (lncRNA), lncRNAp53int1, encoded from the first intron of the p53 tumor suppressor gene. Interestingly, lncRNAp53int1 appears to play an important regulatory role in maintaining the undifferentiated and proliferative state in a manner independent from p53 gene expression. lncRNAp53int1 is expressed at high levels in proliferating, undifferentiated promyelocytes and its levels fall significantly during their terminal differentiation to non-proliferating granulocytes or macrophages. Furthermore, forced overexpression of lncRNAp53int1 blocks terminal differentiation. The observation that the expression of this lncRNA is repressed during terminal differentiation of human leukemic cells and its expression blocks differentiation of leukemic myeloid progenitor cells, is likely to have clinical implications with regards to differentiation therapy for myeloid leukemia. Ongoing research aims to identify the molecular partners that it associates with and the signaling pathways that this lncRNA participates in with the long-term goal of identifying molecules that inhibit lncRNAp53int1 activity and serve as potential therapeutic drugs. The goal of this mini-review is to disseminate these novel and interesting findings to readers and to describe where we think this research is headed and the impact it can have on myeloid leukemias.

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.

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.

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

The study of photonic crystals has emerged as an attractive area of research in nanoscience in the last years. In this work we study the properties of a two-dimensional photonic crystal composed of dielectric rods. The unit cell of the system is composed of six rods organized on the sites of a C6 triangular lattice. We induce a topological phase by introducing an angular perturbation ϕ in the pristine system. The topology of the system is then determined by using the so-called k.p perturbed model. Our results show that the system presents a topological and a trivial phases, depending on the sign of the angular perturbation ϕ. The topological character of the system is probed by evaluating the electromagnetic energy density and analyzing its distribution in the real space, in particular on the maximal Wyckoff points. We also find two edge modes at the interface between the trivial and topological photonic crystals, which present a pseudospin topological behavior. By applying the Bulk-Edge correspondence, we study the pseudospin edge modes and conclude that they are robust against defects, disorder and reflection. Moreover, the localization of the edge modes leads to the confinement of light and the interface behaves as a waveguide for the propagation of electromagnetic waves. Finally, we show that the two edge modes present energy flux propagating in opposite directions, which is the photonic analogue of the quantum spin Hall effect.

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.

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.

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.

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

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.

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.

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.

Safety signal detection, an integral component of Pharmacovigilance (PhV), aims to identify new or known adverse events (AEs) resulting from the use of pharmacotherapeutic products. Post-marketing spontaneous reports from different sources are commonly utilized as a data source for detecting these signals but there are underlying challenges arising from data complexity. This paper investigates the implementation of the Apriori algorithm, a popular method in association rule mining, to identify frequently co-occurring drugs and AEs within safety data. We discuss previous applications of the Apriori algorithm for safety signal detection and conduct a detailed study of an improved method specifically tailored for this purpose. This enhanced approach refines the classical Apriori method to effectively reveal potential associations between drugs/vaccines and AEs from post-marketing safety monitoring datasets, especially when AEs are rare. Detailed comparative simulation studies across varied settings, coupled with the application of the method to vaccine safety data from the Vaccine Adverse Event Reporting System (VAERS), demonstrate the efficacy of the improved approach. In conclusion, the improved Apriori algorithm is shown to be a useful screening tool for detecting rarely occurring potential safety signals from the use of drugs/vaccines using post-marketing safety data.

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.

Hydraulic fracturing has enabled production from unconventional reservoirs in the U.S., but production rates often decline sharply, limiting recovery factors to under 10%. This study optimizes the CO2 huff-n-puff process for multistage-fractured horizontal wells in Wolfcamp A formation in the Delaware Basin. The potential for enhanced oil recovery and CO2 sequestration simultaneously was addressed using a coupled geomechanics-reservoir simulation. Geomechanical properties were derived from a 1D mechanical earth model and integrated into reservoir simulation to replicate hydraulic fracture geometries. The fracture model was validated using a robust production history matching. Fluid phase behavior analysis refined the equation of state, and 1D slim tube simulations determined a minimum miscibility pressure of 4300 psi for CO2 injection. After the primary production phase, various CO2 injection rates were tested from 1 to 25 MMSCFD/well, resulting in incremental oil recovery ranging from 6.3% to 69.3%. Different injection, soaking, and production cycles were analyzed to determine the ideal operating condition. The optimal scenario improved cumulative oil recovery by 68.8% while keeping the highest CO2 storage efficiency. The simulation approach proposed by this study provides a systematic workflow for evaluating and optimizing CO2 huff-n-puff in hydraulically fractured wells, enhancing the recovery factor of unconventional reservoirs.

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.

Cancer is a multifactorial disease that continues to increase. Lignans are known to be important anticancer agents. However, due to the structural diversity of lignans, it is difficult to associate anticancer activity with a particular subclass. Therefore, the present study sought to evaluate the association of lignan subclasses with antitumor activity, considering the genetic profile of the variants of the selected targets. For this, predictive models were built against the targets tyrosine-protein kinase ABL (ABL), epider-mal growth factor receptor erbB1 (EGFR), histone deacetylase (HDAC), Serine/threonine-protein kinase mTOR (mTOR) and Poly [ADP-ribose] polymerase-1 (PARP1). Then, mapped single nucleotide polymorphisms and designed target mutations, and performed molecular docking with the lignans with the best predicted biological activity. The results showed more anticancer activity in the dibenzocyclooctadiene, furofuran and aryltetralin subclasses. The lignans with the best predictive values of biological activity showed varying binding energy results in the presence of certain genetic variants.

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 aim of the present study was to investigate whether chronic undernutrition modifies the response to rostafuroxin in juvenile rats. Chronic undernutrition was induced in male rats using a multideficient diet known as Regional Basic Diet (RBD), mimicking alimentary habits in impoverished regions worldwide. Animals were given RBD – or a control/CTRL normal diet for rodents – from weaning to 90 days, and rostafuroxin (1 mg/kg body mass) or vehicle were orally administered during the last 30 days. Undernourished rats became hypertensive at 55–60 days of age (tail-cuff recording). During the last two days, the rats were hosted in metabolic cages to measure food/energy, water, Na⁺ ingestion, and urinary volume. Blood and kidneys were collected after euthanasia. Rostafuroxin increased food/energy and Na⁺ intake in CTRL and RBD rats but had opposite effects on Na⁺ balance (intake minus urinary excretion): negative in CTRL and positive in RBD. The drug normalized the decreased plasma Na⁺ concentration found in RBD rats, increased urinary volume in RBD but not in CTRL, and decreased and increased urinary Na⁺ concentration in the RBD and CTRL groups, respectively. Rostafuroxin decreased the ouabain-sensitive (Na⁺+K⁺)ATPase and increased the ouabain-resistant, furosemide-sensitive Na⁺-ATPase from proximal tubule cells in both groups, and normalized the systolic blood pressure in RBD without effect in CTRL rats. We conclude that chronic undernutrition modifies the response of blood pressure, caloric metabolism, Na⁺ distribution in liquid compartments, mobilization of Na⁺ from non-osmotic compartments, Na⁺ and water balance, and activity of renal Na⁺-transporting ATPases to rostafuroxin.

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 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.

Little information is available in the literature on the use of cactus pear meal in poultry diets being important to evaluate diets that provide excellent performance and lower production costs. Our objective was to study the use of cactus pear meal in diets of laying hens. In the first study, two diets for male and female chicks were used - 1: 80% reference diet + 20% Miúda cactus pear meal (CPM) and 2: 80% reference diet + 20% Gigante cactus pear meal. The variety Miúda provid-ed better use of metabolizable energy, digestibility coefficient of dry matter, protein, and min-eral matter. In the second study, a control diet was compared to three diets with different levels of CPM for laying hens, in the proportions of 3%, 6% and 9%. No significant differences were found in productive performance. However, there were significant differences in egg quality, texture and color profile of the cooked yolk, egg composition, fatty acids and cholesterol in the yolk. It is possible to use 9% of CPM in the diet of laying hens in semi-intensive system not compromising performance and egg quality, and using 3% of CPM provides higher economic return.

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.

Background and objectives: due to the neurotoxic effect caused by high levels of cortisol, studies suggest that stress and certain psychiatric disorders, such as mood disorders, have influences under the hippocampus, causing a decrease in volume and consequent memory changes. This study aims to evaluate the relationship between hippocampal volume in patients with mood disorders under therapy. Materials and Methods: the PRISMA protocol for systematic reviews was followed. Pubmed, Cochraine and Scielo databases were searched by terms “Hippocampus”, “Mood Disorders” and “MRI”, and variants in other languages, in human, from January 2011 to September 2016. The individual quality of the articles was analyzed using the Cochraine modified scale for clinical trials and the Agency for Healthcare Research and Quality scale for observational studies. Results: all studies showed reduction of hippocampal volume in depressive patients. Change in hippocampal volume is not related to the use of antidepressant. Particularly the sub-region of the subculum is more reduced, without lateralizations. Significant relationship between stress and right hippocampal reduction. The findings seem to point out: a common pathway of hippocampus reduction, mediated by stress, explaining memory deficits due to depression, where the cortisol pathway seems to act; alteration in the prefrontal cortex; reduction in the subiculum related to inhibition of the hypothalamic-pituitary-adrenal axis, corroborating the hypothesis of cortisol. Conclusions: the papers suggest: association between global hippocampal atrophy with mood disorders; reduction of hippocampal subiculum; refractoriness to clinical treatment among patients with lower hippocampal volume.

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.

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.

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.

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.

In this paper, solar irradiance and wind speed forecasts were performed considering time horizons ranging from 10 min to 60 min, under a 10 min time-step. Global horizontal irradiance (GHI) and wind speed were computed using four forecasting models (Random Forest, k-Nearest Neighbours, Support Vector Regression, and Elastic Net) to compare their performance against two alternative dynamic ensemble methods (windowing and arbitrating). Forecasting models and dynamic forecasting ensembles were implemented in Python for performance evaluation. The performance comparison between the prediction models and the dynamic ensemble methods was carried out by evaluating the RMSE, MAE, R² and MAPE, to evaluate whether the dynamic ensemble forecasting method obtained greater. According to the results obtained windowing dynamic ensemble method was the most efficient among the tested. For the wind speed data, by varying its parameter λ (from 1 to 100), a variable performance profile was obtained, where from λ =1 to λ = 74, windowing proved to be the most efficient, reaching maximum efficiency for λ = 19. Windowing was the best method for the GHI analysis, reaching its best performance for λ = 1. The efficiency gain using windowing was 0.56% when using the wind speed model and 1.96% for GHI.

California has experienced a surge in wildfires, prompting research into contributing factors, including weather and climate conditions. This study investigates the complex, multiscale interactions between large-scale climate patterns, such as the Boreal Summer Intraseasonal Oscillation (BSISO), El Niño Southern Oscillation (ENSO), and the Pacific Decadal Oscillation (PDO), and their influence on moisture and temperature fluctuations, and wildfire dynamics in California. The combined impacts of PDO and BSISO on intraseasonal fire weather changes, the interplay between Fire Weather Index (FWI), relative humidity, vapor pressure deficit (VPD), and temperature in assessing wildfire risks, and geographical variations in the relationship between FWI and climatic factors within California are examined. The study employs a multi-pronged approach, analyzing wildfire frequency and burned areas alongside climate patterns and atmospheric conditions. Findings reveal significant variability in wildfire activity across different climate conditions, with heightened risks during specific BSISO phases, La-Niña, and cool PDO. The influence of BSISO varies depending on its interaction with PDO. Temperature, relative humidity, and VPD show strong predictive significance for wildfire risks, with significant relationships between FWI and temperature in elevated regions (correlation, r &gt; 0.7, p ≤ 0.05) and FWI and relative humidity along the Sierra Nevada Mountains (r ≤ -0.7, p ≤ 0.05).

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.

We evaluated the growth of four genetic groups, two pure (pacu and tambaqui) and two hybrids (tambacu and paqui) of Serrasalmidae fish fed with different feeding programs. Over the five months, 30 animals from each genetic group that were evaluated in three cages with 10 animals from each genetic group per cage. A different feeding program was used for each cage, consisting of three commercial diets containing 24, 28, or 32% crude protein (CP) in different combinations. A logistic model or an exponential were used to fit the growth curves. The evaluation time did not allow the adjustment of the logistic model for the tambaqui and paqui groups, as well as for the animals fed the food program with the highest initial protein intake. Pure pacu proved to be an early performing group, while tambacu hybrid reached a higher final weight without slowing down growth.

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).

: The objective of this study was to obtain different dry matter contents and proportions of mel-on plant biomass for silage making. A completely randomized design was adopted with a facto-rial scheme (3 x 2) in four replications. The first factor corresponded to three mixtures of melon biomass on as fed basis (AF) of plant (branch + leaf) and fruit (harvested melon) as follows: 100% plant, 90% plant + 10% fruit, and 100% fruit. The other factor corresponded to the ensiled materi-al with fresh and dehydrated melon plant biomass (40% DM) which was dried in the field, after harvest. Silages with partial dehydration of the biomass available after fruit harvest with 0% and 10% fruit showed the greatest DM values: 297 and 293 g/kg DM, respectively. Silages with 0, 100 and 10% fruit showed medium LAB populations: 5.79, 5.14 and 4.58 logs CFU/g, respectively. Higher concentrations of acetic acid contents were observed in silages with 0 and 10% fresh fruit, which were 14.9 and 14.1 g/kg DM, respectively. Silages with 0% and 100% of dehydrated fruit showed better results for the indicators of good-quality silage. Dehydration improves the fer-mentative profile and quality of the melon silage.

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.

Ascidians are marine invertebrates that synthesize sulfated glycosaminoglycans (GAGs) within their viscera. Ascidian GAGs are considered analogues of mammalian GAGs and possess great potential as bioactive compounds, presenting antitumor and anticoagulant activity. Due to its worldwide occurrence, and therefore, being a suitable organism for large-scale mariculture in many marine environments, our main objectives are to study Microcosmus exasperatus GAGs re-garding composition, structure and biological activity. We also aim to develop efficient protocols for sulfated polysaccharides extraction and purification aiming large-scale production and clinical applications. Ascidians were collected by free diving. GAGs were extracted by proteolytic diges-tion, purified by ion-exchange liquid chromatography, characterized by agarose gel electropho-resis and enzymatic treatments. Anticoagulant activity was evaluated by aPTT assays. Cell cyto-toxicity was evaluated by MTT assays using LLC tumor cell line. Clonogenic assay was performed using LLC cells as well. Tumor cell migration activity was evaluated in vitro by wound healing assays. Our results show that M. exasperatus presents three distinct polysaccharides. These pol-ysaccharides were fractionated into two fractions named SP1 and SP2. M. exasperatus produces a low anticoagulant dermatan sulfate (SP2) and a fucosylated dermatan sulfate composed of a hy-brid chain of dermatan sulfate, chondroitin sulfate and fucose residues with sulfations at position 2 and 6 of the N-acetyl-galactosamine (SP1), which, interestingly, is not susceptible to heparinases treatment and does not present significant anticoagulant activity. Glycans were tested as anti-tumoral agents and presented relevant activity as such. Short-term incubation with TP does not affect LLC tumor cells viability at low concentrations, while long-term incubation with TP de-creased LLC tumor cells growth/proliferation at different concentrations. TP affected LLC tumor cells migration at different concentrations. As a conclusion, we state that M. exasperatus presents great potential as an alternative GAG source compared to porcine or bovine heparin, producing suitable compounds that present antitumoral properties at low concentrations, that do not pos-sess anticoagulant activity, and do not enhance other aspects of malignancy, such as tumor cell migration and invasion. Our perspectives are to apply these molecules in future preclinical studies as a soluble formula, as well as in nanoparticles, for cancer treatment.

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.

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.

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.

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.

This study analysed through the perspective of networks the association between physical activity (PA), sedentary behaviour (SB), and cardiovascular risk factors in overweight low-income schoolchildren from the perspective of complex systems. Methods: The sample consisted of 41 overweight children between 6 and 10 years old (56% girls). PA and SB were analysed by accelerometer, and the measures of cardiovascular factor risk were: anthropometric and hemodynamic measurements, body fat, lipid profile and glucose, cardiorespiratory fitness (CRF), left ven-tricular mass (LVM), and Sokolov-Lyon. Network analysis with Boot-strap-1000 was performed to analyze the association between PA, SB and the cardiovascular risk factors. Results: Moderate to vigorous PA was positive related with Waist circumference (WC; 0.499), HDL-C (0.307), and CRF (0.276), and negatively associated with BMI (-0.251) and Fat (-0.341). For SB, positive associations were seen with WC (0.326), CRF (0.296), LVM (0.250) and Sokolov (0.215). Adicionalment, the centrality indicators highlighted WC as the most important variable in the network. Conclu-sion: Interventions that aim to mitigate the harmful effects of obesity on cardiovascular risk factors in overweight children should consider WC as an important variable in of system.

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%.

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.

The plague caused by the Yersinia pestis bacterium is primarily a flea-transmitted zoonosis of rodents that can also be conveyed to humans and other mammals. In this work, we analyzed the spatial and temporal distribution of rodents’ populations during active and quiescent periods of the plague in the municipality of Exu, northeastern Brazil. The geospatial analyses had shown that all rodent species occurred through the whole territory of the municipality with different hotspots for the risk of occurrence of the different species. Important fluctuation in the rodent populations was observed with a reduction in the wild rodent fauna following the end of a plague epidemic period, mostly represented by Necromys lasiurus and increase of the commensally species Rattus rattus. A higher abundance of rats might lead to an increased exposure of humans populations, favoring spillovers of plague and other rodent-borne diseases. Our analysis contributed to further highlight the role of the wild rodent species as the amplifier hosts and of the commensally rats (Rattus rattus) as the preserver hosts on the quiescent period on that transmission infection area.

Franklinite-Zincochromite-Gahnite solid solutions have been prepared by ceramic or coprecipitation method and its pigmenting capacity as cool ceramic pigments in different glazes (double and single firing frits and porcelain frit) was studied. XRD, UV-Vis-NIR diffuse reflectance, CIEL*a*b* colour analysis, bandgap measurement and the photocatalytic activity over the Orange II azoic dye were carried out for the characterization of samples. Following criteria of high red colouring capacity and high NIR reflectance at minimum Cr amount, Zn(Fe1.8Cr0.2), Zn(Al1.5Cr0.5) and Zn(Al1.3Cr0.5Fe0.2)O4 turn out to be the optimal compositions for an intense reddish cool pigment. All powders show a direct semiconductor behaviour with bandgap around 2 eV, falling in the visible range (620 nm), and according to literature, the associated photocatalytic activity over Orange II with visible light irradiation is moderate in all cases, but the Franklinite-Zincochromite Zn(Fe1.8Cr0.2) stands out.

This research presents a comprehensive bibliometric and sentiment analysis of nanocomposite literature from 1990 to 2024. Employing cutting-edge computational methods, the study delves deep into the progression of microstructural characterization, electrical properties, and mechanical behaviors of nanocomposites. The analysis utilizes advanced Boolean search strategies from the Scopus database, ensuring a thorough extraction of thematic content. The results explore various themes and insights, shedding light on trends evident in the Scopus database, particularly the prominence of research on nanocomposite microstructure, electrical attributes, and mechanical performance. The paper also offers textual analytics and bibliometric data, showcasing critical collaborative efforts and influential studies. Significant discoveries encompass the evolution of research language, shifts in thematic focus, and global contributions, providing a comprehensive perspective on the current landscape of nanocomposite research and its dynamic evolution. Moving forward, the "State-of-the-Art and Gaps Extracted from Results and Discussions" section of the paper delves into the most recent advancements in nanocomposite research. It delves into various types of nanocomposites, with a particular emphasis on their microstructural characteristics, electrical properties, mechanical dynamics, and the application of nanocomposite films. This section identifies key research themes, traces historical progress, and highlights emerging trends while underscoring the significance of collaboration and the influence of pivotal studies that have shaped the field. Lastly, in the "Literature Review Guided by Artificial Intelligence" section, the paper introduces a revised approach for researching nanocomposites through AI-guided techniques. This approach prioritizes articles published in 2023 based on citation frequency. Here, the focus is on exploring the relationship between nanocomposites and their electrical properties, emphasizing their fundamental interactions and impact on electrical characteristics. Various nanocomposite systems are covered, highlighting their composition, structure, and functionality. Findings from recent studies are integrated to provide a comprehensive overview of the current state of knowledge in this area. Notably, the sentiment analysis, anchored by an average sentiment score of 0.638771, underscores a positive trajectory in academic discourse, emphasizing the growing recognition of the potential of nanocomposites. The bibliometric exploration maps the intellectual domain, emphasizing pivotal research themes and the influence of crosslinking time on nanocomposite attributes. While this study is thorough, it acknowledges its limitations and advocates for broader database inclusion in future research endeavors. This work elucidates the prevailing trends in nanocomposite research, emphasizing the indispensable role of computational tools in comprehending this vast wealth of information.

Background: Telomere length (TL) shortening process is associated with several known environment and individual determinants. DNA methylation is the most studied epigenetic process and may be associated with TL. We investigated the associations between DNA methylation and TL in peripheral blood. Methods: Methylation wide association study was conducted in 47 women (37.1±8.8 years) with different nutritional status. Association between TL and DNA methylation levels were explored by univariate and multiple linear regression models, corrected by age and Body Mass Index. Corrections for multiple comparisons by Benjamini‐Hochberg test was also performed. WEBGestalt was used to identify pathways that are responsible for regulating TL. Results: We found negative correlations between TL and BMI (r = -0.641; p = 0.001), abdominal circumference (r = -0.622; p = 0.001) and fat mass (r = -0.656; p = 0.001). 44 CpGs sites were associated with TL, independent of age and BMI. The most of these sites were negatively correlated with TL. For the 7 remained sites, DNA hypomethylation were associated with shorter TL. These CpGs were related to nine different pathways, including thermogenesis, cancer, glutamatergic and serotonergic synapse. Conclusion: There is an epigenetic contribution in TL, independent of nutritional status and age. Genes related to TL are involved in important metabolic pathways.

Lightning discharges in electric power networks generate voltage and current surges that are propagated through the electrical network causing damage and shutdowns in the electrical system. To protect the system against these phenomena, surge arresters are very effective and widely used by electrical utilities in their electric grids. This paper presents a methodology for optimized surge arrester allocation based on genetic algorithm (GA), creating a simulation environment in the software ATP (Alternative Transients Program) to implement the proposed methodology. The optimized allocation procedure is based on a fitness function that minimizes the cost of surge arresters and maximizes the number of protected equipment. To carry out this optimized arrester allocation procedure using ATP may demand too much processing time when running large distribution grids. To overcome this difficulty a procedure is proposed to obtain an overvoltage severity description of the grid and select the most critical electric nodes for the incidence of lightning discharges, in the GA allocation procedure. The case study is applied to the IEEE 123-bus electrical feeder to demonstrate the effectiveness of the proposed methodology.

Starch is a widespread natural polymer used in healthcare applications due to its low cost and antibacterial properties. The use of starch in its many forms and its sometimes combination with metallic nanoparticles have all contributed to the advancement of biomaterials. However, few studies have been conducted on biocomposites composed of jackfruit starch and silver nanopar-ticles (AgNPs). As a result, this research aims to study the physicochemical, morphological, and cytotoxic features of a Brazilian jackfruit (Artocarpus heterophyllus) starch-based scaffold loaded with AgNPs. Gelatinization and chemical reduction were used to synthesize the scaffold and AgNPs, respectively. X-ray diffraction (XRD), differential scanning calorimetry (DSC), scanning electron microscopy coupled with energy-dispersive spectroscopy (SEM-EDS), and Fourier transform infrared spectroscopy (FTIR) were utilized to explore the properties. The findings supported the development of anisotropic, stable, monodispersed AgNPs. The presence of AgNPs in the scaffold matrix was revealed by XRD and SEM-EDS. AgNPs were found to modify the crystallinity, roughness, and thermal stability of the scaffold while leaving its chemical and physical characteristics unchanged. Finally, the scaffolds did not show adverse effects on the L929 cells.

Gluten is a complex mixture of storage proteins in cereals like wheat, barley and rye. Prolamins are the main components of gluten. Their high content in proline and glutamine makes them water-insoluble and difficult to digest in the gastrointestinal tract. Partial digestion generates peptide sequences which trigger immune responses in celiac and gluten-sensitive patients. Gluten detection in food is challenging because of the diversity, in various food matrices, of protein proportions and their immunogenicity. Attempts to develop standard reference materials have been unsuccessful. We present here a summary of recent studies reporting the detection of dominant Gluten Immunogenic Peptides (GIP) sharing epitopes presented in the α-gliadin 33-mer, the most important celiac disease-immunogenic sequence within gluten. GIP were not only detectable and quantifiable in very different kind of difficult to analyze food, but also in stool and urine of celiac patients on a supposedly gluten-free diet (GFD), providing the first simple and objective means to assess adherence to the GFD. Methods to specifically and sensitively detect the most active GIP in food and biological fluids are rational candidates may use similar analytical standard references for determination of the immunopathological risk of gluten exposure in gluten-related diseases.

The sea level rise, a consequence of climate change, is one of the biggest challenges that countries and regions with coastal lowland areas will face in the medium term. This study proposes a methodology for assessing the vulnerability to sea level rise (SLR) on the Atlantic coast of Portugal mainland. Some scenarios of extreme sea level for different return periods and extreme flooding events were estimated for 2050 and 2100, as proposed by the European Union Directive 2007/60/EC. A set of physical parameters are considered for the multi-attribute analysis technique implemented by the Analytic Hierarchy Process, in order to define a Physical Vulnerability Index fundamental to assess coastal vulnerability. For each SLR scenario, coastal vulnerability maps, with spatial resolution of 20 m, are produced at national scale to identify areas most at risk of SLR, constituting key documents for triggering adaptation plans for such vulnerable regions. For 2050 and 2100, it is estimated 903 km² and 1146 km² of vulnerable area, respectively, being the district of Lisbon the most vulnerable district in both scenarios. Results are available through a Web Map Service, for Portuguese public entities, and through a web map viewer for public and communities in general.

Antibiotic resistance (ABR) is concerning issue due to its direct and indirect repercusions on public health, since decreased therapeutic effect of certain antibiotic to treatment complications that can cause death. There are several mechanism as to how ABR can be transferred from one microoorganisms to another, and many of them are dependant many environmental factors. The food supply chain is a environment in which ABR gene transfer can occur is multiple pathways, which generate concerns regarding food safety. Here, we summarize relevant mechanisms which are implied in ABR in food supply chain but also we are addressing routes of transmission and prevalence of ABR, implications on public health, and the application of new alternatives to antibiotics such as antimicrobial peptides, mainly bacteriocins, in order to countermeasure ABR.

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.

Human pluripotent stem cells (PSCs), which include both embryonic and induced pluripotent stem cells, are widely used in fundamental and applied biomedical research. They have been instrumental for better understanding development and cell differentiation processes, disease origin and progression, and can aid in the discovery of new drugs. PSCs also hold great potential in regenerative medicine to treat or diminish the effects of certain debilitating diseases, such as degenerative disorders. However, some concerns have recently been raised over their safety for the use in regenerative medicine. One of the major concerns is the fact that PSCs are prone to errors in passing the correct number of chromosomes to daughter cells, resulting in aneuploid cells. Aneuploidy, characterised by an imbalance in chromosome number, elicits the upregulation of different stress pathways that are deleterious to cell homeostasis, impair proper embryo development and can potentiate cancer development. In this review we will summarise known molecular mechanisms recently revealed to impair mitotic fidelity in human PSCs and the consequences of the decreased mitotic fidelity of these cells. We will finish with speculative views on how the physiological characteristics of PSCs can affect the mitotic machinery and how their suboptimal mitotic fidelity may be circumvented.

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

Adolescents often migrate unaccompanied by a caregiver, facing specific risks. Mentoring has been identified as a strategy with positive results for youth at risk. Its application with unaccompanied minors (UM) refugees and asylum seekers has gained momentum within EU countries receiving refugee citizens from the EU relocation program. Portugal hosts the fourth highest number of Middle East and North Africa UM refugees and asylum seekers among EU countries. Thus, it´s relevant to review strategies and intervention programs, focused on mentoring to inform future programs in Portugal supporting UM. This article aims to describe the use of mentoring strategies towards integrating UM refugees, and how this has been taking place in Portugal. This review has the potential to inform professionals, namely social education professionals involved in the Portuguese refugee relocation program and encourage the discussion about creating based mentoring-based programs for this target group.

Umbu is a tropical fruit with high content of bioactive compounds. However, maturation causes significant losses on nutrient density reducing the nutritional value of this highly appreciated fruit. Thus, the objective of the present work was to encapsulate the Umbu fruit bioactive compounds using spray drying and identify the variables affecting the biomolecules preservation. A Box-Behnken experimental design with 3 factors was set varying inlet temperature, atomization flow rate, and maltodextrin concentration for process otimization. Then the powder physicochemical and chemical properties were characterized, and results were modeled using a polynomial equation. Results revealed that the droplet size and maltodextrin concentration had a significant influence on the conservation of the biomolecules. Drying kinetics favoring fast formation of a particle crust increases encapsulation efficiency. Bioactive compounds retention was achieved by increasing maltodextrin even at high temperatures, where a matrix is formed hindering chemical degradation. Process optimization was validated and revealed low inlet temperatures (122ºC), high atomization flow rate (5kg/h) and high maltodextrin concentration (20%) to be the most desirable conditions for bioactive compounds retention.

At the beginning of December 2019, a new type of coronavirus emerged, SARS-CoV-2. This virus causes COVID-19, a highly contagious disease that can initially present asymptomatically and can also lead to death. Our ecological study goal was to evaluate the incidence, mortality, and lethality rates for COVID-19 between the states of Espírito Santo and Minas Gerais, with time series analysis using secondary and public databases on COVID-19 from January 2020 to July 2022. Prais-Winsten linear regression was used for trend analyses. In 2020, the rate in Espírito Santo was 2.19 times greater than Minas Gerais. This trend continued in 2021, with Espírito Santo's rate being 1.29 times greater. In 2022, Espírito Santo's rate remained 2.65 times higher than Minas Gerais. Furthermore, Espírito Santo had the highest mortality, with the exception of 2021. In turn, Minas Gerais had the highest fatality rate throughout the analyzed pandemic period. The state of Espírito Santo had a higher incidence of COVID-19, as well as higher mortality, when compared to the state of Minas Gerais. In turn, Minas Gerais had a higher fatality rate. Furthermore, both states showed similar trends for mortality, lethality, and incidence in the years 2020 and 2021.

OBJECTIVE: Analyzing the effects of different physical exercise programs on the anthropometric, cardiovascular, metabolic, and strength variables of the elderly participating in health care programs. METHODS: Controlled clinical trial, with 60 elderly participants from health care groups, allocated into four groups: Group of resistance exercises in open-air gyms – GT1 (n = 17); Group of aerobic and localized exercises - GT2 (n = 11); Group of resistance exercises at GT3 gym (n = 17); Control group: non-exercise practitioners - CG (n = 15). Anthropometric (BMI, % body fat, and waist circumference), cardiovascular (SBP, DBP, HR, and DP), metabolic (total cholesterol, triglycerides, and glycemia), and strength variables were evaluated before and after 16 weeks of intervention. Descriptive statistics, Shapiro-Wilk and Kolmogorov-Smirnov normality test, equal variance test, T-Student test, non-parametric Mann-Witney test, ANOVA One Way, multivariate data analysis using data mining and machine learning techniques, Pearson and Spearman correlation tests, Classical Clustering (Agglomerative Hierarchical Method); Principal Component Analysis (PCA), Z score, Fruchterman-Reingold algorithm, Euclidian Similarity Index and, Cohen’s equations were applied. RESULTS: To observe effect size, morphofunctional variables for the GT1 group show a small effect for fat percentage, WHR, and ULS and a medium effect for LLS. For the GT2 group, there was a small effect for fat percentage, WHR, and ULS and a large effect for LLS. For the GT3 group, there was a small effect for the percentage of fat and HC, a medium effect for ULS, and a large effect for LLS. A small effect for glycemia in GT1, a medium effect for glycemia and triglycerides for GT2, a small effect for total cholesterol for GT3, and a large effect for glycemia in CG, with this effect being an increase in this analyte. For the cardiovascular variables, there is a small effect for SBP and HR in GT1, a small effect for DBP, HR, and DP, and a large effect for SBP in GT2, a small effect for SBP, DBP, and HR in GT3 and small effects for SBP and HR in CG. The correlation between BMI (P = 0.0007) and Body Fat% (P = 0.007) with ULS. The variables TG, LLS, and ULS were the ones that differed most in each type of stimulus chosen as an intervention in the present study. CONCLUSIONS: The results of this study indicate that GT2 caused more significant percentage reductions in body mass and BMI. However, GT3 caused greater fat percentage reduction. The GT2 caused the greatest decrease in waist circumference, while the GT3 caused the largest decline in hip circumference. The three groups induced an increase in strength (ULS and LLS). However, in ascending order, GT1 caused the smallest increase, GT2 an intermediate increase, and GT3 the greatest increase in strength, with considerable effects in GT2 and GT3. Finally, GT2 caused a greater percentage reduction in systolic blood pressure (large effect) and double product (small effect) than the other groups. The three methods proved to be efficient, but with particularities that may reflect the choice of one over another due to health conditions, objectives to be achieved, or characteristics of the patient at the time of their choice by the prescriber.

The rapid urban expansion requires an effective management of water resources in small basins, where data are often limited. This article addresses a combination of an empirical hydrological model and Steven Method, a flow extrapolation method that considers the morphological char-acteristics of the cross-section. It also proposes an approach for overcoming the challenges asso-ciated with the lack of information on small water basins in rural to urban transition. A rain-fall event occurred in Pau de Caixeta stream basin (33,7 km²), in the Federal District (DF), in De-cember 2022, was evaluated. The simulation was carried out on the decision support system, Analysis of Complex Basins (ABC), using the rainfall registred by the rain gauge instaled within the monitered area with 3 hours duration and 38 mm of total rainfall, in addiction to the mor-phological and hydraulics characteristics of the study section. The basin presented in this study, has a classification regarding land use: i) urban area: horizontal and vertical residential neigh-borhoods (in the study phase) and rapidly expanding urban infrastructure, ii) rural areas and iii) environmental preservation areas. The results were within the acceptable range of flow extrap-olation errors (16%), indicating the integration of the proposed approaches can guide the devel-opment of impact mitigation strategies and promote sustainable practices in sub-basins.

Cardiovascular diseases (CVDs) remain the leading cause of mortality. Traditional approaches to diagnosing and treating CVDs often involve interpretation, which can lead to errors. The emergence of Artificial Intelligence (AI) and Machine Learning aids the management of CVDs. AI examines amounts of data and identifies patterns offering solutions for disease detection, risk prediction, and patient care. The American Heart Association's 2023 Scientific Sessions (AHA23) showcased studies that highlight AIs potential to revolutionize cardiac healthcare. This article presents a review of these cutting-edge studies exploring how AI is applied in health, the outcomes of these applications, and their implications for clinical practice. The insights gained from this review will contribute to the discussion on AI's role in healthcare and its capacity to enhance outcomes.

After a time away from the classrooms and laboratories due to the global pandemic, the return to the teaching activities during the semester represented a challenge to both teachers and students. Our particular situation in a Microbial Physiology course was the necessity of imparting in a shorter time, laboratory practices that usually take longer. This article describes a two-week long laboratory exercise that covers several concepts in an interrelated way: conjugation as a gene transfer mechanism, regulation of microbial physiology, production of secondary metabolites, degradation of macromolecules and biofilm formation. Utilizing a Quorum Quenching (QQ) strategy, the Quorum Sensing (QS) system of Pseudomonas aeruginosa is first attenuated. Then, phenotypes regulated by QS are evidenced. QS is a regulatory mechanism of the microbial physiology that relays on signal molecules. QS is related in P. aeruginosa to several virulence factors, some of which are exploited in the laboratory practices presented in this work. QQ is phenomenon by which QS is interrupted or attenuated. We utilized a QQ approach based on the enzymatic degradation of the P. aeruginosa QS signals in order to put in evidence QS-regulated traits that are relevant for our Microbial Physiology course.

(1) E-governance is defined as the application of electronic means in the interaction between government and citizens and government and business, as well as in internal government operations to simplify and improve democratic, governmental, and business aspects of governance. Thus, e-governance is built from a paradigmatic dimension such as e-democracy (relationship between government and citizens) and an operational one such as e-government. The objective was to design and validate an instrument to measure e-governance based on three factors: a) e-administration, b) e-services, and c) e-democracy; (2) Methods: Based on the level of importance given to each factor (sample of 2042 Latin American citizens), as well as the relationships between them, an analysis of the importance of each factor is carried out; (3) Results: After the confirmatory analysis, the definitive instrument with which e-governance can be measured by other researchers and future research is obtained, considering the three selection factors, namely: e-administration, e-services and e-democracy; (4) Conclusions: This research contributes to political science through the design and validation of an instrument consisting of 39 items that can be used to measure e-governance according to the dimensions proposed by the United Nations Educational, Scientific and Cultural Organization.

Antibody-based passive immunotherapy has been used effectively in the therapy and prophylaxis of infectious diseases. Outbreaks of the emerging viral infections from arthropod-borne viruses (arboviruses) represent a global public health problem due to the rapid spread and urge actions and treatment of the infected individuals to combat them. Preparedness in advance developing antivirals and studies related to the epidemiology could protect us from damages and losses. Immunotherapy based on monoclonal antibodies (mAbs) has shown to be very specific to combat infectious diseases and several illnesses. Recent advances in the mAb discovery techniques allowed the development and approval of a wide number of therapeutic mAbs. This review focuses on the technological approaches available to select neutralizing mAbs for emerging arbovirus infections and outstanding strategies to obtain highly effective and potent mAbs. The characteristics of mAbs developed as prophylactic and therapeutic antiviral agents for the dengue, Zika, chikungunya and West Nile virus are presented, as well as the protective effect verified in animal model studies.

Sustainability has been playing a major role on the world stage. As with everything in the be-ginning, it was aligned to purely environmental contexts, but with the passing of the years and the new way of producing, marketing, and consuming, this concept has encompassed other as-pects which are the way of producing products and services. In this article, to obtain information on the implementation of sustainable innovation, a search equation was formulated in the SCOPUS database, contemplating the period from 2018 to 2023 as a time interval, with a total of 62 articles. Once the search of the published articles was carried out, 7 categories were formed to classify the research topic of each of the publications with Bibliometrix software, according to the main ob-jective and methodology used in each one. In this sense, 44% of the articles published are related to open innovation, and 26% of the articles are focused on sustainable business models. At the end of the analysis of the implementation of innovation according to the articles consulted, it was possible to establish that sustainable innovation began an important work, in the mitigation of the adverse impacts.

The preparation of new materials based on starch for the development of biodegradable packaging is increasing, however, the poor properties of this biopolymer for this application causes an area of opportunities for the improvement of water vapour permeability (WVP), mechanical properties, thermal properties, hydrophilicity, water absorption, among others. Hence, starch has been combined with other polymers such as polyvinyl alcohol, which has shown an improvement in the mechanical properties of starch, also, the use of clays suggests that the properties of response to water can be improved. Therefore, in this work, the preparation and characterization of starch-PVA-nanoclay films prepared by solvent casting is reported. The results obtained suggest that the sonication of nanoclay is necessary to reach a good dispersion, which promotes a strong interaction among starch-PVA-nanoclay. In addition, the properties of WVP and mechanical properties of films improved with incorporation of nanoclay, the concentration of 0.5% w/v of nanoclay showed to be the best concentration due to concentrations of 1.0 and 1.5% w/v were poorer than 0.5% w/v. Accordingly, the successful incorporation of nanoclays into the matrix starch-PVA suggests that this material is a good candidate for use as packaging.

Sustainable food systems imply the re-use of biowastes and water. In this paper we characterize 30 top soil improvers of anthropogenic, animal and green waste origin, and 11 irrigation waters from rivers, channels, and civil wastewater treatment plants (cWWTPs) for the presence of antimicrobials. Liquid chromatography coupled to hybrid High Resolution Mass Spectrometry (LC-HRMS/MS) was applied to determine 50 different drugs belonging to the classes of sulfonamides (11), tetracyclines (7), fluoroquinolones (10), macrolides (12), amphenicols (3), pleuromutilins (2), diaminopyrimidines (1), rifamycines (1) and lincosamides (1). Biowastes from cWWTPs and animal manure, slurry and litter revealed the highest loads for sulfonamides, tetracyclines, fluoroquinolones, and macrolides (80, 470, 885, and 4,487 ng/g wet weight, respectively) with nor- and cipro-floxacin as marker of anthropogenic sources. In composts and digestates antimicrobials were almost below the determination limits. Re-used waters for irrigation in open field lettuce production resulted contaminated in the range of 12-221 ng/L for sulfonamides, tetracyclines, and fluoroquinolones, against very few detects in channels and surface waters. Antimicrobials Hazard Index (HI) based on Predicted No Effect Concentration for Antimicrobial Resistance (PNEC_AMR) was largely >100 in contaminated top soil improvers from urban and animal sources. Accounting for worst-case inputs from top soil improvers and irrigation water and dilution factors of amended soil, fluoroquinolones only showed a HI around 1 in open fields for lettuce production. The origin of top soil improvers plays a pivotal role for safe and sustainable leafy vegetable productions, to abate the risk of AMR onset in food-borne diseases, as well the transfer of AMR elements to human gut flora.

Integrating systems acquired from third parties and legacies has become of great necessity in companies. This is mainly due to the need to exchange information between entities, such as banks, suppliers, customers, allies, etc. Therefore, it is essential to guarantee the integrity of the data and keep the integration up to date with the changes that can occur. Furthermore, reducing the transaction risk and avoiding losing information is necessary. Implementing this complex task involving technological and business challenges requires appropriate architecture implementation. This article presents an architecture named Dynamic Canonical Data Model through agnostic messages. The contribution involves treating the low link in integrating software units, also called the integration of loosely coupled software units. In particular, it focuses on internal data and external data integration. The proposal reduces their integration, maintenance time, and cost, maximizing their scalability and promoting reuse. To illustrate their components, a case study undertaken at the Mexican Logistics Company Paquetexpress is presented. The company has evaluated different enterprise application integration systems over 26 years to achieve a standardized integration. The article describes an architecture designed and implemented for this delivery company in a real-world case. The implementation’s source code was registered in the National Registry of Copyrights of Mexico.

Thimerosal (TmHg) has been used in thimerosal-containing vaccines (TCV) and various med-icines, although to our knowledge, there are no studies evaluating its effects on microglia. Microglia are a crucial glial component that have important functions in inflammatory diseases, particularly in the Central Nervous System (CNS). They are also considered a key player in neurotoxicity. Furthermore, microglia are responsible for the activation of immune responses and plays an essential role for the maintenance of brain homeostasis. Microglial response is important for the maintenance of CNS immune regulation. Moreover, microglia cells partici-pate in the removal of cellular debris and pathogens, playing an important role as neuroprotectants in several CNS diseases, including neurodegeneration. The objective of this study was to evaluate the effects of TmHg and its metabolite ethylmercury (EtHg) on microglial cells. Our novel findings demonstrated that microglia are more sensitive to TmHg than to EtHg with EC50 1.4 ± 0.4 μM and 2.3± 0.9 μM for 24h, respectively. The mercury compounds also induced the transcription of pro-inflammatory markers s such as IL-1β; iNOS, and to a lesser extent TNF-α expression. Likewise, the increase in Beclin-1 expression - a protein associated to autophagy - and the number of senescent cells indicate that microglia are activated by TmHg and EtHg. The effects of thimerosal may have different consequences depending on the health status and susceptibility of the populations.

Signaling modules that integrate the diverse extra- and intracellular inputs to control cell proliferation are essential during both development and adult stages to guarantee organism homeostasis. Mobs are small adaptor proteins that participate in several of these signalling pathways. Here we review recent advances unraveling Mob4 cellular functions, a highly conserved non-catalytic protein, that plays a diversity of roles in cell proliferation, sperm cell differentiation and simultaneously is involved in synapse formation and neural development. In addition, the gene is often overexpressed in a wide range of tumors and is linked to poor clinical outcomes. Nevertheless, Mob4 molecular functions remain poorly defined, although it integrates the core structure of STRIPAK, a kinase/phosphatase protein complex, that can act upstream of the Hippo pathway. In this review we focus on the recent findings of Mob4 functions, that have begun to clarify its critical role on cell proliferation and development of tissues and individuals.

This work is based on identifying the analysis techniques used to evaluate the antioxidant and anti-inflammatory effects using the zebrafish model. In this context, a literature review was performed with the Web of Science database. We used the terms zebra fish, antioxidant, anti-inflammatory, model, and Danio rerio. Fifty articles were reviewed, of which thirty-three were chosen to perform this review and were classified according to the source of plant extracts, compounds extracted from plants, chemical compounds, and other sources. This paper is an effort to provide a literature review on zebrafish models and elucidate their pros and cons to evaluate anti-inflamatory and antioxidant activity.

The highly adaptable parasite Trypanosoma cruzi undergoes complex developmental stages to exploit host organisms effectively. Each stage involves the expression of specific proteins and precise intracellular structural organization. These morphological changes depend on key structures that control intracellular components’ growth and redistribution. In trypanosomatids, the flagellar attachment zone (FAZ) connects the flagellum to the cell body and plays a pivotal role in cell expansion and structural rearrangement. While FAZ proteins are well-studied in other trypanosomatids, there is limited knowledge about specific components, organization, and function in T. cruzi. This study employed the CRISPR/Cas9 system to label endogenous genes and conduct deletions to characterize FAZ-specific proteins during epimastigote cell division and metacyclogenesis. In T. cruzi, these proteins exhibited distinct organization compared to counterparts in T. brucei. TcGP72 is anchored to the flagellar membrane, while TcFLA-1BP is anchored to the membrane lining the cell body. We identified unique features in the organization and function of the FAZ in T. cruzi compared to other trypanosomatids. Deleting these proteins had varying effects on intracellular structures, cytokinesis, and metacyclogenesis. This study reveals specific variations that directly impact the success of cell division and differentiation of this parasite.