Search Results (6,455)

The primary goal of this study is to introduce the initial phase of developing an impact-based forecasting system for extreme heatwaves, utilizing a novel multivariate index which, at this early stage, already employs a combination of a statistical approach and physical principles related to human body water loss. This system also incorporates a mitigation plan with hydration-focused measures. Since 1990, heatwaves have become increasingly frequent and intense across many regions worldwide, particularly in Europe and Asia. The main health impacts of heatwaves include organ strain and damage, exacerbation of cardiovascular and kidney diseases, and adverse reproductive effects. These consequences are most pronounced in individuals aged 65 and older. Many national meteorological services have established metrics to assess the frequency and severity of heatwaves within their borders. These metrics typically rely on specific threshold values or ranges of near-surface (2 m) air temperature, often derived from historical extreme temperature records. However, to our knowledge, only a few of these metrics consider the persistence of heatwave events, and even fewer account for relative humidity. In response, this study aims to develop a globally applicable normalized index that can be used across various temporal scales and regions. This index incorporates the potential health risks associated with relative humidity, accounts for the duration of extreme heatwave events, and is exponentially sensitive to exposure to extreme heat conditions above critical thresholds of temperature. This novel index could be more suitable/adapted to guide national meteorological services when emitting warnings during extreme heatwave events about the health risks on the population. The index was computed under two scenarios: first, in forecasting heatwave episodes over a specific temporal horizon using the WRF model; second, in evaluating the relationship between the index, mortality data, and maximum temperature anomalies during the 2003 summer heatwave in Spain. Moreover, the study assessed the annual trend of increasing extreme heatwaves in Spain using ERA5 data on a climatic scale. The results show that this index has considerable potential as a decision-support and health risk assessment tool. It demonstrates greater sensitivity to extreme risk episodes compared to linear evaluations of extreme temperatures. Furthermore, its formulation aligns with the physical mechanisms of water loss in the human body, while also factoring in the effects of relative humidity. Full article

Background: Dignity is a key element in end-of-life care. Patients with advanced illness attend the emergency department to seek symptom relief but may find their dignity under threat in these services. Objective: The purpose of the study was to explore the threats to dignity perceived by people with advanced illnesses who are treated in emergency departments. Methods: A descriptive qualitative study was designed for which 18 patients with advanced illnesses were interviewed after being seen in an emergency department. The study complied with the principles of the Declaration of Helsinki for medical research involving human subjects. Results: Two themes were developed from the data analysis that shed light on how patients with advanced illnesses perceive threats to their dignity when seen in emergency departments: (1) “when care focused on diagnosis and treatment limits the dignity of the patient with advanced illness” and (2) “the social dimension of dignity in people with advanced illness in emergency departments”. Conclusions: We conclude that the structural, organizational, and care characteristics of emergency departments may pose a threat to the dignity of people with advanced illnesses who attend the emergency department. Family members, professionals, and other patients can both guarantee and threaten the dignity of people with advanced illnesses when they are treated in emergency departments. Full article

Background/Objectives: The colonization history of house mice reflects the maritime history of humans that passively transported them worldwide. We investigated western house mouse colonization in the Atlantic region through studies of mitochondrial D-loop DNA sequences from modern specimens. Methods: We assembled a dataset of 758 haplotypes derived from 2765 mice from 47 countries/oceanic archipelagos (a combination of new and published data). Our maximum likelihood phylogeny recovered five previously identified clades, and we used the haplotype affinities within the phylogeny to infer house mouse colonization history, employing statistical tests and indices. From human history, we predefined four European source areas for mice in the Atlantic region (Northern Europe excluding Scandinavia, Southern Europe, Scandinavia, and Macaronesia) and we investigated the colonization from these source areas to different geographic areas in the Atlantic region. Results: Our inferences suggest mouse colonization of Scandinavia itself from Northern Europe, and Macaronesia from both Southern Europe and Scandinavia/Germany (the latter likely representing the transport of mice by Vikings). Mice on North Atlantic islands apparently derive primarily from Scandinavia, while for South Atlantic islands, North America, and Sub-Saharan Africa, the clearest source is Northern Europe, although mice on South Atlantic islands also had genetic inputs from Macaronesia and Southern Europe (for Tristan da Cunha). Macaronesia was a stopover for Atlantic voyages, creating an opportunity for mouse infestation. Mice in Latin America also apparently had multiple colonization sources, with a strong Southern European signal but also input from Northern Europe and/or Macaronesia. Conclusions: D-loop sequences help discern the broad-scale colonization history of house mice and new perspectives on human history. Full article

Background: Malnutrition is highly prevalent in patients with head and neck cancer, with relevant consequences in the treatment results. Methods: Multicenter observational study including 514 patients diagnosed with HNC. The morphofunctional assessment was carried out during the first 2 weeks of radiotherapy treatment. A correlation analysis between nutritional variables and groups of malnutrition, a multivariate logistic regression analysis, and a random forest analysis to select the most relevant variables to predict malnutrition were performed. Results: In total, 51.6% were undernourished (26.3% moderately and 25.3% severely). There was a negative correlation between morphofunctional variables and a positive correlation between hsCRP and well vs. moderate and well vs. severe malnutrition groups. The increase in different bioelectrical and ultrasound parameters was associated with a lower risk of moderate and severe malnutrition when groups with different degrees of malnutrition were compared. To predict the importance of morphofunctional variables on the risk of undernutrition, a nomogram, a random forest, and decision tree models were conducted. For the well vs. moderate, for the well vs. severe, and for the moderate vs. severe malnutrition groups, FFMI (cut-off < 20 kg/m²), BCMI (cut-off < 7.6 kg/m²), and RF-Y-axis (cut-off < 0.94 cm), respectively, were the most crucial variables, showing a greater probability of mortality in the two last comparisons. Conclusions: Malnutrition is very prevalent in HNC patients. Morphofunctional assessment with simple tools such as electrical impedance and muscle ultrasound allows an early nutritional diagnosis with an impact on survival. Therefore, these techniques should be incorporated into the daily clinical attention of patients with HNC. Full article

Bioconvection phenomena play a pivotal role in diverse applications, including the synthesis of biological polymers and advancements in renewable energy technologies. This study develops a comprehensive mathematical model to examine the effects of key parameters, such as the Lewis number (Lb), Peclet number (Pe), volume fraction ($\phi$), and angle of inclination $\left(\alpha \right)$, on the flow and heat transfer characteristics of a nanofluid over an inclined cylinder embedded in a non-Darcy porous medium. The investigated nanofluid comprises nano-encapsulated phase-change materials (NEPCMs) dispersed in water, offering enhanced thermal performance. The governing non-linear partial differential equations are transformed into dimensionless ordinary differential equations using similarity transformations and solved numerically via the Network Simulation Method (NSM) and an implicit Runge–Kutta method implemented through the bvp4c routine in MATLAB R2021a. Validation against the existing literature confirms the accuracy and reliability of the numerical approach, with strong convergence observed. Quantitative analysis reveals that an increase in the Peclet number reduces the shear stress at the cylinder wall by up to 18% while simultaneously enhancing heat transfer by approximately 12%. Similarly, the angle of inclination ($\alpha$) significantly boosts heat transmission rates. Additionally, higher Peclet and Lewis numbers, along with greater nanoparticle volume fractions, amplify the density gradient of microorganisms, intensifying the bioconvection process by nearly 15%. These findings underscore the critical interplay between bioconvection and transport phenomena, providing a framework for optimizing bioconvection-driven heat and mass transfer systems. The insights from this investigation hold substantial implications for industrial processes and renewable energy technologies, paving the way for improved efficiency in applications such as thermal energy storage and advanced cooling systems. Full article

Nowadays, there is a growing interest in membrane modification processes to improve their characteristics and the effectiveness of their treatments and reduce the possible fouling. In this sense, in this work, a modification of an ultrafiltration membrane with three different materials has been carried out: reduced graphene oxide (rGO), chitosan and MgCl₂. For both the native and the modified membranes, a study has been carried out to remove the emerging contaminant sulfamethoxazole (SMX). SEM and SEM-EDX analyses have been performed to confirm membrane surface modifications. In the characterisation of the membranes, it is noteworthy that the values of the permeability coefficient, A_w, have been lower in the modified membranes, which is unexpected. Regarding the pollutant removal tests, the influence of pressure and initial concentration on permeate flux and rejections has been studied. Native membrane shows the highest permeate flux values. Comparing the modified membranes, the highest rejection values are obtained with the rGO-modified membrane, which can be explained by its greater hydrophilic character. Finally, a fouling study was carried out, verifying that in almost all cases, fouling occurs after the passage of the pollutant due to the blockage of the membrane pores. Full article

Different sulfates (Ca-, Mg, and Fe- sulfates) have been extensively detected on the Martian surface. As one of the Martian sulfates, the presence of ferrous sulfates will provide valuable clues about the redox environment, hydrological processes, and climatic history of ancient Mars. In this study, three hydrated ferrous sulfates were prepared in the laboratory by heating dehydration reactions. These samples were analyzed using X-ray Diffraction (XRD) to confirm their phase and homogeneity. Subsequently, Raman, mid-infrared (MIR) spectra, visible near-infrared (VNIR) spectra, and laser-induced breakdown spectroscopy (LIBS) were measured and analyzed. The results demonstrate that the spectra of three hydrated ferrous sulfates exhibit distinctive features (e.g., the v₁ and v₃ features of ${\mathrm{S}\mathrm{O}}_4^{2-}$ tetrahedra in their Raman and MIR spectra) that can offer new insights for identifying different ferrous sulfates on Mars and aid in the interpretation of in-situ data collected by instruments such as the Scanning Habitable Environments with Raman & Luminescence for Organics & Chemicals (SHERLOC), SuperCam, and ChemCam, etc. Full article

Tourism still represents a means of generating revenues in the coastal areas in the Mexican Caribbean, despite the growing concern about the social and environmental impacts. The Nichupte Lagoon System (NLS), the most representative lagoon of Quintana Roo State for being in the middle of Cancun’s hotel development, has experienced a continuous drop-off in its water quality due to several factors, including dredging and wastewater discharges from different anthropogenic activities, which modify the flux of nutrients, increase the number of pathogenic microorganisms, and promote physicochemical changes in this ecosystem. Three sampling campaigns (2018, 2019, and 2020) were carried out in the NLS in August, which is the month of greatest tourist occupancy. To evidence the presence of anthropogenic wastewater in the NLS, the caffeine tracer was used, and to determine the water quality, 43 sampling stations were monitored for “in situ” physicochemical parameters (salinity and dissolved oxygen), and water samples were collected for the quantification of nutrients (NO₂⁻ + NO₃⁻, NH₄⁺, SRP and SRSi) and chlorophyll-a (Chl-a). For data analysis, the lagoon was subdivided into five zones (ZI, ZII, ZIII, ZIV, and ZV). Caffeine spatial and time variation evidence (1) the presence of anthropogenic wastewater in all areas of the NLS probably resulting from the tourist activity, and (2) wastewater presence is directly influenced by the coupling of the hydrological changes driven by anomalous rain events and the number of tourists. This same tendency was observed for nutrients that increased from 2018 to 2019 and the trophic state changed from oligotrophic to hypertrophic in all areas, as a result of previous anomalous precipitations in 2018, followed by normal precipitations in 2019. From 2019 to 2020, the nutrients decreased due to the drop in tourism due to COVID-19, promoting fewer nutrients in the lagoon, but, also coupled with an anomalous precipitation event (Cristobal storm), resulted in a dilution phenomenon and an oligotrophic state. The cluster analysis indicated that the least similar zones in the lagoon were the ZI and ZV due to their geomorphology that restricts the connection with the rest of the system. Principal component analysis revealed that wastewater presence evidenced by the caffeine tracer had a positive association with dissolved oxygen and chlorophyll-a, indicating that the arrival of nutrients from wastewater amongst other sources promotes algal growth, but this could develop into an eutrophic or hypertrophic state under normal precipitation conditions as seen in 2019. This study shows the relevance of monitoring in time of vulnerable karstic systems that could be affected by anthropogenic contamination from wastewater inputs, stressing the urgent need for efficient wastewater treatment in the area. The tourist industry in coastal karstic lagoons such as the NLS must have a Wastewater Treatment Program as a compensation measure for the anthropic pressure that is negatively changing the water quality of this highly relevant socio-environmental system. Full article

This study introduces a novel methodology that integrates the Black–Litterman model with Long Short-Term Memory Neural Networks (BL–LSTM). We use predictions from the LSTM as views in the Black–Litterman model. The resulting portfolio performs better than the traditional mean-variance (MV) and exchange-traded funds (ETFs) used as benchmarks. The proposal empowers investors to make more insightful decisions, drawing from a synthesis of historical data and advanced predictive techniques. This methodology is applied to a water market. Investing in the water market allows investors to actively support sustainable water solutions while potentially benefiting from the sector’s growth, contributing to achieving SDG 6. In addition, our modeling allows for companies’ environmental, social, and governance (ESG) scores to be considered in the portfolio construction process. In this case, investors’ decisions take into account companies’ socially responsible behavior in a broad sense, including aspects related to decent work, respect for indigenous communities and diversity, and the absence of corruption, among others. Therefore, this proposal provides investors with a tool for promoting sustainable investment practices. Full article

Background/Objectives: Lebanon, one of the oldest centers of grapevine (Vitis vinifera L.) cultivation, is home to a rich diversity of local grape varieties. This biodiversity is linked to the country’s unique topography and millennia of cultural history. However, the wine industry primarily utilizes international varieties, putting many local varieties at risk of extinction. Methods: In this study, we analyzed 202 samples from old vineyards, home gardens, and private collections using 21 microsatellite markers to assess their identity and genetic diversity. Results: A total of 67 different genotypes were identified, with 34 not matching any existing profiles in the consulted databases, based on comparisons with the European Vitis Database, the Vitis International Variety Catalogue (VIVC), and the databases established in two previous studies conducted in Armenia and Lebanon. Cluster analyses revealed Lebanon’s rich diversity of local grape varieties, highlighting cases of synonymy, homonymy, and misnaming. All loci were polymorphic, with 228 alleles and an average of 11.4 alleles being detected. The highest number of alleles was observed at the VVIV67 locus (19 alleles), while the lowest was found at the VVIQ52 and VVIN73 loci (5 alleles). The observed heterozygosity was 0.732, slightly below the expected value of 0.757, with gene diversity varying among the markers. Conclusions: Of the 67 genetic profiles identified, 34 are absent from national and international databases, underscoring Lebanon as a hotspot for grapevine genetic diversity. This unique genetic variation, which includes several synonyms due to geographic isolation, could provide valuable opportunities for producing distinctive wines and emphasizes the need for further research and documentation. Full article

Background/Objectives: Carbapenem-resistant Enterobacterales (CRE) are a global health threat due to their high morbidity and mortality rates and limited treatment options. This study examines the plasmid-mediated transmission of virulence and antibiotic resistance determinants in carbapenem-resistant Klebsiella pneumoniae (Kpn) and Escherichia coli (E. coli) isolated from Russian hospitals. Methods: We performed short- and long-read whole-genome sequencing of 53 clinical isolates (48 Kpn and 5 E. coli) attributed to 15 genetic lineages and collected from 21 hospitals across nine Russian cities between 2016 and 2022. Results: The plasmid analysis identified 18 clusters that showed high concordance with replicon typing, with all clusters having a major replicon type. The majority of plasmids in the IncHI1B(pNDM-MAR)/IncFIB(pNDM-Mar)-like cluster (79.16%) carried both antibiotic resistance genes (e.g., bla_NDM-1 and bla_OXA-48) and virulence factors (VFs) such as siderophore genes. We hypothesized that hybrid plasmids could play a critical role in the dissemination of antibiotic resistance genes and VFs. Comparative analyses with global plasmid databases revealed high-risk lineages of hybrid plasmids that are predominantly spread throughout Russia at present. Conclusions: Our findings underscore the importance of monitoring plasmid backbones for clinical management, surveillance, and infection control activities. Full article

(1) Background: Impeded resolution of inflammation contributes substantially to the pathogenesis of Alzheimer’s disease (AD); consequently, resolving inflammation is pivotal to the amelioration of AD pathology. This can potentially be achieved by the treatment with specialized pro-resolving lipid mediators (SPMs), which should resolve neuroinflammation in brains. (2) Methods: Here, we report the histological effects of long-term treatment with an SPM, maresin-like 1 (MarL1), on AD pathogenesis in a transgenic 5xFAD mouse model. (3) Results: MarL1 treatment reduced Aβ overload, curbed the loss of neurons in brains especially cholinergic neurons associated with cleaved-caspase-3-associated apoptotic degeneration, reduced microgliosis and the pro-inflammatory M1 polarization of microglia, curbed the AD-associated decline in anti-inflammatory Iba1⁺Arg-1⁺-M2 microglia, inhibited phenotypic switching to pro-inflammatory N1 neutrophils, promoted the blood–brain barrier-associated tight-junction protein claudin-5 and decreased neutrophil leakage in 5xFAD brains, and induced the switch of neutrophils toward the inflammation-resolving N2 phenotype. (4) Conclusions: Long-term administration of MarL1 mitigates AD-related neuropathogenesis in brains by curbing neuroinflammation and neurodegeneration, based on the histological results. These findings provide preclinical leads and mechanistic insights for the development of MarL1 into an effective modality to ameliorate AD pathogenesis. Full article

This work connects the calorimetric responses of different rubber–resin blends with varying resin contents with their alpha relaxation dynamics. We used differential scanning calorimetry and broadband dielectric spectroscopy to characterize the calorimetric and dielectric responses of styrene–butadiene, polybutadiene, and polyisoprene with different resin contents. To model the results, we used the Gordon–Taylor equation combined with an extension of the Adam–Gibbs approach. Thus, we propose a simple and effective model that allows us to estimate the blend dynamics from the temperature dependence of the relaxation times of the pure components and the calorimetric measurement of the glass transition temperature of only one blend composition. By estimating an effective interaction parameter from calorimetry, we achieved accurate alpha relaxation dynamics predictions for different resin concentrations. Our highly predictive approach provides a realistic description of the expected dynamics. This study offers valuable insights into the dynamic properties of polymer compounds, paving the way for the fast and effective development of advanced and more sustainable materials. Full article

Autonomous technologies have revolutionized transportation, military operations, and space exploration, necessitating precise localization in environments where traditional GPS-based systems are unreliable or unavailable. While widespread for outdoor localization, GPS systems face limitations in obstructed environments such as dense urban areas, forests, and indoor spaces. Moreover, GPS reliance introduces vulnerabilities to signal disruptions, which can lead to significant operational failures. Hence, developing alternative localization techniques that do not depend on external signals is essential, showing a critical need for robust, GPS-independent localization solutions adaptable to different applications, ranging from Earth-based autonomous vehicles to robotic missions on Mars. This paper addresses these challenges using Visual odometry (VO) to estimate a camera’s pose by analyzing captured image sequences in GPS-denied areas tailored for autonomous vehicles (AVs), where safety and real-time decision-making are paramount. Extensive research has been dedicated to pose estimation using LiDAR or stereo cameras, which, despite their accuracy, are constrained by weight, cost, and complexity. In contrast, monocular vision is practical and cost-effective, making it a popular choice for drones, cars, and autonomous vehicles. However, robust and reliable monocular pose estimation models remain underexplored. This research aims to fill this gap by developing a novel adaptive framework for outdoor pose estimation and safe navigation using enhanced visual odometry systems with monocular cameras, especially for applications where deploying additional sensors is not feasible due to cost or physical constraints. This framework is designed to be adaptable across different vehicles and platforms, ensuring accurate and reliable pose estimation. We integrate advanced control theory to provide safety guarantees for motion control, ensuring that the AV can react safely to the imminent hazards and unknown trajectories of nearby traffic agents. The focus is on creating an AI-driven model(s) that meets the performance standards of multi-sensor systems while leveraging the inherent advantages of monocular vision. This research uses state-of-the-art machine learning techniques to advance visual odometry’s technical capabilities and ensure its adaptability across different platforms, cameras, and environments. By merging cutting-edge visual odometry techniques with robust control theory, our approach enhances both the safety and performance of AVs in complex traffic situations, directly addressing the challenge of safe and adaptive navigation. Experimental results on the KITTI odometry dataset demonstrate a significant improvement in pose estimation accuracy, offering a cost-effective and robust solution for real-world applications. Full article