Abstract: The dextran sulfate sodium (DSS) colitis model is the most widely used experimental model of inflammatory bowel disease (IBD) due to its simplicity and versatility, with over 7,000 PubMed entries in the last decade and an exponential rise in recent years. Since its initial description in 1985, DSS colitis has been extensively evaluated across species, most notably in mice and rats, and has yielded substantial insights into IBD pathogenesis. However, the model’s multifactorial nature poses a dual challenge: it offers an opportunity but complicates study design, interpretation, and translational relevance. This complexity is worsened by inconsistent reporting, which hampers reproducibility and comparability across studies. The broad use of the DSS-induced colitis model yields numerous insights about the model, which help better understand its complexity, characteristics and limitations. Although DSS colitis is induced locally, inflammation in the colon and the gut barrier destruction may also affect other organs (such as the liver and brain) and their metabolism and molecular responses, which, in turn, influence colitis development, drug response, and the interpretation of results. These intrinsic (intra-experimental) characteristics of the DSS colitis are summarised in the paper (colitis, gut-brain axis, gut-liver axis). In addition, the DSS model is heavily influenced by numerous ex-trinsic (inter-experimental) factors (environmental, microbiological, genetic), which may further complicate the colitis model, the study outcomes, and data interpretation and are also discussed in the paper. As science advances and new data accumulate, understanding the intricate interplay among internal mechanisms, external factors, and technical variables becomes increasingly essential for accurate interpretation of DSS outcomes. This review synthesizes the complexity and interdependence of factors shaping the DSS model, emphasizing the need for meticulous reporting and consideration of methodological nuances to enhance reproducibility, interpretation, and translational value in DSS colitis research. In addition, the review provides practical guidance through a “traps & tricks” subsections and a checklist table designed to provide a framework and practical recommendations to better understand, apply, and interpret DSS model results in the context of broader systemic and methodological considerations.

Abstract: This work introduces a novel conceptual framework that integrates crystallographic visualization techniques with cosmological geometry. Specifically, we reinterpret the crystallo-graphic holography of three-dimensional crystal structures onto a two-dimensional plane within the three-dimensional spatial sector of the Friedmann–Lemaître–Robertson–Walker (FLRW) metric, formulated following the Landau–Lifshitz approach. Within this framework, the surface of a four-dimensional hypermanifold (a 4D sphere) is conceptually interpreted as exhibiting topological features analogous to the “inside–outside” structure of a Klein bottle. This geometrical perspective provides a foundation for analyzing the mass–energy budget of the Universe as determined by the Planck's mission. We examine the present mass–energy composition—including the relative contributions of visible matter (baryonic), and dark energy identified with the zero-point field (ZPF)—within a differential geometric setting. These components are ultimately represented through a crystallographic holography–based formulation of the Planck observational mass–energy budget.

Abstract: With the rapid advancement of deep learning, differential privacy has become a key technique for protecting sensitive data with a formal guarantee of privacy. By injecting noise and enforcing privacy budgets, differentially private deep learning (DP-DL) systems are able to protect individual data points yet still maintain a model’s utility. However, recent studies reveal that DP-DL systems can be vulnerable to different types of attacks throughout their lifecycle. Naturally, this has attracted the attention of both academia and industry. Critically, these risks are not the same as those associated with traditional deep learning. This is because the differential privacy mechanism itself introduces new attack surfaces that adversaries can exploit. Our work focuses on the distinct vulnerabilities that can arise at the data, algorithm, and architecture levels. By analyzing representative attacks and corresponding defenses, this survey highlights emerging challenges and outlines promising research directions. Overall, our aim is to make differential privacy more robust and deployable in real-world deep learning systems.

Abstract: Crop simulation models and irrigation decision support systems (IDSS) are essen-tial tools for improving water-use efficiency in agriculture, particularly in Mediterra-nean and semi-arid regions where water scarcity is a major constraint. However, many operational platforms are either too complex and data-demanding for widespread adoption or too simplified to adequately simulate crop responses to the combined ef-fects of temperature, water stress, and elevated CO2. This paper presents the Easy Sim-ulator Crop Model (EaSiCroM), a modular, low-parameterisation decision support system designed to simulate daily crop growth, soil water dynamics, and irrigation requirements. EaSiCroM simulates canopy development through a be-ta-function-derived leaf area index (LAI) trajectory and Beer–Lambert canopy cover (CC), with growth progressively constrained by temperature (Tlim) and water stress (Kstress and KScc). Biomass accumulation is estimated through a water-productivity (WP) approach, optionally complemented by a radiation-use efficiency (RUE) path-way. A Michaelis–Menten sub-model accounts for the CO2 fertilisation effect on WP and RUE. The soil water balance includes a two-stage bare-soil evaporation formula-tion and supports multiple irrigation triggering strategies. EaSiCroM is implemented as a Docker-containerised web application supporting single-crop, multi-plot, and near-real-time irrigation modes. The model requires a limited parameter set, operates at daily time steps, and integrates user-provided canopy observations (field or remote sensing) for adaptive irrigation scheduling. Its modular architecture and accessible in-terface make it suitable for both research and operational irrigation management in water-scarce agricultural systems.

Abstract: Background: The clinical management of refractory Small Intestinal Bacterial Overgrowth (SIBO) and persistent gastrointestinal dysmotility represents a significant challenge, as these symptoms are often resistant to standard antibiotic treatments. While frequently categorized as functional disorders, the chronicity and systemic nature of these presentations suggest a possible underlying involvement of the autonomic nervous system. We explore the hypothesis that systemic iron dysregulation, rather than isolated dysbiosis, may contribute to these neuro and gastrointestinal manifestations. Hypothesis: We propose that iron overload mediated by HFE mutations, potentially exacerbated by low ferroxidase activity (ceruloplasmin), may lead to the accumulation of non-transferrin-bound iron (NTBI) in its reactive ferrous state (Fe2+). In this framework, we examine whether a lack of efficient iron chaperoning creates a pro-oxidative environment that could interfere with normal autonomic function. Mechanism: The suggested mechanism involves the Fenton reaction, where excess Fe2+ facilitates the generation of hydroxyl radicals. It is hypothesized that this localized oxidative stress may affect the unmyelinated neurons of the myenteric plexus, potentially leading to autonomic dysregulation. Such an environment could impair intestinal motility, thereby creating a substrate for recurrent and refractory SIBO. Furthermore, this iron dysregulation may act as a nutrient for pathogenic microbiota. This availability supports bacterial proliferation and biofilm formation, further contributing to the refractory nature of SIBO. Clinical Relevance: This model suggests that in patients with overlapping HFE variants and low ceruloplasmin, refractory SIBO may be a symptom of a broader metabolic dysregulation. Consequently, therapeutic strategies could consider the management of the systemic iron burden. Therapeutic phlebotomy is discussed as a potential intervention to reduce reactive iron levels, which might mitigate oxidative stress and support the stabilization of autonomic gastrointestinal function.

Abstract: The substantial computational and memory demands of Large Language Models (LLMs) during fine-tuning are partially addressed by Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA. However, their static low-rank configurations overlook heterogeneous learning sensitivity across layers, leading to suboptimal capacity allocation. We propose Adaptive-PEFT (AP-PEFT), a novel dynamic PEFT framework that introduces a real-time, layer-specific rank adjustment mechanism. This is accomplished via a lightweight module that assesses layer contributions using gradient information, combined with a dynamic rank strategy involving growth and shrink thresholds and a smooth transition for stability. Comprehensive experiments on diverse LLMs (from 3B to 8B parameters) and datasets show AP-PEFT achieves superior task performance and enhanced resource efficiency. AP-PEFT consistently demonstrates competitive or improved metrics in memory usage, compute utilization, latency, throughput, and energy consumption compared to state-of-the-art PEFT baselines and full fine-tuning. This work underscores the importance of dynamic parameter allocation for achieving an optimal balance between performance and efficiency in LLM fine-tuning.

Abstract: Item response theory (IRT) models are widely used in the social sciences to analyze multivariate discrete data that include cognitive test items. In many applications, the performance of two groups is compared using IRT modeling. The assessment of differential item functioning (DIF) plays a central role in this context, as it evaluates whether specific items function differently across groups; that is, whether their item parameters differ between groups. DIF detection is commonly based on statistical inference using item fit statistics. The mean deviation (MD) and root mean square deviation (RMSD) statistics are two widely used item fit measures. However, in the literature and in empirical research, these statistics are typically treated only as effect size measures (i.e., point estimates), and formal statistical inference for them is largely lacking. To address this gap, this article proposes confidence interval (CI) estimation for the MD and RMSD statistics based on asymptotic theory and a computationally efficient parametric bootstrap method. A simulation study was conducted to evaluate the proposed CI estimation approaches and demonstrated their validity. Across both item fit statistics, for DIF and non-DIF items, and across all simulation conditions, the results indicate that CI estimation based on the parametric bootstrap using empirical percentiles performed best and outperformed both the parametric bootstrap with normal distribution-based CIs and the asymptotic theory-based approach. It is therefore recommended that CI estimation for MD and RMSD statistics be routinely reported in addition to point estimates in empirical research.

Abstract: The large‑scale deployment of photovoltaic (PV) inverters in distributed energy resource (DER) ecosystems has created a highly connected environment where telemetry, remote access, and cloud platforms play a central operational role. Unlike smart meters, however, PV inverters have not been systematically examined from a privacy perspective, despite continuously generating fine‑grained data that can reveal sensitive information about users and installations. This preprint presents the first comprehensive analysis of privacy leakage vectors in modern PV inverter ecosystems, covering device‑level measurements, local interfaces, fieldbus protocols, cloud platforms, and external actors such as installers, aggregators, and utilities. Through a technical examination of inverter telemetry and widely adopted DER communication protocols (SunSpec Modbus, Modbus TCP, IEEE 2030.5), we identify structural risks including telemetry oversharing, metadata exposure, behavioural inference, cloud retention leakage, and installer‑side overprivilege. Our findings show that inverter telemetry can reveal occupancy patterns, behavioural routines, consumption habits, and installation characteristics with high fidelity. We conclude by outlining initial recommendations for telemetry minimization, metadata reduction, and cloud governance, establishing the foundation for a dedicated privacy‑by‑design framework for PV inverters and DER systems.This work establishes that PV inverters represent a first-order privacy threat in the modern home, demanding immediate attention from manufacturers, standard-setting bodies, and policymakers.

Abstract: This study explores the sensitivity differences between behavioral experiments and verbal reports in translation quality evaluation. Results indicate that behavioral metrics (e.g., response times) are significantly more sensitive to syntactic-pragmatic manipulations (phrase order) than verbal reports. Translations with congruent phrase order received higher ratings and faster response times compared to those with incongruent order. However, most participants explicitly denied phrase order's influence in verbal reports. Lexical equivalence showed no significant impact on explicit ratings but increased cognitive effort, as indicated by slower response times for approximate lexical matches. These findings reveal a critical dissociation between implicit cognitive processes and explicit awareness in translation evaluation. The study highlights that translation assessment involves both implicit System 1 processes and explicit System 2 reasoning, offering new cognitive insights for translation research and practical implications for translator education and machine translation assessment. By bridging cognitive science and translation studies, this research contributes to a paradigm shift: translation quality is not merely what evaluators say it is, but what their cognitive behavior reveals it to be.

Abstract: Coffee is a very important world commodity because of the countries involved in its production, along with the total cultivated area, production volume, consumption and economic impact. In Mexico, the coffee producing area locates mainly in hilly terrain of southern Mexico under agroforestry systems predominantly owned by smallholders. Low productivity is faced specially in the state of Oaxaca as a result of inadequate management practices such as aged plantations and deficient practices on pruning and plant nutrition. In order to evaluate the effect of N-P-K inorganic fertilizer application an experiment was carried out at three plantations located in the coastal coffee producing region of the state of Oaxaca, Mexico. Three levels of Nitrogen, Phosphorus and Potassium were evaluated using a randomized complete block design with four replications. The experiments initiated on plantations with three and four years since planted with the objective of having at least one harvest for yield evaluation. The results showed that Nitrogen application increased coffee yield on both varieties of Arabica coffee: Typica and Oro Azteca.

Abstract: Persistent and systemic disruptions—such as pandemics, geopolitical crises, and climate-related events—have exposed critical vulnerabilities in global supply chains, highlighting the urgent need for dynamic and adaptive resilience strategies. This paper proposes a novel immune system-inspired dynamic model for designing resilient, adaptive, and financially viable supply chains under severe disruptions. The model integrates innate and adaptive response mechanisms, including organizational memory as a dynamic capability that enables supply chains to learn from past disruptions and improve future responses. Unlike traditional models focused solely on structural redundancy or flexibility, this framework combines operational, financial, and learning dimensions within a unified system modeled through nonlinear differential equations. To validate the model, we conducted a scenario-based analysis, simulating three configurations: (1) a Total System Collapse without adaptation or learning, (2) a Baseline Resilience scenario with innate responses only, and (3) an advanced scenario with active organizational memory and adaptive mechanisms. Results demonstrate that the presence of learning and adaptive capacities significantly enhances both operational and financial resilience, reducing disruption intensity and accelerating recovery. Furthermore, a comprehensive sensitivity analysis was performed on three critical parameters: rate of active adaptation, organizational memory accumulation rate, and supply chain vulnerability. Findings reveal that higher adaptation rates and stronger organizational memory dramatically improve supply chain resilience, while higher structural vulnerability leads to systemic failures that cannot be mitigated by reactive measures alone. This study offers a quantitative and interdisciplinary contribution to supply chain resilience theory and provides practical guidelines for managers and policymakers to invest in adaptive capabilities, institutionalize learning processes, and reinforce structural robustness. The proposed model serves as a foundation for designing next-generation resilient supply chains, capable of surviving and thriving under persistent global uncertainty.

Abstract: Background: Patients described as high-need, high-cost (HNHC) represent a subset of individuals with complex multimorbidity whose healthcare trajectories are char-acterised by recurrent instability and intensive use of acute care services. Concepts such as trajectory disruption, resilience, and complex adaptive behaviour are widely discussed in health systems research, yet empirical evidence linking these ideas to longitudinal patient monitoring remains limited. The PaJR (Patient Journey Record) monitoring system was designed using principles from complex adaptive systems theory, enabling longitudinal observation of patient trajectories in real-world care. Objective: This study develops a complex adaptive system–informed theory of instability phases within patient trajectories using longitudinal monitoring data generated by the PaJR system. Methods: Analyses draw on two PaJR monitoring datasets used for complementary purposes: a MonashWatch cohort dataset comprising 100 patients and 1,137 monitoring calls used to illustrate trajectory dynamics, and an Irish monitoring dataset comprising 286 patients and 11,108 monitoring calls over 18 months used to examine signal distributions and instability patterns. Monitoring calls capture structured signals across multiple domains including illness, medication, healthcare utilisation, social support, environmental factors, and self-care. Results: Instability signals were concentrated within a minority of monitoring observations, producing a long-tail distribution of alert intensity. Alerts frequently occurred in clusters across consecutive monitoring calls, with approximately 63% of alert calls occurring immediately after a previous alert. Alerts were also commonly multi-domain, with approximately 42% involving disturbances across more than one domain simultaneously. These observations support an instability–plasticity framework that integrates empirical monitoring data with concepts from complex adaptive systems and resilience theory, interpreting clusters of patient-reported signals preceding hospital admission as indicators of declining resilience and increasing trajectory plasticity. Conclusions: Longitudinal relational monitoring can reveal instability patterns within patient journeys that are not visible through episodic health system data. These findings help empirically ground emerging theories of complex healthcare trajectories and suggest that recognising instability phases may support earlier and more adaptive responses for patients with complex healthcare needs.

Abstract: Around 1637, Pierre de Fermat famously wrote in the margin of a book that he had a proof showing the equation aⁿ + bⁿ = cⁿ has no positive integer solutions for exponents n greater than 2. This statement, now known as Fermat’s Last Theorem, remained unproven for centuries despite the efforts of countless mathematicians. Andrew Wiles’s work in 1994 finally provided a rigorous proof of Fermat’s Last Theorem. However, Wiles’s proof relied on advanced mathematical techniques far beyond the scope of Fermat’s time, raising questions about whether Fermat could have truly possessed a proof using only the methods available to him. Wiles’s achievement was widely celebrated, and he was awarded the Abel Prize in 2016; the citation described his proof as a “stunning advance” in mathematics. Combining short and elementary tools, we prove the Beal conjecture, a well-known generalization of Fermat’s Last Theorem. The present work potentially offers a solution closer in spirit to Fermat’s original idea.

Abstract: Ecosystem disruption is a significant challenge of the contemporary age, arising from substantial CO₂/CO emissions resulting from dependence on fossil fuels as a primary energy source. Scholars across several fields are striving to mitigate these severe greenhouse gas emissions. The most promising method is absorbing carbon and transforming it into sustainable energy. We sought to diminish CO levels by electrocatalytic reduction using innovative catalytic surfaces, namely transition metal phosphides (TMPs). During this work, VP is recognized as a very effective surface for CO reduction and the synthesis of methane, methanol, and formaldehyde at -0.68 V. Further, hydrogen evolution does not pose a challenge for any surface, despite all TMPs facilitating CO reduction. Overall, predictions from these DFT-guided predictions, experimentalists can get insight for their experimental validation and synthesize of active catalysts for CO conversion and green energy production.

Abstract: This study addresses the conflict between data privacy and modeling performance in financial technology by proposing a federated learning-based privacy-preserving framework. The research first analyzes the sensitivity of user transaction data and the issue of cross-institutional data silos, highlighting the limitations of traditional centralized modeling in terms of privacy and compliance. To overcome these issues, a distributed collaborative modeling mechanism is designed, where participants train models locally and achieve global optimization through parameter uploading and weighted aggregation, thus avoiding centralized storage and transmission of raw data. Differential privacy and secure aggregation are introduced to ensure that individual information is not exposed during parameter exchange, enhancing the overall privacy protection of the system. Furthermore, to address the non-independent and non-identically distributed nature of financial data, a personalized regularization term is incorporated to mitigate the impact of distribution differences across data sources, thereby improving adaptability and robustness in heterogeneous environments. Experiments, including comparisons with mainstream methods and multi-dimensional sensitivity analyses, verify the effectiveness and superiority of the proposed method under privacy-preserving conditions, as shown by improvements in AUC, ACC, F1-Score, and Precision. The results demonstrate that the framework can ensure data security while maintaining strong predictive performance and stability. In summary, this study not only achieves secure modeling of financial data but also provides a feasible direction and reference for further research on privacy-preserving algorithms in financial technology applications.

Abstract: Antisense oligonucleotides (ASOs) are emerging therapeutic agents that modulate gene expression at the RNA level, offering distinct therapeutic advantages over conventional small-molecule drugs and biologics. By directly targeting RNA, ASOs expand the spectrum of druggable targets to include those previously considered "undruggable" and enable shorter development timelines with improved research and development efficiency. These attributes position ASOs as a highly promising platform for precision and personalized medicine. Recent advances in chemical modification strategies and delivery technologies have markedly accelerated the clinical translation. This review systematically examines the technological evolution of ASOs therapeutics, detailing their mechanisms of action, key chemical modification strategies, and advanced delivery systems. It also provides a comprehensive overview of the current global clinical landscape, including approved drugs, discontinued candidates, and ongoing clinical trials. Finally, this review discusses the major challenges facing the field and outlines future directions, with the aim of informing subsequent basic research and clinical development efforts.

Abstract: Background/Objectives: tRNAs, tRNAomes, aminoacyl-tRNA synthetases (AARS), first proteins, the ribosome and the genetic code coevolved. We utilize sequence data to reconstruct key steps in establishing the first code on Earth. Methods: Networks were constructed to describe initial tRNAome and AARSome evolution. Results: tRNA-34 wobble modifications and tRNA-37 modifications were necessary to evolve the code, as were additional tRNA modifications, so diverse tRNA modification en-zymes (i.e., histidyl-tRNA -1 GTP synthase) are among first proteins. tRNA-linked chemistry brought asparagine, glutamine, cysteine and possibly additional amino acids into the code. tRNA, tRNA modifications and tRNA-linked chemistry were core founding innovations for code evolution. Coevolution of AARSomes was also essential. Class II and class I AARS have distinct folds but are nonetheless homologs by se-quence. Early AARS enzymes folded around Zn motifs. Networks were generated for tRNAomes and AARSomes in ancient Archaea, because Archaea are the closest living organisms to the last universal common ancestor. Conclusions: The first code on Earth was surprisingly ordered, and the few apparent deviations from regular order can yet be explained. Early in evolution of the code, innovation was more strongly selected than accuracy. The code froze, however, because of evolving fidelity mechanisms. A historical record was documented in tRNA and the genetic code and has been pre-served in living organism sequence.

Abstract: Photoacoustic imaging (PAI) is an emerging hybrid biomedical imaging modality that combines the high molecular contrast of optical excitation with the deep tissue penetration of ultrasound detection. This review presents recent advances in PAI-based techniques for the detection and characterization of gynecological and gynecological diseases in women, with particular focus on endometriosis and uterine-related disorders. We summarize the application of PAI across preclinical and translational studies, highlighting progress in photoacoustic microscopy, spectroscopic photoacoustic imaging, and endoscopic and probe-based implementations for noninvasive, high-resolution tissue evaluation. The role of functional and contrast-enhanced PAI approaches is discussed, emphasizing their ability to enhance diagnostic sensitivity, enable longitudinal monitoring, and provide detailed information on vascular, biochemical, and structural tissue characteristics. Furthermore, the expanding applications of PAI in assessing uterine, cervical, and ovarian pathologies, including tumor detection and tissue remodeling, are reviewed. Finally, current challenges, limitations, and future directions toward clinical translation are addressed. Collectively, this review underscores the potential of photoacoustic imaging as a powerful, noninvasive platform for early diagnosis, disease monitoring, and improved management of women’s health conditions.

Abstract: Regulated river corridors downstream of large dams face competing geomorphic pressures: sediment starvation and channel incision (the hungry water effect) and episodic tributary-driven aggradation during floods. Quantifying their net balance over multi-decadal timescales is essential for flood risk assessment and sediment management. This study presents the first LiDAR-based DEM of Difference (DoD) analysis of the 21 km Loire river corridor downstream of the Villerest dam (constructed 1984, France), using co-registered DEMs from 2003 and 2022 (19-year monitoring period). A uniform minimum level of detection (minLoD) of 0.10 m was applied, consistent with airborne LiDAR vertical accuracy (±0.05–0.15 m RMSE). The corridor exhibited dominant aggradation: mean DoD = +0.172 m, net deposition volume = +300,841 m³, and mean annual vertical change rate ≈ 9.1 mm/yr. Despite sediment-starved conditions immediately downstream of the dam, net deposition dominates the corridor budget. Results are consistent with tributary sediment inputs mobilised during four major flood events (2003, 2008, 2016, 2021), though mechanistic attribution requires coupled hydrological–sediment-transport modelling. Localised erosion, restricted to 5.6% of the corridor area, reflects residual hungry water effects along channel margins. These findings provide the first quantitative morphological baseline for this regulated corridor and suggest a progressive reduction in floodplain storage capacity with implications for downstream flood risk in the Roanne agglomeration.

Abstract: In 1992, Hudzik and Landes [Math. Ann.] derived a breakthrough generalization of the triangle inequality for two nonzero elements in normed linear spaces, which was generalized to finitely many nonzero elements independently in 2006 by Dragomir [Bull. Aust. Math. Soc.] and in 2007 by Kato, Saito and Tamura [Math. Inequal. Appl]. We derive a non-Archimedean version of Hudzik-Landes-Dragomir-Kato-Saito-Tamura inequality.