Abstract: Neonatal hypoxic ischemic encephalopathy (HIE) is a common birth complication that can cause death or lifelong disabling conditions like cerebral palsy, epilepsy, and autism. It is well established that maternal infection and inflammation are significant risk factors for HIE but reasons for this increase in neurological risk to the offspring remain unknown. Inflammation or infection are associated with epigenetic changes and may contribute to the increased risk of neurodevelopmental disability in exposed offspring. Here, we analyzed and compared DNA methylation patterns in brain monocytes isolated from control, maternal immune activation (MIA), and an inflammation sensitized HIE (IS-HIE) mouse model at postnatal day 7. We found that maternal inflammation induced significant methylation differences in neonates relative to control samples in both MIA and IS-HIE samples with no significant differences identified between the MIA and IS-HIE groups. MIA samples showed hypermethylation at loci involving craniofacial development and transcription factors important for regulating neurodevelopment and immune function. MIA samples also demonstrated significant hypermethylation at multiple mitochondrial genome CpGs. These findings suggest that maternal inflammation induces epigenetic alterations in fetal brain immune cells that are detectable in neonates. These changes may contribute to heightened neurodevelopmental risk in offspring following hypoxic injury, highlighting potential molecular pathways for future therapeutic targeting.

Abstract: We show the Born rule P = |ψ|² is the unique probability rule consistent with thermodynamic constraints on record formation, conditional on the regime where Landauer-type bounds apply. The derivation proceeds from five operational postulates: normalization, phase information loss in record formation, interference consistency, tensor product factorization, and continuity. The key physical insight is that creating a classical measurement record requires that phase information is not retained in the record accessible to observers, a not-reversible operation with entropy flow to the bath and Landauer cost k_B T ln 2 per bit. The squared modulus emerges as the unique probability rule that (i) eliminates phase to produce positive probabilities, (ii) preserves interference effects before measurement, and (iii) satisfies standard probability axioms. This thermodynamically motivated derivation complements Gleason's theorem: where Gleason proves the rule is mathematically necessary (dimension ≥ 3), we show it is the unique rule realizable through record formation under these constraints (all dimensions including d=2). The framework provides a concrete answer to "why squared?": the irreversible formation of a classical record, on a Hilbert space whose norm is preserved by unitary evolution, admits no other consistent probability rule.

Abstract: Foodborne diseases and food poisoning caused by bacterial pathogens is a significant global health as well as economic concern. While synthetic compounds are widely used as preservatives to ensure food safety, growing concerns regarding their potential health risks and the rise of antimicrobial resistance have driven the search for natural alternatives. Essential oils (EOs) and their individual bioactive constituents, known as essential oil components (EOCs), have emerged as promising, eco-friendly candidates for food preservation due to their robust broad-spectrum antibacterial properties. This review provides comprehensive mechanistic insights into how individual EOCs exert their antibacterial effects, detailing the disruption of bacterial cell membranes, inhibition of vital metabolic enzymes and ATP synthesis, modulation of virulence gene expression, and the prevention and eradication of biofilms. Furthermore, the review explores the practical applications and limitations of EOCs in food systems, addressing challenges such as chemical instability, toxicity at high doses, and adverse organoleptic effects. It also highlights advanced formulation strategies, such as micro/nano-encapsulation, nano-emulsions, and chemical derivatization, which significantly enhance EOC stability, bioavailability, and overall preservative efficacy. Ultimately, understanding the multifaceted mechanisms of individual EOCs paves the way for their optimized and sustainable use, ensuring global food safety.

Abstract: The paper deals with comparative analysis of machined surfaces by classic electrical discharge machining (EDM) and hybrid ultrasonic EDM of CoCr alloys, using computer vision aimed at emphasizing the advantages of this hybrid technology. The analysis revealed generally the superior stability of EDM+US process against classic EDM explained by better evacuation of debris from the working gap due to ultrasonically induced cavitation. This key phenomenon also contributed to the enhancement of machining rate by removing the material in liquid state and also the in solid state from the microgeometry peaks but also reducing the surface roughness if the power on the ultrasonic chain is optimzed.

Abstract: Latina and Black female students are more likely than their White and Asian counterparts to leave science majors after their first-year of college. Supportive peer mentorship has been shown to positively impact science retention but limited data exists on the peer-mentoring impact to first-year Latinas and Black science students. We present an assessment of a peer mentoring intervention supporting the experience of first-year science female students at an urban public university during the COVID-19 pandemic.

Abstract: Determining which sensor modalities carry genuine discriminative signal for CNC monitoring—and how many can be removed before performance degrades—is a practical question that prior work rarely answers quantitatively under leakage-resistant evaluation. We address this through a systematic cross-validated ablation study on a 9-class CNC toolpath and condition classification task, combining three toolpath strategies (adaptive, face, pocket) with three conditions (air-cutting, active-cutting, and damaged-spindle). Using 120 operation files from a desktop CNC mill with six consistently active sensors (17 channels per sensor) plus 8 machine-level electrical features, we evaluate six model families across 690 cross-validated runs spanning five cumulative feature-ablation levels (110–56 features) and ten temporal resolutions. To handle the fusion challenge, we introduce MM-DTAE-LSTM, a multi-modal denoising temporal attention encoder with unidirectional LSTM-based classification that combines learned modality gates, cross-modal attention, and a self-supervised denoising objective. Key findings on this single-machine, single-material dataset: (1) MM-DTAE-LSTM reaches 96.3% ± 4.7% accuracy at a 98-feature configuration (excluding proximity and pressure), leading all baselines by 3.1–5.2 points, though differences are not statistically significant at n=5 folds; (2) reducing the feature set by 49% (to accelerometer, gyroscope, temperature, RMS audio, and electrical channels) retains 92.5% ± 8.3% for the encoder while XGBoost drops to 84.4%, a loss of 10.7 points from its full-feature peak; (3) at full features, baselines are competitive (Random Forest: 95.6%, XGBoost: 95.1%); and (4) one-way ANOVA reveals that pressure channels encode session-level barometric confounds (F > 2,000) rather than machining dynamics, explaining baseline degradation when confound-prone channels are removed. These results suggest that core inertial, acoustic, and machine-level modalities may be sufficient for effective CNC operation classification on similar platforms, providing sensor-selection and temporal-configuration guidance for cost-effective monitoring deployments. Generalization to industrial machines, diverse materials, and production environments requires further validation.

Abstract: Microplastics (MPs) are emerging pollutants of global concern, posing significant ecological and human health risks. They are frequently detected in stormwater systems, with urban runoff serving as a major transport pathway into the environment. Green storm-water infrastructure, particularly bioretention systems (BRS), offers a promising approach to mitigate these risks by filtering and retaining various contaminants. However, the occurrence of MPs in BRS and their capacity in retaining these pollutants remain largely unexplored in the literature, despite being critical for stormwater management and water quality protection. Therefore, this study examines the occurrence, vertical distribution, and retention of MPs within field-installed BRS, emphasizing their role in reducing MPs transport. Field samples were collected at depths of 2, 12, and 24 inches below the surface and processed in the laboratory for MP detection and quantification. The results revealed an average concentration of 1,095 particles per kg of dried sediment, with fragments (microplastics shape) accounting for 78.54% of total MPs. Although no clear vertical distribution pattern was observed, MPs accumulated predominantly at 24 inches, indicating their transport through the media and the retention capacity of BRS (surface and mid layer) in capturing microplastics from stormwater environments. Integrating BRS into urban stormwater infrastructure provides dual benefits: improved stormwater management and reduced plastic pollution. This study highlights the importance of optimizing bioretention design and media composition to improve the removal efficiency of MPs.

Abstract: Religion, culture, and ethnic heritage play a significant role in shaping migrant identities. This paper investigates the interplay of these factors in the identity formation of African Christian migrants in Europe. In particular, it analyzes how second-generation (2G) migrants integrate Western secular values with Pentecostal orientations to facilitate upward social mobility. The analysis is based on a critical review of existing literature, supported by selected ethnographic case studies and qualitative interviews discussed in the cited works. By drawing on empirical research from various European contexts, this study aims to provide a rigorous and multidimensional understanding of intergenerational identity reconstruction among 2G African Christians. By centering the Pentecostal family as a primary site of socialization, this paper explores how 2G African Christians both distance themselves from indigenous African spirit cosmologies and adapt elements of these cosmologies to pursue secular, achievement-oriented objectives. This dialectical engagement highlights a generational shift: while first-generation migrants depend heavily on religion and religious institutions for integration, 2G migrants prioritize secular aspirations as they navigate socioeconomic structures, negotiate belonging, and construct new forms of transnational identity.

Abstract: This study extends the application of the 3M&I (human, Material/Machine, Money, and Information resources) body science framework to explore the wisdom law of body wealth and energy in additive-multiplicative systems, integrating both artificial and natural bodies. The research formalizes the mechanisms underlying body intelligence and survival through look-ahead and nano-transforming schemes grounded in a static-stable system perspective, modeled across two- and three-dimensional spaces. We propose refinements to Matsui’s original motion equations by introducing an ad-renewable formulation (ME) and a generalized version, MW = ZL, to capture both cumulative and progressive dynamics in body schemes. A ternary/pair-map framework is developed to address three-body systems and dualism-based challenges, extending beyond traditional two-body models. The framework also incorporates a wealth-additive scheme, reinterpreting the flow retention concept to prioritize energy/wealth maximization over cost minimization. Finally, we introduce Chameleon’s objective function—defined as (revenue × lead time) / departure—as a metric for optimizing marginal diversity within sustainable development contexts. The proposed model offers a theoretical foundation for predictivity versus sustainability processing and integrates body system modeling with broader eco-entropy and sustainability goals.

Abstract: Background/Objectives: Mycoplasma pneumoniae (MP) is a frequent cause of community-acquired pneumonia, but it is increasingly recognized for extrapulmonary complications, specifically Mycoplasma pneumoniae-induced rash and mucositis (MIRM). This systematic review aims to comprehensively assess the incidence, diagnostic criteria, clinical features, and outcomes of oral mucositis in patients with confirmed MP infection. Methods: A systematic review was conducted following PRISMA guidelines across PubMed, Web of Science and Scopus, covering the period 2015–2025. Inclusion criteria encompassed in vivo studies, case reports, and case series in English focusing on MP-associated mucositis. Methodological quality was assessed using JBI checklists for case-based evidence and the Newcastle-Ottawa Scale for cohort studies. Two clinical cases were reported. Results: Out of 242 identified records, 42 studies were included, involving 140 patients with a notable male predominance (62%). Oral involvement was reported in 92.9% of cases, often characterized by severe ulcerations, hemorrhagic crusting, and debilitating pain. Intensive Care Unit admission was required in 21.5% of cases due to severe systemic or mucosal disease, with 14.3% necessitating parenteral nutrition. Quality assessment indicated moderate-to-high methodological rigor across most included studies. Conclusions: MIRM represents a significant clinical entity where oral mucositis is a dominant feature, often preceding or overshadowing respiratory symptoms. Early recognition by oral health professionals is crucial to avoid misdiagnosis, ensure appropriate multidisciplinary care, and implement supportive or immunomodulatory therapies that reduce morbidity and hospitalization length.

Abstract: Alzheimer’s disease (AD) is characterized by progressive cognitive decline and the accumulation of amyloid β (Aβ) plaques and tau neurofibrillary tangles. Beyond genetic and proteostatic mechanisms, a growing body of work has revived infection- and dysbiosis-based models of AD, including the antimicrobial protection hypothesis in which Aβ participates in innate immune defense. Here, we reanalyzed ribosomal-depleted (Ribo Zero) RNA-seq data from dorsolateral prefrontal cortex (DLPFC) samples from the Mount Sinai Brain Bank cohort (GSE53697) to screen for non-human transcripts. Reads underwent quality control and adapter trimming, taxonomic classification with Kraken2, Bayesian re-estimation with Bracken, and differential abundance testing with edgeR. Across 17 samples (9 advanced AD; 8 controls), we detected low-biomass microbial signals with a disease-associated shift. Acinetobacter radioresistens was enriched in the AD group (FDR = 0.018), whereas several taxa were relatively enriched in controls (including Lactobacillus iners; FDR = 0.051). In silico analysis of an A. radioresistens biofilm-associated protein homolog identified multiple amyloidogenic hexapeptides and surface-exposed regions in an AlphaFold2 structural model, consistent with a hypothetical cross-seeding capacity. Given the technical challenges of brain microbiome inference from post-mortem RNA-seq (contamination, low microbial biomass, and host background), these findings should be interpreted as hypothesis-generating and warrant orthogonal validation.

Abstract: Bioeconomy policy requires timely, economy-wide evidence; however, two persistent measurement constraints remain: official input–output (IO) tables are published with substantial time lags, novel start-up and novel prospective or hybrid bio-based activities are rarely identified as separate sectors in national accounts. This paper develops an applied methodology that addresses both limitations by combining IO nowcasting with a reduced-dimensional sector-embedding procedure. Using Ireland’s national IO system and an existing bioeconomy IO framework as the accounting backbone, we update the 2015 table to 2022 through calibration to macroeconomic control totals, providing a timely structural baseline. We then introduce a transparent method for constructing new bioeconomy sectors based on dominant input shares, import intensity, and output allocation, while preserving national accounting identities. The approach is demonstrated for aquaculture systems, anaerobic digestion scenarios, and plant-based protein value chains. Demand-driven Leontief multipliers reveal substantial heterogeneity in domestic propagation effects across activities and development stages. The framework offers a resource-efficient and replicable tool for evaluating bioeconomy strategies under real-world data constraints.

Abstract: Effective management of reservoir water for irrigation is crucial in arid regions prone to drought and water shortages. However, evaporation losses from reservoirs remain poorly understood. Direct measurements typically quantify evaporation only at the measurement site rather than across the entire reservoir. This study introduces the Turbulent Exchange Approach for Reservoir Evaporation Estimation (TEAREE). The TEAREE is a simple model that integrates a bulk aerodynamic formulation with Landsat 8–9 satellite water-surface temperature data and meteorological observations to estimate spatially distributed daily reservoir evaporation. The TEAREE model was first evaluated at Elephant Butte and Caballo reservoirs in New Mexico, USA, and subsequently applied across multiple reservoirs with diverse climatic conditions to demonstrate its applicability for estimating open-water evaporation. Daily evaporation was obtained by upscaling satellite overpass-time evaporation estimates using the daily-to-instantaneous vapor pressure deficit ratio (k_e) and wind speed. The model performed strongly across 12 lakes (R² = 0.91–0.99; RMSE = 0.27–0.85 mm/day) compared with the bulk aerodynamic (B_AER) method. Comparison with eddy covariance (EC) evaporation also showed good agreement. Monte Carlo analysis indicated moderate uncertainty associated with k_e variability, supporting the operational use of a constant k_e = 0.95 for daily upscaling.

Abstract: Neural decoding has demonstrated that population activity contains behaviorally relevant information, yet predictive accuracy alone does not constitute mechanistic explanation. Decoding models establish statistical mappings between neural responses and task variables but leave the underlying computational processes underdetermined. We argue that neural computation is more appropriately framed within a dynamical state-space perspective, in which population activity reflects the evolution of latent states governed by structured transition operators. Across empirical and theoretical work, neural trajectories increasingly appear as low-dimensional, nonlinear flows shaped by recurrent circuit structure and contextual inputs. This shift reframes the central scientific objective: not merely extracting representations, but learning the evolution operator that governs state transitions. However, even accurate reconstruction of latent dynamics does not guarantee mechanistic validity. Observational data typically constrain only an equivalence class of admissible operators, rendering the inferred dynamics structurally non-identifiable. We therefore propose that causal neural dynamics must be defined through perturbation and experimental design. By introducing directional constraints on state transitions, targeted interventions collapse equivalence classes and enable identification of operators that remain valid under manipulation. In this framework, evolution operators are treated as falsifiable hypotheses whose mechanistic status depends on predictive stability under perturbation. This perspective recasts neural modeling as the search for perturbation-validated dynamical laws governing population activity, moving the field from decoding-based description toward causal dynamical explanation.

Abstract: Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia worldwide. Its hallmarks are extracellular amyloid-beta (Aβ) plaques and intracellular hyperphosphorylated tau forming neurofibrillary tangles, leading to synaptic dysfunction and neuronal loss. Despite extensive research, the mechanisms driving these proteinopathies and the contribution of genetic, molecular, and environmental factors remain unclear. Objective: This review summarizes the molecular mechanisms underlying AD and the factors influencing its onset and progression. Methods: A narrative review of peer-reviewed studies from PubMed, Scopus, and Web of Science was conducted. Relevant articles on neuropathology, molecular pathways, genetic susceptibility, oxidative stress, mitochondrial dysfunction, neuroinflammation, and metabolic and lifestyle risk factors were analyzed. Results: AD is marked by Aβ accumulation and tau pathology, causing synaptic and neuronal loss. Key mechanisms include abnormal amyloid precursor protein processing, tau hyperphosphorylation, oxidative stress, mitochondrial dysfunction, neuroinflammation, and calcium dysregulation. Genetic variants (APP, PSEN1, PSEN2, APOE ε4) increase risk, while aging, cardiovascular disease, diabetes, and lifestyle factors further influence disease onset and progression. Conclusion: AD arises from complex interactions among molecular and environmental factors. Understanding these pathways is essential for developing preventive strategies and effective therapies, with personalized approaches offering future promise.

Abstract: Water colour is an important optical proxy for trophic status and water quality, but its integration into regulatory assessment frameworks is still limited. This study assesses the potential of the Forel–Ule Index (FUI) derived from Sentinel-2 as a proxy indicator to support the assessment of the ecological status of reservoirs under the European Union’s Water Framework Directive (WFD). Seventeen reservoirs located in semi-arid Mediterranean climate agricultural basins in southern Portugal (Sorraia, Sado, and Guadiana) were analysed, combining 4,316 FUI observations (2017–2024) with in situ water quality data and official WGD ecological status classifications. FUI values covered virtually the entire scale (1–21), with most observations between 12 and 18 and with marked spatial and seasonal contrasts, particularly between more transparent reservoirs and persistently turbid ones, probably eutrophicated reservoirs. Principal component analysis showed that the first component (PC1, 39.5% of variance) represents a trophic gradient dominated by turbidity, chemical oxygen demand and chlorophyll-a, and is positively, albeit moderately, correlated with FUI (Spearman’s ρ = 0.439, p < 0.001), while the second component, dominated by nitrogen, showed no significant association. The ordinal logistic regression relating the FUI to the ecological status classes of the WFD captured the expected quality gradient, with FUI values between 10–13 reliably identifying the status ‘Good’ (probability > 0.70), but with greater uncertainty for the intermediate range (14–16) and a tendency to underestimate “Poor/Bad” conditions when the FUI > 16. Overall, the FUI proves to be a low-cost, high-frequency screening and early warning tool that is effective in detecting good conditions and state transitions. However, it should be complemented by physical-chemical and biological metrics when a fine distinction between WFD classes is required.

Abstract: The mutually reinforcing synergy between the development of the new energy industry and comprehensive land remediation is crucial for integrating ecologically fragile areas into the national "dual carbon" goals and supporting regional high-quality development. Based on an analysis of the challenges and opportunities facing the new energy industry in ecologically fragile areas, as well as the mechanisms for mutual promotion between new energy industry development and land remediation, this paper explores pathways for comprehensive land remediation coordinated with new energy development. Drawing on local practices, it further distills five typical models. The results show that: 1) The development of the new energy industry in ecologically fragile areas faces multiple challenges, including a fragile ecological environment, inadequate infrastructure, a mismatch between resource supply and demand, and land use conflicts. Against the backdrop of the energy transition, breakthroughs in key technologies, and the guidance of territorial spatial planning, the value of wind and solar resources in these areas is becoming increasingly prominent, offering broad prospects for the new energy industry. 2) The development of the new energy industry and comprehensive land remediation in ecologically fragile areas are mutually reinforcing. Factors such as resource endowment, ecological (environmental) constraints, new quality productive forces, and investment and financing mechanisms interact and integrate, leading to differentiated pathways for synergy. 3) Based on the focus of new energy industry development and the primary objectives of remediation, five remediation models are identified: ecological restoration-led land reclamation model, resource development-led land consolidation model, industry collaboration-led land consolidation model, technology innovation-led land consolidation model and integrated development model. Each model has distinct priorities and applicable scenarios. This study will provide a reference for new energy development and sustainable development in ecologically fragile areas, including desertified and Gobi desert areas, coal mining subsidence areas, and areas rich in wind, solar, and hydro energy resources.

Abstract: To address the core challenge in integrative medicine—the semantic incommensurability of heterogeneous medical data arising from divergent cognitive paradigms across medical systems—this paper proposes a multi-track cognition framework for global integrative medicine. Adopting a decoupled design of "flexibly customizable and extensible cognitive tracks with a fixed unified core architecture", this framework constructs exclusive cognitive tracks preserving the native logic for each medical system, takes the homeostatic representation network of multiple dimensions of human eight physiological systems as the general quantitative mediation benchmark, and establishes the system-level mapping relationship constrained by three core rules: cluster correspondence, network emergence, and context dependence, to realize the standardized transformation and system-level fusion of multi-source heterogeneous medical data. Empirical verification shows that the semantic alignment accuracy of this framework reaches 91.27%, the model goodness of fit ≥0.85, and the accuracy is improved by 32.14% compared with the traditional single-point linear mapping method. The determination results have a strong consistency with clinical expert judgments, which can provide a feasible and general technical support for basic research of integrative medicine, whole-cycle management of chronic diseases, and individualized health intervention.

Abstract: Background: Small-cell lung cancer (SCLC) represents an aggressive malignancy associated with a poor prognosis, underscoring the critical demand for enhanced monitoring methodologies. Circulating tumor DNA (ctDNA) constitutes a promising non-invasive biomarker; however, reports employing highly sensitive, tumor-informed assays in SCLC remain scarce. This investigation aimed to assess the clinical utility of a personalized ctDNA monitoring strategy for predicting therapeutic outcomes and resistance in SCLC patients. Methods: This prospective, observational study enrolled patients diagnosed with unresectable SCLC. Whole exome sequencing was conducted on baseline tumor specimens to design customized 16-plex multiplex PCR panels. Serial blood samples were obtained at baseline, at six-week intervals during treatment, and upon disease progression. Detection of ctDNA-based minimal residual disease (MRD) was performed using a tumor-informed assay (Huajianwei® bespoke MRD) with ultra-deep sequencing. Results: Among seven evaluable patients, the baseline ctDNA-MRD positivity rate was 100%. A significant positive correlation was observed between baseline ctDNA levels and radiographic tumor burden (r = 0.821, P = 0.034). Longitudinal analysis indicated that patients exhibiting an early decline in MRD levels (n=5) demonstrated a trend toward superior progression-free survival (PFS) compared to those with an MRD increase (n=2) (P = 0.0665, hazard ratio (HR) = 0.24 (95% CI: 0.02 - 3.19)). Notably, elevation in MRD preceded radiographic progression by as much as 135 days in certain instances. Conclusions: Tumor-informed ctDNA-MRD monitoring effectively mirrors tumor burden and offers early prediction of treatment response and clinical outcomes in SCLC. ctDNA kinetics may serve as a crucial prognostic indicator, presenting the potential to inform personalized management approaches and facilitate earlier therapeutic interventions compared to conventional imaging techniques.

Abstract:

This study investigates the influence of selected technical and technological parameters on cutting forces and power consumption during the milling of medium-density fibreboards. The main objective was to experimentally measure orthogonal cutting force components (F_x, F_y, F_z) and electrical power consumption under varying spindle speeds (14 000, 16 000 and 18 000 rpm), feed speed (6, 8 and 10 m/min), and milling strategies (conventional and climb), and to evaluate the suitability of the obtained data for predictive modelling. Cutting forces were measured using a Kistler 9257B piezoelectric dynamometer, and power consumption was recorded by a three-phase power quality analyser. Statistical analysis confirmed significant effects of machining parameters on force components, total cutting force, and power consumption. Spindle speed showed the strongest influence on total cutting force and power consumption, while milling strategy predominantly affected F_x and F_y components. Power consumption increased with increasing spindle speed. Based on the measured data, several machine learning models were developed to predict the total cutting force. After model comparison using RMSE, R², training time, and model size, a Fine Tree model was identified as the most suitable, achieving high prediction accuracy without signs of overfitting. The results confirm that experimentally obtained force and energy data are suitable for reliable predictive modelling in CNC milling of MDF.