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Legitimacy is defined as the generalized perception that an entity's actions are appropriate within a socially constructed system of norms, values, and beliefs. While this concept is well established in organizational literature, consumer perceptions of food quality labels' legitimacy remain underexplored. To address this gap, we adapted the organizational legitimacy framework-cognitive, regulative, pragmatic, and moral dimensions-to the context of quality labels and developed a corresponding measurement instrument. Perceived legitimacy was conceptualized as a second-order formative construct composed of four first-order formative dimensions. Using a mixed qualitative-quantitative approach, we designed and validated indicators for each dimension. Our methodology involved: • Qualitative exploration to generate indicators aligned with each legitimacy dimension. • Quantitative validation through a survey of 600 French consumers. • Partial Least Squares (PLS) modeling to test reliability and validity. This article details the development process and validation of the proposed instrument. Despite some contextual limitations, the model offers a novel framework for understanding the multifaceted nature of legitimacy in consumer evaluations of labels. The results confirm the relevance of legitimacy as a construct in label studies and provide useful insights for consumer behavior research. The method can be replicated in other labeling or geographical contexts.

Trauma exposure and post-traumatic stress disorder (PTSD) are a global public health concern, especially in low and middle-income countries (LMICs). University students are a unique population that has been found to have increased levels of trauma exposure and PTSD, yet limited clinical outcome and neurobiological data exist, especially in LMICs, on treatment responses to evidence-based trauma-focused treatments for PTSD among university students in LMICs. The development and integration of neuroimaging tools in psychotherapy enable more robust integration of data from clinical psychology and neuroscience. Our protocol is the first to use functional Near-infrared spectroscopy (fNIRS) to investigate the treatment response of a first-line trauma-focused cognitive behavioural therapy, namely, prolonged exposure therapy, for the treatment of PTSD. Using a pilot randomised controlled trial, university students diagnosed with PTSD will be assessed using clinical outcome measures and fNIRS to ascertain prefrontal cortical increases and decreases in persons diagnosed with PTSD. Participants will be randomised to either the experimental condition (PE) or a comparative control condition (supportive counselling). Participants will be assessed at baseline, post-intervention, and at 12 and 24-week follow-ups using the same measures and imaging.•Recruit and assess university students using standard clinical measures and fNIRS for PTSD.•Assign enrolled students to either PE (experimental condition) or supportive counselling (control condition) for a minimum of 8 sessions.•Assess students at the end of treatment and at 12 and 24-week follow-ups using the same clinical measures and fNIRS for any changes in PTSD.

: Flow cytometry is a powerful tool for immunophenotyping, but its application in feline samples is challenging due to species-specific blood characteristics, a paucity of standardised protocols and high reagent costs. These limitations may compromise sample quality, antibody performance, and the reproducibility of the results. : An optimal protocol for extracellular immunophenotyping of feline leukocytes from peripheral whole-blood was developed, adapting established human flow cytometry methods. Key adaptations included improved blood collection process, assessing sample preservation, and titrating antibodies. Detailed MIFlowCyt-compliant information on cytometer configuration, compensation procedures, gating strategy, and controls is provided in Supplementary File S2. : The enhanced blood collection significantly improved erythrocyte lysis rendering the samples more suitable for cytometric analysis. Baseline leukocytes viability, assessed by trypan blue exclusion was 98-100%. The use of a cellular antigen stabilisation reagent preserved feline peripheral whole-blood samples without detectable loss of surface antigen expression for up to 14 days. Antibody titration showed that most monoclonal antibodies were effective at 1.5 µL, reducing usage by up to 85%, while CD5 required 3 µL. : This improved, cost-effective and reproducible protocol address major technical limitations in feline flow cytometry and provides a practical framework for the reliable leukocyte immunophenotyping in clinical diagnostics, research, and comparative immunology.

Population-Based Training (PBT) has the drawback of using fixed, pre-programmed mutation and selection rules to optimize hyperparameters, which are not always flexible across reinforcement learning (RL) tasks. To address this, we introduce LLM-Guided Population-Based Reinforcement Learning (LPBRL), a scalable methodology in which the reasoning capability of Large Language Models (LLMs) is used to manage population evolution dynamically. LPBRL operates through a six-phase cycle in which the LLM analyzes real-time performance measurements from parallel workers and produces adaptive population-update recommendations as a substitute for static rules. In contrast to conventional PBT, and unlike prior LLM-assisted optimization frameworks that typically operate outside the recurrent population loop, LPBRL places language-model reasoning directly inside the selection-mutation stage of training. This enables task-aware hyperparameter adaptation that improves convergence speed and training stability. We evaluated LPBRL on CartPole-v1 with 8 parallel workers over 150 episodes and observed clear gains over conventional PBT, with best- and average-reward convergence improving by 62.5 percent and 68.2 percent, respectively. Although the approach requires access to LLM APIs and compatible RL tooling such as Stable-Baselines3, the results show strong potential for large-scale training workflows in which adaptive hyperparameter control is essential. Overall, the empirical findings support the claim that language-model reasoning can make effective optimization decisions in RL while preserving the practical strengths of population-based training.•Large Language Models are integrated as adaptive decision-makers inside the recurrent population-evolution loop, replacing static task-agnostic mutation and selection rules with context-aware reasoning.Real-time worker metrics, trajectory trends, and LLM-guided hyperparameter adaptation accelerate convergence and improve stability across discrete and continuous-control RL settings.•The methodology provides a reproducible implementation path with structured prompts, deterministic parsing, bounded updates, and compatibility with multiple RL algorithms (PPO, SAC, TD3), supporting large-scale applications.

Electron beam absorbed current (EBAC) microscopy can provide spatially resolved electrical information that conventional probe methods and local scanning probes often miss in nanoparticulate thin films. Here we present the development of a practical scanning electron microscopy (SEM)-based methodology to qualitatively assess conductivity uniformity and electrical continuity in thin films spanning two electrodes using TiO2 films as a model system. The approach combines a purpose-built insulating holder, readily made without clean-room and advanced lithography access, with embedded electrodes and a measurement configuration to visualize current pathways and identify electrically disconnected regions that may not be apparent from morphology alone. Line scans and 2D maps enable rapid screening of film quality, highlighting cracks, and poor particle connectivity. The workflow is designed for reproducibility and can be adapted to other semiconducting or weakly conducting thin films where microscale continuity is critical to device performance. Fabricate a biasable dual-electrode SEM holder by embedding bent copper plates in an insulating resin body and machining a defined deposition channel (drilled to expose copper + resin in one plane), enabling repeatable electrode gaps and robust external connections for EBAC measurements. Prepare thin films that reliably bridge the electrode gap by using a diluted, well-dispersed nanoparticle suspension (e.g., TiO2 in IPA) and controlled drop-casting into the channel; confirm continuous coverage using standard SEM imaging before electrical mapping. Map conductivity uniformity in situ using EBAC (line scans and 2D maps under applied voltage) to rapidly locate conductive pathways and diagnose electrically disconnected regions that may appear morphologically continuous, providing a reproducible screening workflow adaptable to other weakly conducting thin films.

Transition state or minimum energy path finding methods constitute a routine component of the computational chemistry toolkit. Standard analysis involves trajectories conventionally plotted in terms of the relative energy to the initial state against a cumulative displacement variable, or the image number. These dimensional reductions obscure structural rearrangements in high dimensions and may often be history dependent. This precludes the ability to compare optimization histories of different methods beyond the number of calculations, time taken, and final saddle geometry. We present a method mapping trajectories onto a two-dimensional projection defined by a permutation corrected root mean square deviation from the reactant and product configurations. Energy is represented as an interpolated color-mapped surface constructed from all optimization steps using a gradient-enhanced Gaussian Process with the inverse multiquadric kernel, whose posterior variance contours delineate data-supported regions from extrapolated ones. A rotated coordinate frame decomposes the RMSD plane into reaction progress and orthogonal distance. We show the utility of the framework on a cycloaddition reaction, where a machine-learned potential saddle and density functional theory reference lie on comparable energy contours despite geometric displacements, along with the ratification of the visualization for more complex reactions, a Grignard rearrangement, and a conrotatory bicyclobutane ring opening. • Dimensionality Reduction: Maps optimization histories onto a 2D plane defined by distance-to-reactant and distance-to-product. • Landscape reconstruction: Interpolates sparse optimization samples onto a continuous energy surface with data-driven smoothing to visualize basin topologies. • Validation: Facilitates the direct projection of reference electronic structure calculations onto landscapes generated by machine-learned interatomic potentials.

Rejection rates of upper-limb prosthetic hands remain high, largely due to limitations in control performance and usability. Most commercially available prosthetic hands rely on a fixed and limited set of predefined control modes, which restrict the execution of complex daily activities and reduce their acceptance by users. Consequently, there is a growing need for advanced control strategies that enable more natural and intuitive prosthetic hand operations. Multimodal sensing approaches that combine surface electromyography with inertial measurement unit and gyroscope data, together with accurate finger joint angle measurements, have shown promise for proportional and continuous control techniques. However, the availability of well-structured, synchronized datasets to support the development, training, and evaluation of such methods remains limited. This study aims to create a standardized, synchronized multimodal dataset to enable the benchmarking and training of proportional, continuous-control models for next-generation prosthetic hands and broader human-robot interaction and rehabilitation applications. The study employed a synchronized multimodal data acquisition framework, integrating surface EMG with kinematic measurements, enabling a comprehensive analysis of neuromuscular and movement data. Finger joint angles were obtained using an optical motion capture system without reliance on data gloves, with markers placed directly on the skin, which may introduce soft tissue artifacts and inter-subject variability.

This study provides a comprehensive mapping of the global research landscape on algae, microalgae, and seaweed in food-related applications over the period 1989-2024, with particular emphasis on food additives, nutraceuticals, functional foods, and bioactive compounds. Based on 929 records retrieved from the Web of Science Core Collection, a tailored bibliometric framework is employed that integrates performance analysis, science mapping, thematic evolution, and reference-year analysis, thereby linking publication dynamics with the intellectual and conceptual structure of the field. The findings indicate a pronounced and sustained growth in algae-based food research, characterized by extensive international collaboration and a rising emphasis on functional ingredients and health-promoting compounds. Thematic and conceptual analyses delineate algal extracts, bioactive compounds, polysaccharides, proteins, and lipids as the principal knowledge clusters. The evolutionary trajectory of the field reveals a progression from primarily biochemical characterization toward more application-oriented research in food, nutraceutical, and functional health domains. Collectively, these results provide a systematic evidence base to support researchers, industry stakeholders, and policymakers in understanding current research priorities and in identifying emerging directions in algae-based food innovation.

This study presents a keyword-guided cross-attention framework for automated radiological report generation from 3D FLAIR MRI brain tumor images. The architecture integrates M3D-CLIP as the image encoder. Hierarchical keyword extraction is performed using fine-tuned KeyBERT and BioBERT semantic embeddings in a 768-dimensional space. Six cross-attention layers fuse visual features with clinical keywords across four hierarchical levels: abnormality type, lesion characteristics, anatomical location, and lateralization. A four-layer transformer decoder generates captions autoregressively. The BraTS2020 dataset containing 369 glioma patients paired with TextBraTS radiological descriptions was preprocessed with center-focused slice selection of 32 from 155 slices and spatial interpolation to 256 × 256 resolution. Training on NVIDIA RTX 3050 GPU for 15 epochs using AdamW optimizer achieved loss reduction from 4.16 to 1.33. Evaluation on 20 test samples demonstrated BLEU-1 of 0.5359, BLEU-2 of 0.3969, and ROUGE-L of 0.5051, with generated captions accurately capturing clinical information for decision support applications. •Multi-modal fusion through keyword-guided cross-attention integrating visual MRI features with hierarchical clinical terminology•Transformer-based autoregressive generation conditioned on enriched image-keyword representations•Comprehensive evaluation using BLEU and ROUGE metrics on brain tumor caption generation task.

A new, low-cost LED-LDR Portable Colorimeter-Multimeter (LED-LDR PCM) was developed as an educational tool for undergraduate chemistry students. This device constructed from inexpensive and available components in the Iraqi markets. These included a multimeter, a white LED light source, a light dependent resistor (LDR), a battery, and a sample cell (cuvette). This device serves as platform for teaching essential principles of the systematic deviations from the Beer-Lambert law with polychromatic LED sources, build calibration curves, and teaching self-filtering effect phenomenon. The LED-LDR PCM device showed a high accuracy and precision in the determination of methyl red dye in tap water samples, with RSD% values from 0.791% to 3.076% and recovery values ranging from 99.131% to 101.562%. The performance of the educational LED-LDR PCM device was evaluated using greenness and Whiteness assessment tools, such as GAPI, MoGAPI, CaFRI, and the Multi-Color Assessment (MA) Tool. A comparative analysis against reported methods revealed that the educational LED-LDR PCM device is more sustainable and environmentally friendly, with a high overall Whiteness score of 66.9%, showed the scores of reported methods. This project highlights a new direction for the development of simple, cost-effective analytical devices that are ideal for educational purposes analytical devices and aligned with the principles of green analytical chemistry.

Entomopathogenic nematodes (EPNs) are important for the biological control of insect pests. At the same time, EPNs are excellent research tools for understanding the molecular and functional bases of the insect defense against parasitic nematode infection. Implementing insect models and natural hosts forms an important strategy for characterizing EPN virulence factors and insect anti-nematode immune responses. The Indian meal moth Plodia interpunctella is a world-wide insect pest of stored-products and processed food commodities. This insect species is also commonly infected by EPNs, and therefore, it can be used as a natural host to determine how insect pests interact with EPNs during infection. Obtaining this information is critical because it will allow agricultural practitioners to design improved EPN management tactics in the field. Here we describe a protocol for infecting P. interpunctella larvae with the EPNs Steinernema carpocapsae and S. hermaphroditum. The method is outlined below:•Third instar P. interpunctella larvae are infected with infective juveniles of either S. carpocapsae or S. hermaphroditum. Uninfected control larvae are exposed to sterile water only.•Insect survival is recorded at regular intervals.•Survival curves are constructed and results are statistically analyzed to compare the P. interpunctella larval mortality against the EPNs.

A reproducible workflow is presented that integrates fruit&#x2011;health stratification, stringent surface sterilization, culture&#x2011;based isolation, molecular identification, enzyme phenotyping, and a low&#x2011;injury needle&#x2011;transfer pathogenicity assay to isolate, classify and functionally characterize bacterial endophytes, saprophytic colonizers and pathogens associated with postharvest tomato (Solanum lycopersicum) fruits. The method is designed to distinguish ecological guilds (endophytes vs soft&#x2011;rot pathogens vs saprophytes) rather than simply list "bacteria present", and can be implemented in standard microbiology laboratories without specialized equipment. Tomato fruits were stratified by fruit-health status and surface&#x2011;sterilized to distinguish internal endophytes from epiphytic and saprophytic surface&#x2011;associated communities. All bacterial isolates were cultured on tryptone soya agar, purified, assigned to an ecological niche (healthy or spoiled/diseased fruits), and tested for in planta pathogenicity on tomato fruits. All the isolates were identified using biochemical and 16S rRNA gene sequencing, while preliminary phenotypic screening was used to quantify cell wall&#x2011;degrading activities relevant to soft&#x2011;rot. The workflow yielded 14 characterized bacterial isolates spanning three ecological groups (non&#x2011;pathogenic endophytic Bacillus species, soft&#x2011;rot&#x2011;inducing Enterobacterales, and saprophytic colonizers), with ecological niche separation statistically supported by Fisher's exact test (p < 0.001). The method can be adapted to other fruit or vegetable systems to link bacterial community composition with plant health outcomes.&#x2022;Provides a low-injury and contamination-reduced approach for fruit pathogenicity assays based on a needle&#x2011;transfer inoculation technique adapted for routine microbiology laboratories.&#x2022;Enables the functional differentiation of endophytic, saprophytic, and pathogenic bacterial isolates relevant to fruits and vegetables through combined ecological sources, in planta pathogenicity, and enzyme phenotyping.&#x2022;The approach is adaptable to multiple fruit and vegetable host crops in a resource-limited and efficient laboratory setting.

The nudged elastic band (NEB) method is the standard approach for finding minimum energy paths and transition states on potential energy surfaces. Practical NEB calculations require several pre-processing steps: endpoint minimization, structural alignment, and initial path generation. These steps are typically handled by ad-hoc scripts or manual intervention, introducing errors and hindering reproducibility. We present a fully automated, open-source Snakemake workflow for small gas phase molecules that couples modern machine learning potentials (PET-MAD) to the eOn saddle point search software. Each step of the calculation lifecycle is encoded as an explicit dependency graph, from model retrieval and endpoint preparation through path initialization and band optimization. The workflow resolves all software dependencies from conda-forge, ensuring identical execution across platforms. Validation on the HCN to HNC isomerization demonstrates that the automated pipeline recovers the known single-barrier energy profile and product energy without manual intervention.

Systematic error undermines the internal validity of randomized controlled trials (RCTs). Elucidating how risk of bias (RoB) domains distort effect estimates strengthens evidence trustworthiness. Low back pain (LBP) research provides a representative field for this investigation, with spinal manipulative therapy (SMT) serving as a widely utilized intervention. To explore the association between risk of bias domains and effect estimates of SMT treatment in LBP trials. RCTs from the Cochrane systematic reviews which have examined the effect of SMT for acute and chronic LBP will be included. The Cochrane RoB 2 tool will be used. The influence of RoB domains on effect estimates for pain intensity and physical functioning will be explored through univariable and multivariable meta-regression models. Models will be adjusted for confounders identified through a directed acyclic graph, including sample size, trial registration, country income level, and comparator type, while exploring interactions. This study builds upon research in exercise therapy, investigating whether the association between RoB and effect estimates is consistent across other non-pharmacological LBP interventions. Findings may help refine the application of GRADE on SMT, and improve evidence synthesis and decision-making for researchers and policymakers.

We developed an artificial shark uterus system that enables the simultaneous maintenance of multiple embryos within a single container. In previous designs, group housing of late-stage embryos was unsuccessful because the activation of one embryo stimulated others, triggering collective hyperactivity. This led to severe skin abrasions due to repeated contact with the rubber mesh covering the container opening. The key modifications and functional principle of the new system are as follows:&#x2022;The rubber mesh was eliminated and replaced with an acrylic cylinder that provides vertical space above the incubation container.&#x2022;This vertical space allows activated embryos to swim upward, reducing physical contact between active and inactive individuals.This method increases incubation density and broadens the applicability of artificial uterine technology to shark species with high fecundity.

Over the past few decades, Naematelia aurantialba (commonly known as "Mushroom Jin Er") has attracted significant attention due to its medicinal properties and nutritional value. To address the existing gap in genetic manipulation tools for molecular biology research in N. aurantialba, we employed Agrobacterium tumefaciens-mediated transformation to introduce exogenous genes into the blastospores of N. aurantialba for the first time. We successfully established a genetic transformation method for N. aurantialba. Moreover, this work not only shows that N. aurantialba blastospores are an excellent recipient material for future molecular studies but also provides strong technical support and a valuable reference for accelerating genetic research related to N. aurantialba. Key methodology points include: Establishment of an Agrobacterium-mediated transformation system for the blastospores of N. aurantialba for the first time. Transformants with hygromycin B resistance were screened, and molecular identification results confirmed that the exogenous gene had been successfully introduced into the genome of blastospores. Phenotypic analysis showed that the blastospores of transformants had GUS activity and green fluorescence signals, confirming that the gus gene and gfp gene could be expressed in the transformants.

Children with Autism Spectrum Disorder (ASD) frequently experience psychomotor impairments, limited participation in physical activities, and reduced quality of life (QoL). Exercise shows promising benefits, but physiotherapy approaches in ASD are underreported, with limited evidence supporting a structured framework that systematically targets motor and functional outcomes. This study protocol describes a single-blinded randomised controlled trial evaluating the effectiveness of a structured physiotherapy intervention on motor skills, physical activity levels, and behavioural and QoL among children with mild to moderate ASD. This trial will include 64 children aged 6-10 years with ASD. Participants will be randomly assigned to an intervention group or a control group. Both groups will receive usual care therapy, equated between groups. The intervention group will also receive structured physiotherapy twice weekly for 12 weeks, each session lasting 60 min. The intervention integrates coordination, balance, strength, and endurance training. Outcome assessments will be conducted at baseline and post-intervention using validated instruments: GARS-3, BOT-2, GLTEQ, CBCL and PedsQL. Data analysis will follow the intention-to-treat principle and will examine between-group and within-group changes across all outcome measures. Qualitative data will be analysed using thematic analysis to characterise themes reflecting participants' engagement and parents' and caregivers' perspectives on the intervention.

The increasing cultural diversity of healthcare systems requires strategies that support effective communication and equitable care for patients from diverse cultural and linguistic backgrounds. Cultural concordance refers to situations in which patients and healthcare professionals share relevant cultural or linguistic characteristics that may facilitate communication and mutual understanding. In some healthcare organizations, this concept has been operationalized through organizational strategies aimed at aligning patients with healthcare professionals who share similar cultural or linguistic backgrounds. However, limited research has explored how these initiatives are experienced in clinical practice. The OHANA (Optimizing Healthcare through Alignment and Nurturing Across cultures) protocol was developed to investigate experiences of culturally concordant care within healthcare organizations that have implemented cultural concordance systems. This multicenter qualitative study, coordinated by the Fondazione Policlinico Universitario A. Gemelli IRCCS in Rome in collaboration with several Italian hospitals, will explore the perspectives of both healthcare professionals and patients. Data will be collected through semi-structured interviews and analyzed using inductive qualitative content analysis to identify themes related to communication, perceived benefits, and challenges associated with culturally concordant care. The findings are expected to inform strategies to support culturally responsive healthcare. Key points:&#x25aa;Multicenter qualitative study protocol exploring experiences of culturally concordant care in hospital settings.&#x25aa;Investigation of perspectives from both healthcare professionals and patients involved in cultural concordance systems.&#x25aa;Use of semi-structured interviews and inductive qualitative content analysis to examine how culturally concordant care is experienced in clinical practice.