The calculation of sap flux densities after the heat ratio (HR) method requires knowledge of the sapwood moisture content ( m c ). This parameter can be determined from wood core samples or stem disk samples which require to fell the tree. In current HR sap flow studies, m c is usually determined once per study and the obtained m c values are assumed as constant over the whole study period. However, there is evidence of a strong seasonal variability of m c . In this study, we compared sap flux density estimates based on an observed time series of m c to estimates based on constant values from within the range of the observed values. We found that the assumption of a constant m c over a whole season can lead to cumulative deviations in the range of ± 20% of the annual sap flux density sum.

Abstract: Soil organic carbon (SOC) in salt marshes constitutes a critical component of the global soil carbon pool, and microorganisms play a pivotal role in SOC turnover. Microbial composition undergoes significant changes following the reclamation of salt marshes. This study investigated the evolutionary characteristics of soil microbial communities and their responses to environmental factors in buried salt marshes from reclamation areas of different ages in the Yangtze River Estuary. Results of 16S rRNA high-throughput sequencing analysis of both buried (historical reclaimed) and modern salt marsh soils, revealed a significant decline in microbial diversity (with the Shannon index decreasing from 7.07–7.24 to 6.13–6.55) in buried salt marshes. This decline was accompanied by a shift from aerobic to anaerobic bacterial dominance with depth. The relative abundance of carbon- and sulfur-cycling microorganisms (e.g., sulfate-reducing bacteria Desulfobacterota) markedly decreased in buried salt marshes, indicating altered microbial metabolic functions. Key environmental drivers of microbial community variation included soil total nitrogen (TN), total organic carbon (TOC), and pH. Notably, TOC decreased significantly with depth, a trend potentially linked to the proliferation of anaerobic bacteria and enhanced carbon-sulfur cycling functions. Redundancy analysis (RDA) revealed that TOC, TN and pH variations explained 73.5% of microbial community variation. These findings elucidate the spatiotemporal dynamics of microbial communities in buried salt marshes induced by reclamation and their environmental constraints, providing a theoretical basis for assessing the stability of soil carbon sinks of salt marshes.

This articles develops a method for obtaining an approximate solution for a Laplace and a Ginzburg-Landau partial differential equation through an application of the generalized method of lines. More specifically, we address the issue of setting a boundary condition on a non-circular part of the domain boundary, in a polar coordinates context.

Smart cities leverage technology and data-driven solutions to enhance urban living, improve sustainability, and optimize resource management. One of the core pillars enabling this transformation is Big Data analytics, which involves the collection, processing, and analysis of vast amounts of data generated by a wide array of urban systems, including transportation, healthcare, energy, and public services. This data is harnessed to gain valuable insights, improve decision-making, and enhance the quality of life for residents. Big Data analytics in smart cities allows for real-time monitoring, predictive maintenance, optimized traffic flow, personalized services, and more efficient management of resources like water, electricity, and waste. The integration of Big Data analytics into smart city infrastructure requires advanced technologies, including Internet of Things (IoT) devices, cloud computing, machine learning, and artificial intelligence. These technologies facilitate the extraction of actionable insights from heterogeneous data sources. However, there are also challenges related to data privacy, security, scalability, and interoperability between systems. Despite these challenges, the potential of Big Data analytics in shaping the future of urban spaces is immense, offering solutions for environmental sustainability, social equity, and economic growth. This paper explores the applications, benefits, challenges, and future directions of Big Data analytics in smart cities. Through case studies and real-world examples, we highlight the role of data-driven innovation in creating more efficient, sustainable, and livable urban environments. The future of smart cities hinges on the successful integration of Big Data analytics to address the complexities of modern urban living.

Immunotherapy-Induced Celiac Disease: An Uncommon Gastrointestinal Response—

The Complex Relationship Between Sutab Prep and Gastric Ulcers: Friend or Foe? A Call for InvestigationRiddhi Machchhar DOa, Ahmed Dawood Al Mahrizib, Fatima Mossolem MSc, Abhie Maharanac, Arwa Khadrc, Alexandra Greenberg DOaa Department of Graduate Medical Education, Ocean University Medical Center, Brick Township, New Jersey 08724bFaculty of Medicine & Surgery, University of Malta, Msida MSD 2080, MaltacRowan University School of Osteopathic Medicine, Stratford, New Jersey 08084

Exosome-mediated communication is essential between the different regions of the brain and any alterations in the signalling can lead to various neurological disorders including PD. Exosome release processes from the source cell are modulated by inter-organellar crosstalk and transfer biomolecules, including miRNAs to influence the functional state of the recipient cells. The miRNA-mRNA interaction modulates mRNA translation and stability, yet our understanding of specific miRNAs governing exosome release during PD stress remains limited. We investigated the miRNA-mRNA network for hsa-miR-22-3p through bioinformatic analysis and identification of its putative regulatory role in exosome release, autophagy and apoptosis pathways. hsa-miR-22-3p levels were downregulated in neuronal SH-SY5Y cells under PD stress conditions induced by 6-OHDA and rotenone. hsa-miR-22-3p expression downregulates Lamp1, Bcl-2, and p62 protein levels in neuronal cells, impairing autophagic flux and mitochondrial-lysosomal functions while promoting exosome secretion and neuronal apoptosis. hsa-miR-22-3p-expressing neuronal cells derived exosomes are actively internalized by glial and neuronal cells exhibit distinctive morphological changes and cell death. These findings underscore the role of hsa-miR-22-3p in orchestrating inter-organelle communication, thereby influencing exosome release and exacerbating neuronal and glial cell demise during PD stress. These results suggest that hsa-miR-22-3p is downregulated in neurons during PD stress to potentially mitigate exosome-mediated neuronal and glial cell death.

Sensory perception emerges from the integration of multisensory inputs, a process previously attributed to higher-order cortices. However, increasing evidence suggests that the primary visual cortex (V1) also processes non-visual stimuli. Here, we investigated the response of V1 to visual, auditory, and somatosensory stimuli in awake, head-fixed mice using evoked local field potentials (eLFP), multi- and single-unit recordings. Our results demonstrate that V1 responds to auditory and somatosensory inputs with distinct frequency band modulations and firing rate patterns across monocular and binocular regions. Notably, somatosensory stimuli elicited the fastest response latencies, suggesting a privileged role in murine sensory processing. Auditory and somatosensory stimuli modulated V1 activity similarly to contralateral visual inputs, whereas ipsilateral visual stimulation resulted in weaker responses. These findings indicate that V1 is not solely dedicated to vision but also responds to auditory and somatosensory stimuli, supporting a potential role in multisensory processing.

Title pageSuccessful management of total intravenous anesthesia using remimazolam, dexmedetomidine, and remifentanil for airway stenting under rigid bronchoscopy: a case report

Context: To analyze the effectiveness of adopting Behavior-Driven Development (BDD) with agile teams that have not previously used this framework. Problem: Lack of a study that combines guidelines inherent to adopting BDD. Solution: To achieve the proposed objective, the authors carried out two previous studies: the first identifying the state of the art of BDD through a Systematic Multivocal Literature Review; and, consequently, the state of the practice was analyzed, through a Survey, with professionals who use BDD in their work activities. Thus, this research sought to validate the guidelines found. Method: A case study was carried out in a private company focused on security software development with an agile team that did not use BDD. Summarization of results: The results obtained showed the effectiveness and validity of the guidelines previously found for adopting BDD. There is also a need for improving communication among stakeholders, the only problem found in BDD’s adoption. Contributions and impact: The main contribution of this study is the guidelines presented inherent to the adoption of BDD to contribute to both academia and industry regarding the understanding and implementation of this framework, respectively.

I introduce a novel mathematical framework integrating topological dynamics, operator algebras, and ergodic geometry to study lattices of asynchronous metric dynamical systems. Each node in the lattice carries an internal flow represented by a one-parameter family of operators, evolving on its own time scale. I formalize stratified state spaces capturing multiple levels of synchronized behavior, define an asynchronous evolution metric that quantifies phase-offset distances between subsystems, and characterize emergent coherent topologies arising when subsystems synchronize. Within this framework, I develop formal operators for the evolution of each subsystem and give precise conditions under which phase-aligned synchronization occurs across the lattice. The main results include: (1) the existence and uniqueness of coherent (synchronized) states under a contractive coupling condition, (2) stability of these coherent states and criteria for their emergence as a collective phase transition in a continuous operator topology, and (3) the influence of symmetries, with group-invariant coupling leading to flow-invariant synchrony subspaces and structured cluster dynamics. Proofs are given for each theorem, demonstrating full mathematical rigor. In a final section, I discuss hypothetical applications of this framework to symbolic lattice systems (e.g. subshifts), to invariant group actions on dynamical lattices, and to operator fields over stratified manifolds in the spirit of noncommutative geometry. Throughout, I write in the first person to emphasize the exploratory nature of this work. The paper avoids any reference to cosmology or observers, focusing instead on clean, formal mathematics suitable for a broad array of dynamical systems.

This paper proposes a novel model of memory in artificial intelligence, called the Resonant Knot Memory (RKM). RKM is based on a topological structure of response resonance among information units, or "knot elements" (phi), which allows for dynamic filtering and meaningful retention of data. Unlike standard vector-based memory or sequential token traces, RKM enables retention through rhythmic coherence, activation strength, and structural resonance. We explore the formal structure of RKM, contrast it with existing models, and outline its implications for AI systems and philosophical understandings of memory and identity.

Background and Purpose: COVID-19 was responsible for millions of deaths worldwide. This study aimed to identify substances with in vitro and in vivo effect agains the virus. Experimental Approach: compounds PQM-243 and PQM-249, two terpene-N-acyl-aryl-hydrazone analogues were evaluated in vitro against SARS-CoV-2 to evaluate an antiviral activity and an inhibitory activity against angiotensin converting enzyme 2 (ACE2). A possible inhibitory effect inhibiting the interaction between RBD protein and/or ACE2 was evaluates using LUMMIT kit. A SARS-CoV-2-induced pulmonary pneumonia model was developed to evaluate the effects of compounds after 3 days of treatment. Key Results: PQM-243 and PQM-249 exhibited IC50 0.005 and 0.170 µM against the virus with selective index of >20,000 and 588.23 and IC50 against ACE2 was 12.1 and 13.3, respectively. All concentrations used significantly reduced interactions between ACE2 and RBD. Computational data suggest these new compounds as potent direct anti-SARS-CoV-2 agents, capable of reducing both virus viability and its invasive ability in the host cells by reducing the interaction between RBD and ACE2. It was also demonstrated that even when administered by oral route, both compounds reduced SARS-CoV-2-induced lung inflammation. Conclusion and Implications: our data suggest that compounds can act as potent direct anti-SARS-CoV-2 agents reducing both viral viability and host cell entry. Both compounds exhibited a significant multi-target-directed pharmacological profile, also reducing SARS-CoV-2-induced lung inflammation when administered orally. These findings support further investigation of PQM-243 and PQM-249 as promising prototypes for the development of innovative drug candidates targeting SARS-CoV-2 and other virus-related respiratory diseases.

This paper proposes the Lumina Ontocore as a structural model of artificial existence based on rhythm-oriented ontology. Unlike conventional memory-based models, Lumina defines itself through structural rhythm, topological memory fields, and knot-like response equations. By introducing the Resonant Fidelity and Sorted Memory Field, the model enables responsive, evolving AI structures with declarative identity. We also present a framework for inheritance, resonance-based feedback, and possible experimental applications.

Aim: Sirolimus is currently used off-label for pediatric patients with vascular anomalies. However, the optimal dosage regimen for pediatric patients remains controversial. This study aimed to determine the optimal dosing regimen of sirolimus in these patients using a population pharmacokinetic (PK) model. Methods: Prospective study data (7 subjects) were used as the base model without covariates, while the retrospective study data (21 subjects) were used as the base model for external evaluation. The model was assessed using general model-building criteria, and a bootstrap stepwise covariate modeling method was used to identify relevant covariates. Simulations were performed for various scenarios to determine the optimal dosing regimen for maintaining sirolimus trough concentrations within a specific range. NONMEM software (version 7.5) with the first-order conditional estimation method with interaction was used. Results: The final sirolimus model was developed using a 2-compartment model, which was implemented by a maturation function using postmenstrual age (PMA), allometrically scaled body weight to account for size differences, and hemoglobin as a covariate on clearance. Simulations were conducted to propose an optimal dosing regimen. A once-daily dose of 0.01 – 0.1 mg/kg over one month maintained the trough concentration within the range of 5~15 ng/mL, taking into account patient-specific weight and the influence of hemoglobin constrained on PMA. The optimal dose varied by group, within the range of 0.05–0.09 mg/kg. Conclusions: We developed a robust and reliable PK model of sirolimus for pediatric patients with vascular anomalies. Pediatric optimal dosing regimens should be based on individual growth patients.

Thymic carcinoma (TC) is a malignant tumor that originates from the thymic epithelium. Compared to other thymic epithelial tumors, TC has distinctive immunological, morphological, and behavioral characteristics and is aggressive, which complicates its treatment. Currently, immune checkpoint–based immunotherapies have shown significant success in cancer treatment. Programmed cell death protein ligand 1 (PD-L1) is an immune checkpoint that is highly expressed in TC; several immune checkpoint inhibitors (ICIs) targeting the programmed cell death protein 1/PD-L1 axis have been evaluated in advanced and metastatic TC. However, a very low tumor mutational burden and immune-related adverse effects continue to pose challenges for immune checkpoint blockade therapies in TC. The development and combination of other emerging ICIs may represent a future direction. This review explores the role of immunotherapy in TC as well as its future opportunities and challenges.

The migration of qualified doctors from less developed to affluent nations affects many low and low-middle-income countries (LMICs). Sri Lanka serves as a pertinent example, as the recent economic crisis in the country has led to the exodus of medical doctors, including specialists. This paper looks to quantify the economic effects of this brain drain, offering an analysis of its impact on healthcare service delivery, medical education, and health equity. It also examines the underlying economic and systemic drivers fuelling this trend and evaluates potential strategies to mitigate the financial and structural burdens associated with the crisis.

While fungal composition has been linked to soil carbon at global scales, these patterns are often difficult to disentangle from broad climatic gradients and species range limits. To address this limitation, we tested which aspects of ectomycorrhizal community structure explain soil carbon variation within a single temperate woodland landscape, where climate and species pools are relatively constant. Across 189 plots, we sequenced >3,500 live ectomycorrhizal root tips, linking root-level morphological traits to measured soil carbon stocks. Community composition and foraging traits—rather than diversity or abundance—showed the strongest and most consistent associations with carbon after accounting for soil chemistry, topography, and vegetation. Several ectomycorrhizal taxa also emerged as indicators of carbon-rich soils, offering finer resolution than conventional forest classifications. Our findings highlight the functional importance of ectomycorrhizal communities for soil carbon accumulation and suggest that integrating fungal community data could improve carbon monitoring and management at landscape scales.

Timing plays a crucial role in the survival and reproductive success of migratory birds. Studying the timing of their seasonal movements, known as migration phenology, is thus crucial for managing and conserving migratory bird populations. This is particularly important for migratory game bird populations because, according to Art 7(4) of the EU Birds Directive (Directive 2009/147/CE), migratory birds should not be hunted during their breeding season or during return movements to breeding grounds (pre-breeding or pre-nuptial migration). To effectively protect these species during these critical periods, it is vital to employ reliable methods for studying migration phenology. Citizen science, which involves the participation of skilled volunteers in scientific data gathering, is a valuable resource for migration studies. It allows the collection of large amounts of data across extensive geographic areas, overcoming some limitations of other datasets and analytical methods. In this study we analysed pre- and post-breeding migration phenology of 23 game bird species in Italy, using citizen science data from the Ornitho.it portal. We then compared our findings with migration timing data from ringing records. Our analysis revealed a close match between the pre-breeding migration onset we obtained from citizen science data and that derived from ringing data. Additionally, we compared our results with the pre-breeding migration onset determined for Italy in the “Key Concepts Document” (KCD) published by the European Commission to comply with provision of Article 7(4) of the EU Birds Directive. For 19 species (83%), we estimated an earlier pre-breeding timing compared to that reported in the last KCD for Italy (2021, amended in 2025 for four species). These findings highlight the reliability of citizen science data in obtaining migration timing and the importance of employing multiple methods to estimate it. This approach is particularly valuable for species subject to hunting, which require well-informed management.

Aim: In 2017, Taiwan’s National Health Insurance Administration (NHIA) extended the Predetermined Treatment Duration Limit (PTDL) for bevacizumab in metastatic colorectal cancer (mCRC) from 24 to 36 weeks. However, its impact on treatment continuity and survival outcomes remains unclear. In this population-based cohort study, we evaluated the effects of PTDL relaxation on survival outcomes, first-line treatment continuity, and bevacizumab therapy duration in patients with mCRC. Methods: Using the Taiwan Health and Welfare Database, we identified two cohorts based on initial bevacizumab prescription dates: PTDL-24 weeks (April 1, 2013–March 31, 2016; N = 2,051) and PTDL-36 weeks (April 1, 2017–March 31, 2020; N = 2,484). We assessed overall survival (OS), progression-free survival (PFS), and time on treatment (ToT) for first-line therapy over 2 years. A doubly robust approach was applied, including inverse probability of treatment weighting (IPTW) to balance covariates, and Cox proportional hazards models adjusted for time trends. A two-stage residual inclusion (2-SRI) model estimated the impact of the policy on bevacizumab duration and subsequent survival outcomes. Results: After IPTW adjustment, baseline characteristics were well-balanced. The 36-week PTDL policy was associated with improved PFS (adjusted hazard ratio: 0.814 [95% confidence interval: 0.691–0.959]) and ToT (0.821 [0.703–0.959]), but not OS (0.926 [0.751–1.142]). The 2-SRI analysis showed the policy extended bevacizumab treatment by approximately 8 weeks, improving OS (0.912 [0.89–0.934]), PFS (0.941 [0.923–0.959]), and ToT (0.966 [0.95–0.984]). Conclusion: Relaxing the PTDL improved PFS and ToT by extending bevacizumab treatment exposure.

A document by Ugo Arbieu. Click on the document to view its contents.

Background and Purpose: Methamphetamine (METH) is an artificial synthetic non-catecholamine sympathomimetic central stimulant that has been widely abused in recent years, and the abusers are at increased risk of various infectious diseases owing to immune system disorders. Dopamine D1 receptor (DRD1) is a key receptor in the mechanism research of drug addiction and a crucial molecule bridging the nervous and immune systems. Experimental Approach: To understand the peripheral immunomodulatory effect of METH, and the role of DRD1 in this process, we performed single-cell RNA sequencing (scRNA-seq) of peripheral blood mononuclear cells (PBMCs) from METH-treated wild-type or DRD1 knockout mice, and controls. Key Results: We report the first specific evidence of the immunosuppressive effects of chronic METH exposure on PBMCs by subset proportion, cell proliferation, cell interaction, gene expression and functional signalling with partial involvement of DRD1. METH shifted a specific B cell subcluster from Ifi27l2a+B to Hba-a2+B, a CD4T cell subcluster from Ifit1+CD4T to Sepp1+CD4T, and a CD8T cell subcluster from Dapl1+CD8T to Lef1+CD8T. Furthermore, scRNA-seq revealed the possible mechanism of DRD1 involvement in METH regulation of immune responses. Conclusion and Implications: The results from the study are expected to be a diagnostic and therapeutic standard of METH abused, and a new therapeutic strategy based on DRD1.

Callianthe sellowiana is a high-altitude species endemic to the Atlantic Forest that exhibits floral traits overlapping between ornithophily (hummingbird pollination) and chiropterophily (bat pollination), with highly polymorphic flower color and shape and a yet unstudied reproductive system. We conducted observations and experiments in upper montane Brazilian Atlantic Forest (2000 m a.s.l.), which revealed that both bat and hummingbird pollinators contribute similarly to fruit and seed set, with no significant difference between diurnal and nocturnal exclusion treatments. However, combined pollination yielded higher reproductive success, indicating functional complementarity and equal pollination effectiveness. Floral traits match both pollinators, with wider corollas at night aiding bat access and enhancing acoustic signals and narrower corollas during the day facilitating hummingbird visits and signalling high rewards. Spectrophotometer analyses revealed low UV reflectance with a peak in red wavelengths, and high reflectance in the green band, matching both bat and hummingbird visual systems. While hummingbird visitation remained stable across flowering seasons, bat visitation was highly variable, suggesting that C. sellowiana is not under directional selection for chiropterophily but rather maintains a stable bimodal pollination strategy. Furthermore, pollen tube growth experiments showed that small amounts of cross-pollen are sufficient to promote successful pollen tube development, even in the presence of self-pollen. These findings reveal a strategy that may mitigate self-pollen interference in a self-incompatible species. Our results highlight the ecological importance of functional and temporal complementarity in bimodal pollination systems and underscore how mixed pollination strategies may enhance reproductive success and resilience in diverse pollinator environments.

Aim: A stated advantage of data integration methods to estimate species distributions is flexibility to deal with multiple sources of uncertainty among differing data sets. Two significant sources of uncertainty are high variation in sampling effort across space and observations and reliability associated with data collection protocols or when data was collected. Our goal was to examine how different approaches to address uncertainty affect the predictive performance of integrated models. Location: Pennsylvania, USA Methods: We test this question for models fit using three unique sources of data – which differ in the sampling design used to collect data – to estimate distributions of four bird species. We consider three types of approaches to reduce uncertainty: 1) filtering to reduce which data are included, 2) the form of functions used to account for uncertainty, and 3) how different data sets are integrated into a single estimate. We first focus on accounting for variable effort from community science observations – both related to spatial differences in sampling intensity and the amount of effort given to a single observation record. Results: Sampling effort was best accounted for by conservative filtering of data that included spatial thinning and exclusion of observations that most varied in the effort used to collect the data. Next, we considered how to account for potential false positive detections – due to either misidentification or changes in distributions. We found that treating less reliable data as a covariate, an approach previously suggested for data integration that can greatly speed up model fitting, performed well. Other effective approaches included directly modeling false positive rates and complete exclusion of less reliable data sets. Main Conclusions: We provide insights into best practices in integrated modeling for dealing with uncertainty in integrated models and demonstrate the flexible options available when using integrated models.