Abstract: Background and Objectives: Emerging clinical data indicate that glucose levels in women with type 1 diabetes (T1D) fluctuate throughout the menstrual cycle. However, data concerning the adolescent population remain sparse. This study aimed to compare continuous glucose monitoring (CGM)-derived glycemic metrics between the early follicular and late luteal phases in adolescent girls with T1D in order to establish a clinical basis for phase-specific treatment adjustments. Materials and Methods: Ninety-nine menstrual cycles from 34 female adolescents with T1D were retrospectively analyzed. Time in range (TIR), time above range (TAR), time below range (TBR), mean glucose, and glucose variability were compared between the early follicular and late luteal phases. Additional analyses were performed to evaluate the impact of different insulin delivery methods and to assess glycemic metrics across subgroups by glycemic control. Results: During the late luteal phase, TIR was significantly lower, while TAR and mean glucose were significantly higher compared to the early follicular phase (p < 0.05). Although the continuous subcutaneous insulin infusion (CSII) group achieved better overall glycemic metrics then the multiple daily injections (MDI) group, phase-related fluctuations in TIR and TAR reached significance precisely within the CSII group. Furthermore, glycemic shifts in TAR, TBR and mean glucose were especially pronounced in adolescents with tight glycemic control (HbA1c < 7.0%). Conclusions: The late luteal phase is associated with significant glycemic deterioration in adolescent girls with T1D, particularly in those with optimal glycemic control. These findings underscore the need for targeted education and menstrual-cycle-tailored insulin dose adjustments.

Abstract: Livestock production systems in East Africa depend heavily on forage resources, yet productivity and quality of available forages vary widely across agroecological zones. This study evaluated the influence of soil type and altitude on biomass production, biomass allocation, and forage quality of three improved tropical forage grasses—Massai (Megathyrsus maximus), Mestizo (a Urochloa hybrid blend), and Talisman (Urochloa hybrid)—across five experimental sites in Tanzania and Kenya. Field trials were established using a randomized complete block design with three replicates per site. Measurements included cumulative dry matter yield, root: shoot ratio, and nutritive yield expressed as metabolizable energy and crude protein per hectare. Root: shoot ratios varied significantly among species, soil types, and altitudes, with higher ratios observed in Mestizo and Talisman, clay-loam soils, and high-altitude sites. Biomass production was highest in sandy-loam soil and mid-altitude environments. Massai consistently produced the highest cumulative dry matter yield across locations. Significant genotype × environment interactions influenced both productivity and nutritive yield. Metabolizable energy and crude protein yields varied considerably among sites, emphasizing the importance of site-specific forage selection to maximize biomass production and nutritional value in East African livestock systems.

Abstract: Sugar sorghum has emerged as a highly promising crop for bioethanol production owing to its high biomass yield potential and its remarkable capacity to accumulate fermentable sugars in the stalks. The aim of this study was to evaluate the productivity, sugar accumulation capacity, and bioethanol potential of 12 sugar sorghum accessions under the conditions of Northern Kazakhstan. Field experiments were carried out over the 2024-2025 growing seasons, during which key agronomic and technological traits were assessed, including green biomass yield, juice yield, total soluble solids (Brix), sugar concentration, and theoretical ethanol yield. The analysis of variance revealed that biomass yield was predominantly driven by weather conditions (p < 0.001), whereas sugar concentration was significantly influenced by genotype (p < 0.05). Several genotypes, namely Volonter, Kapital, Sevilya, Flagman, Chayka, and Sauri, consistently exhibited high sugar productivity and bioethanol potential across years, confirming considerable genetic variability in these traits. Storage of stem juice resulted in sugar losses of up to 30.7%, indicating the necessity for rapid processing of raw biomass after harvest. Under laboratory fermentation conditions, juice from the Sevilya genotype (17.9 Brix) achieved a sugar-to-ethanol conversion efficiency of 74.3% relative to the theoretical yield. Overall, the findings confirm the suitability of sugar sorghum for bioethanol production and highlight its strong potential as an energy crop under the arid agroclimatic conditions of Northern Kazakhstan.

Abstract: Traditional discriminative recommenders score and rank items indexed by single item IDs, whereas Semantic ID-based generative recommendation formulates recommendation as conditional generation of Semantic ID token sequences. This shift offers a unified view of retrieval and ranking and shows promising scaling properties, but the literature is fragmented across tokenization and quantization choices, model backbones, and training and decoding protocols, making systematic comparison difficult. To address this, we present the first survey that organizes the field, with four pivotal contributions. First, we introduce a unified five-stage reference pipeline: Representation Layer, Tokenization, Generative Backbone, Training, and Inference. This pipeline standardizes terminology and exposes shared structure. Second, grounded in this pipeline, we map existing methods into a fine-grained typology along semantic granularity, architectural coupling, and learning objectives. Third, based on this structured view, we provide a scaling-oriented perspective that connects component-level decisions to expressiveness, efficiency, and empirical performance, clarifying trade-offs. Finally, we synthesize open challenges and concrete directions that follow from the identified bottlenecks. To support reproducibility and controlled ablations across stages, we release UniGenRec (https://github.com/hupeiyu21/UniGenRec-A-universal-generative-recommendation-toolbox), an open-source modular toolbox implementing the proposed pipeline.

Abstract: This paper examines how the body of research and innovation on electric cooking for low and middle income countries has evolved to the extent that electric cooking is now influencing energy system performance. Methods: The paper synthesises recent evidence from pilots, market developments, and system-level analysis across Africa and Asia, focusing on demand patterns, utility economics, carbon finance mechanisms, and emerging digital and financing models. Results: Electric cooking is increasingly acting as a system-strengthening demand, rather than a system stressor. Two reinforcing mechanisms are identified: (i) an electricity revenue loop, in which increased consumption improves utility and mini-grid viability and supports further investment; and (ii) a carbon finance loop, enabled by metered methodologies and measurable emissions reductions, which can improve household affordability and accelerate adoption. The analysis also highlights the importance of diversified demand (household, commercial, and institutional), which improves load factors and aligns demand with generation. However, a persistent planning blind spot remains, with cooking demand largely excluded from energy models. Conclusions: Electric cooking is moving from proof of concept toward system integration, but scale is constrained by affordability, reliability, tariff design, fuel stacking, institutional fragmentation, and carbon market uncertainty. The findings suggest that electric cooking should be treated as a core component of energy system design, requiring coordinated policy, planning, and financing to realise its full potential.

Abstract: This study investigated the feasibility of using blood feather transcriptomics to detect sex-differences and gene response to physiological changes in chickens. The identification of molecular markers associated with metabolism in poultry typically requires invasive sampling of tissues, such as liver. Feathers represent a promising non-invasive biological source of RNA: the quill pulp of growing feathers contains living cells capable of active transcription. Growing feathers were collected from 150-day-old male and female chickens (Bionda Piemontese, slow-growing breed) raised under a free-range system and fed two finishing diets differing in lipid content: low-lipid (LL, ether extract 3.6%) and high-lipid (HL, ether extract 9.3%). RNA was extracted from quill pulp and subjected to whole RNA-Seq analy-sis. Differential gene expression and functional enrichment analyses were performed us-ing the RaNA-Seq platform. A total of 17,360 transcripts were detected and used for downstream analyses. Across all individuals, three genes associated with ether lipid metabolism (PLA2G10, PLA2G4F, and ENPP6) were consistently upregulated in chickens fed the HL diet. Sex-specific responses were also observed. In roosters, HL feeding significantly affected genes involved in lipid transport and metabolic regulation within the PPAR signaling pathway, including APOA1 and SLC27A4. In contrast, hens showed differential expression primarily in pathways related to apelin signaling, extracellular matrix remodeling, and cardiovascular function rather than classical lipid metabolism pathways. These findings demonstrate differential responses to dietary treatments between males and females and reveal metabolic differences, confirming the need for sex-specific anal-yses in this local breed. In conclusion, feather RNA-Seq successfully captured diet-induced molecular responses and revealed sex-specific metabolic adaptations to dietary lipid levels. This study demon-strates that quill pulp represents a practical and ethically favorable alternative to tradi-tional tissue sampling and may support future nutrigenomic and genetic improvement studies. The findings support the development of non-invasive biomarkers applicable to genetic selection and precision nutrition, ultimately supporting more sustainable poultry production.

Abstract: Cancer initiation is commonly interpreted through mutation-centered models in which tumor development results from the progressive accumulation of genetic alterations. Although this framework remains essential, it does not fully account for the long latency of many cancers, the persistence of cellular phenotypes after removal of environmental stressors, or the stable epigenetic changes associated with chronic metabolic and inflammatory disease. This article proposes a testable theoretical framework termed Temporal Genomic Memory. The model suggests that prolonged biological exposures, including chronic inflammation, metabolic stress, oxidative injury, immune dysregulation, and environmental pressure, may be progressively encoded within epigenetic and RNA-mediated regulatory systems. These signals may be compressed into relatively stable molecular information signatures that shape future transcriptional responses. Under triggering conditions such as aging, immune decline, renewed inflammation, or metabolic imbalance, these stored regulatory states may be reactivated through molecular recall mechanisms, thereby altering cellular behavior and increasing susceptibility to oncogenic transformation. A simplified mathematical representation is introduced to describe biological signal accumulation, regulatory compression, and recall activation over time. The hypothesis does not replace somatic mutation theory; rather, it adds a complementary temporal-regulatory layer linking metabolic history, epigenetic memory, mitochondrial signaling, and cancer initiation. A practical experimental strategy is proposed to examine whether prolonged metabolic stress can generate persistent epigenetic and transcriptional signatures after stress withdrawal.

Abstract: The ability of cells to translate optical radiation into biochemical signals, i.e., optotransduction, plays an important role in emerging therapeutic strategies, with a relevant influence on inflammation. However, a systemic understanding of the molecular pathways underlying the transduction of these physical stimuli is still lacking. In this work, we present a molecular map of optotransduction reconstructed from the literature and provide its representation as pathway, using the standard Systems Biology Markup Language. This representation enables network-based analyses and allows us to investigate the differential effect of stimuli wavelengths and overlap with other forms of physical transduction, namely mechanotransduction.

Abstract: Student safety during daily school transportation remains a major concern, particularly in systems that rely mainly on GPS tracking and manual supervision. Existing approaches often lack proactive safety mechanisms for monitoring both student attendance and driver condition in real time. This paper presents MUTMA’INN derived from the Arabic word “مطمئن”, meaning being reassured, at peace, or tranquil, reflecting the system’s role in ensuring the safety and security of students during transportation. The proposed system is an AI-powered school bus safety framework designed to improve the security and reliability of daily student transportation in alignment with Saudi Vision 2030’s Quality of Life Program. The proposed system consists of two integrated components: a cross-platform Flutter mobile application for parents, drivers, and school administrators, and a Python-based edge system connected to Firebase for real-time synchronization. The framework automates student attendance through facial recognition at the bus gate, reducing manual effort and the risk of human error. In addition, it monitors the driver using contactless remote photoplethysmography and facial analysis techniques to estimate heart rate and detect signs of fatigue or emotional distress. When abnormal conditions are detected, immediate alerts are sent to administrators to support timely intervention. By combining mobile computing, edge intelligence, computer vision, and cloud services into a unified platform, MUTMA’INN provides a proactive approach to school transportation safety. The proposed framework demonstrates how AI can support safer and more intelligent student transit systems.

Abstract: Accurate estimation of meal composition from food images can support safer and more reliable insulin bolus decision-making for individuals with Type 1 diabetes. Existing food recognition and nutrition estimation systems are often designed for general dietary logging and do not directly integrate food analysis with personalized insulin therapy parameters. This study presents an image-based nutrition estimation and insulin decision-support module developed within the AI-assisted Diabetes Care (AIDCARE) platform. The proposed system uses a convolutional neural network (CNN) to classify food items from a single meal image and retrieves reference nutritional values from a food composition database. A separate multimodal large language model (MLLM)-based estimation component is then used to estimate portion size, allowing carbohydrate and nutrient values to be scaled according to the observed serving. A curated food image dataset containing 40 food categories was used to evaluate three CNN architectures: ResNet50, Inception V3, and EfficientNet-B0. EfficientNet-B0 achieved the best classification performance, with 94.91% validation accuracy, 95.55% precision, 94.87% recall, and 94.90% F1-score. The portion-estimation component achieved an MAE of 12.27 g and an RMSE of 15.11 g. The estimated carbohydrate value is combined with user-specific clinical parameters, including the insulin-to-carbohydrate ratio and insulin sensitivity factor, to generate advisory bolus guidance. To support safety, the system requires user confirmation or correction of the recognized food category and estimated portion before insulin guidance is displayed. The proposed system is intended for advisory decision support only and is not designed to replace clinical judgment or autonomous insulin delivery systems.

Abstract: Dengue remains a major public health problem in tropical and subtropical regions, particularly in Latin America. Acute kidney injury (AKI) is one of the severe complications associated with dengue and has been linked to worse clinical outcomes, including prolonged hospitalization, need for renal replacement therapy, and increased mortality. This review aimed to summarize the available evidence on the epidemiology, pathophysiology, clinical manifestations, diagnosis, management, and prognosis of dengue-associated AKI, while also providing an overview of the literature from Latin America. This manuscript was developed as a narrative review. For the Latin America-specific overview, a focused structured search was conducted in PubMed, ScienceDirect, Cochrane Library, LILACS, and Web of Science, including studies published up to December 2025. The available data suggest that AKI in dengue is multifactorial, involving plasma leakage, renal hypoperfusion, endothelial dysfunction, tubular injury, rhabdomyolysis, thrombotic microangiopathy, and inflammatory renal damage. Clinically, AKI has been associated with oliguria, proteinuria, elevated serum creatinine, renal replacement therapy, and higher mortality. Only four eligible indexed studies from Latin America were identified in our search, all from Brazil, with small sample sizes and incomplete reporting of renal outcomes; however, additional unpublished or non-indexed local data may exist. In summary, dengue-associated AKI is a relevant complication of severe dengue, but the evidence available from Latin America remains limited. These findings highlight the need for improved renal surveillance and standardized reporting in dengue-endemic settings across Latin America.

Abstract: Photovoltaic (PV) systems are fundamentally limited by spectral mismatch between the solar spectrum and semiconductor band gaps, resulting in thermalization and transmission losses that reduce overall efficiency. This paper presents a critical review of spectral management approaches, focusing on solar spectrum splitting as a means to improve energy conversion. Existing strategies, including multijunction solar cells, optical spectrum splitting, dispersive and diffractive systems, luminescent solar concentrators, hybrid photovoltaic–thermal systems, and photonic filtering, are analyzed and compared. While these approaches improve spectral utilization, they are often constrained by fabrication complexity, alignment sensitivity, angular dependence, or inherent energy losses. A qualitative, integrative literature review methodology is used to evaluate performance, limitations, and implementation feasibility across these technologies. The analysis shows that no current approach simultaneously achieves high efficiency, low complexity, and robust performance under diffuse illumination. Photonic spectrum splitting combined with independently operated photovoltaic channels is identified as a promising direction. However, the absence of experimental validation remains a limitation, and future work should focus on developing compact, alignment-tolerant systems for practical applications.

Abstract: West Nile Virus (WNV) belongs to the orthoflavivirus genus and is part of the Flaviviridae family, which includes the Japanese encephalitis virus, Dengue virus, Zika virus, and yellow fever virus. WNV circulates among birds and mosquitoes, posing infection risks to humans and mammals. The significant rise in WNV's geographic spread and infection rates over the past five decades has prompted urgent public health concerns, driving the need for accelerated vaccine research. The development of a vaccine for WNV infection presents several challenges, primarily due to the virus's complex biology, the risk of cross-reactivity with other flaviviruses, safety concerns such as Antibody-dependent enhancement (ADE), and the economic and logistical hurdles in vaccine production. Despite significant research efforts, no human vaccine has been approved, although several candidates are in various stages of development. The current review offers a comprehensive summary of the latest progress and the concomitant challenges in the development of vaccines. It also discusses the role of host-pathogen interaction, host immunity, viral immune evasion, and disease pathogenesis in facilitating the advancement of vaccines.

Abstract: Amid COVID-19-related in-person school closures in 2021, an agent-based simulation grounded in social impact theory was implemented and documented to investigate the effects of in-person school closure on nonmedical prescription opioid use among adolescents in Ontario, Canada. The results of model simulations forecasted an alarming rebound effect in the opioid use prevalence after the lifting of in-person school closures and identified secure medication storage in households as an effective strategy for mitigating associated risks. This study evaluates this result by comparing the baseline projection from the previously published study with newly released 2023 data from the Ontario Student Drug Use and Health Survey. Furthermore, it employs the developed agent-based model to simulate the projection through 2030 and assesses the efficacy of secure medication storage in households for the coming years. The study confirms that the previously published simulation projection for 2023 closely aligns with observed data, showing nonmedical prescription opioid use prevalence among Ontario adolescents nearly doubling from 2021 to 2023. Additionally, the results show that nonmedical prescription opioid use prevalence among youth is projected to remain at these elevated levels. Critically, the findings suggest that the temporal window for effective secure medication storage interventions has elapsed, and these interventions are now expected to have minimal impact on reducing this increase, even when applied extensively. The agreement between reported predictions and observed data demonstrates that a simulation model with relevant conceptual foundation can accurately predict future trends and provide sufficient lead time for policymakers to implement interventions within critical time-sensitive windows to alter undesirable trajectories before public health crises escalate.

Abstract: Modern dietary debates remain highly polarized among competing nutritional paradigms, including low-fat, Mediterranean, plant-based, vegan, low-carbohydrate, ketogenic, and animal-based dietary models. Despite decades of nutritional guidelines and extensive epidemiological research, chronic diseases—including obesity, type 2 diabetes mellitus (T2DM), atherosclerotic cardiovascular disease (ASCVD), autoimmune disorders, cancer, and neurodegenerative diseases—continue to rise globally. These trends raise an important question: are prevailing nutritional frameworks adequately aligned with human physiology, metabolic biology, and long-term systems resilience?This paper proposes an Integrative Orthomolecular Medicine (IOM) Systems Medicine framework for evaluating human diets based not solely on caloric intake or macronutrient composition, but on broader biological principles including metabolic compatibility, metabolic flexibility, nutrient density and bioavailability, mitochondrial energetics, inflammatory regulation, biological barrier integrity, oxidative-reduction balance, and cumulative toxicological burden.We first examine evolutionary and physiological foundations of human nutrition, emphasizing omnivorous adaptation, fuel-switching physiology, fasting metabolism, and the evolutionary importance of energetic resilience during periods of food scarcity, migration, hunting, and prolonged physical exertion. Particular attention is given to the human capacity for metabolic flexibility—the ability to transition between glucose utilization, fatty acid oxidation, and ketone metabolism according to energetic demands and nutrient availability. We propose the Energetic Resilience Principle, which suggests that nutritional systems should be evaluated not solely according to glycemic control, but also according to their effects on mitochondrial energetics, fuel adaptability, endurance capacity, fasting tolerance, and long-term physiological resilience. Particular attention is also given to the absence of a clearly established minimum dietary carbohydrate requirement in the presence of adequate protein and fat intake.We then compare major dietary models—including the Standard American Diet (SAD), Mediterranean, plant-based and vegan, low-carbohydrate, ketogenic, and carnivore/elimination-based approaches—across multiple domains relevant to metabolic health and systems biology. Particular attention is given to the potential consequences of chronic dependence on highly refined, continuously fed, hyperinsulinemic metabolic states, including impaired metabolic flexibility, mitochondrial stress, oxidative imbalance, and reduced physiological adaptability.Special attention is given to the nutritional and toxicological characteristics of both plant- and animal-derived foods. While plant foods provide fiber, phytonutrients, vitamins, and numerous bioactive compounds, they may also contain naturally occurring defense compounds such as lectins, oxalates, phytates, alkaloids, and gluten-related proteins, in addition to agricultural contaminants including pesticides, herbicides, and microplastics. Conversely, animal-derived foods may bioaccumulate persistent fat-soluble pollutants and environmental contaminants. The paper further proposes that plant-heavy and animal-heavy dietary systems may differ in dominant toxicological exposure profiles, including relative tendencies toward water-soluble agricultural contaminants and plant defense compounds versus fat-soluble bioaccumulated environmental pollutants. Accordingly, this paper proposes that no modern dietary system is entirely toxin-free, and that dietary strategies should instead be evaluated according to cumulative toxicological burden, nutrient sufficiency, metabolic effects, mitochondrial support, and biological compatibility.Finally, this paper proposes a hierarchical IOM Systems Nutrition framework emphasizing: • low glycemic burden, • low ultra-processing burden, • low cumulative toxicological burden from both natural and industrial exposures, • nutrient sufficiency, • metabolic flexibility, • mitochondrial support, • preservation of energetic resilience, • and long-term physiological adaptability.Within this framework, nutrition is viewed not merely as a source of calories or macronutrients, but as a systems-level regulator of mitochondrial energetics, metabolic resilience, endocrine signaling, inflammatory regulation, biological integrity, adaptive stress responses, and long-term physiological resilience. The framework proposed is intended as a comparative systems-based model for evaluating dietary compatibility with human physiology and adaptive metabolism, rather than a universal prescription for any single dietary pattern.

Abstract: Obesity represents a growing global health challenge, driving the need for safer and more effective therapeutic strategies. Natural products, particularly medicinal plants, have gained increasing attention as potential sources of anti-obesity agents due to their diverse bioactive compounds and multi-target mechanisms. The genus Scutellaria (Lamiaceae) is rich in phytochemicals, especially flavonoids such as baicalin, baicalein, and wogonin, which have been reported to modulate key metabolic pathways involved in obesity. This review aims to comprehensively summarize current evidence on selected Scutellaria species with potential anti-obesity activity, focusing on their phytochemical profiles and pharmacological mechanisms. A literature search was conducted using PubMed, Scopus, and Google Scholar databases, and relevant studies were selected based on predefined inclusion criteria. The findings indicate that Scutellaria-derived compounds may exert anti-obesity effects through multiple mechanisms, including inhibition of adipogenesis, regulation of lipid metabolism, improvement of energy homeostasis, and suppression of obesity-associated inflammation. Preclinical studies provide substantial evidence supporting these biological activities; however, clinical validation remains limited. In conclusion, Scutellaria species represent promising candidates for the development of novel anti-obesity therapies. Further studies, particularly well-designed clinical trials, are necessary to confirm their efficacy, safety, and therapeutic applicability in humans.

Abstract: Laser-ignited particle combustion is critical to energy, aerospace, and defense applications, yet understanding its physicochemical mechanisms is hindered by poor reproducibility in combustion data from randomly packed samples. While classical theory attributes data inconsistency to variations in packing density, we propose instead that consistency of the surface layer morphology—given the nanoscale laser penetration depth—is the dominant factor. A two-dimensional Discrete Element Model showed that increasing particle layers markedly reduces surface topography conformity, while gravitational settling maintains packing density near its theoretical maximum. An innovative constrained droplet method was developed for sample preparation, integrating multi-stage sieving, equal-circle packing in a circle theory, alongside droplet deposition to build multilayer samples mirroring computational models. In-situ laser ignition diagnostics revealed that key combustion metrics—spectral profiles, temporal evolution, ignition delay, and combustion duration—exhibit a rapid decline in consistency with increasing layers, closely matching the simulated decay in surface morphology conformity. Contrary to long-held assumptions, this work robustly shows that surface morphology governs laser-ignition experimental reproducibility. This paradigm-shifting finding redefines the controlling mechanism in laser-ignited combustion of random particle packings, thereby provides a method for refining sample preparation and enables the accurate determination of key parameters that remain elusive with conventional approaches.

Abstract: Physics-informed neural networks (PINNs) provide a data-efficient frameworkfor solving partial differential equations, but improving their accuracy often requires enlarging multilayer perceptron backbones, which increases parameter countand computational cost. This study investigates whether PINN performance canbe improved while keeping the underlying MLP lightweight. We introduce the Cannistraci-Muscoloni-Gu Generalized Logistic-Logit Function (CMG-GLLF) as a learnable activation function for compact PINNs. To make CMG practicalfor PINN training, we reformulate its implicit logit-phase approximation into anexplicit differentiable form using a one-step Newton approximation, reducing numerical instability and computational overhead. Empirical validation on Burgers’equation shows that the explicit CMG formulation substantially outperforms boththe implicit CMG implementation and fixed tanh activation. We further show that alayer-wise CMG design achieves a favorable accuracy-parameter trade-off, addingonly two trainable parameters per hidden layer while improving over vanilla MLPsin most settings. In addition, we evaluate transponder-based contextual modula-tion, which adaptively modulates hidden-layer representations according to thenetwork input. Across Burgers, Allen-Cahn, and diffusion-reaction benchmarks,Transponder-NS consistently improves over parameter-matched vanilla MLPs andachieves the best overall ranking, with approximately order-of-magnitude errorreductions on Burgers and Allen-Cahn. Combining CMG with transponder modu-lation further improves performance on Allen-Cahn and remains competitive acrosstasks. Finally, parameter-level analysis on Allen-Cahn shows that learned CMG parameters differ from the fixed Tahn and that transponder modulation varies acrossboth layers and nodes, providing explainability on why CMG and transpondercould outperform vanilla networks through depth-dependent modulation behavior.These results suggest that learnable activation functions and contextual modulationoffer a practical route toward lightweight, accurate, and explainable PINNs.

Abstract: Membrane Distillation (MD) is a heat-driven seawater desalination technology that uses a hydrophobic microporous membrane as its core component. Due to its low energy consumption, high separation efficiency, and ability to handle high-concentration saline wastewater, it has become an effective solution to the shortage of freshwater resources. Neverless, issues such as membrane wetting, membrane fouling, and low membrane flux severely limit its large-scale application. Composite membranes prepared using metal-organic framework (MOF) materials as fillers have become a research hotspot due to their advantages, such as permeable microporous channels, customizable pore structures, and modifiable active sites. These properties enable them to effectively reduce temperature polarization and concentration polarization phenomena. This article describes the characteristics of metal-organic framework materials and their current applications in the field of membrane distillation. Comparative analysis of the applicability of MOF polycrystalline membranes and MOF composite membranes in membrane distillation. Discussed the working principle of MOFs in enhancing the performance of membrane distillation. Finally, the problems and challenges associated with the use of MOFs in membrane distillation applications were analyzed. Aims to provide theoretical guidance for the application of metal-organic framework materials in the field of membrane distillation seawater desalination.

Abstract: The TM7x strain is a genetic variant of the bacterium Nanosynbacter lyticus, which belongs to the Saccharibacteria phylum within the Candidate Phyla Radiation (CPR) or Patescibacteria group. Its biology differs significantly from that of other bacterial phyla, and its ecological role in the oral cavity remains largely undefined. Through a organyzed and comprehensive literature review, we aim to define the role this bacterium plays within the oral ecosystem. We identified relevant studies from primary sources, included scientific articles from preclinical and clinical studies obtained from three digital databases. The bacterial strain TM7x is an obligate epibiont that exhibits autonomous energy me-tabolism and utilizes a type IV pili system to adhere to its direct host, Schaalia odontolytica. It interacts with its host in two stages: initially as an epipatobiont and subsequently as an episymbiont. TM7x plays a complex ecological role by modulating the host’s metabolism and structure toward a less virulent phenotype resistant to phage attack, while also in-fluencing the human host through immunomodulation and tissue protection. This organism has transitioned from being considered 'biological dark matter' to a key model for understanding coevolution within the human microbiome. Its ability to protect the host from phages, induce protective biofilms, and suppress destructive inflammatory responses positions it as a vital component of human oral microbiome homeostasis.