Abstract: Cloud-based large language models (LLMs) have demonstrated near-human performance in medical applications; however, their clinical deployment is constrained by concerns regarding patient privacy, data security, and network dependence. Locally deployable, open-weight LLMs may provide a privacy-preserving alternative for resource-limited or security-sensitive environments. We evaluated two families of locally deployed models, Google Gemma3 (1B, 4B, 12B, and 27B parameters; vision enabled in models since 4B) and GPT-OSS-20B, using 1,200 multiple-choice questions from the Taiwan Pulmonary Specialist Board Examinations (2013–2024), including 1,156 text-only and 44 text-and-image items across 26 categories. A cloud-based GPT-4 Turbo model served as a reference. Models were queried locally via Ollama. Accuracy was analyzed by year and category using repeated-measures ANOVA with Tukey-adjusted pairwise comparisons. GPT-OSS-20B achieved the highest overall accuracy (58–78 correct answers per 100 questions) and significantly outperformed all Gemma-3 variants (p < 0.001), while Gemma3-27B ranked second. No statistically significant difference was observed between GPT-OSS-20B and GPT-4 Turbo after Tukey adjustment. Larger models showed improved accuracy but longer inference time. These findings suggest that selected open-weight LLMs deployed on-device can approach the performance of cloud-based models in structured medical examinations, with trade-offs between accuracy, modality support, and computational efficiency.

Abstract: Background: Rising kidney discard rates and uncertainty around accepting higher-risk donor kidneys highlight the need for decision-support tools that integrate donor and recipient factors and communicate risk in ways that are understandable and usable at the time of offer. Conventional indices (e.g., KDPI/KDRI) provide population-level signals but do not deliver individualized, cognitively accessible information aligned with real-time clinical workflows. Objective: To describe how key transplant stakeholders—patients, coordinators, and providers—interpret and evaluate a prototype Kidney Risk Calculator app that generates donor–recipient–specific survival projections, and to identify the content, format and features, and functionality needed for clinically meaningful, patient-centered decision support. Design: Qualitative study using focus groups and individual interviews. Setting: University of New Mexico Hospital (UNMH) Kidney Transplant Center. Participants: Five patients (four transplant candidates and one patient advocate), three transplant coordinators, and five transplant providers (3 attending physicians and 2 advanced practice practitioners). Methods: Semi-structured sessions (45–60 minutes) with 13 stakeholders (patients, coordinators, and providers) included a live app demonstration and explored usability, interpretability, contextual information needs, perceived clinical utility, and anticipated barriers/facilitators. Data were collected via one coordinator focus group, one patient focus group, and five provider interviews; sessions were recorded, transcribed, de-identified, and analyzed using inductive reflexive thematic analysis. Results: Stakeholders affirmed the value of personalized projections as an adjunct to clinical judgment, particularly for higher-risk offers. Participants prioritized: 1) Content—clear education on hepatitis C virus (HCV)-positive donors and Public Health Service (PHS) risk criteria; plain explanations of Calculated Panel Reactive Antibody (CPRA); and framing that makes time on dialysis and trade-offs salient; 2) Format & Features—plain-language narratives, percentages rather than decimals, simple visuals, minimized acronyms, U.S. customary units, and a stepwise (“TurboTax‑like”) input flow preferred by patients; and 3) Functionality—attention to cognitive load and workflow alignment, given phone-based time pressure and digital-access constraints. Stakeholders emphasized that the tool’s value hinges on clarity, context, and workflow fit—not predictive accuracy alone. Limitations: Single‑center, formative prototype study with a modest sample; findings are illustrative and may have limited transferability. Participants reacted to a demonstration rather than using the app during real‑time offer calls; convenience/email recruitment and Zoom‑only English sessions may introduce selection bias; team involvement in app development may contribute residual confirmation bias despite mitigation. Conclusions: Early stakeholder input suggests that a kidney offer decision support tool should integrate individualized predictions with plain language explanations, contextual information that addresses common misconceptions, workflow aligned functionality, and accessible outputs. Tools designed and implemented with these features may support acceptance of medically complex kidneys and may help reduce offer bypass and organ discard. These inferences reflect stakeholder perceptions in a formative qualitative study and warrant prospective evaluation.

Abstract: The significant heat generation during refueling of hydrogen pressure tanks might exceed the permissible 85 °C temperature limit for type IV tanks consisting of a thermoplastic liner and a carbon fiber composite overwrap. Common countermeasures like hydrogen pre-cooling or long filling times are energy and time consuming, hence in this paper passive means through thermally better suited materials are examined. Therefore state of the art and alternative materials are first characterized and finally compared using a transient heat model. The different material combinations are compared for maximum temperature and weight in a typical filling scenario. As alternative liner materials thermoplastics filled with short carbon fibres, minerals and graphite and concerning the composite overwrap copper coated carbon fibres were chosen to improve thermal properties. The findings show that the liner is the bottleneck while transferring heat from the inner to the outer tank surface. Using graphite filled thermoplastic as liner material shows the highest potential regarding thermal optimization with only little weight increase. Using additionally copper coated carbon fibres reduces the maximum temperature further, but at a high weight increase. This article is a revised and expanded version of a paper, which was presented at the 15th EASN International Conference, in Madrid, Spain, in October 2025 [1].

Abstract: Recent advances in large language models (LLMs) have made it feasible to use them as automated debugging tutors, but it remains unclear how much can be gained by moving from single-model tutors to multi-agent councils with separated roles. We study this question in an offline simulation on 200 debugging cases drawn from an online judge, spanning 20 problems split into course-style and contest-style challenge tracks. We compare four single-model tutors based on current frontier models with four councils that assign models to Architect, Skeptic, Secretary, Pedagogue, and Mentor roles and operate in both Blind and Guided modes. Single-model tutors achieve near-perfect repair on course problems but perform less reliably on challenge cases and often rewrite large portions of student code, show non-negligible false positive rates, and leak full or near-full solutions in a substantial share of hints. Councils designed around measured model strengths improve both technical and pedagogical behaviour. On the challenge track, the best council raises patch success by 12.2 percentage points over the best single tutor, while reducing false positives, shrinking median patch size, improving hint localisation, and cutting solution leakage in Blind mode from about one fifth of hints to under ten percent. Councils also exhibit higher stability across reruns and produce hints that two independent instructors consistently rate as more useful and better scaffolded. Guided mode, where internal components see a reference solution, yields further technical gains but introduces leakage risks that require prompt tightening and a sanitising Secretary to control the flow of ground truth. Additional trap experiments with poisoned reference solutions show a mix of resistance and fail-safe collapse rather than systematic poisoning of hints. These results indicate that orchestration and information flow are powerful levers and that well-designed councils can provide more reliable and pedagogically aligned debugging support than strong single-model tutors alone.

Abstract: Background/Objectives: Head and neck squamous cell carcinoma (HNSCC) frequently exhibits resistance to targeted therapies, including cetuximab. Identifying key drivers of tumor progression and elucidating the mechanisms underlying therapeutic resistance are essential for improving clinical outcomes. This study aimed to investigate the role of Caveolin-2 (CAV2) in HNSCC proliferation and cetuximab resistance. Methods: Prognosis-associated genes in HNSCC were screened using the TCGA database. The functional role of CAV2 in cell proliferation and apoptosis was assessed via CCK-8, colony formation, and flow cytometry assays. Mechanistic insights were obtained through co-immunoprecipitation, ubiquitination assays, and proteomic analysis. The impact of CAV2 on cetuximab sensitivity was evaluated both in vitro and in a xenograft mouse model. Results: CAV2 emerged as a top prognostic candidate. Knockdown of CAV2 significantly suppressed HNSCC cell proliferation and induced apoptosis. Mechanistically, CAV2 interacted with and stabilized the PACT protein, thereby inhibiting PKR activation via the ubiquitin–proteasome pathway. Notably, CAV2 deficiency markedly enhanced the sensitivity of HNSCC cells and tumor xenografts to cetuximab treatment. Conclusions: These findings establish CAV2 as a critical driver of HNSCC progression and cetuximab resistance through post-translational regulation of the PACT–PKR axis. Targeting CAV2 may therefore represent a promising strategy to potentiate the efficacy of EGFR-targeted therapy in HNSCC.

Abstract: In agricultural practice, botanical extracts have emerged as promising biostimulants that can modulate key metabolic and redox processes in crops, thereby increasing stress resistance and productivity. This review provides a comprehensive synthesis of current knowledge on how botanical extracts influence plant metabolism and redox homeostasis, with particular emphasis on their role in adaptive cellular responses. Additionally, it examines how agronomic practices, such as nutritional strategies, water availability, light regimes, and preharvest biostimulant applications, can be utilized to increase the bioactive composition and efficacy of these extracts. By integrating recent advances in metabolomics and transcriptomics, this review outlines the biochemical and molecular reprogramming triggered by botanical extracts, identifies knowledge gaps, and outlines future research directions to optimize their use in sustainable agriculture. The sections comprising the review are an introduction that establishes the context and objective of the manuscript. The second section describes the bioactive constituents found in botanical extracts from different species, along with their metabolic and redox effects. The third section describes the plant response to the botanical extracts. The fourth section describes the metabolic and gene expression reprogramming that occurs following the application of a botanical extract. The last section presents the conclusion and future directions envisioned by the authors.

Abstract: Background: Cutaneous melanoma remains a highly lethal malignancy once metastatic. Current prognostic stratification relies primarily on staging and serum lactate dehydrogenase (LDH), which incompletely captures inter-patient biological heterogeneity. Increasing evidence highlights the importance of tumour–immune interactions in melanoma progression and response to therapy. Aim: This narrative review summarises and critically evaluates current evidence on circulating cytokines as prognostic and biologically informative biomarkers in melanoma, with particular emphasis on the immunotherapy era. Main findings: Several circulating cytokines—most consistently interleukin-6 (IL-6) and interleukin-8 (IL-8)—are associated with adverse outcomes in advanced melanoma. However, baseline elevations predominantly reflect tumour burden and systemic inflammation, indicating prognostic rather than treatment-specific predictive value. In contrast, early on-treatment changes, particularly decreases in IL-8, may better capture evolving tumour–immune interactions during immune checkpoint inhibitor therapy. C-reactive protein (CRP), a downstream marker of IL-6 signalling, similarly reflects systemic inflammatory status and carries reproducible prognostic significance. Early circulating tumour DNA (ctDNA) dynamics demonstrate strong associations with response and survival and may provide complementary insight into tumour burden kinetics. Conversely, cytokines central to effective antitumour immunity, such as interferon-γ (IFN-γ), are more reliably characterised at the tumour transcriptional level than by circulating protein measurements. Conclusions: Circulating cytokines represent biologically meaningful but methodologically challenging biomarkers in melanoma. Their most realistic clinical role lies in complementing established prognostic factors within integrated biomarker frameworks rather than functioning as standalone tests. Standardization of pre-analytical handling, assay platforms, and sampling time points, together with prospective validation, is essential before broader clinical implementation.

Abstract: Large language models (LLMs) require a significant redesign in solutions to preserve privacy in data-intensive applications due to their text-generation capabilities. Indeed, LLMs tend to memorize and emit private information when maliciously prompted. In this paper, we introduce Private Association Editing (PAE) as a novel defense approach for private data leakage. PAE is designed to effectively remove Personally Identifiable Information (PII) without retraining the model. Experimental results demonstrate the effectiveness of PAE with respect to alternative baseline methods. We believe PAE will serve as a critical tool in the ongoing effort to protect data privacy in LLMs, encouraging the development of safer models for real-world applications.

Abstract: Unmanned aerial vehicle (UAV) remote sensing has evolved from experimental imaging into an operational diagnostic infrastructure supporting climate-smart agriculture through high-resolution, flexible, and timely crop observation. This review synthesizes advances in UAV platforms, multisensor payloads, artificial intelligence (AI) analytics, and multisource data fusion to evaluate their combined potential for monitoring heterogeneous smallholder systems. A PRISMA-guided analysis of 59 studies (2013–2024) classified sensing architectures, analytical approaches, and application domains across diverse agroecological contexts. Integrated UAV–AI frameworks improve detection of crop stress, yield variability, biomass distribution, and phenological dynamics compared with conventional monitoring, particularly when multimodal sensor data are fused with satellite and ground observations. Predictive performance and diagnostic reliability increase when spectral, thermal, and structural datasets are analyzed jointly using machine-learning or deep-learning models. However, scalability remains constrained by operational, infra-structural, and regulatory factors, especially in resource-limited systems. These findings demonstrate that integrated sensing–analytics systems form a critical foundation for scalable climate-smart agricultural transformation and data-driven decision support across farm, landscape, and institutional scales.

Abstract: This paper provides a rigorous examination of eight fundamental architectural deficiencies that render the Linux kernel unsuitable for deployment in safety-critical avionics. These deficiencies include inadequate temporal determinism, the absence of physical memory isolation, driver-induced contamination of global kernel state, an excessively large and unbounded Trusted Computing Base (TCB), open and nondeterministic system semantics, insufficient inter rocess fault containment, unstable kernel behavior due to continuous patching, and a highly complex toolchain that imposes prohibitive DO-330 qualification burdens. Through a technical and standards-aligned analysis, this paper demonstrates that Linux cannot satisfy the determinism, verifiability, isolation, and lifecycle stability required for airworthiness certification, making it inherently incompatible with certifiable airborne platforms.

Abstract: Translational virology, characterized as “from bench to bedside”, covers all issues from basic research through clinical evaluation and final registration and drug/vaccine approval. It covers the identification of the cause of disease, screening of potential prophylactic or therapeutic agents, evaluation in animal models, confirmation of activity in human clinical trials, registration and approval. The recent COVID-19 pandemic represents a perfect example of translational virology, which demonstrated an unprecedented cooperation from the identification of the SARS-CoV-2 to the rapid development of potential repurposed and novel drugs and vaccines for both prophylactic and therapeutic applications. After confirmation of therapeutic and prophylactic efficacy in animal models, clinical phase I-III evaluation was carried out in an overlapping strategy, reducing the development time significantly. To maximize the chances of success, vaccines based on whole viruses, protein and peptide subunits, viral vectors and nucleic acids were developed in parallel. Based on good safety profiles and robust immune responses, COVID-19 vaccine candidates were granted emergency use authorization worldwide allowing the start of mass vaccinations. More than 13.6 billion COVID-19 vaccine doses have been administered, and although severe adverse events have been registered millions of lives have been saved. Due to emerging SARS-CoV-2 variants vaccine re-engineering has been required as part of translational virology. Vaccine production, storage, transport and distribution have also been given attention.

Abstract: This paper presents a conceptual framework, the AI-Augmented Interview Framework (AAIF), requiring empirical validation before deployment. No interviews have been conducted; all thresholds, weights, and KPI linkages are conjectures pending empirical testing. The accelerating adoption of AI-powered development tools (GitHub Copilot, ChatGPT, Claude) is transforming software engineering practice. Industry surveys indicate that over 75% of professional developers now use AI coding assistants regularly (noting potential self-selection bias in survey samples), yet fewer than one in four organizations assess AI fluency during technical interviews. AAIF proposes a structured five-stage interview methodology (Stage 0 fundamentals gate plus four AI-augmented stages) for evaluating developer competencies in AI-mediated environments. The framework assesses: (1) toolchain fluency and prompt engineering, (2) AI output evaluation and critical reasoning, (3) system-oriented problem solving with AI integration, and (4) meta-reasoning about AI limitations, ethics, and failure modes. We develop evaluation rubrics with behaviorally anchored rating scales, propose configurable decision thresholds, and provide an integrated risk framework addressing bias, fairness, legal compliance, and ethical dimensions. The novelty lies in the systematic integration of established methods from industrial-organizational psychology, software engineering, and risk management for the specific and underexplored problem of assessing developers who use AI tools. A detailed four-phase empirical validation protocol is proposed as a key contribution.

Abstract: Colorectal cancer is a major healthcare burden, and modern management of non-metastatic disease relies heavily on guideline concordant care with a basis in histopathologic staging and empiric systemic therapy. While multidisciplinary care pathways and standardized guidelines have improved outcomes at a population level, they fall short in addressing the inter-patient and intra-tumoral heterogeneity that drives lack of response, recurrence, and unnecessary toxicity. Using a hypothetical patient journey, our commentary highlights how current practice often fails to align with patient needs despite being “guideline concordant”. We discuss limitations of current treatment paradigms and the shortcomings of even modern tools like genomic profiling, highlighting the continued need for complementary approaches.We hypothesize that functional precision medicine approaches have the potential to complement existing treatment paradigms and may contribute to improved therapeutic stratification. We provide illustrative examples of potential utility drawn from our recent colorectal cancer clinical correlation study where we reported an association between assay outcomes and clinical response in a retrospective cohort, as well as the ability to identify intra-patient heterogeneity in ex-vivo drug response, suggestive of phenotypically distinct subpopulations with differential drug sensitivity. Further investigation leading to integration of these or similar technologies alongside genomic and minimal residual disease assessments could refine therapy selection and improve existing surveillance strategies.Ultimately, we suggest that while guideline concordant care remains necessary, it is not sufficient for all patients, and that with continued research efforts utilizing functional precision medicine technologies, colorectal cancer management can move toward a personalized framework that maximally benefits patient outcomes.

Abstract: This paper presents a generative AI frameworkfor producing structured symbolic sequences with fine-grainedexpressive control. The approach introduces a compact tokenrepresentation combined with phrase-aware latent alignment tosupport coherent generation across variable-length segments. Byintegrating sequence-level regularization directly into attention,the model balances structural consistency and diversity withoutrelying on explicit post-processing constraints. Empirical analysisshows that the method maintains stable distributional behavioracross expressive dimensions, highlighting its suitability forcontrollable symbolic generation tasks.

Abstract: CEMP-rs stars are often interpreted as signatures of intermediate (i-) process nucleosynthesis during early AGB evolution, yet no observed pattern has been shown to favor the i-process over a simple r+s combination. We present a new analysis of TYC 6044-714-1 based on high-resolution UVES spectra and state-of-the-art 1D and 3D non-LTE modelling, deriving precise atmospheric parameters, elemental abundances, and barium isotopic ratios. Modelling of the Ba II 4934 Å line indicates that 86% of barium originates from the s-process, while the derived [Ba/Eu] = 0.25 dex further supports s-process dominance.

Abstract: This paper propose a formal framework for AIagents that unifies semantic reasoning with resource-aware con-trol. Agents act via sparse policies over structured semantic fields,bounded by entropy and sparsity budgets. We define a typedoperational semantics, prove soundness and stability, and derivea sparse free-energy objective with phase transitions. The calculusis categorically structured, maps to unistochastic dynamics, andcompiles to executable policies with verified runtime bounds.This yields a foundation for interpretable, thermodynamically-plausible agent design.

Abstract: Foremost, the structural-, functional- and behavioural traits of birds relate directly or indirectly to powered flight, an elite mode of locomotion which has importantly made them what they are - ‘specialist and extreme animals’. Placing them at the pinnacle of the evolutionary hierarchy, birds possess exceptional biological specialisations which have conferred profound survival advantages. The adaptive novelties of birds are particularly exhibited by the exemplary morphological and physiological refinements of their respiratory system, the lung-air sac system. To contribute to the ongoing discussions and debates on the impacts of the existing extreme environmental conditions (ECs) on the biology of birds, here, a perspective is posed that the adaptive specialisations which birds acquired ostensibly under different ECs may have undermined their capacity of efficiently adjusting to different ones. To explain the viewpoint, the following aspects are considered: the specialist- and extreme biology of birds; the prevailing harsh ECs which are brutally impacting on birds and; the consequences from their enduring the harsh conditions which include among others global warming and habitat devastation. It is contended that under the existential threats, the adaptive capacities of birds appear to have declined, rendering them more vulnerable to external stressors. It is urged that urgent conservation measures, especially of the most threatened species of birds, are necessary.

Abstract: In this study we explore various non-destructive methods for the determination of density of 27 non-porous natural stones. Among the methods investigated the most accurate method was found to be the mass-based suspension method that uses Archimedes principle, with costs of equipment less than 20$. We have used this density measurement method to measure densities of natural stones and copper reference cube in the range of 1.07 – 8.93 g×cm-3, for stones that have volumes less than 16.4 cm3. The measurement are in excellent agreement with more precise methods that use a 4 decimal place analytical balance. The measurement uncertainty of the method was assessed with a Cu density reference cube and was found to be of the order of 0.1% in measuring the volume of stones with arbitrary shape. Finally, we provide details of the design features of a new liquid-based pycnometer that can measure the density of irregular shape natural stones without the need to form a powder. This pycnometer can also be used to measure density changes in liquids as a function of temperature and solute concentration.

Abstract: The hierarchy problem, the anomalous weakness of gravity relative to other fundamental forces, remains one of the most persistent challenges in theoretical physics. Here we introduce the Information Lattice Model (ILM), a framework that reinterprets gravity not merely as spacetime curvature but as the macroscopic manifestation of directed informational flow across a permeable, multi-layered lattice spanning our observable 4-dimensional brane and a higher-dimensional bulk. In the ILM, mass-energy density modulates the bandwidth of inter-node links, naturally accounting for gravitational weakness via trans-layer dilution and resolving the black hole information paradox through lattice-mediated data transfer. The model aligns with recent developments in braneworld scenarios, Causal Dynamical Triangulations, and holographic principles, while generating two falsifiable predictions: anomalous attenuation in high-frequency gravitational wave spectra detectable by LIGO/LISA, and non-linear deviations in quantum decoherence rates near lattice saturation. Beyond its physical core, the ILM offers a speculative but theoretically grounded framework for technosignature detection, proposing that advanced intelligences may be identified by their degree of lattice sovereignty, and for Unidentified Anomalous Phenomena (UAP), reinterpreted here as bulk-brane perturbation events. We also identify consciousness as a participatory interface within the lattice architecture, whose neural complexity functions as a biological transducer of bulk-brane informational coupling. This structural continuity between mind and lattice suggests that UAP encounters may constitute bilateral informational exchange events sharing common substrate in the higher-dimensional bulk.

Abstract: The ZNF469 transcription factor and collagen-homologous matrix contributor, first related to recessively inherited brittle cornea syndrome, was found variant in 8 patients with Ehlers-Danlos syndrome and an additional 14 from the literature with related connective tissue findings. Systematic documentation of skin, skeletal, cardiovascular, and neuro-autonomic findings in the 8 patients supported the diagnosis of Ehlers-Danlos hypermobile type, component diagnoses of aneurysms-dissections or blue sclerae-skeletal change predominating in 9 patients having cardiovascular screening or 5 carriers among many in brittle cornea syndrome families. Locations of these 22 patient variants along with 60 related to EDS and 68 to brittle cornea syndrome from the ClinVar database were spread throughout the 3953 amino acid ZNF469 coding sequence. Heterozygous variants except for 3 in a zinc finger region were associated with diagnoses of Ehlers-Danlos syndrome or its component findings, all documented or inferred biallelic ZNF469 variations except for one associated with brittle cornea syndrome. Limitations of this study point to the need for matching of systematically evaluated patients with the multiple DNA variants inherent in complex disease, network action exemplified by the fibrillar participation and regulatory feedback of ZNF469.