Search Results (2,893)

Introduction: Influenza has a major impact on public health. The best way to prevent it is through vaccination. In Catalonia, influenza vaccination has been recommended for children aged 6 to 59 months since the 2023–24 season. Objective: To assess the association between the implementation of this vaccination program and changes in influenza diagnoses in primary care and influenza-related hospitalizations in all age groups. Materials and Methods: Quasi-experimental study with interrupted time series (ITS) analysis. All influenza diagnoses made in primary care (PC) and all influenza-related hospitalizations in the Central Catalonia health region between October 2018 and August 2025 were included. The monthly aggregated cases were analyzed using segmented negative binomial regression models that accounted for temporal trends, the onset of COVID-19, and the introduction of systematic pediatric influenza vaccination. Results: A total of 6804 influenza diagnoses made in PC and 3252 hospitalizations for influenza were analyzed. A statistically significant decrease was observed in the percentage of influenza diagnoses in PC in the 2–4 (13.5% vs. 10.6%) and 5–14 (26.1% vs. 16.3%) age groups. In the ITS analysis conducted in primary care (PC) settings, the vaccination period was significantly associated with a 13% reduction in expected influenza cases among individuals aged 15–64 years (RR 0.87 [0.78; 0.99]). After sensitivity analysis, these results were no longer statistically significant. The ITS analysis in the hospital setting has not shown a significant reduction in expected influenza cases or in expected admissions. Conclusions: Systematic influenza vaccination in children aged 6 to 59 months has not been shown to be associated with a reduction in influenza cases in primary care or hospitals settings during the early stages of implementation of the new vaccination program. Full article

As artificial intelligence (AI) becomes increasingly embedded in organizational processes, questions about its implications for leadership have gained growing relevance. However, the existing literature remains fragmented, often addressing strategy, leadership capabilities, governance structures, or ethical concerns in isolation, without explaining how these dimensions interact to shape leadership effectiveness in AI-driven environments. This study conducts a PRISMA-guided systematic review of 33 peer-reviewed articles to examine how AI-embedded leadership is conceptualized across contexts. By synthesizing findings across strategic, human, and governance domains, the analysis identifies recurring patterns and structural relationships in the literature. The results indicate that effective leadership in AI-intensive settings is not determined solely by technological adoption or digital competencies, but by the alignment between the depth of AI integration in decision-making processes, leaders’ capacity to interpret and oversee algorithmic outputs, and the presence of governance mechanisms that ensure transparency, accountability, and trust. While some studies highlight potential opportunities associated with AI, these remain less systematically developed compared to the extensive focus on challenges and emerging risks. On this basis, the study introduces the AI-Leadership Configurational Framework (ALCF), a multi-level model that conceptualizes leadership effectiveness as the outcome of systemic alignment. The framework integrates previously disconnected debates and provides a coherent foundation for future empirical research on leadership in the algorithmic age. Full article

Bacillus species are extensively studied, utilized, and commercialized biocontrol agents, demonstrating significant effectiveness in managing a variety of plant diseases. Bacillus possesses a robust intrinsic biosynthetic ability, capable of producing a diverse array of antimicrobial metabolites, including dipicolinic acid (DPA; 2,6-pyridinedicarboxylic acid), which exhibits antifungal properties and serves as a principal structural component of Bacillus spores. This study revealed that DPA exhibits significant antibacterial activity against the hazardous soybean pathogen Xanthomonas citri pv. glycines (Xcg), with an EC₅₀ value of 53.2 μg/mL. DPA inhibited Xcg swimming motility, extracellular protease activity, and biofilm formation, while inducing significant membrane irregularities in Xcg cells. DPA treatment downregulated the expression of several Xcg membrane integrity-related genes, including cirA, czcA, czcB, emrE, and tolC. The preventive and curative application of 500 μg/mL DPA reduced Xcg symptoms by 82.7% and 83.8%, respectively, and induced the accumulation of the isoflavone genistin in soybean leaves. DPA exhibited only weak toxicity in the zebrafish model, suggesting its potential suitability for agricultural commercialization. Overall, this study provides the first detailed characterization of the antibacterial mechanism of DPA against a phytopathogenic bacterium, Xcg, and identifies DPA as a previously underexplored antibacterial metabolite from Bacillus and Paecilomyces with potential for disease management. Full article

A new control method for efficiency optimization in systems composed of parallel converters is presented in this paper. The proposed methodology considers the individual efficiency surfaces for given ratings of power and voltage and determines the optimum operating point for each converter such that the global system efficiency is maximized throughout the entire operating spectrum. Furthermore, a supervisory control strategy is proposed to manage the power-sharing of the converters according to the optimal surfaces provided by the methodology, enabling a performance enhancement for the system by improving its efficiency. Different approaches can be used to implement the active current sharing (ACS) scheme, and in-depth discussions are provided to guide the designer through the tradeoffs to achieve the desired transient and steady-state behavior for the system. Experimental results show that under light load operation, an improvement of 8.5% is achieved in comparison with a conventional technique of equal power-sharing. This points out that the proposed strategy is especially applicable and can significantly improve the performance of systems powered by batteries or renewable sources. Full article

Warm rain events occur when moist air masses containing elevated precipitable water produce high rainfall rates capable of generating local flash floods. Catalonia, located on the northeastern Mediterranean coast of the Iberian Peninsula, is regularly affected by such episodes: approximately 70% of daily precipitation events exceeding 10 mm with fewer than ten cloud-to-ground lightning flashes can be classified as warm rain. The current research aimed to identify the meteorological conditions most conducive to heavy warm rain episodes in Catalonia. These cases are commonly associated with flash flood episodes in the study region. We utilized rain gauges, lightning data, radar, and model fields, combined with radio sounding profiles. First, we identified and characterized warm rain cases, and second, we have selected some relevant cases to characterize the phenomenon. These events occur predominantly along the Catalan coast during the warm season, typically following the passage of a cold front, and are associated with shallow convective clouds producing little or no lightning. However, the key determining factor is a characteristic vertical thermodynamic profile: a moist and saturated lower troposphere with high precipitable water beneath a low- to mid-level thermal inversion, and weak instability concentrated near the surface. Furthermore, local wind convergence plays a principal role in the rainfall pattern. Full article

Background/Objectives: In recent years, research on psychosis has increasingly focused on prevention, aiming to implement early interventions that mitigate or reduce its impact. Within this framework, the analysis of linguistic markers in individuals with at-risk mental states (ARMS) has proven valuable for identifying those at risk and predicting psychosis onset. Artificial intelligence tools, particularly natural language processing (NLP), have emerged as effective resources for detecting these language-based indicators. This study aims to synthesize the existing scientific evidence on linguistic markers analyzed through NLP techniques in individuals with ARMS. Methods: A systematic review following the PRISMA 2020 protocol was conducted. Three databases (PubMed, PsycInfo, and Scopus) were searched for published articles from their inception to October 2025. Rayyan software was used to manage references and article downloads. Out of ninety initial search results, fifteen studies involving 1313 participants from diverse groups were included in the review. Results: The findings indicated that alterations in semantic coherence, syntactic complexity, referential cohesion, and speech/content poverty differentiated ARMS individuals from healthy controls. Several of these markers, analyzed with NLP methods, predicted the onset of psychosis with accuracy levels ranging from 79% to 100%, although these findings should be interpreted with caution due to the significant methodological heterogeneity and variability in sample sizes across the included studies. Conclusions: NLP techniques offer a powerful approach for detecting language alterations that distinguish ARMS individuals and provide meaningful predictions of psychosis onset, highlighting their potential as a complement to traditional clinical assessments for early identification and prevention. Full article

Neurofilament light chain (NfL) is a promising biomarker of axonal injury across acute and chronic neurodegeneration, which can improve drug discovery and disease monitoring models. Traditional in vivo animal models cannot fully mimic human pathophysiology of neurodegenerative diseases (NDDs), but in vitro models based on human cells solve this problem, reducing the time and cost of drug testing. We developed an electrochemical immunosensor for NfL detection in cell culture media to monitor acute neuronal injury in in vitro models. The biosensor was designed in two configurations: the label-free system, which directly detects NfL in the sample via the antibody–antigen interaction, and the sandwich configuration, which incorporates two additional antibodies. Detection was examined using electrochemical techniques, including cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and chronoamperometry (CA). The sensor demonstrated a detection limit of 3–9 pg mL⁻¹, and a dynamic working range spanning from 10 up to 10⁷ pg mL⁻¹. Importantly, NfL was successfully detected in physiological media collected from cultured neurons that were differentiated from the long-term human neuroepithelial-like stem cells. This discovery highlights the platform’s applicability for in vitro neurodegenerative models. The immunosensor offers a sensitive, scalable, and cost-effective alternative for neurodegeneration detection in drug testing applications. Full article

Background: Women with atrial fibrillation experience a higher lifetime risk of ischemic stroke, greater stroke severity, and worse functional outcomes than men. Preventive strategies focused on AF detection may therefore miss critical opportunities for early intervention in women. Methods: We developed a decision-analytic Markov model using real-world primary care data from Catalonia (Spain) to evaluate an artificial intelligence (AI) enabled strategy for upstream thromboembolic risk detection. The intervention combined electronic health record–based risk prediction, targeted digital rhythm screening, and individualized anticoagulation. Lifetime clinical and economic outcomes were estimated for adults aged ≥65 years, with pre-specified sex-stratified analysis. Results: Compared with usual care, the AI-enabled strategy reduced ischemic stroke, major adverse cardiovascular events, and long-term disability. Absolute reductions in stroke and disability were greater in women, reflecting higher baseline thromboembolic risk. Per 1000 high-risk women, the strategy prevented more strokes and generated larger quality-adjusted life-year gains than in men. From both healthcare payer and societal perspectives, the intervention was cost-saving in women, driven by reductions in stroke-related disability and long-term care. Conclusions: AI-enabled upstream thromboembolic risk detection may deliver particularly important benefits for older women and represents a promising approach to reduce sex-based inequities in stroke prevention. Full article

Background/Objectives: The question addressed in the current work is to develop a simulation of a pharmaceutical process (DNA encapsulation within lipid nanoparticles using a microfluidic micromixer) which will be of utility to the end users (laboratory-scale formulation development). The simulation and the microfluidic approach also address sustainability issues, such as reducing the environmental impact of the process itself, and reducing the need for physical testing. The paper details the implementation and validation, taking into account key performance indicators and control parameters. Methods: The main method applied for simulation development is a novel multi-agent approach to incorporate stochastic probabilistic behavior, combined with theoretical definitions from the process experts and relevant literature, and data/results from laboratory-scale experiments with different parameter configurations. Results: The simulation was implemented as a representation of the real physical process, reproducing the relationships between process parameters (flow rates) and experimental key performance indicators (capsule diameter, poly dispersion index, encapsulation efficiency). The simulation results demonstrated a general agreement with the empirical results and provided useful predictive insights for the laboratory experiments. Conclusions: The simulation has potential as a support tool for laboratory experiments to reduce physical testing and indicate the most promising configurations on which to focus, with potential savings in time, resources and other costs. Full article

Epistemology remains tacitly anthropocentric: it treats knowledge as something produced and validated through human cognitive capacities such as understanding, intuition, and transparent justification. Yet contemporary science and artificial intelligence increasingly depend on non-human systems that generate mathematically valid results, empirically successful models, and operationally reliable inferences that no human can fully survey or interpret. This article develops xenoepistemics, a structural theory of non-anthropocentric knowledge. The central claim is that epistemic evaluation must be reformulated in terms of system-level properties—reliability, robustness, counterfactual sensitivity, and domain transfer—rather than mentalistic notions such as belief or understanding. I offer (i) a definition of xenoepistemic systems as systems that track structure in a target domain without requiring human-style semantic access; (ii) a minimal account of epistemic agency without minds that avoids trivialization; and (iii) a non-circular trust framework that distinguishes empirical success from epistemic legitimacy using independent validation regimes. This paper addresses a reflexive worry—that a human-authored theory cannot dethrone human epistemology—by separating standpoint from object: xenoepistemics is articulated by humans but is not about human cognition. I discuss the pragmatic value of xenoepistemic knowledge production, the limits of independent verification for opaque systems, domain-relative thresholds for xenoepistemic authority, and the problem of constitutionally human-inaccessible knowledge. Finally, I diagnose and formalize the Marcusian regress paradox: recurrent goalpost-shifting, whereby every machine competence is reclassified as irrelevant once achieved. Xenoepistemics reframes this debate by treating non-human knowledge as a present reality requiring new norms, not as a future curiosity. Full article

Anti-analysis techniques, also known as evasive techniques, enable Windows malware to detect and evade dynamic inspection environments, undermining the effectiveness of virtual-machine and sandbox-based inspection. Despite extensive prior research, no unified classification has been paired with a large-scale empirical evaluation of static detection capabilities for these behaviors. This paper addresses this gap by presenting a comprehensive classification and detection framework. We consolidate 94 anti-analysis techniques from academic, community, and threat-intelligence sources into nine mechanistic categories and derive corresponding YARA rules for static identification. In total, 82 YARA signatures were authored or refined and evaluated on 459,508 malware and 92,508 goodware samples. After iterative refinement using precision thresholds, 42 rules achieved high accuracy (≥75%), 16 showed moderate precision (50–75%), and 24 were discarded due to unreliability. The results indicate strong static detectability for firmware- and BIOS-based checks, but limited precision for timing-based evasions, which frequently overlap with benign behavior. Although YARA provides broad coverage of observable artifacts, its static nature limits detection under obfuscation or runtime mutation; our measurements therefore represent conservative estimates of technique prevalence. All validated rules are released in an open-source repository to support reproducibility, improve incident-response workflows, and strengthen prevention and mitigation against real-world threats. Future work will explore hybrid validation, container-evasion extensions, and forensic attribution based on signature co-occurrence patterns. Full article

Agroindustrial residues represent an abundant and underutilized source of carbon-rich materials for environmental remediation. In this study, annatto processing waste (Bixa orellana), a largely unexplored lignocellulosic by-product generated during pigment extraction, was converted into hydrochar via hydrothermal carbonization at 200 °C for 3 h. The resulting hydrochar (HC-AW) exhibited a predominantly amorphous carbon structure with retained oxygen-containing surface functionalities, and a solid yield of 44%, indicating efficient biomass conversion under subcritical conditions. Adsorption performance toward tetracycline was evaluated through pH-dependent experiments, kinetic modeling, equilibrium isotherms, and thermodynamic analysis. Maximum adsorption occurred under near-neutral conditions (pH ≈ 7), consistent with the interplay between tetracycline speciation and the hydrochar surface charge (pH_PZC ≈ 6.3), highlighting its potential applicability under realistic water treatment conditions without pH adjustment. Kinetic data were well described by the pseudo-second-order model, while equilibrium results were best fitted by the Langmuir model, with a maximum adsorption capacity of 14.94 mg g⁻¹ at 30 °C. Thermodynamic analysis indicated a spontaneous and slightly endothermic adsorption process. Overall, the results highlight the potential of annatto-derived hydrochar as a low-cost adsorbent and provide insight into the relationship between surface properties and adsorption behavior governing antibiotic removal from aqueous systems. Full article

Brown trout (Salmo trutta) populations at the southern edge of the species’ distribution are increasingly exposed to interacting climatic, biotic, genetic, and anthropogenic pressures. This study provides an integrative assessment of the drivers of variation in brown trout density, biomass, and size structure across six Mediterranean river basins in Catalonia (NE Iberian Peninsula), based on long-term standardized electrofishing surveys (2016–2025; 88 sites). Generalized linear mixed models revealed that mean summer temperature, genetic introgression from hatchery-derived Atlantic lineages, and the density of introduced fish species were the most consistent negative predictors of total density, biomass, and juvenile abundance (<120 mm FL). Hydrological and nutrient variables showed comparatively weak or non-significant effects relative to thermal and biotic predictors. Fishing regulations significantly influenced the biomass and density of larger trout (>220 mm FL), with lower values in harvest-allowed sections, whereas total density was less responsive to regulation. These findings indicate that Mediterranean brown trout populations are primarily constrained by thermal conditions, genetic integrity, and biological invasions, with implications for conservation and fishery management under ongoing climate warming. Full article

In desert ecosystems, deep-rooted plants like Alhagi sparsifolia contribute not only to wind prevention and sand fixation but also to the transport of carbon into deep soil layers through their root systems. However, the sources and stabilization mechanisms of soil organic carbon (SOC) following plant carbon input remain unclear. This study investigated a dominant A. sparsifolia community at the southern edge of the Taklimakan Desert. We analyzed plant traits and the vertical distribution (0–200 cm) of SOC fractions—particulate organic carbon (POC), mineral-associated organic carbon (MAOC), and calcium/iron-bound organic carbon (Ca/Fe-OC)—along with carbon sources (microbial biomass, microbial necromass, and plant residue). As growth advanced, stem and root biomass increased, while leaf and thorn biomass remained stable. SOC and POC decreased by 5.38–29.43% with soil depth, whereas MAOC and Ca/Fe-OC increased by 32.34–48.15%. Plant residue contributed more to SOC (average 30.56%) than microbial necromass (8.28%), and both contributions increased by 9.60–167.68% with soil depth. No significant correlation was found between plant residue and SOC fractions, but a significant correlation with microbial necromass. In conclusion, although plant residues constitute the primary source of SOC in desert ecosystems, microbial necromassa exerts a stronger influence on SOC stability. Full article

This study presents a comparative analysis of residential segregation among the three largest foreign-born populations residing in Spain: Moroccans, Romanians, and Colombians. Using data from Spain’s Population and Housing Census (INE), the research analyzed the segregation index across 34 urban areas in medium-sized cities. Three urban areas in Catalonia were selected for the intraurban case studies, and Pearson correlation coefficients were calculated for 60 sociodemographic variables. The objectives were to identify indicators that helped diagnose potential segregation contexts and to explore residential patterns by country of origin. The study was grounded in a central premise: the foreign-born population cannot be treated as a homogeneous group, as aggregation conceals group-specific inequalities and differentiated spatial configurations. The findings showed that segregation occurred. Moroccans exhibited the highest levels of segregation, which was associated with socioeconomic vulnerability and also a marked residential preference for central urban areas. Colombians displayed lower levels of segregation and greater territorial dispersion, pointing to broader residential access. Romanians presented intermediate and heterogeneous patterns, which combined localized concentrations with peripheral settlement. The results highlighted how intraurban differentiation emerged from interactions between different migrant profiles, housing opportunity structures, and urban morphology, providing an empirical basis on which to design targeted urban policies. Full article