Abstract: The Atherogenic Index of Plasma (AIP), ratio between triglycerides and high-density lipoprotein cholesterol, has been associated with cardiovascular (CV) events, metabolic syndrome and hypertension (HT)-related vascular organ damage. However, the majority of the published studies suffer from important limitations, such as the cross-sectional or retrospective nature and the performance in selected Asian populations only. To overcome these limitations, we performed an analysis of the data collected in the Pressioni Arteriose Monitorate E Loro Associazioni (PAMELA) study, providing longitudinal information on the relationships between AIP, diabetes mellitus (DM), HT and left ventricular hypertrophy LVH) in a western european general population. At the study entry baseline data were collected in 2035 subjects, while longitudinal data were obtained in 1412 subjects examined for a median follow-up time amounting to 10.7 years. 50.6% of the subjects were males. aged 50.9±13.7 (mean±SD) years. During the follow-up AIP did not significantly change (from -0.11±0.3 to -0.12±0.29 a.u., P=NS), while systolic BP and plasma glucose significantly increased with a significant relationship with HT, DM and LVH development (P <0.0001). At multivariable analysis, AIP significantly and independently predicted all the above-mentioned outcomes, with the exception of HT development based on home BP. The same significant association was detected for fatal and non-fatal CV events. These data provide evidence that in a general European population characterized by a low CV risk AIP significantly and independently predicts HT, DM and LVH development and is significantly associated with the composite outcome of CV morbidity and mortality.

Abstract: Over the last 20 years Deep-Eutectic-Solvents (DES) have been making a significant impact in the field of chemistry, with applications in nanotechnology, biomass transformation, electrochemistry pharmaceuticals and a host of other applications that includes catalysis. Considering the importance of chiral organocatalysis for the selective synthesis of drugs, pharmaceuticals and fragrances etc. DESs were quickly harnessed as the media for carrying out organocatalytic transformations. In this review, we discuss some of the most important examples from the literature that have made an impact in the field over the last 5 years. A more recent development has been the incorporation of DESs in structured and self-organized gel-like assemblies that are known as EutectoGels. These soft structures offer a more defined and compact environment that can influence stereoselectivity by pre-organizing the reactants in three-dimensional space, and potential control the types of transition states that can be formed.

Abstract: As the generation of data in the life and health sciences expands rapidly, there is a growingneed for professionals and students in these fields to master core bioinformatics skills,particularly those relating to Unix-like systems, most commonly used in bioinformatics. Thispaper introduces two key contributions to address this need: (1) A Unix curriculum for lifescientists with little or no command line experience, based on progressive Unix skill levels forbioinformatics and (2) An implementation of this curriculum into a series of interactive onlinetutorials deployed through Sandbox.bio - an open-source platform for learning bioinformaticsthat embeds a command line in the browser, which removes barriers related to softwareinstallation and access. We performed an overall evaluation of this teaching framework indifferent contexts. This inclusive, sustainable approach provides widespread access toessential bioinformatics skills for Life Science students and professionals alike.

Abstract: Reinforcement learning (RL) agents are increasingly deployed for voltage control in power distribution networks. However, their opaque decision-making creates a significant trust barrier, limiting their adoption in safety-sensitive operational settings. This paper presents XRL-LLM, a novel framework that generates natural language explanations for RL control decisions by combining game-theoretic feature attribution (KernelSHAP) with large language model (LLM) reasoning grounded in power systems domain knowledge.We deployed a Proximal Policy Optimization (PPO) agent on an IEEE 33-bus network to coordinate capacitor banks, tap changers, and shunt regulators, successfully reducing voltage violations by 90.5% across diverse loading conditions. To make these decisions interpretable, KernelSHAP identifies the most influential state features. These features are then processed by a domain-context-engineered LLM prompt that explicitly encodes network topology, device specifications, and ANSI C84.1 voltage limits.Evaluated via G-Eval across 30 scenarios, XRL-LLM achieves an explanation quality score of 4.13/5. This represents a 33.7% improvement over template-based generation and a 67.9% improvement over raw SHAP outputs, delivering statistically significant gains in accuracy, actionability, and completeness (p< 0.001, Cohen’s d values up to 4.07). Additionally, a physics-grounded counterfactual verification procedure which perturbs the underlying power flow model, confirms a causal faithfulness of 0.81 under critical loading.

Abstract: Valuably, the International Code of Zoological Nomenclature provides, under its principle of priority, that a species name that has been declared a synonym of an earlier proposed species remains contestable and open to future research. However, as the Code is concerned with namenclature and not with taxonomic concepts, it places no restrictions on declarations of synonymy, enabling them to be published without supporting evidence. This freedom, which is at the core of the synonymy problem contributes to taxonomic inflation and constrains estimates of diversity. From a theoretical perspective, evidence in an unsupported declaration devolves to that of one specimen, the holotype. Furthermore, in the absence of other evidence, the declaration further devolves to one of opinion, with readers left to judge its value based on their knowledge of the declarator. This approach, based on typification, is contrasted with one in which a species exists independently of our perception and is viewable as samples of its local populations. Rather than an arbiter of usage, the holotype then acts only as a referent to its local source population, whose properties define much of the species concept. A worked example of these concepts using data from the planktonic Foraminifera supports the view that evidence of synonymy lies in the source populations of the holotypes.

Abstract: (1) Background: Privacy usability in IoT smart home companion applications remains an underexplored domain despite mounting regulatory requirements and accelerating user adoption. Heuristic evaluation offers a scalable pathway to privacy usability assessment, yet validated frameworks for applying such methods in real industrial settings are scarce. This study presents the first empirical application of the ABCDE Privacy Framework, a ten-heuristic instrument grounded in Nielsen’s usability principles and Privacy by Design, to an IoT companion application developed with a major European home appliance manufacturer. (2) Methods: A structured workshop was conducted with a multidisciplinary team of seven participants (five industry professionals and two researchers) following a two-round protocol: a qualitative heuristic discussion phase (Round 1) and an individual scoring phase (Round 2). Data were analysed through MAXQDA. (3) Results: Average heuristic scores ranged from 3.6 (H9: error recovery) to 4.8 (H6: recognition; H10: documentation), with an overall mean of 4.32. Six second-order themes were identified, including Transparency Asymmetry, Centralised but Decontextualised Privacy, and Shared Household Complexity. (4) Conclusions: The ABCDE Privacy Framework is feasible, time-efficient, and analytically productive in real industrial contexts, generating design-relevant insights and enabling cross-role team alignment within a two-hour session. These findings support its potential as a scalable tool for Privacy by Design practice in IoT product development.

Abstract: Background/Objectives: Congenital Talipes Equinovarus (CTEV) is a prevalent musculoskeletal deformity requiring precise management to prevent lifelong disability. While the Ponseti method and Dennis Brown Splint (DBS) are global standards, relapse remains a critical challenge in resource-constrained regions like the Eastern Cape. This study aimed to evaluate the clinical effectiveness of the DBS in maintaining foot correction, preventing relapse, and promoting functional mobility in paediatric patients post-Ponseti treatment at two Eastern Cape hospitals. Methods: A retrospective clinical evaluation was conducted of the medical records of 33 paediatric patients (51 feet) aged 2–12 months, diagnosed with idiopathic clubfoot. Participants were sampled from Frere Tertiary Hospital (n=23) and Bedford Orthopaedic Hospital (n=10). Outcome measures included changes in Pirani scores and goniometric dorsiflexion angles. Adherence was categorised by follow-up frequency, and qualitative clinical notes were reviewed to identify systemic barriers to successful bracing. Results: The Frere Hospital cohort showed a statistically significant improvement in Pirani scores, decreasing from a mean of 4.20 (±0.90) to 0.59 (±0.47) (p < 0.001), with a relapse rate of 13%. In contrast, the Bedford Hospital cohort exhibited non-significant improvements and a 50% relapse rate. Adherence was strongly associated with out-comes; patients with high follow-up frequency achieved significantly better correction. Systemic factors, including fitting errors and supply chain issues, were identified as primary drivers of poor outcomes. Conclusions: The DBS is biomechanically effective for maintaining correction, but its clinical success is highly contingent on systemic support, practitioner expertise, and caregiver adherence.

Abstract: Objectives: The mechanism of action of genes related to lactate metabolism in papillary thyroid carcinoma (PTC) is still unclear. In this study, key genes that play a role in PTC were selected from the known genes related to lactate metabolism, and their roles in promoting lactate metabolism in PTC cells were investigated. Methods: Through bioinformatics analysis and cell experiments, the roles of the relevant genes in lactate metabolism and their roles in the occurrence and development of PTC were verified. Results: Through bioinformatics analysis, 12 candidate genes were obtained. Through qRT-PCR experiments, it was confirmed that the expressions of TIMP1 and DPP4 were higher in thyroid papillary carcinoma than in normal PTC cells. By inhibiting the expression of TIMP1 and DPP4 using siRNA, the invasion and proliferation abilities of PTC could be reduced. Compared with normal thyroid cells, the contents of lactic acid and LDHA in PTC cells were higher. Knocking down the expression of TIMP1 and DPP4 would reduce the lactate production ability of PTC cells, and TIMP1 and DPP4 promoted the accumulation of lactate in PTC cells.

Abstract: The integration of artificial intelligence (AI) into solar energy systems has emerged as a transformative pathway to enhance efficiency, reliability, and sustainability in renewable energy. This review provides a comprehensive examination of recent advances in AI-driven optimization and integration strategies across photovoltaic and solar thermal technologies. A particular emphasis is placed on machine learning and deep learning techniques applied to solar irradiance forecasting, maximum power point tracking, fault detection, energy management, and predictive maintenance. Unlike earlier reviews that focused on isolated applications, this work highlights the systemic role of AI in enabling smart grids, hybrid systems, and large-scale energy storage integration. The novelty of this contribution lies in mapping the evolution from traditional control methods to intelligent, self-adaptive frameworks that couple physical modeling with data-driven approaches, offering a structured roadmap for future developments. Furthermore, the review identifies challenges such as data scarcity, computational demand, and interpretability of AI models, while outlining opportunities for process intensification, resilience, and techno-economic optimization. By bridging technical progress with implementation prospects, this article provides an updated reference for researchers, policymakers, and industry stakeholders seeking to accelerate the deployment of AI-enhanced solar energy solutions.

Abstract: Triptolide (TPL), a diterpenoid derived from the Chinese medicinal plant Tripterygium wilfordii, exhibits broad-spectrum biological and pharmacological activities, although its clinical translation is limited by systemic toxicity. Recent advances in the development of TPL derivatives have created new therapeutic opportunities. This review summarizes current knowledge of triptolide, with a focus on TPL’s toxicity profile, derivative strategies, and antitumor mechanisms. There are at least ten cancer types studied by research. We also summary the plant origin and traditional uses of the plant, TPL’s pharmacokinetics (PK), and relevant clinical trials against tumors. The main mechanism of TPL antitumor effect is interfere ATPase of XPB via covalently binding to it, as well as inducing rapid depletion of RPB1 via hyperphosphorylation and ubiquitination. We also reviewed systematic toxicity including neuro, cardio, oto, nephron, hepato, digestive hemato and reproductive toxicity. Finally, we searched the clinical trials through three platforms against tumor and concluded that Minnelide has great potential against solid tumor in clinic. By critically evaluating TPL and its derivatives from multiple dimensions, this review aims to inform future strategies that maximize therapeutic efficacy while minimizing adverse effects.

Abstract: The main objective of the study is to reduce the dwell time of wagons at stations and to improve the efficiency of shunting locomotive utilization. The problem has a combinatorial nature, since an increase in the number of loading and unloading fronts leads to a sharp growth in the number of feasible service variants. During the research, a mathematical model describing the servicing process of industrial sidings was developed. The study addressed the problem of determining the optimal sequence of wagon deliveries and the optimal distribution of workload among shunting locomotives. Under conditions where two or more shunting locomotives are used, an optimization method based on the indicator of wagon-hours reduction (σ) was proposed for allocating loading and unloading fronts. Using combinatorial properties, it was shown that many possible allocation variants are symmetric, which allowed the development of a mathematical solution that simplifies the search for an optimal solution. Computational results demonstrated that, at the hypothetical railway station “N-1”, applying the optimal service sequence reduces wagon dwell time by 21% compared with an arbitrary sequence. At the hypothetical station “N-2”, distributing wagon groups between two shunting locomotives improves the efficiency of the servicing process by 26% compared with using a single locomotive. Based on the proposed mathematical model and algorithm, a practical software tool was developed that enables the automatic determination of service sequences for loading and unloading fronts. The software allows the identification of optimal servicing orders, analysis of alternative variants, and evaluation of the efficiency of shunting locomotive utilization.

Abstract: The CO₂-emissions are rapidly rising with new records and the transport sector is considerably contributing to GHG emissions. The critical transition towards electrification and sustainable development demands a radical change in the transport industry. One of many solutions is to analyze the environmental benefits of optimized vehicle production and recycling of the vehicle components after its usable life to reduce dependency on limited raw materials. Electric motor is one of the most crucial powertrain components, yet studies on the overall ecological profile of production and end of its useable life is limited. This study examines the life cycle assessment (LCA) impacts of electric motors used in passenger cars and potential recycling of its materials. The analysis covers production and recycling of components, crucial elements, and permanent magnets. The results show that housing and rotor production have the highest impacts mainly due to steel, aluminum and permanent magnets. The findings discuss e-motor recycling innovations, state-of-the-art methods and emission reduction potentials of recycling. This paper also covers the understanding that a significant transformation to optimize the resource consumption in manufacturing of crucial vehicle powertrain component and reduce waste after end-of-life could bring combined ecological advantages.

Abstract: Rising energy costs and strict CO₂ traceability regulations create demand for monitoring energy and CO₂ emissions in manufacturing. This paper presents a framework for modelling component-wise energy models with deployable accuracy. In many factories, power meters log data at a sampling rate of 1–2 Hz, so short start-up peaks of components are underestimated. Manufacturers want to exploit this information to support operational decisions, such as peak shaving and optimising energy contract costs. To enable data-driven decisions with limited measurement infrastructure, energy models must extrapolate component behavior from sparse data. The framework is based on power measurements in accordance with ISO 14955-3, ensuring that the load characteristics required for subsequent modelling are known. The measurements are then segmented, and regressions are fitted for each segment. As a case study considering the mist extractors of two different machine tools, the proposed segmentation achieved determination coefficients (R²) of up to 0.94 in the complex ramp-up phase. The resulting models are compact, interpretable, and suited for energy monitoring on edge devices. The contribution is a reproducible framework for delivering peak-aware, component-level energy models from low-frequency industrial power meter data.

Abstract: Loanword Research on Anglicisms has largely centered on lexical borrowing and phonological adaptation, with comparatively limited attention to morphosyntactic integration in recipient grammars. This study examines the syntactic behavior of single-word Anglicisms in Mexican Spanish, drawing on phonetically classified corpora of 131 monosyllabic Anglicisms with mon-ophthongs extracted from spontaneous speech by Spanish–English bilinguals in the Tijuana–San Diego border region. Building on prior acoustic analyses based on F1 and F2 vowel measure-ments, the study investigates the relationship between phonological adaptation and morphosyn-tactic integration. Results reveal a gradient pattern of incorporation. Anglicisms exhibiting Span-ish-like phonetic properties tend to occupy canonical syntactic positions and show greater com-patibility with Spanish functional morphology, whereas phonetically non-adapted forms more frequently resist morphological marking and display island-like behavior within otherwise Spanish clauses. The analysis examines distribution across nominal, adjectival, and prepositional domains, as well as object positions, enabling a fine-grained assessment of degrees of morpho-syntactic integration. The former is illustrated as follows: (1) Guardo cash ([kaʃ]) por si acaso (2) Si hacen match ([mæʧ]), puede funcionar Adopting a usage-based and contact-oriented perspective for syntactic borrowing (Bybee, 2015), the study is situated within the Matrix Language Frame model (Myers-Scotton, 1993; Muysken, 2000) and recent approaches to insertional borrowing (Poplack & Dion, 2012; Onysko & Win-ter-Froemel, 2011). A central contribution lies in establishing a principled link between morpho-syntactic behavior and an independently motivated phonetic classification, offering convergent evidence for the systematic integration of Anglicisms into Spanish grammar. At a broader ana-lytical level, the study advances debates on syntactic borrowing and contact-induced change by demonstrating that Anglicisms are subject to Spanish morphosyntactic constraints rather than functioning as unconstrained lexical insertions, and by developing an interface-based account of borrowing that captures the gradient nature of grammatical incorporation in contact settings and contributes a corpus-based, empirically grounded perspective to typologies of borrowing in Spanish contact linguistics

Abstract: A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: describe briefly the main methods or treatments applied; (3) Results: summarize the article’s main findings; (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article, it must not contain results which are not presented and substantiated in the main text and should not exaggerate the main conclusions.

Abstract: The present study examined the effect of Enterococcus durans cell free supernatant (CFS) on interleukin (IL) 8, 10 and 1β gene expressions in the intestinal cell line HT-29 treated with Staphylococcus aureus CFS. HT-29 cells were incubated with E. durans CFS or S. aureus CFS, or S. aureus CFS plus E. durans CFS. All concentrations of E. durans CFS did not show cytotoxicity, while the highest treatment (44.9 μg/mL) with S. aureus CFS induced significant cell death. S. aureus CFS did not modify IL-1β gene expression, while E. durans CFS alone or in combination with S. aureus CFS reduced it. Treatment with S. aureus CFS induced greater expression of the IL-8 gene compared to S. aureus CFS plus E. durans CFS. S. aureus CFS alone or in combination with E. durans CFS increased the expression of the IL-10 gene, while E. durans CFS alone did not modify it. These results suggest a potential protective role of the E. durans secretome in mitigating the inflammatory environment in intestinal cells. This treatment could be useful to protect against possible contact with dangerous soluble microbial products present in food.

Abstract: In structural equation modeling (SEM), model fit is commonly evaluated on a single sample, which limits evidence about out-of-sample generalisability in HR research. This study adapts repeated cross-validation to SEM and introduces a normalised generalisation gap (NG) index to quantify fit deterioration on unseen data relative to training fit. Using employee survey data (N = 1,040), we estimated a model in which quiet quitting and passive quitting predict work engagement and evaluated RMSEA across repeated 10×10 folds. In-sample fit indices were stable across training subsamples, yet test-set fit was consistently weaker. The NG index operationalises this decline as a standardised ratio, enabling transparent reporting of overfitting risk and model transportability. The procedure supports more robust evaluation of measurement models and structural relations in people analytics applications.

Abstract: Salmonella enterica remains a major threat to animal and human health because of its broad host range, increasing antimicrobial resistance (AMR), and capacity to form biofilms. Biofilm formation enhances bacterial persistence in host tissues, farm environments, food-processing systems, and clinical reservoirs, while also contributing to their tolerance against antibiotics, disinfectants, and other stresses. However, biofilm capacity is not uniform across serovars and is influenced by host adaptation, niche specialization, and accessory genome content. This review synthesizes current knowledge on the relationship between biofilm formation, AMR, and serovar-specific adaptation in Salmonella. It examines biofilm-associated traits across various hosts (e.g., gastrointestinal tract and gallbladder, and environmental (e.g., food-production and clinical) niches, and discusses comparative evidence from genomic, transcriptomic, proteomic, and metabolomic studies. Particular attention is given to the emerging concept of comparative biofilmomics, which integrates phenotypic and multi-omics data across diverse serovars and host sources to identify conserved and niche-specific determinants of persistence. This framework may help define high-risk lineages that couple multidrug resistance (MDR) with enhanced biofilm-forming capacity. A better understanding of these linked traits will support the development of more targeted interventions for controlling persistent Salmonella in veterinary, food production, and public health settings.

Abstract: In a previous work [Bakonyi et al., Eur. Phys. J. Plus 137, 871 (2022)], in-plane magnetoresistance results were reported on a thin strip-shaped foil sample of nanocrys-talline (nc) Ni metal. These studies have been by now complemented with the measure-ment of the temperature dependence of the resistivity as well as the field dependence of the resistivity and the Hall effect on the same sample at 3 K and 300 K in polar magnetic fields up to 140 kOe, i.e., with the magnetic field perpendicular to the strip plane. Due to the strong contribution of the grain-boundary scattering in the nc state, the residual re-sistivity was about 11 % of the room-temperature value. The polar magnetoresistance (PMR) showed a similar behavior to the previously reported transverse magnetoresistance (TMR), yielding an anisotropic magnetoresistance (AMR) value in good agreement with the AMR previously deduced from the in-plane MR data. As to the Hall effect, the results for the ordinary (Ro) and the anomalous (Rs) Hall coefficient fitted rather well into the rather dispersed reported data of bulk Ni at both temperatures. However, a closer look of the Rs values for nc-Ni revealed that at 300 K it is larger and at 3 K it is smaller than the corresponding bulk Ni values obtained on samples with the same zero-field resistivity as our nc-Ni foil. It will be discussed briefly that these deviations may be attributed to the nanocrystalline state containing a large density of grain boundaries.

Abstract: Metal forming processes play a key role in modern manufacturing, but they are characterized by high energy consumption and low overall efficiency. In this context, precise methods for monitoring the operational state and cycle-dependent metrics of manufactured parts are essential to implement energy optimization strategies. This article presents a data-driven and non-intrusive methodology to identify, in real time, the part under production and to estimate both cycle time and energy consumption per part. The method relies exclusively on electrical measurements taken at the main switchboard and at the first process-stage switchboard. These signals are used to calculate electrical quantities such as root mean square (RMS) current and active power, and a machine-learning (ML) approach is proposed to automatically identify the part in production. To this end, time-domain features are extracted directly from the signals, while time-frequency features are extracted using Continuous Wavelet Transform (CWT). These features are employed to train Support Vector Machine (SVM) classifiers optimized via grid search. Experimental results show that the model achieves a test accuracy of 99.9%. Once the production state is identified, the system estimates cycle time and energy per cycle in real time. Approximately 58,000 production cycles, corresponding to several part types were characterized.