Staphylococcus aureus is a microorganism frequently associated with implant-related infections, owing to its ability to produce biofilms. These infections are difficult to treat because antimicrobials must cross the biofilm to effectively inhibit bacterial growth. Although some antibiotics can penetrate the biofilm and reduce the bacterial load, it is important to understand that the results of routine sensitivity tests are not always valid for interpreting the activity of different drugs. In this review, a broad discussion on the genes involved in biofilm formation, quorum sensing, and antimicrobial activity in monotherapy and combination therapy is presented that should benefit researchers engaged in optimizing the treatment of infections associated with S. aureus biofilms.

Introduction: Infections associated with medical implants pose significant challenges to healthcare, demanding the development of new effective antimicrobial strategies. This study focuses on the creation of a novel biological membrane with enhanced antimicrobial properties by with the incorporation of either vancomycin or silver nanoparticles. Methods: Pericardium samples were impregnated with either silver nanoparticles (AgNPs) or vancomycin (VAN). The antimicrobial activity of the membranes was assessed through qualitative microbiological analysis, biofilm production and animal in vivo model of infection. Histological analysis was performed on the pericardium samples, and quantitative microbiological analysis was conducted on the tissue from the animal model and biocompatibility evaluation. Statistical analyses were carried out using appropriate tests. Results: Microbiological tests confirmed both VAN and AgNP showed inhibition halos against bacteria. Histological analysis indicated minimal inflammatory response and bacterial presence in VAN and AgNP groups compared to the control group revealed reduced bacterial presence. Biomechanical testing showed that polypropylene had the lowest tensile strength, while AgNP-impregnated pericardium exhibited lower resistance than VAN-impregnated and control pericardium. Conclusion: Lyophilized and decellularized bovine pericardium as a substrate, combined with the incorporation of vancomycin or silver nanoparticles, provides a versatile platform for combating infections associated with medical implants.

The global need for rapid diagnostic methods for pathogen identification and antimicrobial susceptibility testing (AST) is underscored by the increasing bacterial resistance and limited therapeutic options, especially critical in sepsis management. The evolution from traditional phenotype-based methods to rapid molecular and mass spectrometry techniques has significantly decreased result turnaround times, improving patient outcomes. Despite advancements, the complex decision-making in antimicrobial therapy often exceeds the capacity of many clinicians, highlighting the importance of antimicrobial stewardship programs (ASP). These programs, integrating mHealth and eHealth, leverage technology to enhance healthcare services and patient outcomes, particularly in remote or resource-limited settings. However, the application of such technologies in antimicrobial management remains underexplored in hospitals. The development of platforms combining antimicrobial prescription data with pharmacotherapeutic algorithms and laboratory integration can significantly reduce costs and improve hospitalization times and mortality rates. Yet, the effectiveness of empirical antibiotic therapy, influenced by rapid diagnostic tests, becomes cost-effective when error rates exceed 20%, emphasizing the need for technological integration in ASPs to combat the rising challenge of antimicrobial resistance (AMR) and healthcare sustainability.

Introduction: Silver nanoparticles have been extensively investigated in dental and orthopedic materials. However, the impregnation of bone graft with silver nanoparticles has been poorly investigated. Objectives: The aim of this study was to evaluate the in vitro antibiofilm activity of a bovine bone impregnated with silver nanoparticles. Methods: Bone scaffolds from cancellous bovine femur were used for the tests and impregnated with silver nanoparticles (50nm) by physical adsorption. Silver nitrate minimal inhibitory and bactericidal concentration were performed Staphylococcus aureus, Pseudomonas aeruginosa, Candida albicans, Enterococcus faecalis, Acinetobacter baumannii, and Escherichia coli. Disc diffusion tests for silver nanoparticles susceptibility and quantification of biofilm production on plate and bone with sessile cell count were also performed. Results: All pathogens were susceptible to silver with low minimal inhibitory concentration (0.25 – 4 mg/L). The scaffold impregnated with silver nanoparticles presented a significant reduction in the biofilm cells for all microorganisms with a reduction of more than 3 logs in colony forming units count. Conclusion: Bone scaffolds impregnated with silver nanoparticles can significantly reduce biofilm, and it can be a strategical material to be used as an implant for different approaches.

This study introduces a novel approach by proposing a dressing impregnated with vancomycin and chlorhexidine for postoperative care, specifically targeting patients undergoing orthopedic surgeries. The research design involved a pilot study with a randomized control group, aiming to evaluate the vancomycin with chlorhexidine dressing's efficacy and safety. A total of 12 patients used impregnated dressing and 9 used the same dressing without impregnation. Despite limitations, including the small sample size and single-center study location, our findings demonstrate the safety of the impregnated dressing in trauma surgeries, indicating potential applicability in broader surgical contexts. In conclusion, this study contributes to the discourse on preventive strategies for SSIs, presenting a pioneering approach with the vancomycin and chlorhexidine-impregnated dressing. Future research endeavors, incorporating larger-scale studies and addressing study limitations, are crucial for advancing the understanding and implementation of effective postoperative care strategies.

Background: Antibiofilm activity of silver nanoparticles has been extensively investigated in common bacteria. Metallo-β-lactamase producing Gram-negative bacteria are hard-to-treat microorganisms with few therapeutic options, and silver nanoparticles were not evaluated on the biofilm of these bacteria. Objectives: The aim of this study was to evaluate the antibiofilm activity of a bone scaffold impregnated with silver nanoparticles in NDM-producing Gram-negative bacilli. Methods: Bone scaffolds from bovine femur were used for the tests and impregnated with silver nanoparticles (50 nm) by physical adsorption. Silver nitrate minimal inhibitory and bactericidal concentrations (MIC and MBC) were performed on NDM-producing Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa. Disc diffusion tests for silver nanoparticles susceptibility and quantification of biofilm production on plate and bone with sessile cell count were performed. Results: The MIC results demonstrated that silver nitrate had an antimicrobial effect on all microorganisms, inactivating the growth of isolates from a concentration of 8 µg/mL. MBC results showed that E. coli 16.211 was the only isolate to present MIC different from MBC, with a value of 16 µg/mL. Conclusion: Bone scaffolds impregnated with silver nanoparticles can significantly reduce biofilm, and it can be a strategic material to be used as an implant for different approaches.

Background: Staphylococcus aureus is a major contributor to biofilm-associated infections, which complicate the treatment of chronic conditions like osteomyelitis and prosthetic infections. Bio-films, characterized by bacterial communities encased in a protective matrix, hinder the effec-tiveness of antimicrobial agents and contribute to persistent infections. Objective: This study aimed to evaluate the activity of delafloxacin, a novel fluoroquinolone, against biofilm-forming S. aureus strains, including methicillin-resistant (MRSA) and methicillin-sensitive (MSSA) isolates. Methods: Delafloxacin’s efficacy was compared with traditional antibiotics (vancomycin, line-zolid, doxycycline, sulfamethoxazole/trimethoprim, and rifampicin) using minimal inhibitory concentration (MIC) assays, including testing at different pH levels to simulate acidic biofilm conditions. Synergistic interactions between delafloxacin and other antimicrobials were assessed using the checkerboard method. Biofilm eradication was tested through minimal biofilm eradi-cation concentration (MBEC) assays, biomass quantification, and cell viability (MTT assay). Re-sults: The results showed that all isolates were susceptible to vancomycin and linezolid, with varying resistance to other antibiotics. Delafloxacin demonstrated potent activity, with MIC re-ductions observed in acidic conditions. Synergistic effects were minimal, with only a few isolates showing synergy with vancomycin and doxycycline. Biofilm eradication tests revealed limited success in eradicating biofilms at breakpoint concentrations. However, rifampicin in combination with delafloxacin significantly reduced biofilm biomass in some isolates. No significant changes in cell viability were noted across treatments. Conclusion: This study highlights delafloxacin's potential against biofilm-associated infections, particularly in acidic environments. Despite the challenges in biofilm eradication, delafloxacin combined with rifampicin shows promise for im-proved treatment strategies.

Differences in fish assemblages’ structure and their relation with environmental variables (due to the variations in sampled seasons, habitats, and zones), were analyzed in two adjacent estuaries on the north Pacific coast of Mexico. Environmental variables and fish catches were registered monthly between August 2018 and October 2020. Multivariate analyses were conducted to define habitats and zones based on their environmental characteristics, and the effect of this variability on fish assemblages’ composition, biomass, and diversity (α and β) was evaluated. A total of 12,008 fish individuals of 143 species were collected using different fishing nets. Multivariate analyses indicated that fish assemblages’ structure was different between zones due to the presence, height, and coverage of distinct mangrove species. Additionally, factors such as depth and salinity showed effects on fish assemblages’ diversity (α and β-nestedness), which presented higher values in the ocean and remained similar in the rest of the analyzed zones and habitats. These results and the differences in species replacement (β-turnover) indicate the singularity of fish assemblages at estuaries (even in areas very close to the ocean), and the necessity to establish local management strategies for these ecosystems.

Colombia has one of the largest numbers of internally displaced populations in the world and recently entered a period of post-conflict. These socio-political processes and trends have increased the migration of people towards cities and accordingly are affecting the distribution and occurrence of tropical diseases in its urban and peri-urban areas. Studies have suggested that many human phenomena such as urbanization scale according to the size of human populations regardless of cultural context. But other studies show that health epidemics such as malarial and human immunodeficiency virus infections, follow a scale-free distribution in terms of population size and density. We explore these relationships and dynamics in a tropical context using statistical analyses and available geospatial data to identify the scale dynamics between urbanization processes and disease transmission in Colombia. We found that rural populations and certain disease dynamics were described by power-laws that are frequently mentioned in urbanization studies. However, we found that malaria presented higher intensity of infection in human settlements of less than 50,000 individuals, particularly for ethnic indigenous populations. Results indicate that epidemics and urbanization dynamics do indeed follow scales in Colombia; findings that differ from previous epidemiological studies such as those for malarial infection. Additionally, we identified trends showing that malarial infections become endemic in peri-urban areas. Targeting such peri-urban locations and certain demographic groups are key for managing public health issues in the urbanizing tropics.

The quantification of sea surface temperature (SST) through space platforms has revolutionized how we obtain information at a global level. However, the main disadvantage of obtaining SST with satellite images consists of its inherent coarse spatial resolution. One solution could be the use of downscaling algorithms to create sequences of matrices at a higher resolution. We used the same SST data source from the MODIS-Aqua sensor at three spatial resolutions of 9 km, 4.5 km, and 1 km in the Gulf of California. Based on an open-source algorithm, the original SST images were downscaled to 4.5 km, 1 km, 500 m, 250 m, and 125 m per pixel scales. Results indicate a strong linear relationship between the original SST-MODIS data and the modeled data for all spatial resolutions. This study demonstrates the feasibility of an open-source downscaling algorithm to enhance the spatial resolution of SST images in a marginal sea.

Enzymes serve as biocatalysts for innumerable important reactions; however, their application has limitations, which could be overcome by using appropriate immobilization strategies. Here, a new support for immobilizing enzymes is proposed. This hybrid organic-inorganic support is composed of chitosan—a natural, nontoxic, biodegradable, and edible biopolymer—and sodium polyphosphate, which was the inorganic component. Lipase B from Candida antarctica (CALB) was immobilized in microspheres by encapsulation using these polymers. The characterization of the composites (by infrared spectroscopy, thermogravimetric analysis, and confocal Raman microscopy) confirmed the hybrid nature of the support, whose external part consisted of polyphosphate and core was composed of chitosan. The immobilized enzyme had the following advantages: possibility of enzyme reuse, easy biocatalyst recovery, increased resistance to variations in temperature (activity declined from 60°C and the enzyme was inactivated at 80°C), and increased catalytic activity in the transesterification reactions. The encapsulated enzymes were utilized as biocatalysts for transesterification reactions to produce the compound responsible for the aroma of jasmine.

The Ni-laterite deposit at the San Felipe plateau, located 30 km northwest of Camagüey, in cen-tral Cuba, is the best example of a clay-type deposit in the Caribbean region. San Felipe resulted from weathering of mantle peridotites of the Cretaceous Camagüey ophiolites. In this study, a geochemical and mineralogical characterization of two profiles (83 and 84) from the San Felipe deposit has been performed by XRF, ICP-MS, quantitative XRPD, oriented aggregate mount XRD, SEM, FE-SEM and EMPA. Core 83 with a length of 23 m, and drilled in the central part of the plateau, presents a notable concentration of cryptocrystalline quartz fragments and a rather poor content in NiO, averaging 0.87% wt. Core 84, which is 12 m long, and drilled at the border of the plateau, lacks silica fragments and presents a higher NiO content, averaging 1.79 %wt. The smectite structural formulae reveal that they evolve from trioctahedral to dioctahedral towards the top of the laterite profiles. Quantitative XRD analyses indicate that smectite is a dominant Ni bearing phase, accompanied by serpentine and minor chlorite. Serpentine, as smectite, is en-riched in the less soluble elements Fe3+, Al and Ni towards the top of the profiles. Core 83 seems to have been affected by collapses, replenishments, whereas core 84 may have remained undis-turbed.

The viewpoint and reaction of a country towards climate change are shaped by its political, cultural, and scientific backgrounds, in addition to the distinct characteristics of its evolving climate and the anticipated and actual consequences of the phenomenon in the times ahead. A region's climate has a significant impact on how water is managed and used, mostly in the primary sector (agroforestry systems, livestock, and agriculture), and both the distribution of ecosystem types and the amount and spreading of species on Earth. As a result, the environment and agricultural practices are affected by climate, so evaluating both distribution and evolution is extremely pertinent. Towards this aim, the climate distribution and evolution in the S. Francisco River basin (SFRB) is assessed in three periods (1970−2000, 1981−2022) in the past and 20412060 in the future from an ensemble of GCMs under two SSPs (Shared Socioeconomic Pathways), SSP2-4.5 and SSP5-8.5. The Köppen-Geiger (KG) climate classification system is analyzed, and climate change impacts are inferred for this watershed located in central-eastern Brazil, covering an area equivalent to 8% of the country. Results predict the disappearance of the hot-summer (Csa) and warm-summer (Csb) Mediterranean climates, and a reduction/increase in the tropical savanna with dry winter (Aw)/dry summer (As). A striking increase in the semi-arid hot (BSh-steppe) climate is predicted with a higher percentage (10%) under SSP5-8.5. The Source and the mouth of SFRB are projected to endure the major impacts of climate change that is followed by a predicted increase/decrease in temperature/precipitation. Future freshwater resource availability, and quality for human use will all be impacted. Consequences on ecosystems, agricultural and for the socioeconomic sectors within the SFRB might deepen the current contrasts between regions, urban and rural areas, and even between population groups, thus translating to a larger extent, the inequality that still characterizes Brazilian society. Maps depicting land use and cover changes in the São Francisco River basin from 1985 to 2022 highlight tendencies such as urbanization, agricultural expansion, deforestation, and changes in shrubland and water bodies. Urban areas fluctuated slightly, while cropland significantly increased from 33.57% to 45.45%, and forest areas decreased from 3.88% to 3.50%. Socioeconomic data reveals disparities among municipalities: 74.46% with medium Human Development Index (HDI), 0.59% with very high HDI, and 9.11% with low HDI. Most municipalities have a Gross Domestic Product (GDP) per capita below US$ 6,000. Population distribution maps show a predominance of small to medium-sized urban and rural communities, reflecting the basin's dispersed demographic and economic profile. To build routes towards a shared sustainable goal—namely, to adapt and reduce the expected climate change impacts in SFRB—it is imperative that integrated measures be conducted with the cooperation of stakeholders, local population, and decision-makers.

This study aims to provide a comprehensive analysis of the More Light for Amazon (MLA) program, examining the roles played by each stakeholder involved in the concession process and identifying the limitations faced for program success. The research employs a content analysis methodology, analyzing a variety of documents, including the Program Operational Manual, Commitment Terms, news articles, and concessionaires' notes. The findings reveal the crucial role of the government as an inducer of actions, establishing objectives and guiding norms for the private sector. Conversely, concessionaires assume the role of program implementers but encounter specific limitations in remote locations, challenging the provision and maintenance of the electrical system in beneficiary communities. The implementation of microgrid systems through concessions enhances coordination and integration between generation and distribution services, allowing for increased government control and ensuring transparency, efficiency, and program effectiveness. These identified elements represent significant challenges for the implementation of public policies in remote regions of the Amazon. Overcoming these challenges take coordinated and strategic actions involving both the government and concessionaires to ensure the complete fulfillment of energy needs in MLA program beneficiary communities.

Abstract: Endoglin (ENG) is a causative gene of type 1 hereditary hemorrhagic telangiectasia (HHT1). HHT1 patients have arteriovenous malformations (AVMs) in multiple organs, including the brain. In mice, Eng deletion induced by R26RCreER or SM22aCre leads to AVM development in the brain and other organs. We have reported that the severity of brain AVM in mice with activin receptor-like kinase 1 (Alk1, an HHT2 causative gene) mutation is correlated with the number of Alk1 negative endothelial cells (ECs). We hypothesized that increase of Eng negative ECs will enhance AVM severity. To increase EC Eng deletion, we used a codon improved cre (icre), which is more potent in recombination of floxed alleles than the wild type (WT) cre. R26RCreER;Eng^f/f mice that have Rosa promoter driving and tamoxifen (TM) inducible WT cre expression globally and PdgfbiCreER;Eng^f/f that have Pdgfb promoter driving and TM inducible icre expression in ECs were treated with 3 intra-peritoneal injections of TM (2.5 mg/25g of body weight) to delete Eng globally or in the ECs. AAV-VEGF was stereotactically injected into the brain to induce brain focal angiogenesis and brain AVM. We found that iCre caused more Eng deletion in the brain indicated by lower level of Eng proteins (p<0.001) and fewer Eng positive ECs (p=0.01) than mice with WT cre. Mice with iCre mediated Eng deletion have more abnormal vessels (p=0.02), CD68⁺ macrophages (p=0.002), and hemorrhage (p=0.04), and less vascular pericyte and smooth muscle coverage than mice with WT cre. In addition, arteriovenous shunts were detected in the intestines of icre mice, a phenotype that have not been detected in WT cre mice before. RNAseq analysis showed that 8 out of the 10 top upregulated pathways identified by Gene Ontology (GO) analysis are related to the inflammation. Therefore, increase of Eng deletion in ECs exacerbates AVM severity, which is associated with enhanced inflammation. Strategies that can reduce Eng negative ECs could be used to develop new therapies to reduce AVM severity for HHT1 patients.

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.

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.

The 2022-2023 Chilean summer showed increased temperatures and similar burned area, compared to the 2016-2017 season, where more than 500,000 hectares were compromised, mainly in the rural areas. After a brief review, it is revealed that the effects of forest fires on soil and hydrological properties are barely debated in Chile. Here, we showed a climatological analysis where temperature records in the 2016-2017 season were unusual, as well as another unexpected increase in the summer of 2022-2023, resulting in high-severity fires known as ‘mega-fires’ or “storm-fires”. Mega-fires affect forest plantations and native forests mainly from 33º S (Maule Region) to 39º S (Los Ríos Region) and they are expected to become frequent due to climate change, moving from the north to the south. We present an overview of the influence of wildfires on soil components in the most affected areas (inland, Coastal, and Andes ranges), their hydrological impacts, and potential erosion risk due to high winter precipitation. We propose several management practices that could help to prevent or mitigate these events, including pre-and post-fire interventions, such as afforestation and seeding, selective logging, mulching, erosion barriers, soil preparation, and dam monitoring. We argue that any effective plan in fire-prone and affected areas should include a combination of actions taken at the hillslope scale at integral ecosystem management, whose effectiveness should be monitored and verified regionally at the watershed scale.

(1) Background: Patients with schizophrenia have higher mortality, with cardiovascular diseases being the first cause of mortality. This study aims to estimate the excess risk of hospital admission for cardiovascular events in schizophrenic patients, adjusting for comorbidity and risk factors. (2) Methods: The APNA study is a dynamic prospective cohort of all residents in Navarra, Spain. 505889 people over 18 years were followed for five years. The endpoint was hospital admissions for a cardiovascular event. Direct Acyclic Graphs (DAG) and Cox regression were used. (3) Results: Schizophrenic patients had a Hazard Ratio (HR) of 1.414 (95% CI 1.031-1.938) of hospital admission for a cardiovascular event after adjusting for age, sex, hypertension, type 2 diabetes, dyslipidemia, smoking, low income, obesity, antecedents of cardiovascular disease, and smoking. In non-adherent to antipsychotic treatment schizophrenia patients, the HR was 2.232 (95% CI 1.267-3.933). (4) Conclusions: Patients with schizophrenia have a higher risk of hospital admission for cardiovascular events than persons with the same risk factors without schizophrenia. Primary care nursing interventions should monitor these patients and reduce cardiovascular risk factors.

For more than ten years, Central Chile faces drought conditions, which impact crop production and quality, increasing food security risk. Under this scenario, implementing management practices that allow increasing water use efficiency is urgent. The study was carried out in kiwifruit trees, located in the O’Higgins region, Chile; for season 2018-2019 and 2019-2020. We evaluate nine vegetation indices in the VNIR and SWIR regions derived from Sentinel-2 (A/B) satellites to know how much variability in the canopy water status could explain. Over the study's site were installed sensors that continuously measure the leaf's turgor pressure (Yara Water-Sensor). A strong correlation between turgor pressure and vegetation indices was obtained with the Spearman's rho coefficient ($\rho$). However, the NIR range's indices were influenced by the vegetative development of the crop rather than its water status. Red-edge showed better performance as the vegetative growth did not affect it. It is necessary to expand the study to consider higher variability in kiwifruit's water conditions and incorporate the sensitivity of different wavelengths.

Water scarcity is a key challenge to global development. In Brazil, the Sao Francisco River Basin (SFB) has experienced water scarcity problems because of decreasing streamflow and increasing demands from multiple sectors. However, the drivers of decreased streamflow, particularly the potential role of surface-groundwater interaction, have not been yet investigated. Here, we assess long-term trends in baseflow, quickflow, and streamflow of the SFB during 1980–2015 and constrain the most likely drivers of observed decreases through trend analysis of precipitation (P), evapotranspiration (ET), and terrestrial water storage change (TWS). We found that over 82% of the observed decrease in streamflow can be attributed to a significant decreasing baseflow trend (< -20 m3 s-1 y-1) along the SFR with spatial agreement between decreased baseflow, increased ET, and irrigated agricultural land. We also noted a decrease in TWS across the SFB with trends exceeding -20 mm y-1. Overall, our findings indicate that decreasing groundwater contributions (i.e., baseflow) is providing the observed reduction in total SFR flow. A lack of significant P trends indicates that only P variability likely has not caused the observed baseflow reduction, mainly in the Middle and Sub-middle SFB. Therefore, groundwater and surface withdrawals may be likely a driver of water scarcity over the SFB.

Athletes are exposed to high-intensity loads to promote athletic performance, however without appropriate evaluation for its effects. This study investigates the effects of four types of exhaustion exercises on Heart Rate Variability (HRV) and Poincaré features as markers of central fatigue; Creatine Kinase (CK) and blood lactate concentrations ([LA-]b) as biomarkers of peripheral fatigue. To achieve this purpose, ten healthy volunteers were exposed to exhaustive exercise using isotonic-, isometric-, aerobic-, and anaerobic-fatigue protocols. HRV Poincaré features, standard deviation of instantaneous beat-to-beat R-R interval variability (SD1) and standard deviation of continuous long-term R-R interval variability (SD2) variables were collected. Central fatigue was tested through the sympathetic stress index (SS) and the sympathetic and parasympathetic index (SS/SD1). Blood samples were collected at the beginning and at the end of the exercises to determine CK and [LA-]b. The SD1 decreased in each exercise protocol, while the SS and SS/SD1 increased. [LA-]b and CK increased at the end of each protocol and correlated with SD1 and SS/SD1. HRV, CK, and [LA^-]_b are acute markers to detect, both central and peripheral fatigue; sensitive to the type, duration, and intensity of exercise, being HRV a novel noninvasive marker, simple and useful for sports coaches and athletes.

Enzyme immobilization is known to enhance polypeptide resistance towards environmental changes, particularly in terms of their stability. The Y509E mutant of β-xylosidase from Geobacil-lus stearothermophilus (XynB2Y509E) (which also bears xylanase activity) has been immobilized in chitosan spheres through either entrapment or covalent bond formation methods. In the immo-bilization by entrapment, besides observing a total loss of xylanase activity, it was found that chitosan concentration affects the process efficiency. Additionally, the maturation time of the spheres and the alkaline pH used to precipitate the spheres affect the β-xylosidase activity of the enzyme. In covalent bond immobilization, it was found that the pH during the modification with glutaraldehyde, as well as the subsequent protein binding, were key factors in preserving the dual enzymatic activity. The activity expressed after immobilization by covalent bonding was 23% higher compared to the activity expressed following entrapment immobilization, with values of 122.3 and 99.4 U. g-1, respectively. Both biocatalysts showed increased thermal and pH stability, along with an improved storage capacity, as they retained 40% and 88% of their activi-ty after being stored at 4 C for two months. Moreover, XynB2Y509E immobilized by covalent binding also exhibited outstanding reusability, retaining a 92% of activity after ten cycles of re-use.

The performance of modern wireless communication systems is highly dependent on the adoption of multiple antennas and the associated signal processing. In 5G and 6G networks, beamforming and beam management become challenging tasks due to aspects such as user mobility, increased number of antennas, and the adoption of higher frequencies. Artificial intelligence, and more specifically, machine learning, are efficient tools to reduce the complexity involved in generating beams and the overhead associated with beam management without sacrificing system performance. Therefore, AI-aided beamforming and beam management have received a lot of attention recently. This article presents a complete survey on this topic, emphasizing open problems and promising directions. The discussion includes architectural and signal processing aspects of modern beamforming and beam management. The article presents communication problems and respective solutions using centralized/decentralized, supervised/unsupervised, semi-supervised, active, federated, and reinforcement learning.

Abstract: Understanding the reliability and sensitivity of jump testing is essential to determine the neuromuscular progress of athletes and make informed decisions. This study aimed to assess the reliability and sensitivity of countermovement jump (CMJ) and drop jump (DJ) tests metrics in female volleyball players. Sixteen semi-professional female volleyball players participated in the study. Intrasession and intersession reliability of CMJ and DJ metrics were evaluated using a randomized cross-over design. Dual force platform was used to collect data, and several dependent variables were calculated using forward dynamics. Intraclass correlation coefficients (ICC), coefficients of variation (CV) and minimum difference (MD) were calculated. The third attempt presented the highest values for both test, and changes in jump strategy in DJ. CMJ is consistent across sessions, while DJ also shows intersession differences (p<0.05) in 11 out of 15 variables. Most CMJ variables exhibit excellent reliability while DJ variables have lower reliability and higher variability. Concentric and eccentric impulses for DJ showed excellent reliability. Most CMJ variables showed excellent reliability and variability, DJ had lower reliability, higher variability, and lower sensitivity. These findings provide valuable information when assessing athletes' performance and highlight the reliability, sensitivity, and variability in jump performance for CMJ and DJ tests.

Many estuarine ecosystems and the fish communities that inhabit them have undergone substantial changes in the past several decades, largely due to multiple interacting stressors that are often of anthropogenic origin. Few are more impactful than droughts, which are predicted to increase in both frequency and severity with climate change. In this study, we examined over five decades of fish monitoring data from the San Francisco Estuary, California, U.S.A, to evaluate the resistance and resilience of fish communities to disturbance from prolonged drought events. High resistance was defined by the lack of decline in species occurrence from a wet to a subsequent drought period, while high resilience was defined by the increase in species occurrence from a drought to a subsequent wet period. We found some unifying themes connecting the multiple drought events over the fifty-year period. Pelagic fishes consistently declined during droughts (low resistance), but exhibit a considerable amount of resiliency and often rebound in the subsequent wet years. However, full recovery does not occur in all wet years following droughts, leading to permanently lower baseline numbers for some pelagic fishes over time. In contrast, littoral fishes seem to be more resistant to drought and may even increase in occurrence during dry years. Based on the consistent detrimental effects of drought on pelagic fishes within the San Francisco Estuary and the inability of these fish populations to recover in some years, we conclude that freshwater flow remains a crucial but not sufficient management tool for the conservation of estuarine biodiversity.

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.

Hereditary Hemorrhagic Telangiectasia (HHT), also known as Osler-Weber-Rendu syndrome, is a rare and inherited vascular disorder, characterized by the development of arteriovenous malformations (AVMs) in various organs and telangiectasia (small AVM) in the mucocutaneous. The majority of HHT patients have haploinsufficiency of genes involved in the transforming growth factor-beta (TGFβ) signaling pathway, including endoglin (ENG), activin receptor-like kinase 1 (ALK1, also known as ACVRL1), or SMAD4. Active angiogenesis is also required for telangiectasia and AVM development. Anti-angiogenic strategies have been tested in patients and animal models extensively. However, the exact mechanisms for telangiectasia and AVM development remain unclear. In this review, we discuss recent advances in identifying disease mechanisms, and potential therapeutic targets.

The present study aimed to develop a mathematical model to estimate the performance of an indoor rowing event of 6000m through an 500 m all out test in junior athletes. We selected141 subjects from the Brazilian rowing confederation database (15.9 ± 1.0 years), subsequently randomised the sample to the development (~70%) and cross-validation (~30%) groups of the mathematical model. Performance data for 500m and 6000m were collected (there was a 48h washout between one test and anoth-er).Subsequently, the mathematical model: Time(min) 6000m = { [( Time(s) 500-m * 6) * 2] / 60} + 1.3, was developed by arithmetic modeling using machine learning and Regression analysis, being tested by intraclass correlation coefficient (ICC), Concordance correlation coefficient (CCC), Validity (Cb ), preci-sion (ρ) and bland-Altman plotting. The prediction of the result of 6000m through the mathematical model utilizando apenas o desempenho de 500m “all-out” test showed a significant reliability in de-velopment group (r2 = 0.730, ICC = 0.753; CCC = 0.895, Cb = 0.879, ρ: 0.957, pure error: 0.2 secounds (1.0% estimative error)); and in cross-validation group (r2 = 0.710, ICC = 0.747; CCC = 0.888, Cb = 0.875, ρ: 0.903, pure error: 0.3 secounds (1.4% estimative error)). When comparing the estimated re-sults of the 6000m performance by the mathematical model with the real performance of 6000m per-formed in indoor rowing, no statistical differences were observed. In addition, the mathematical model did not present a significant proportion bias in relation to the 6000 m performance in both groups. The mathematical model for predicting 6000m performance through a 500m fast test was significant for national level junior rowing athletes.

The Harmonic Graphs Conjecture states that there exists an asymptotic relation involving the Harmonic Index and the natural logarithm as the order of the graph increases. This conjecture, grounded in the novel context of Prime Graphs, draws upon the Prime Number Theorem and the sum of divisors function to unveil a compelling asymptotic connection. By carefully expanding the definitions of the harmonic index and the sum of divisors function, and leveraging the prime number theorem's approximations, we establish a formula that captures this intricate relationship. This work is an effort to contribute to the advancement of graph theory, introducing a fresh lens through which graph connectivity can be explored. The synthesis of prime numbers and graph properties not only deepens our understanding of structural complexity but also paves the way for innovative research directions.

Water conflicts have been a significant issue in Brazil, especially in the Sao Francisco River basin. Subseasonal forecasts, up to a 60-day forecast range, can provide information to support decision-makers in managing water resources in the river basin, especially before drought events. Predictability in the subseasonal forecasts range is a research topic as this range contains mixed dependence on weather and seasonal phenomena. Numerical models may inherit this mixed dependence on their skills. The objective of this paper is to evaluate the Eta Model simulations to reproduce the drought events between the years 2011 and 2016. Two sets of 60-day simulations were produced; one started in September (SO) and the other in January (JF) each year. These months were chosen to evaluate the model skill to reproduce the onset and the middle of the rainy seasons in Central Brazil, where the Upper Sao Francisco River is located. The SO simulations reproduced the observed spatial distribution of precipitation but underestimated the amounts. Precipitation errors exhibit large variability across the subbasins. The JF simulations also reproduced the observed precipitation distribution but overestimated it in the Upper and Lower subbasins. The JF simulations better captured the interannual variability of precipitation. The 60-day simulations were discretized in six 10-day accumulations to assess the intra-monthly variability. They showed that simulations can capture the onset of the rainy season and the small amounts of the rainy months that occurred in these severe drought years.

Climate change in the coming decades could intensify extreme events such as severe droughts. Combined with the possible increase of water demands, these changes exert a large pressure on the water systems. In order to confirm this assumption, a set of scenarios was proposed in this study to consider the combined impact of climate changes and the increase in water demand on the main multiple-use reservoirs of São Francisco River, Brazil. For this purpose, five CMIP6 climate models were used considering two greenhouse gas emissions scenarios: the SSP2-4.5 and SSP5-8.5. The naturalized and withdraw streamflows were estimated to the adopted reser-voirs considering all existing, new and projected demands. The conjunction of scenarios indicat-ed an increase in Potential Evapotranspiration, a possible significant reduction in water availa-bility, a growth in water demand mainly for irrigation and a substantial reduction in the perfor-mance of the evaluated reservoirs.

Microbes found in soil can have a significant impact on the taste and quality of wine, also referred to as wine terroir. To date, wine terroir has been thought to be associated with physical and chemical characteristics of the soil. However, there is a fragmented understanding of the contribution of vineyard soil microbes to wine terroir. Additionally, vineyards can play an important role in carbon sequestration, since the promotion of healthy soil and microbial communities directly impact greenhouse gas emissions to the atmosphere. We review 24 studies that explore the role of soil microbial communities in vineyards and their influence on grapevine health, grape composition, and wine quality. Studies spanning 2015 to 2018 laid a foundation by exploring soil microbial biogeography in vineyards, vineyard management effects, and the reservoir function of soil microbes for grape-associated microbiota. On the other hand, studies spanning 2019 to 2023 appear to have a more specific and targeted approach, delving into the relationships between soil microbes and grape metabolites, the microbial distribution at different soil depths, and microbial influences on wine flavor and composition. Next, we identify research gaps and make recommendations for future work. Specifically, most of the studies utilize targeted sequencing (16S, 26S, ITS) which only reveals community composition. Utilizing high-throughput omics approaches such as shotgun sequencing (to infer function) and transcriptomics (for actual function) is vital to determine the specific mechanisms by which soil microbes influence grape chemistry. Going forward, understanding long-term effects of vineyard management practices and climate change on soil microbiology, grapevine trunk diseases, and the role of bacteriophages in vineyard soil and wine making would be a fruitful investigation. Overall, the studies presented shed light on the importance of soil microbiomes and their interactions with grapevines in shaping wine production. However, there are still many aspects of this complex ecosystem that require further exploration and understanding to support sustainable viticulture and enhance wine quality.

Previous work has shown that perturbation analysis in algorithmic information dynamics can uncover generative causal processes of finite objects and quantify each of its element's information contribution to computably constructing the objects. One of the challenges for defining emergence is that the dependency on the observer's previous knowledge may cause a phenomenon to present itself as emergent for one observer at the same time that reducible for another observer. Thus, in order to quantify emergence of algorithmic information in computable generative processes, perturbation analyses may inherit such a problem of the dependency on the observer's previous formal knowledge. In this sense, by formalizing the act of observing as mutual perturbations, the emergence of algorithmic information becomes invariant, minimal, and robust to information costs and distortions, while it indeed depends on the observer. Then, we demonstrate that the unbounded increase of emergent algorithmic information implies asymptotically observer-independent emergence, which eventually overcomes any formal theory that any observer might devise. In addition, we discuss weak and strong emergence and analyze the concepts of observer-dependent emergence and asymptotically observer-independent emergence found in previous definitions and models in the literature of deterministic dynamical and computable systems.

The properties of photonic quasicrystals ultimate rely on their inherent long-range order, a hallmark that can be quantified in many ways depending on the specific aspects to be studied. We use the Lempel-Ziv measure, a basic tool for information theoretic problems, to characterize the complexity of the specific structure under consideration. Using the generalized Fibonacci quasicrystals as our thread, we adress the relation between the optical response and the associated complexity.

Monitoring is an essential component in ecosystem management and leveraging existing data sources for multiple species of interest can be one effective way to enhance information when making management decisions. Here we analyzed juvenile Chinook Salmon (Oncorhynchus tshawytscha) bycatch data that has been collected by the recently established Enhanced Delta Smelt Monitoring program (EDSM), a survey designed to estimate the abundance and distribution of the San Francisco Estuary’s (estuary) endangered Delta Smelt (Hypomesus transpacificus). Two key aspects of the EDSM program distinguish it from other fish surveys in the estuary: a stratified random sampling design and the spatial scale of its sampling effort. We integrated the EDSM dataset with other existing surveys in the estuary and used an occupancy model to assess detection probability differences across gear types. We saw no large-scale differences in size selectivity, and while detection probability varied among gear types, cumulative detection probability for EDSM was comparable to other surveys due to the program’s use of replicate tows. Based on our occupancy model and sampling effort in the estuary during spring of 2017 and 2018, we highlighted under-sampled regions that saw improvements in monitoring coverage due to EDSM. Our analysis also revealed that each sampling method has its own benefits and constraints. Although the use of random sites with replicates as conducted by EDSM can provide more statistically robust abundance estimates relative to traditional methods, the use of fixed stations and simple methods such as beach seine may provide a more cost-effective way of monitoring salmon occurrence in certain regions of the estuary. Stronger inference on salmon abundance and distribution can be made by leveraging the strengths of each survey’s method. Careful consideration of these trade-offs is crucial as the management agencies of the estuary continue to adapt and improve their monitoring programs.

Hydrogen gas is seeping from the sedimentary basin of São Franciso, Brazil. The seepages of H₂ are accompanied by helium whose isotopes reveal a strong crustal signature. Geophysical data indicates that this intra-cratonic basin is characterized by i) a relatively high geothermal gradient, ii) deep faults delineating a horst and graben structure and affecting the entire sedimentary sequence, iii) an archean to paleoproterozoïc basements enriched in radiogenic elements and displaying mafic and ultramafic units, and iv) a possible karstic reservoir located 400 m below the surface. The high geothermal gradient could be due to a thin lithosphere enriched in radiogenic elements, which can also contribute to a massive radiolysis process of water at depth, releasing an important amount of H₂. Alternatively, ultramafic rocks that may have generated H₂ during their serpentinization are also documented in the basement. The seismic profiles show that the faults seen at the surface are deeply rooted in the basement, and can drain deep fluids to shallow depths in a short time scale. The carbonate reservoirs within the Bambuí group which forms the main part of the sedimentary layers are crossed by the fault system and represent good candidates for temporary H₂ accumulation zones. The formation by chemical dissolution of sinkholes located at 400 m depth might explain the presence of sub-circular depressions seen at the surface. These sinkholes might control the migration of gas from temporary storage reservoirs in the upper layer of the Bambuí formation to the surface. The very high fluxes of H₂ escaping out of these structures which have been recently documented are, however, in disagreement with the newly developed H₂ production model in the Precambrian continental crust. They either question the validity of these models or the measurement methodology.

Background: Cleft lip and palate can affect the development of the maxilla, which may create a midfacial deficiency as well as an interference of the facial growth pattern and dentofacial esthetics. Objective: Estimate the chronological age of complete fusion of the spheno-occipital synchondrosis in cleft lip and/or palate patient´s and a control group, using cone beam computed tomography images. Methods: In this retrospective study, 125 patients were enrolled (cleft lip and/or palate group (n=91); control group (n=34)). Age comparison was made with Chi-square test and a Kaplan-Meyer analysis determined the median time to reach complete fusion of the spheno-occipital synchondrosis (p&lt;0.05). Results: The experimental group showed statistically significant differences in the median time for complete ossification between males and females (p=0.019). No statistically significant differences were found in the control group between males and females (p=0.104). The median time for complete ossification of the spheno-occipital synchondrosis was, for males, 15.0 years in both groups; for females, it was 14.0 years in the experimental group and 13.0 years in the control group. Conclusions: The present study showed no differences in the ossification of the spheno-occipital synchondrosis between individuals with and without cleft lip and/or palate.

Transcription is carried out in most eukaryotes by three multimeric complexes (RNA polymerases I, II and III). However, plants contain two additional RNA polymerases (IV and V), which have evolved from RNA polymerase II. RNA polymerases II, IV and V contain both common and specific subunits that may specialize some of their functions. In this study, we conducted a search for the genes that putatively code for the specific subunits of RNA polymerases IV and V, as well as the corresponding of RNA polymerase II in olive. Based on the homology with the genes of Arabidopsis thaliana, we identified 13 genes that putatively code for the specific subunits of polymerases IV and V, and 16 genes that code for the corresponding specific subunits of polymerase II in olive. The transcriptomic analysis by RNA-Seq revealed that the expression of the RNA polymerases IV and V genes was induced during the initial stages of fruit development. Given that RNA polymerases IV and V are involved in the transcription of long non-coding RNAs, we investigated their expression and observed relevant changes in the expression of this type of RNAs. Particularly, the expression of the intergenic and intronic long non-coding RNAs tended to increase in the early steps of fruit development, suggesting their potential role in this process. The positive correlation between the expression of RNA polymerases IV and V subunits and the expression of non-coding RNAs supports the hypothesis that RNA polymerases IV and V may play a role in fruit development throughout the synthesis of this type of RNAs.

Natural host defensins, also sometimes termed antimicrobial peptides, are evolutionarily conserved. They have been studied as antimicrobials, but some pharmaceutical properties, undesirable for clinical use, led to the development of synthetic molecules with constructed peptide arrangements and/or peptides not found in nature. The leading development currently is synthetic small molecule nonpeptide mimetics, whose physical properties capture the characteristics of the natural molecules, and share their biological attributes. We studied brilacidin, an arylamide of this type, for its activity in vitro against fungi (40 clinical isolates, 20 species) that the World Health Organization has highlighted as problem human pathogens. We find antifungal activity at low concentrations for many pathogens, which indicates further screening for activity, particularly in vivo, is justified to evaluate this compound, and other mimetics, as attractive leads for development of effective antifungal agents.

In the last decade, the integration of robots into agricultural tasks has significantly transformed food production from planting to harvest. The precision and efficiency of a robot allow for specialized crop management in cases such as; plant disease identification, optimization of water and fertilizer use, monitoring of environmental and soil conditions, among others. That is, the adoption of robots in agricultural practices through intelligent automation increases crop yields and decreases environmental impact. Intelligent automation faces contemporary challenges such as climate change and population growth, promoting sustainable food security. Therefore, this article presents a review of the state of the art of modular robots and their applications in agriculture. Modular robots have the ability to reconfigure and adapt to different tasks, promoting versatile solutions for the agricultural sector. These solutions are linked to the robot's control systems, which can be centralized, decentralized, and hybrid. Firstly, centralized control allows for unified management and coordination in high-precision tasks. Secondly, the decentralized approach offers flexibility and robustness in changing environments where adaptability is required. Thirdly, hybrids incorporate features of both control types balancing control and autonomy to increase efficiency and effectiveness in practical applications. In some cases, control systems incorporate bio-inspired motion control techniques, where the robot mimics natural movements and behaviors to improve its adaptability in performing a task. For example, the chemosynthesis model, which is a biological process where bacteria convert an inorganic compound into energy, has been adapted for individual robots to explore and navigate their environment. Therefore, this article presents an overview of modular robots, the incorporation of bio-inspired motion control techniques, and their convergence towards the sustainable solution of contemporary problems in the agro-industry.

Clostridial infections in cattle are a significant concern for Brazilian livestock. These diseases are caused by various species of Clostridium, which are known for their ability to produce potent toxins. Botulism in cattle is a serious and often fatal condition caused by the ingestion of neurotoxins produced by C. botulinum. This bacterium thrives in decomposing organic matter, such as spoiled feed, carcasses, and contaminated water. Tetanus while less common, tetanus is a serious disease following contamination of wounds with Clostridium tetani spores. It results in muscle stiffness, spasms, and often death due to respiratory failure. Blackleg (C. chauvoei) is this disease primarily affects young cattle, leading to acute lameness, swelling, and high fever. Malignant edema (C. septicum and others) is characterized by rapid onset of swelling at wound sites, malignant edema can occur after injuries or surgical procedures. Enterotoxemia is triggered by the rapid growth of C. perfringens in the gut following excessive carbohydrate intake. This leads to toxin production that causing sudden death. In conclusion, clostridial bovine infections remain a persistent challenge for Brazilian cattle farmers. With continued focus on vaccination, good management practices, and research, the impact of these diseases can be minimized, safeguarding the livestock industry's economic viability.

Objective: To analyse the association between maternal risk factors such as age, body mass index (BMI) and cigarette smoking, with perinatal outcomes. Materials and Methods: This a retrospective analysis from prospectively collected data performed at Hospital Universitario de Torrejón (Madrid, Spain), between September 2017 and December 2019. All pregnant women with singleton pregnancies and non-malformed live fetuses attending their routine ultrasound examination at 11+0 to 13+6 weeks' gestation were invited to participate. Association between preeclampsia, preterm birth, gestational diabetes mellitus (GDM), small for gestational age (SGA) or fetal growth restricted (FGR) neonates and type of delivery according to maternal age, BMI, and cigarrete smoking was studied. Logistic mixed models were used to analyze the data. Results: 1921 patients were included in the analysis. Women of 40 years old had a significantly higher risk of having GDM (Odds Ratio (OR) 1.61, 95% Confidence Interval (CI) 1.08 to 2.36), and SGA neonates (OR 1.54, 95% CI 1.00 to 2.37). Women with a BMI &lt;18 had an increased rate of birth SGA and FGR neonates (OR 3.28, 95% CI 1.51 to 7.05, and OR 3.73, 95% CI 1.54 to 8.37, respectively), whereas women with a BMI ≥35 had a higher risk of GDM (OR 3.10, 95% CI 1.95 to 4.89). Smoking increased the risk of having SGA and FGR neonates (OR 1.83, 95% CI 1.36 to 2.46, and OR 1.91, 95% CI 1.29 to 2.78). Conclusions: Advanced maternal age, low or high BMI, and smoking status are significant risk factors for pregnancy complications. Both clinicians and society should concentrate their efforts on addressing these factors to enhance reproductive health.

The early 2-factor (E2F) family of transcription factors, including E2F1-8, plays a critical role in apoptosis, metabolism, proliferation, and angiogenesis within glioblastoma (GBM). However, the specific functions of E2F transcription factors (E2Fs) and their impact on the malignancy of Bevacizumab (BVZ)-responsive GBM subtypes remain unclear. This study used data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to explore the impact of eight E2F family members on the clinical characteristics of BVZ-responsive GBM subtypes and possible mechanisms of recurrence after BVZ treatment.To predict the possibility of GBM patient survival and progression, we classified and comparedthe expression of E2Fs according to BVZ-responsive subtypes and employed a machine learning method-TreeBagger, one random forest algorithm. Multiple bioinformatics analyses have shown that the significant increased E2F8 post BVZ treatment may enhance the function of angiogenesis and stem cell proliferation, indicating one of the mechanisms of GBM recurrence after treatment. In addition, BVZ treatment in unresponsive GBM patients may potentially worsen disease progression. Our findings suggest that E2F family members play important roles in GBM malignancy and BVZ treatment response, highlighting their potential as prognostic biomarkers and therapeutic targets and recommending precise BVZ treatment for GBM patients.

A study analyzed financial statements in the beer sectors of Italy and Spain during 2019-2021 using compositional data analysis (CoDa) to gauge pre- and post-pandemic financial health and aid sector-level decision-making. Geometric averages were employed to mitigate statistical issues, offering more accurate insights than arithmetic averages. The exploratory, quantitative methodology calculated key financial ratios and profitability metrics. Results indicated satisfactory short-term solvency. Returns and margins remained subpar despite pandemic policies. Geometric averages proved essential in portraying sector health accurately, underscoring the need for their use in classic brewing sector ratios. The study also showcased CoDa's efficacy in diagnosing sectoral financial health for informed decision-making. Furthermore, a web analysis revealed varying transparency levels in communicating non-financial indicators, with Spain excelling in waste, water, and energy disclosures, while Italy prioritized energy and waste with less transparency.

Human non-conscious reasoning is one of the most successful procedures developed to solve everyday problems in an efficient way. This is why the field of artificial intelligence should analyze, formalize and emulate the multiple ways of non-conscious reasoning with the purpose of applying them in knowledge based systems, neurocomputers and similar devices for aiding people in the problem-solving process. In this paper, a framework for those non-conscious ways of reasoning is presented based on object-oriented representations, fuzzy sets and multivalued logic.

Marine renewable energy technologies have a great potential in supplying clean electricity to millions of people across the globe, if technical and economic conditions are in right. So far, ocean energy projects are commonly started by SMEs or educational institutions with limited budgets. Therefore, any effort to reduce expenses is of great value. One of the areas involving substantial expenses are the inevitable seabed inspection prior to deployment of marine renewable energy device. Detailed seabed inspections can also reduce the risk of associated with deployment of structures on uneven seabed, especially marine renewable energy devices with gravity foundations. By reducing the costs and risks of such surveys prior and during the installation phases, the feasibility of marine renewable energy projects can be more favoured and competitive. In this perspective, this study proposes a cost and time effective technique for seabed surveys. The proposed technique involves the use of high precision and inexpensive sonar systems and underwater optical cameras integrated into a versatile and compact subsea monitoring platform. It also involves simple and practical data acquisition and processing protocols that do not requires hi expertise for operation. The results obtained showed that high resolution bathymetric maps and detailed seabed inspections imagery can be acquired. This study concludes that a simple and inexpensive subsea monitoring platform comprising a multibeam, dual beam and video cameras can be effective for high resolution seabed inspection and bathymetric measurements for marine energy applications.

In recent years, news media has been greatly disrupted by the potential of technologically driven approaches in the creation, production, and distribution of news products and services. Artificial intelligence (AI) has emerged from the realm of science fiction and has become a very real tool that can aid society in addressing many issues, including the challenges faced by the news industry. The ubiquity of computing has become apparent and has demonstrated the different approaches that can be achieved using AI. We analyzed the news industry’s AI adoption based on the seven subfields of AI: (i) machine learning; (ii) computer vision (CV); (iii) speech recognition; (iv) natural language processing (NLP); (v) planning, scheduling, and optimization; (vi) expert systems; and (vii) robotics. Our findings suggest that three subfields are being developed more in the news media: machine learning, computer vision, as well as planning, scheduling, and optimization. Other areas have not been fully deployed in the journalistic field. Most AI news projects rely on funds from tech companies such as Google. This limits AI’s potential to a small number of players in the news industry. We make conclusions by providing examples of how these subfields are being developed in journalism and present an agenda for future research.

Coincident changes in abundance and behavior pose a challenge for interpreting abundance data from monitoring programs. In the San Francisco Estuary, long-term monitoring documented the declines of many species including the anadromous Longfin Smelt (Spirinchus thaleichthys). We identified seasonal patterns in reginal presence of Longfin Smelt through its life cycle using monitoring data and generalized additive modelling. We then investigated the year-to-year variability in the seasonal patterns of presence using functional data analysis (FDA). FDA separated the variability due to population size from variability due to differences in timing of presence. We found that Longfin Smelt have consistent seasonal distribution patterns and that two trawl types were needed to accurately describe those patterns. After accounting for variability due to year-class strength, shifts in the timing of presence were evident in three regions. The most variable period for the upstream regions Suisun Bay and West Delta was for age-0 fish in summer and for the downstream region Central Bay was for age-0 fish in late fall. This manifested as a delay in the typical fall re-occupation of upstream regions that comprise the study area for another monitoring study (Fall Midwater Trawl). Thus, a portion of the recent reductions in Fall Midwater Trawl abundance of Longfin Smelt resulted from changes in behavior rather than a decline in abundance. The presence of multiple monitoring surveys allowed analysis of distribution from one data set to aid interpretation of patterns in abundance from another monitoring survey. This study highlights how identifying portions of the life cycle with the most and least variability in distribution can help inform the types of management strategies that will be most effective. It also illustrates an analytical method that can be used to address the problem of confounded effects of abundance and behavior on patterns in monitoring data.

Abstract— Flight Control System is an integrated avionics system equipped with the minimum required components for an autonomous flight. This paper focuses on the Hardware Design of the Flight Control System and presents specific details of the components and its interface. The system architecture is based on Field Programmable Gate Array and Digital Signal Processor. Employing these two processors in the flight control system would improve the Flight Control System performance in terms of fast sequential processing of high-level control algorithms. In addition to Field Programmable Gate Array and Digital Signal Processor, the flight control computer system will also make use of Global Positioning System and Micro Electro Mechanical System sensors. The project will be implemented using Altera’s System On Programmable Chip builder, currently known as Qsys – Platform Designer implemented in Quartus-II. The system employs Nios-II processor which is 32-bit soft-core embedded-processor architecture designed especially for the Altera’s family of Field Programmable Gate Array. From conceptualization to final design, this paper presents the functionality of the different modulus and complex interfaces employed in this Flight Control System.

Angiogenesis is a hallmark of ovarian cancer (OC) it promotes rapid cell growth and the associated metastasis. Identifying new bioactive compounds to target angiogenesis may provide valuable paradigms as therapeutic strategies. Melatonin is a well-characterized indoleamine that possesses important anti-angiogenic properties in a set of aggressive solid tumors. Herein, we evaluated the role of melatonin therapy on the angiogenic signaling pathway in OC of an ethanol-preferring rat model that mimics the same pathophysiological conditions occurring in women. OC was chemically induced with a single injection of 7,12-dimethylbenz(a)anthracene (DMBA) under the ovarian bursa. After the rats developed serous papillary OC, half of the animals received i.p. injections of melatonin (200 µg/100 g body weight/day) for 60 days. Serum levels of melatonin were higher following therapy, and the expression of its receptor MT1R was significantly increased in OC-bearing rats, regardless of ethanol intake. TGFB1, a transforming growth factor-beta1, was reduced only after melatonin treatment. Importantly, vascular endothelial growth factor (VEGF) was severely reduced after melatonin therapy in animals given or not given ethanol. Conversely, the levels of VEGF receptor 1 (VEGFR1) was diminished after ethanol consumption, regardless of melatonin therapy, and VEGFR2 was only reduced following melatonin. Hypoxia-inducible factor (HIF)-1α was augmented with ethanol consumption, and notably, melatonin significantly reduced their levels. Collectively, our results suggest that melatonin attenuates angiogenesis in OC of an animal model of ethanol consumption; this provides a possible complementary therapeutic opportunity for concurrent OC chemotherapy.

The environmental vulnerability diagnosis of a river basin depends on a holistic analysis of its environmental aspects and degradation factors. Based on this diagnosis, the definition of priority areas where interventions for environmental recovery should be carried out is fundamental, since financial and natural resources are limited. In this study, we developed a methodology to assess these fragilities using an Environmental Vulnerability Index (EVI) that combines physical and environmental indicators related to the natural sensitivity of ecosystems and their exposure to anthropogenic factors. The EVI developed was applied to the headwater’s region of the São Francisco River basin, Brazil. The proposed index was based on the AHP multi-criteria analysis and was adapted to include four variables representative of the study area: Land Use Adequacy, Burned Area, Erosion Susceptibility and Quantitative Water Balance. The EVI analysis highlighted that the presence of easily erodible soils, associated with sloping areas and land uses above their capacity, generate the most vulnerable areas in the headwaters of the São Francisco River basin. The results of this study indicate where resources should be applied for environmental preservation in the basin under study, directing the allocation of efforts to areas with lower resilience in maintaining ecosystem services.

Adequate public spaces and urban green areas are key criteria for urban development and infrastructure implementation of healthy cities. Latterly, there has been an increasing number of research methods using artificial intelligence (AI) to monitor, quantify, and control the state of these spaces with an aim towards pioneering research in Urban Studies. However, in informal areas, open-data access tends to lack adequate and updated information, making it difficult to use AI methods. Hence, we propose a methodology for restricted open data collection and preparation for future use in machine learning or spatial data science models for similar areas. To that extent, we examine two peripheral and low-income neighborhoods in Quito, Ecuador, La Bota and Toctiuco, to analyze their public spaces, urban green areas, points of interest, and road networks, and how they address healthy cities criteria. We develop an original methodological approach that combines an index of proximity, accessibility, quantity, and quality of these spaces with geospatial and network analysis techniques. Results indicate that the connectivity and structure of these spaces are centralized and nodal, representing exclusion and segregation. This work provides insights on potential healthy spaces, and information to urban planners and policymakers in decision-making for healthy urban infrastructure.

The aim of this work was to describe the physiological limitations induced under drought conditions in young sweet orange trees, including the crop capability to recuperate its physiological status once re-watering, and after a water withholding. The trial was conducted during two consecutive months (July and August) in two-years old sweet orange trees. Three irrigation treatments were arranged: A full irrigation treatment; (FI); a low-frequency deficit-irrigation treatment (LFDI); and a sustained-deficit irrigation (SDI) treatment. The obtained results showed that for Stem values below -1.5 MPa, significant decreases in gas exchange rates, evidencing diffusive limitations to drought stress. Additionally, there was evidence of increased osmolyte synthesis, compatible as a defensive response to prevent oxidative damage. Likewise, the isohydric behaviour observed also resulted in significant decreases in gs even for moderate stress situations. In addition, it was observed that in the LFDI treatment, the stress levels reached caused a significant increase in the levels of Pro and MDA, which were not equal to those detected in FI during the recovery periods, which leads us to assume that potential values below -1.8 MPa would be causing significant oxidative damage with no capacity for subsequent recovery, and probably affecting the final yield.

The use of large-scale antenna arrays grants considerable benefits in energy and spectral efficiency to wireless systems due to spatial resolution and array gain techniques. By assuming a dominant line-of-sight environment in a massive MIMO scenario with favorable propagation, we derive analytical expressions for the sum-capacity. % Then, we show that convenient simplifications on the sum-capacity expressions are possible when working at low and high SNR regimes. % Furthermore, in the case of a high SNR regime, it is demonstrated that the Gamma PDF can approximate the PDF of the instantaneous channel sum-capacity as the number of BS antennas grows. A second important demonstration presented in this work is that a Gamma PDF can also be used to approximate the PDF of the summation of the channel's singular values as the number of devices increases. Finally, it is important to highlight that the presented framework is useful for LPWAN's as we show that the transmit power of each device can be made inversely proportional to the number of BS antennas.

In this brief letter we report our initial results on the application of deep-learning to the massive MIMO channel estimation challenge. We show that it is possible to estimate wireless channels and that the possibility of mitigating pilot-contamination with deep-learning is possible given that the leaning model underwent an extensive training-phase and that it has been presented with a large number of different channel conditions.

The Physical Random Access Channel (PRACH) plays an important role in LTE and LTE-A systems. It is through the PRACH channel that the user equipment (UE), based on eNodeB's timing estimates, aligns its uplink transmissions to the eNodeB's uplink and gain access to the network. One of the initial operations executed by the PRACH receiver at eNodeB side is the translation of the PRACH signal back to base band, $i.e.$, center the PRACH signal around DC. This operation is a necessary step for preamble detection and can be carried out through a time-domain frequency shift operation. Therefore, in this paper we present the hardware architecture and implementation details of a configurable and optimized FPGA-based time-domain frequency shifter. It is a hardware-efficient and accurate architecture for converting the relevant received PRACH signal into base band before further signal processing. The architecture is manly based on a customized Numerically Controlled Oscillator (NCO), which is used for generating complex exponentials employing only adders, a Look-Up Table (LUT) and plain logic resources. The main advantage of the proposed hardware architecture is that it completely eliminates the need for storing a large number of long complex exponential sequences by employing a single LUT and exploiting quarter wave symmetry of the basis waveform. Our simulation results show that the proposed customized NCO architecture provides high Spurious Free Dynamic Range (SFDR) signals using a minimal amount of FPGA resources. Moreover, the proposed architecture exhibits spur-suppression ranging from 62.13 to 153.58 dB without using Taylor Series correction.

This paper proposes the use of compressive sensing to tackle the Massive MIMO channel estimation problem. As our results show compressive sensing-based estimators perform as well as the optimum MMSE estimator.

In this letter, we advocate that it is possible to mitigate Pilot Contamination in Massive MIMO systems by scrambling the pilot sequences with a Base Station (BS) scrambling sequence. It is possible if a set of sequences is carefully designed to meet the orthogonality property defined in this letter. Each BS possesses its own scrambling sequence that can be reused the same way frequency reuse is applied to cell deployment. The main advantage of the prosed pilot generation scheme is that the frequency reuse factor can be set to 1, the most aggressive one, while the scrambling sequences can be reused with much less aggressive reuse factors (e.g. 4, 7, 9, 12, etc.), which in consequence results in pilot contamination mitigation and increased system's performance.

The aim of this study is to evaluate three different strategies to improve classification accuracy in a highly fragmented semiarid area. i) Using different classification algorithms: Maximum Likelihood, Random Forest, Support Vector Machines and Sequential Maximum a Posteriori, with parameter optimisation in the second and third cases; ii) using different feature sets: spectral features, spectral and textural features, and spectral, textural and terrain features; and iii) using different image-sets: winter, spring, summer, autumn, winter+summer, winter+ spring+summer; and a four seasons combination. A 3-way ANOVA is used to discern which of these approaches and their interactions significantly increases accuracy. Tukey-Kramer contrast using a heteroscedasticity-consistent estimation of the kappa covariances matrix was used to check for significant differences in accuracy. The experiment was carried out with Landsat TM, ETM, and OLI images corresponding to the period 2000-2015. A combination of four images was the best way to improve accuracy. Maximum Likelihood, Random Forest and Support Vector Machines do not significantly increase accuracy when textural information is added, but do so when terrain features are taken into account. On the other hand, Sequential Maximum a Posteriori increases accuracy when textural features are used, but reduces accuracy substantially when terrain features are included. Random Forest using the three feature subsets and Sequential Maximum a Posteriori with spectral and textural features had the largest kappa values, around 0.9.

Cryptococcus neoformans is a multidrug-resistant human pathogenic yeast responsible for infections in immunocompromised patients. Here, Itraconazole (ITR), a commercial antifungal drug with low effectiveness against C. neoformans, was combined with different synthetic peptides Mo-CBP3-PepII, RcAlb-PepII, RcAlb-PepIII, PepGAT, and PepKAA. The mechanisms of action responsible for the synergistic effect were evaluated for the best combinations by Fluorescence Microscopy (FM). The synthetic peptides enhanced the activity of ITR by 10-fold against C. neoformans. Our results demonstrated that the combinations could induce pore formation in the membrane and overaccumulation of ROS on C. neoformans cells. Our findings indicate that our peptides successfully potentialize the activity of ITR by reducing it by 10-fold to reach antifungal activity against C. neoformans. Therefore, synthetic peptides are potential molecules to act as co-adjuvant agents in treating Cryptococcal infections.

The Nanosecond Pulsed Electric Field (nsPEF) or Nano Pulsed Stimulation (NPS) is a cutting-edge technology that applies a series of high-voltage electric field pulses of extremely short duration, typically on the order of nanoseconds, to cells. This technique induces the formation of nanopores and activate ionic channels, thereby altering cellular activity. Recent simulations suggest that nsPEF also triggers a novel phenomenon, Protein-mediated Electroporation, whereby pores are formed through the voltage-sensing domain of ionic channels. In this study, we employed Molecular Dynamics simulations and machine learning techniques to investigate the Voltage Sensing Domain (VSD) of a cardiac channel under nsPEF stimulation. Specifically, we characterized VSD structures that form pores helping for the understanding of the physical chemistry underlying the defibrillation effect of nsPEF. Our analysis involved the clustering of 142 replicas simulated for 50 ns under the nsPEF stimulus. Subsequently, we identified the representative structures of each cluster and calculated the free energy between them. Our results demonstrate that although VSD structures are ubiquitous among ionic channels, they exhibit distinct responses to nsPEF stimuli. These findings have significant implications for the development of novel nsPEF-based therapies for cardiac diseases.

Artisanal and Small-scale Gold Mining (ASGM) has caused several environmental impacts, resulting in significant siltation of water bodies due to the deposition of sediments on river banks. Based on this perspective, this study aims to investigate the water bodies and regions most impacted by mining activities, especially in relation to the increase in the Total Suspended Solids (TSS) caused by ASGM, focusing on the territories of Suriname and French Guiana, over the period from 2017 to 2023, through the creation of an algorithm in Google Earth Engine. The research also aims to map and describe active mining in this region using the Classification and Regression Tree (CART) method, which achieved an overall accuracy of 82% and a kappa index of 0.77. The results reveal that from 2017 to 2024 there was an increase of 148.09 km² of mining, with an average increase in TSS of up to 167 mg/l in water bodies most affected by mining activities. Finally, the continued importance of using remote sensing technologies, such as GEE, together with innovative methodological approaches, to monitor and manage natural resources in a sustainable manner is highlighted.

The production of high-quality wines is one of the primary goals of modern oenology. In this regard, it is known that the potential quality of a wine begins to be determined in the vineyard, where the quality of the grape, initially, and later that of the wine, will be influenced by the soil properties. Given the spatial variability of the fundamental soil properties related to the potential grape production, such as texture, soil organic matter content, or cation exchange capacity, it seems that a uniform management of a vineyard is not the most optimal way to achieve higher grape quality. In this sense, the delineation of zones with similar soil characteristics to implement site-specific management is essential, reinforcing the interest to incorporate technologies and methods to determine these homogeneous zones. A case study was conducted in a 3.3 ha vineyard located near Évora, South of Portugal. A non-contact sensor (DUALEM 1S) was used to measure soil apparent electrical conductivity (ECa) in the vineyard, and later, a kriged ECa map was generated. ECa and elevation maps were utilised to delineate homogeneous zones (management zones, MZs) in the field through a clustering process. MZs were validated using some soil properties (texture, pH, organic matter -OM, phosphorous -P2O5, potassium -K2O, sum of the exchange bases -SEB; and cation exchange capacity -CEC) which were determined from 20 soil samples taken in the different MZs. Additionally, validation was also performed using Rasch measures, which were defined based on the formula-tion of the objective and probabilistic Rasch model, integrating the information from the afore-mentioned soil properties at each sampling location. The comparison of the MZs was more evident with the use of the Rasch model, as only one value was to be employed in each MZ. The use of ECa constitutes an efficient technological tool for implementing site-specific man-agement in viticulture, which allows for the improvement of decision-making processes by con-sidering the inherent spatial variability of the soil.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) persists throughout the world with over 65 million registered cases of survivors with post-COVID-19 sequelae, also known as LongCOVID-19 (LongC). LongC survivors exhibit various symptoms that span multiple organ systems, including the nervous system. To search for neurological biomarkers of LongC, we investigated the soluble proteins present in the plasma and the proteins associated with plasma neuronal-enriched extracellular vesicles (nEVs) in 33 LongC patients with neurological impairment (nLongC), 12 COVID-19 survivors without any LongC symptoms (Cov), and 28 pre-COVID-19 healthy controls (HC). COVID-19 positive participants were infected from 2020-2022, not hospitalized, and were vaccinated or unvaccinated before infection. IL-1b was significantly increased in both nLongC and Cov and IL-8 elevated in only nLongC. Both brain-derived neurotropic factor and cortisol were significantly elevated in nLongC and Cov compared to HC. nEVs from people with nLongC had significantly elevated protein markers of neuronal dysfunction, including amyloid beta 42, pTau181 and TDP-43. This study shows chronic peripheral inflammation with increased stress after COVID-19 infection. Additionally, differentially expressed nEV neurodegenerative proteins were identified in people recovering from COVID-19 regardless of persistent symptoms.

Several methods have been tried to estimate air temperature using satellite imagery. In this paper, the results of two machine learning algorithms, Support Vector Machine and Random Forest, are compared with Multivariate Linear Regression, TVX and Ordinary kriging. Several geographic, remote sensing and time variables are used as predictors. The validation is carried out using four different statistics on a daily basis allowing the use of ANOVA to compare the results. The main conclusion is that Random Forest with residual kriging produces the best results (R$^2$=0.612 $\pm$ 0.019, NSE=0.578 $\pm$ 0.025, RMSE=1.068 $\pm$ 0.027, PBIAS=-0.172 $\pm$ 0.046), whereas TVX produces the least accurate results. The environmental conditions in the study area are not really suited to TVX, moreover this method only takes into account satellite data. On the other hand, regression methods (Support Vector Machine, Random Forest and Multivariate Linear Regression) use several parameters that are easily calculated from a Digital Elevation Model, adding very little difficulty to the use of satellite data alone. The most important variables in the Random Forest Model were satellite temperature, potential irradiation and cdayt, a cosine transformation of the julian day.

After 29 years of clinical practice treating odontogenic infections of the head, neck and oral cavity, my co-contributors and I sought to determine by a review of the literature and three clinical cases if Prevotella species were significant opportunistic pathogens in odontogenic infections of all kinds in the oral and maxillofacial region. We performed a PubMed review of 40 articles using terms associated with odontogenic infections, pericoronitis, Prevotella and antibiotics used to treat common oral and maxillofacial infections. Three cases were reported, and the treatment was discussed. An additional 26 articles were reviewed to describe the pathogenesis of Prevotella. Given the opportunity to become virulent—such as post-extraction wound healing of an extraction site adjacent to an intact dentition, excessive plaque and calculus, nearby gingivitis or periodontitis, or an immunocompromised host—we wondered if Prevotella could be targeted with antimicrobials mitigating, or even resolving, odontogenic infections in those cases refractory to conservative surgical therapy that includes source control. We found that Prevotella is a significant opportunistic pathogen in odontogenic infections. Prevotella is susceptible to Metronidazole (Flagyl), and it is effective in mitigating the disease process when basic principles of the treatment of odontogenic infections are employed. Infections not responsive to this empiric therapy should be cultured to determine the opportunistic organism’s sensitivity and susceptibility to specific antibiotic therapy.

The spatial and temporal changes in social vulnerability to natural hazards in Mexico are analyzed. To this end, using census data from 2000, 2010, and 2020 and a statistical method, different indices were computed, and with a GIS-based approach, patterns of social vulnerability are examined. In addition, a risk assessment test for severe weather (thunderstorms, hailstorms, and tornadoes) is made out. The results show different common social vulnerability driving factors in the three analyzed years, with root causes that have not been addressed since the beginning of the century. Likewise, a wider gap between Mexico's most and least vulnerable populations is identified. The changes in spatial patterns respond to different historical situations, such as migration, urbanization, and increased population. Also, poverty, ethnicity, and marginalization factors located in very particular regions in Mexico have remained relatively the same in the last few years. These situations have strongly influenced the spatial-temporal distribution of vulnerability in the country. The role of social vulnerability in the disaster risk to extreme events such as thunderstorms, hailstorms, and tornadoes in Mexico is fundamental to understanding changes in disaster distribution at the national level, and it is the first step to generating improvements in integrated risk management.

The use of unmanned aerial aircrafts (UAVs) is governed by strict regulatory frameworks that prioritize safety. To guarantee safety, it is necessary to acquire and maintain situational awareness (SA) throughout the operation. Existing Canadian regulations require pilots to operate their aircrafts in visual line-of-sight. Therefore, the task of acquiring and maintaining SA primary falls to the pilots. However, the development of aerial transport is entering a new era with the adoption of a highly dynamic and complex system known as advanced air mobility (AAM), which involves UAV operating autonomously beyond visual line-of-sight. SA must therefore be acquired and maintained primarily by each UAV through specific technologies and procedures. In this paper, we review these technologies and procedures in order to decompose the SA of the UAV in the AAM. We then use the system modeling language to provide a high-level structural and behavioral representation of the AAM as a system having UAV as its main entity. In a case study, we analyze one of the flagships UAVs of our industrial partner. Results show that this UAV doesn’t have all the technologies and methodologies necessary to achieve all the identified SA goals identified for the safety of the AAM. This work is a theoretical framework intended to contribute to the realization of the AAM, and we also expect to impact the future design and utilization of UAVs.

This study presents an in-depth analysis and evaluation of self-healing networks as an innovative solution to combat the escalating threat of ransomware attacks. Recognizing the limitations of traditional network security methods in the face of advanced cyber threats, the research focuses on the development and implementation of self-healing networks, characterized by their dynamic, intelligent response systems. Utilizing a combination of artificial intelligence and machine learning algorithms, these networks demonstrate remarkable adaptability and resilience. Through rigorous simulations, the study examines the efficacy of self-healing networks in detecting, isolating, and recovering from ransomware attacks, with an emphasis on their ability to learn and evolve from each incident. The research highlights the strengths and limitations of these networks and discusses their potential applications in various sectors. The findings suggest that self-healing networks, with their proactive and adaptive defense mechanisms, represent a significant advancement in cybersecurity strategies, offering a robust solution against a range of cyber threats. Future work is recommended to further enhance the capabilities of self-healing networks, particularly in the areas of speed, detection accuracy, and quantum-resistant security measures.

This research looks into the evolving dynamics of ransomware, shifting from conventional encryption-based attacks to sophisticated data exfiltration strategies. Employing the Bidirectional Encoder Representations from Transformers (BERT) model, the study analyzes network traffic patterns to detect ransomware activities, offering new insights into their covert operations. The findings emphasize the need for advanced AI tools in cybersecurity, highlighting the significance of adapting and innovating defense strategies to counter the changing landscape of ransomware threats. The study contributes to a deeper understanding of ransomware evolution and underscores the importance of integrating AI in cybersecurity practices.

Predicting long-term student achievement is a critical task for teachers and for educational data mining. However, most of the models do not consider two typical situations in real-life classrooms. The first is that teachers develop their own questions for online formative assessment. Therefore, there are a huge number of possible questions, each of which is answered by only a few students. Second, online formative assessment often involves open-ended questions that students answer in writing. These types of questions in online formative assessment are highly valuable. However, analyzing the responses automatically can be a complex process. In this paper, we address these two challenges. We analyzed 621,575 answers to closed-ended questions and 16,618 answers to open-ended questions by 464 fourth-graders from 24 low-SES schools. Using linguistic features of the answers and an automatic incoherent response classifier, we built a linear model that predicts the score on and end-of-year national standardized test. We found that despite answering 36.4 times fewer open-ended questions than closed questions, including features of the students’ open responses in our model improved our prediction of their end-of-year test scores. To the best of our knowledge, this is the first time that a predictor of end-of-year test scores has been improved by using automatically detected features of answers to open-ended questions on online formative assessments.

: Predicting long-term student learning is a critical task for teachers and for educational data mining. However, most of the models do not consider two typical situations in real-life classrooms. The first is that teachers develop their own questions for formative assessment. Therefore, there are a huge number of possible questions, each of which is answered by only a few students. Second, formative assessment often involved open-ended questions that students answer in writing. These types of questions in formative assessment are highly valuable. However, analyzing the responses automatically can be a complex process. In this paper, we address these two challenges. We analyzed 621,575 answers to closed-ended questions and 16,618 answers to open-ended questions by 464 fourth-graders from 24 low-SES schools. We constructed a classifier to detect incoherent responses to open-ended mathematics questions. We then used it in a model to predict scores on an end-of-year national standardized test. We found that despite answering 36.4 times fewer open-ended questions than closed questions, including features of the students’ open responses in our model improved our prediction of their end-of-year test scores. To the best of our knowledge, this is the first time that a predictor of end-of-year test scores has been improved by using automatically detected features of answers to open-ended questions on formative assessments.

We provide a detailed presentation of the functional renormalisation group (FRG) approach to weakly-interacting Bose-Bose mixtures, including a complete discussion on the RG equations. To test this approach, we examine thermodynamic properties of balanced three-dimensional Bose-Bose gases at zero and finite temperatures and find a good agreement with related works. We also study ground-state energies of repulsive Bose polarons by examining mixtures in the limit of infinite population imbalance. Finally, we discuss future applications of the FRG to novel problems in Bose-Bose mixtures and related systems.

We argue that several potentials proposed recently for the analysis of the vibrational-rotational spectra of diatomic molecules and their thermodynamic properties exhibit a flaw. One can easily show that the parameters $D_e $ and $r_e$ in those potentials are not the dissociation energy and equilibrium bond length, respectively, as the proposers believe. We show how to overcome the mistake in a simple and quite general way.

This work presents updated reconstructions of watershed runoff to San Francisco Estuary from tree-ring data to AD 903, coupled with models relating runoff to freshwater flow to the estuary and salinity intrusion. We characterize pre-development freshwater flow and salinity conditions in the estuary over the past millennium and compare this characterization with contemporary conditions to better understand the magnitude and seasonality of changes over this time. This work shows that the instrumented flow record spans the range of runoff patterns over the past millennium (averaged over five, ten, twenty and one hundred years), and thus serves as a reasonable basis for planning-level evaluations of historical hydrologic conditions in the estuary. Over annual timescales we show that, although median freshwater flow to the estuary has not changed significantly, it has been more variable over the past century compared to pre-development flow conditions. We further show that the contemporary period is generally associated with greater spring salinity intrusion and lesser summer-fall salinity intrusion relative to the pre-development period. Thus, salinity intrusion in summer and fall months was a common occurrence under pre-development conditions and has been moderated in the contemporary period due to the operations of upstream reservoirs, which were designed to hold winter and spring runoff for release in summer and fall. This work also confirms a dramatic decadal-scale hydrologic shift in the watershed from very wet to very dry conditions during the late 19th and early 20th centuries; while not unprecedented, these shifts have been seen only a few times in the past millennium. This shift resulted in an increase in salinity intrusion in the first three decades of the 20th century, as documented through early records. Population growth and extensive watershed modification during this period exacerbated this underlying hydrologic shift. Putting this shift in the context of other anthropogenic drivers is important in understanding the historical response of the estuary and in setting salinity targets for estuarine restoration. By characterizing the long-term behavior of San Francisco Estuary, this work supports decision-making in the State of California related to flow and salinity management for restoration of the estuarine ecosystem.

The boosted interest in the use of rare earth elements (REEs) in modern technologies had also increase the necessity of their recovery from various sources, including raw materials and wastes. Though Hydrometallurgy plays a key role in these recovery processes, some drawbacks (apparent or not) of these processes, had led to the development of a more environmental friendship subclass named Solvometallurgy, in which non-aqueous solvents substituted to the aqueous media of the hydrometallurgical processing. The non-aqueous solvents chosen for these usages are, besides ionic liquids (ILs), the so-named Deep Eutectic Solvents (DEEs). The utilization of DEEs included the leaching of REEs from the different sources containing them, and also in the separation-purification steps necessary for the yielding of these elements, normally as oxides or salts, in the most purified form. This work reviewed the most recent literature (2023 year) about the utilization of deep eutectic solvents in the recovery of REEs from various sources, and in the coupling of these two (DESs and REEs) to derived compounds to be used in other fields.

This article is about school evaluation. The objective was to explain critically the mediating relationships between educational evaluation and the production of school knowledge from the perspective of the Dialectical Historical Materialism. With this interest, a documentary analysis was made, obtaining as result four explanatory categories: production, subsumption, distribution and exchange of knowledge, which form a concrete totality with distinctions and antagonistic relationships between them, limited by their opposite. It is concluded that the evaluation as a mediating form lacks antagonistic pair and consequently of control by opposition, emerging from the school daily as autonomous and sovereign entity, oppressive of school subjects.

Among the possible improvements of Autonomous Emergency Braking (AEB) systems, reducing the intensity of the automatic braking process by studying the kinematics and general behavior of the pedestrian while crossing is crucial to determine the progressiveness of the braking, or replacing part of the braking process by an evasive maneuver when a collision is imminent. This paper proposes the integration of an autonomous avoidance system (Automatic Emergency Steering, AES) that acts directly on the steering system to generate an evasive maneuver and avoid a possible pedestrian collision (OPREVU-AES system), as well as the assessment of its effectiveness compared to a commercial AEB system. OPREVU and VULNEUREA are research projects in which INSIA and CEDINT have cooperated to improve driving assistance systems and the safety of pedestrians and cyclists through Virtual Reality (VR) techniques. The analysis of the kinematic and dynamic response of the OPREVU-AES system is conducted in CarSim© software. The effectiveness evaluation procedure is based on the reconstruction of a sample of road vehicle-to-pedestrian crashes (INSIA-UPM database), using the PCCrash® software, and taking as an indicator the probability of head injury severity (ISP). The results show that the AEB system would have prevented part of the collisions, especially after the incorporation of the OPREVU-AES system. In most of the cases where avoidance is not possible, a significant reduction of the ISP is achieved.

Non-linear loads in circuits cause the appearance of harmonic disturbances both in voltage and current. In order to minimize the effects of these disturbances and, therefore, to control over the flow of electricity between the source and the load, they are often used passive or active filters. Nevertheless, determining the type of filter and the characteristics of their elements is not a trivial task. In fact, the development of algorithms for calculating the parameters of filters is still an open question. This paper analyzes the use of genetic algorithms to maximize the power factor compensation in non-sinusoidal circuits using passive filters, while concepts of geometric algebra theory are used to represent the flow of power in the circuits. According to the results obtained in different case studies, it can be concluded that the genetic algorithm obtain high quality solutions that could be generalized to similar problems of any dimension.

Cities exemplify the evolving world with changing demographics and climates. Urban green spaces play a crucial role in improving the quality of life of people through their potential to mitigate temperatures. Therefore, comprehending their impact is of para-mount interest. Given the challenges in obtaining temperature data from urban locations, this study develops Artificial Neural Networks (ANNs) to predict daily and hourly temperatures in Valladolid, Spain, with a particular focus on urban allotment gardens and a forested urban park. ANNs were built and evaluated from various combinations of inputs (X), hidden neurons (Y) and outputs (Z) under the practical rule of "making net-works simple, to obtain better results". The best performing model was 6-Y-1 ANN archi-tecture with an impressive result of Root Mean Square Error (RMSE) = 0.42°C in the urban garden called Valle de Arán. However, other five ANN architectures were also tested (7-Y-5; 6-Y-5; 7-Y-1; 3-Y-Z and 2-Y-1). ANNs dedicated to urban temperature analysis hold immense potential for urban planning and research, aiding in under-standing the urban climate, forecasting future temperatures, identifying temperature mitigation strategies and even for managing urban crops

Monitoring training load, recovery, and wellness in maturation ages is even more relevant than in adulthood, especially during congested schedules. The objective of this study was to quantify and describe the differences in external and internal load, recovery, and perceived wellness in elite under-16 female players during a period of three competitions in seven days, based on their participation during matches. An observational design was used to quantify external load (total distance, high-speed distance, and PlayerLoad), internal load (differential sRPE), as well as recovery and wellness of 18 international under-16 players during a development tournament. The external load pattern of the starting players was undulating, with matches showing the highest internal load values compared to training sessions and non-starters. Non-starters players (whether training or matches) showed similar external load and differential sRPE values throughout the tournament (p&gt;0.05). Starting players experienced higher internal load than non-starters during matches (p&lt;0.037), without differences between training sessions (p&gt;0.05). No changes were seen in wellness or recovery (all p-values &gt;0.05), except for non-starters in session 3 compared to session 7 (p=0.024). Starting players had more weekly total minutes (p=0.018), differential sRPE (breath: p=0.002; leg: p=0.002; cognitive: p=0.003), total distance (p=0.003), and PlayerLoad (p=0.008) than non-starters. Our results indicate differing load patterns between starters and non-starters, which do not affect wellness and recovery differently. Starters accumulate more weekly total load. Coaches can use these findings to guide decisions and load compensations in high competition contexts for young female soccer players.

Climate change has become a major concern for developing countries given the risk that it posses on energy and food independence, and on general productivity. Despite having an energy system with low carbon intensity when compared to other Latin American countries, Colombia is already facing climate change impacts and requires urgent efforts to mitigate them. As a developing country, the challenge is bigger as policies for economic growth should be in line with the global commitment of reducing greenhouse gas emissions. With the aim of contributing to the design of climate policies, this study assesses the impact of economic development on the environment by examining the validity of the Environmental Kuznets Curve hypothesis for Colombia. Statistically validated and stable autoregressive distributed lag models are estimated for three different environmental indicators: carbon dioxide emissions, methane emissions, and ecological footprint. Moreover, the effects of other variables such as urbanization, foreign direct investment, value added of agricultural and industrial sectors, and energy use are analyzed with dynamic simulations. Empirical evidence supports a long-run equilibrium relationship among investigated variables and the existence of an inverted U-shaped EKC relationship between Gross Domestic Product (GDP) and methane emissions, and GDP and ecological footprint. Shifting to renewable energy sources and leveraging the use of cleaner technologies in agricultural and industrial sectors are found to be key for economic growth without harming the environment.

The purpose was to reflect on the sustainability of artisanal and informal mining in the Peruvian Andean zone by 2022. The type of research was basic, non-experimental design, ex post facto scope. The sample consisted of: statistics on crime prevention due to socio-environmental conflicts from the National Prosecutor's Office and the Ombudsman's Office, reports of interventions targeted at artisanal and informal miners. The procedure focused on gathering secondary information for a documentary review analysis that is based on proven and disseminated past facts. Artisanal and informal mining is strengthened in the Peruvian Andean zone by incidence and force: 1) the social license and agreements by community assembly, 2) the flexibility in compliance with the formalization, preventive and environmental protection laws, 3) the 50% increase in socio-environmental conflict due to mining activity, 4) a 25% progressive increase in the production costs of agricultural and livestock activities. The direct consequences on environmental sustainability are: 1) the delimited reconversion of the change in agricultural land use in a third of the communal territories in basin headwaters; 2) displacement of environmentally friendly economic activity by 20% in basin headwaters; 3) the impossibility of modification or intervention in water surfaces, bofedales by communal agreements.

While digitization can make the health industry faster and more efficient, there are many financial, social and organizational obstacles and barriers to its implementation. This article offers a systematic review of the main troubles detected from the literature review. Also there is a proposal of new kind of restrictions to pay attention. Finally, its offer an heterodox reading about the problem analyzed.

Italy suffered heavily with the new pandemic crisis caused by the novel coronavirus SARS-CoV-2. Given the low number of tests performed on the early stages of the outbreak, Italy lost track of most of infections. We use a modified SEIR model to reconstruct the most realistic infection curve using the hospitalization curve of the registered data. Using this method we estimated that, by the end of the first infection wave, about 3-4% of the population will have been infected by the virus. Following the same process, the number of deaths is projected to be between 100000 to 115000. We also find a significant correlation between the number of tests performed, the fraction of undocumented infections and the rate of change dI/dt of the real infection curve. We conclude that herd immunity is not enough to contain further spread of the disease inside the country.

The calculation of power flow in power systems with the presence of harmonics has been properly studied in the scientific literature. However, power flow calculation considering interharmonic components is still an open question. Traditional methods based on the IEEE1459 standard have proven to be valid and accurate only for linear and sinusoidal systems, but have been criticized for non-linear and non-sinusoidal systems because they are not able to explain correctly the current and voltage interactions beyond the active power. This paper proposes the use of a novel mathematical framework called geometric algebra (GA) to study the power flow considering the interaction of current and voltage harmonics and interharmonics. The use of GA enables the precise determination of the direction and magnitude of the total and single active power flow for each component, as well as other power elements related to the non-active power due to cross interaction. Moreover, this paper makes a novel contribution to the definition of interharmonics in geometric algebra space that has not been done before. To test the validity of the method, both linear and non-linear circuits are proposed and solved by applying voltages and currents with harmonic and interharmonic components. The results obtained show that power flow can be analyzed under the prism of the principle of energy conservation (PoCoE) in a way that allows a better understanding of the power spectrum due to the interaction of harmonics and interharmonics of voltage and current.

This paper proposes an upper bound for mine productivity and also a simple truck dispatch rule that demonstrates how tight the upper bound can be using simulation. It also proposes a greedy search to approximate the productivity upper bound, which is faster and often exact. Uncertainty is added to the simulation in order to verify how the productivity responds to it. Typically, the productivity upper bound is less tight close to its saturation point as a function of the number of trucks, where adding more trucks only increases queues. Furthermore, more uncertainty in the model typically leads to a less tight upper bound. The results are conducted using real data from an open pit mine in Brazil.

Risk management of electric power transmission lines requires knowledge from different areas such as environment, land, investors, regulations, and engineering. Despite the widespread availability of databases for most of those areas, integrating them into a single database or model is a challenging problem. Instead, in this paper, we use a single source, the Brazilian National Electric Energy Agency’s (ANEEL) weekly reports, which contains decisions about the electrical grid, comprising most of the areas. Since the data is unstructured (text), we employed NLP techniques such as stemming and tokenization to identify keywords related to common causes of risks provided by an expert group on energy transmission. Then, we used models to estimate the probability of each risk. Our results show that we were able to estimate the probability of 97 risks out of 233.

The aim of the current study has been to assess externalizing problems from a gender perspective in the juvenile population. This objective has been evaluated though the Child and Adolescent Assessment System questionnaire (SENA), to measure the dimension of externalized problems. A total of 128 Spanish students took part in the survey, comprising 58 (45.3%) male and 67 (52.3%) female, aged between 12 and 16 years old (M= 13,7; Sd= 1,14). The sample was taken from three High- Schools (state and private-subsidized) in Madrid, Spain. The research for this study was conducted through a descriptive, inferential, quantitative, ex post facto design. The data analysis was performed using SPSS 27.0. The result revealed an increase in externalized problems: Attention (M= 2.65, Sd=.88), Anger Control (M= 2.27, Sd=.85) and Hyperactivity- Impulsivity (M= 2.26, Sd=.68). In addition, female students score highly in Attention Problems (F= 4.124; Sig.: .004), Hyperactivity-Impulsivity (F: 6.18; Sig.: <.001), and Anger Control (F: 2.3; Sig.: .062). This study highlights the impact and increase on students externalized mental health problems currently. It is essential to have a protective environment and promote the development of emotional intelligence, encouraging the mental health in young people. This article is part of the findings of the research project “Therapeutic Innovation Classroom for the accompaniment of teenager with Serious Mental Disorder” (Reference: 2021V/EU-UNED/02).

The article explores Qualitative Comparative Analysis (QCA), an approach for establishing causal relationships between conditions and outcomes. QCA, applied in case studies and empirical analyses, facilitates generalizations for future research and addresses causal complexity by identifying various causal "recipes" leading to the same outcome. Three widely used QCA variants are Crisp-Set QCA (csQCA), Multi-value QCA (mvQCA), and Fuzzy-Set QCA (fsQCA), each with unique approaches. The article emphasizes the significance of bibliometric analysis in mapping a research field's intellectual structure, revealing key contributions and trends. In QCA, bibliometric analysis identifies leading researchers, affiliations, and influential countries. Results show QCA's widespread adoption across disciplines, with continuous growth in article production since 2013. The favored variant is fsQCA, followed by csQCA, and China notably contributes to QCA-related research. In conclusion, QCA proves a potent tool for discerning complex causal relationships in empirical studies, with its increasing interdisciplinary use indicating substantial potential for advancing scientific knowledge.

This study addresses the problem of accurately predicting azimuth and elevation angles of signals impinging on an antenna array employing Machine Learning (ML). Using the information obtained at a receiving system when a transmitter’s signal hits it, a Decision Tree (DT) model is trained to estimate azimuth and elevation angles simultaneously. Simulation results demonstrate the robustness of the proposed DT-based method, showcasing its ability to predict the Direction of Arrival (DOA) in diverse conditions beyond the ones present in the training dataset, i.e., the results display the model’s generalization capability. Additionally, the comparative analysis reveals that DT-based DOA estimation outperforms the state-of-the-art MUltiple SIgnal Classification (MUSIC) algorithm, establishing DTs as competitive alternatives for DOA estimation in signal reception systems.

This paper documents the development of a special case of multilingual Automatic Speech Recognition model, specifically tailored to attend two languages spoken by the majority of Latin America, Portuguese and Spanish. The bilingual model combines Language Identification and Speech Recognition developed with the Wav2Vec2.0 architecture and trained on several open and private speech datasets. In this model, the feature encoder is trained jointly for all tasks and different context encoders are trained for each task. The model is evaluated separately on two tasks: language identification and speech recognition. The results indicate that this model achieves good performance on speech recognition and average performance on language identification, training on a low quantity of speech material. The average accuracy of the language identification module on the MLS dataset is 66.75%. The average Word Error Rate in the same scenario is 13.89%, which is better than average 22.58% achieved by the commercial speech recognizer developed by Google.

In recent years the field of computer science has experienced great changes, due to the incredible advances in the field of artificial intelligence. Deep Learning models are responsible for most of them since the biggest milestones occurred in 2012 when AlexNet won the image classification challenge called ImageNet. These models have demonstrated great performances in different types of complex tasks like image restoration, medical diagnosis or object recognition. Their disadvantages are related to their high data dependency, which forces experts in the field to follow a precise methodology to obtain accurate models. In this paper, we describe a complete workflow that begins with the management of the raw data until the in-depth interpretation of the performance of the models. This should be taken as a high-level consultation document describing good practices that should be applied. Apart from the step-by-step methodology, we present different use cases that correspond to the two main problems of the field: classification and regression.

Europium is one of the most reactive lanthanides and humans use it in many different applica-tions, but we still know little about its potential toxicity and cellular response to its exposure. Two strains of the eukaryotic microorganism model Tetrahymena thermophila adapted to high concentrations of two Eu(III) compounds (EuCl3 or Eu2O3) were obtained and compared to a control strain and cultures treated with both compounds. In this ciliate, EuCl3 is more toxic than Eu2O3. LC50 values show that this microorganism is more resistant to these Eu(III) compounds than other microorganisms. Oxidative stress originated mainly by Eu2O3 is minimized by overexpression of genes encoding important antioxidant enzymes. The overexpression of metallothionein genes under treatment with Eu(III) compounds supports the possibility that this lanthanide may interact with the -SH groups of the cysteine residues from metallothi-oneins and/or displace essential cations of these proteins during their homeostasis function. Both lipid metabolism (lipid droplets that fuse with europium-containing vacuoles) and au-tophagy are involved in the cellular response to europium stress. Bioaccumulation, together with a possible biomineralization to europium phosphate, seems to be the main mechanism of Eu(III) detoxification in these cells.

In this article, we investigate limitations of importing methods based on algorithmic information theory from monoplex networks into multidimensional networks (such as multilayer networks) that have a large number of extra dimensions (i.e., aspects). In the worst-case scenario, it has been previously shown that node-aligned multidimensional networks with non-uniform multidimensional spaces can display exponentially larger algorithmic information (or lossless compressibility) distortions with respect to their isomorphic monoplex networks, so that these distortions grow at least linearly with the number of extra dimensions. In the present article, we demonstrate that node-unaligned multidimensional networks, either with uniform or non-uniform multidimensional spaces, can also display exponentially larger algorithmic information distortions with respect to their isomorphic monoplex networks. However, unlike the node-aligned non-uniform case studied in previous work, these distortions in the node-unaligned case grow at least exponentially with the number of extra dimensions. On the other hand, for node-aligned multidimensional networks with uniform multidimensional spaces, we demonstrate that any distortion can only grow up to a logarithmic order of the number of extra dimensions. Thus, these results establish that isomorphisms between finite multidimensional networks and finite monoplex networks do not preserve algorithmic information in general and highlight that the algorithmic information of the multidimensional space itself needs to be taken into account in multidimensional network complexity analysis.

Background: Many risk-modifying factors affect cancer incidence and mortality rates. Solar UVB reduces cancer risk by producing vitamin D, which has many mechanisms to reduce risk of cancer incidence, progression, and metastasis. Obesity increases cancer risk through several mechanisms, including increased inflammation. Method and Materials. We present an ecological study (a type of observational study) of incidence and/or mortality rates for cancers during 2007–2016 of 17 anatomical sites with respect to solar ultraviolet-B (UVB) doses and obesity prevalence. Data were averaged for 12 geographical regions. Results: Solar UVB doses were significantly inversely correlated with incidence rates for three types of cancer for males. They were inversely correlated with mortality rates for five cancers for males and four for females. Obesity prevalence was directly correlated with mortality rates for three cancers for males and seven for females. Discussion: The ecological approach has certain limitations, such as its sensitivity only to regional variations in relation to specific risk-modifying factors. In this study, these limitations include numerous factors influencing cancer risk and the fact that the data are averaged across entire regional populations, which may not reflect individual risks. Despite this, the study reveals important correlations. Specifically, it found that solar UVB exposure is linked to a lower risk of certain cancers, while obesity is associated with an increased risk of some cancers in France. These findings are consistent with results from a similar study in the United States on cancer rates between 2016 and 2020. The association between obesity and cancer risk aligns with other observational studies. However, it is uncertain to what extent higher solar UVB exposure or elevated serum 25-hydroxyvitamin D levels might mitigate the impact of obesity on cancer risk. Reducing cancer mortality in France may be achievable through vitamin D supplementation and efforts to lower obesity prevalence.

In southern South America, zonal flora, defined by macroclimatic conditions, is strongly influenced by an aridity filter near 29°S. Cushion bogs, high-altitude wetlands along the Andes, display homogeneous flora with cushion species dominance, contrasting with zonal vegetation. Despite being influenced by microclimatic conditions, these ecosystems experience varied environmental effects. This study identifies environmental filters affecting bog communities along a broad-scale latitudinal gradient from 15°S to 42°S. We analyzed 420 bogs and 284 species across three macroclimatic regions with distinct summer, winter, and transitional arid rainfall regimes. Using variance partitioning and membership-based regionalization models, we examined the impacts of climatic, edaphic, and spatial variables on beta diversity. We also assessed species' niche overlap and the influence of environmental filters on the communities' phylogenetic diversity. Results show that species turnover and niche overlap vary with macroclimatic differences, delineating three distinct regions. Notably, phylogenetic clustering in the driest part of the gradient (23°S —24°S) highlights the impact of the environmental filter. Aridity and temperature variations at a broad scale serve as environmental filters shaping the composition of bog communities across southern South America.

The objective of this research article is to determine if social and cultural capital are factors that cause inequalities in the level of knowledge in civic and citizenship education, between Latin American and European countries. To achieve this purpose, information from the National Study of Civic and Citizen Education -ICCS-, of the International Association for the Evaluation of Educational Achievement -IEA-, of the year 2016, is used. Methodologically speaking, the Educational Production Function -EPF- is estimated and subsequently, the Oaxaca-Blinder decomposition technique is applied to quantify the differences in civic and citizenship education and see how much they are explained by the characteristics of the student and their family. the school, the social and cultural capital. As a main result, educational inequalities were found in favor of European countries, and are due to a greater extent to differences in school resources, between Latin American and European countries, followed by differences in social and cultural capital, therefore, it is the European students who make the best use of and benefit from the differences in school endowments and in social and cultural capital.

Tree biomass and diversity relationship in mixed forest impacts on forest ecosystem services provisions. Tree biomass yield is driven by several aspects such as species identity, site condition, stand density, tree age as well as tree diversity expressed as species mingling and structural diversity. By comparing diverse degrees of tree mixture in natural forests we can insight on the ecosystem services provision level and dynamic. Two monitoring sites in the Castilian Northern Plateau (Spain) have been analyzed to disentangle the relationships between biodiversity levels and tree biomass yield. Two permanent one ha squared plots were established at Llano de San Marugan and Valdepoza. In each plot all individual trees were measured (diameter and height), georeferenced and its species identity defined. Tree species in the two sites were Pinus sylvestris, Pinus nigra, Pinus pinea, Quercus pyrenaica, Quercus ilex, Quercus faginea and Juniperus thurifera. From these datasets ten diversity indices that fall in three categories (species richness indices, species compositional/mingling indices and vertical structural indices) were used as predictor variables to fit several candidate models. By merging the trees by site (without considering the species identity) selected models include individual tree basal area as explanatory variable combining by addition or interaction with diversity indices. When species are analyzed independently structural diversity impacts on biomass yield in combination (additive or multiplicative) with tree size is negative Pinus nigra and positive for the other species.