Abstract: The semantic interpretation of actions is deeply intertwined with how change unfolds over time, space, and interaction. Prior theoretical and computational work has suggested that explicitly modeling three-dimensional motion---including object positions and orientations evolving through time---should offer a privileged pathway for encoding fine-grained verb meaning, especially for distinctions such as \textit{roll} versus \textit{slide}. At the same time, the vast majority of multimodal language models rely almost exclusively on two-dimensional visual inputs, implicitly assuming that such projections suffice to ground linguistic meaning. In this work, we revisit this assumption through a systematic and tightly controlled comparison of visual and motion-based modalities. We construct self-supervised encoders over both 2D video observations and 3D trajectory data, and probe the resulting representations for their capacity to discriminate verb-level semantic categories. Contrary to prevailing intuition, our empirical analysis reveals that representations learned from 2D visual streams are competitive with, and in some cases indistinguishable from, those derived from explicit 3D trajectories. These findings complicate the widely held belief that richer environmental encodings automatically lead to superior semantic representations, and suggest that the relationship between perceptual fidelity and linguistic abstraction is more nuanced than often assumed. Our study offers early evidence that effective verb representation may emerge from multiple perceptual pathways, motivating a rethinking of how embodiment and modality interact in multimodal language learning.

Abstract: This study proposes a spatiotemporal framework with cross-node dependency modeling to address anomaly identification in distributed systems under highly dynamic workloads, multi-node coupling, and complex topology changes. The framework first builds a multi-scale temporal representation module that extracts local trends, short-term disturbances, and long-term correlations from monitoring sequences to enhance sensitivity to system dynamics. It then introduces a dynamic structure learning mechanism that generates time-dependent structural graphs based on call chains, resource contention, and link interactions, allowing the model to capture dependency drift caused by topology changes during system operation. On this basis, the framework designs a structure propagation and joint embedding module that fuses temporally enhanced features with structural dependency representations into a unified system state vector, enabling the model to understand anomaly propagation paths and cross-node interactions from a global perspective. A latent-space anomaly measurement function is used to identify possible bottleneck anomalies and improve detection performance for complex system behaviors. To validate the method, extensive experiments are conducted on large-scale open cluster monitoring data, evaluating model performance under hyperparameter sensitivity, environment sensitivity, and data sensitivity settings. The results show that the proposed framework maintains stable performance under structural noise, topology perturbations, and workload fluctuations, and achieves higher accuracy, recall, and F1 scores than multiple representative baselines, demonstrating the advantages of spatiotemporal joint modeling for anomaly detection in distributed systems.

Abstract: Industrial bioprocesses remain constrained by their inability to directly monitor what happens inside cells in real time. Instead, they rely on external measurements such as nutrient or metabolite levels in the culture medium that only provide delayed and indirect clues about the cell's internal state. Riboswitches, RNA elements that respond to specific small molecules, offer a powerful alternative. Acting as intracellular biosensors, they can detect metabolites at nanomolar to micromolar concentrations within milliseconds. When coupled with gene regulatory systems, riboswitches enable dynamic feedback control, allowing cells to sense imbalances and autonomously adjust their metabolism. This transition from external, delayed monitoring to internal, responsive regulation has the potential to transform microbial production systems into self-optimising factories that are more efficient, adaptive, and resilient.

Abstract: Introduction: Anemia is a global public health concern affecting mainly women of reproductive age (WRA) and preschoolers (PSC,6-59 months) due to their higher demand for iron. The etiology of anemia is multifactorial, and nutritional anemia is the most common type worldwide and is predominantly due to deficiencies in iron, vitamin B12, and folate. This study aimed to assess the associated factors, etiology, and prevalence of anemia among WRA and preschoolers in the GBAO region in Tajikistan. Methods: We conducted a cross-sectional study in the GBAO province of Tajikistan between April 2021 and September 2021, enrolling 500 WRA and 500 children aged 6-59 months from six districts of the GBAO region. The survey was administered through a structured questionnaire at the household level, and information was collected on sociodemographic characteristics, reproductive history, dietary intake, nutritional status, and maternal factors. Blood and stool samples were also collected for micronutrient deficiencies and helminthic infections. Data analysis began with univariate analysis, followed by multivariate logistic regression in Stata (version 18). Results: Biochemical assessment of 473 WRA and 390 preschoolers was conducted; 17.3% of WRA and 15.4% of PSC were anemic. After adjustment, the following factors were positively associated with anemia in WRA living in the GBAO region; body mass index ( overweight (OR 0.313, p value 0.020), gravidity (OR 0.326,p value 0.009), low ferritin (OR 8.549,p value <0.001), elevated serum transferrin receptor levels (OR 4.817,p value <0.001) and Vitamin B12 deficiency (OR 0.18,p value 0.050). In contrast, in children, maternal education (OR 2.35, p value 0.029), maternal anemia (OR 4.998, p value 0.001), age under 2 years (OR 2.6, p value 0.016), and low ferritin (OR 5.67, p value <0.001) showed a positive association with anemia. Conclusions: Anemia in women was strongly associated with low ferritin, high STFR, gravidity, BMI, and vitamin B12; in children, with maternal education, maternal anemia, age <24 months, and low ferritin. These findings underscore the urgent need for locally tailored interventions, both in the short term and the long term.

Abstract: The need for simpler, yet accurate and physically sound, methods to predict the lift and pressure distributions over asymmetric delta wings, particularly at high angles of attack with attached shock wave, motivates the development of an alternative approach presented in this paper. By employing a geometric transformation and postulating a functional similarity between linear and nonlinear solutions, a straightforward algebraic technique for pressure estimation is developed. This approach bridges the solution in the central nonuniform flow region to the exact solutions in the uniform flow regions with attached shock waves near the leading edges, in a manner analogous to methods used for supersonic starting flow at high incidence. The method is shown to reproduce established results for both symmetric and yawed delta wings within a limited error. It yields a compact, explicit expression for the normal force coefficient, formulated as a weighted average of the pressure coefficients from the two uniform flow regions. A pathway for extending the approach to the upper surface, where the uniform flow is governed by swept Prandtl-Meyer relations is also outlined. Although classical analytical approaches for delta wings were established decades ago, the proposed method provides a tractable alternative tool for modern fast engineering analysis.

Abstract: The ongoing environmental challenges posed by soil degradation and desertification are of particular concern. This situation is particularly alarming in agricultural and pastoral areas in communities in northern Senegal, as it compromises the food security of human communities. Considering this situation, composting is regarded as a pivotal instrument within agrosystems, facilitating the utilization of organic livestock and agricultural waste for the purpose of transforming it into fertilizer for crop cultivation. Development cooperation projects sometimes involve the transfer of scientific knowledge to develop products adapted to the conditions of the area targeted by the intervention. The study aims to examine the basic properties of the soils, analyse the composting process and the compost, and evaluate the effect of the compost obtained on the soil within the framework of a development cooperation project. Sampling has been carried out in several agro-livestock communities in Northern Senegal; physical and chemical parameter analyses were carried out on materials in the composting phase, compost, soils, and soils amended with compost and manure. The results indicate that the soil dedicated to cultivation in the areas studied are characterized by a predominantly sandy texture and exhibit significantly low levels of nitrogen and organic matter. The resulting compost has contributed significantly to improving the soils where it has been applied and therefore improve crop production, thereby highlighting the Kanel region.

Abstract: On-road vehicles are a primary source of urban air pollution. It is known that high-emitting vehicles represent a fraction of the fleet but contribute significantly to the total emissions. Usually, road transportation emission inventories do not capture the impact of these types of vehicles, underestimating emissions. This study introduces a simple method to refine vehicle emission inventories by incorporating data from Ecuador's Inspection and Maintenance (I/M) program. We analyzed I/M data from Quito to develop a correction factor for the Vehicular Emissions INventory (VEIN) model, accounting for the higher emissions from vehicles that fail inspection. Our analysis shows that while less than 10% of gasoline and 20% of diesel vehicles failed inspection, their emissions were substantially higher; for instance, accounting for reproved vehicles produced 60% more CO, 18% more NMVOC, 40% more PM2.5, and 34% more PM10 . These findings demonstrate that incorporating I/M data is crucial for accurately quantifying vehicular pollution. The proposed methodology offers a way to create more accurate emission estimates, providing a tool for policymakers to manage air quality.

Abstract:

This study presents the complete sequencing and comparative genomic analysis of Leishmania (Viannia) naiffi and Leishmania (Viannia) shawi, species of epidemiological relevance in the Brazilian Amazon. Genome assemblies yielded sizes of 32.13 Mb and 32.51 Mb, with 8,170 and 7,767 annotated genes, respectively. Predicted gene functions were primarily related to catalytic, binding, and ATP-dependent activities. Pangenome analysis revealed a core genome of 6,256 genes alongside notable species-specific differences, including 46 and 25 unique genes in L. naiffi and L. shawi. Functional screening identified pharmacologically promising proteins such as calpains, ABC transporters, and notably, GSK-3. Ploidy analysis indicated tetraploidy on chromosome 8 in L. naiffi and chromosome 2 in L. shawi. Genetic variability assessment detected 34,480 SNPs in L. naiffi and 26,562 in L. shawi, indicating greater genomic diversity in the former. Phylogenetic inference based on the polA1 gene confirmed the placement of both species within the Leishmania (Viannia) subgenus. These findings advance Leishmania genomics knowledge by highlighting unique genetic signatures, regions of high variability, and potential therapeutic targets. This work establishes a foundation for future research on evolution, pathogenicity, and drug development for leishmaniasis.

Abstract: Parenting stress and parenting practices are key indicators of family relational well-being, yet less is known about these indicators among adolescent mothers in resource-constrained settings. This study draws on cross-sectional survey data from 931 adolescent mother–child dyads in the Eastern Cape, South Africa (first parity 11–19 years). Mothers completed an adapted Parenting Stress Scale and the Parenting Young Children (PARYC) measure, and children were assessed using the Mullen Scales of Early Learning. We examined associations between parenting stress, parenting activities, and developmental functioning using multivariate regression. Adolescent mothers reported elevated stress, shaped by child characteristics, intergenerational caregiving dynamics, and household resource strain. While mothers frequently engaged in playful and responsive activities, they reported greater difficulty with limit setting and proactive parenting behaviors. Children showing age-appropriate cognitive and socio-emotional development were more likely to have mothers who engaged regularly in playful activities and reported higher parenting enjoyment. Associations were small to moderate (ANOVA p<0.001, d=0.48). Findings highlight adolescent parenting as a site of both vulnerability and resilience, suggesting that interventions may be strengthened by addressing stress regulation, playful engagement, intergenerational support, and economic stability as interlinked indicators of family well-being.

Abstract: This review explores inulin as a versatile dietary fiber, covering its extraction, characterization, functional properties, applications, chemical modifications and sustainability. It first presents conventional and emerging extraction methods, including hot water, microwave and ultrasound‑assisted extraction, enzymatic processes and pulsed electric fields, together with purification by ion‑exchange resins and membrane filtration, and analytical techniques (FTIR, HPLC, TLC) used to identify and quantify inulin. The influence of degree of polymerization and molecular weight distribution on solubility, rheology, thermal stability and sweetness is then discussed, highlighting inulin’s role as a fat and sugar replacer in complex food matrices. The manuscript reviews applications in food, pharmaceutical, agricultural and cosmetic fields, focusing on prebiotic effects, texture modulation, drug delivery and biostimulant potential. Significant attention is given to chemically modified inulins (butyrate, acetyl, carboxymethyl, and other functionality derivatives) that provide enhanced stability, bioactivity, encapsulation efficiency, and sensory characteristics. Finally, the review examines economic, environmental, and social aspects, focusing on the valorization of chicory and artichoke by-products and positions inulin as a key ingredient for the developed of healthier and more sustainable products.

Abstract: Endoperoxides represent a distinctive class of highly oxygenated terpenoids characterized by the presence of a cyclic peroxide (–O–O–) bond, a structural feature that underlies their remarkable chemical reactivity and broad spectrum of biological activities. Naturally occurring endoperoxides are widely distributed in terrestrial and marine organisms, including plants, algae, fungi, and bryophytes, and play important roles in chemical defense and ecological interactions. This review provides a comprehensive overview of naturally occurring endoperoxide-containing terpenoids, with particular emphasis on their distribution in nature, structural diversity, and biological activity profiles. Special attention is given to their antiprotozoal and anticancer properties, exemplified by artemisinin and related compounds, which remain among the most effective antimalarial agents and show promising antineoplastic potential. Using structure–activity relationship (SAR) concepts and modern computational approaches, including the PASS (Prediction of Activity Spectra for Substances) system, we analyze trends linking peroxide-containing motifs to biological function. Comparative evaluation of experimental and predicted activities highlights key structural features associated with antiprotozoal, antineoplastic, and anti-inflammatory effects. The review underscores the significance of endoperoxides as a rich source of bioactive scaffolds and discusses their potential for further development as therapeutic agents, despite challenges related to stability and reactivity.

Abstract: To address API abuse and unauthorized data access in multi-tenant systems, this paper proposes a full-stack security gateway framework based on zero-trust access and policy verification. The system integrates Envoy Gateway and the OPA (Open Policy Agent) policy engine at the API ingress layer, combining the OAuth 2.1 authorization protocol with JWT token authentication to achieve fine-grained tenant identity management. To support dynamic resource access, a policy inheritance mechanism based on GraphQL Schema injection is designed, enabling millisecond-level data access permission validation. Experiments demonstrate that under million-request-level testing, the model achieves an average authentication latency of 74.2 ms, with a 28% increase in security event detection rate and a false positive rate reduced to 1.9%. This research provides a highly scalable, auditable security baseline architecture for data security governance in multi-tenant web platforms.

Abstract: Semantic interoperability remains a critical challenge in Spatial Data Infrastructures (SDIs), particularly when aligning authoritative taxonomies with collaborative folksonomies. Recent advances in Large Language Models (LLMs) offer new avenues for automated semantic interpretation, yet these 'sub-symbolic' approaches often lack the logical rigor required for structured geospatial data. This paper evaluates the capability of LLMs – specifically distinguishing between traditional architectures and emerging Large Reasoning Models (LRMs) – to perform semantic alignment between the Brazilian national cartographic standard (EDGV) and OpenStreetMap (OSM). Using a formal ontology as a prompting scaffold, we tested seven model versions (including ChatGPT 5.0, DeepSeek R1, and Gemini 2.5) on their ability to identify semantic equivalents and generate valid ontological mappings. Results indicate that while traditional LLMs struggle with hierarchical structures, reasoning-oriented models demonstrate significantly improved capacity for complex inference, correctly identifying many-to-one (n:1) relationships across linguistic barriers. However, all models exhibited limitations in generating syntactically valid OWL code, revealing a gap between semantic comprehension and formal structuring. We conclude that a neuro-symbolic approach, using ontologies to ground AI reasoning, provides a viable pathway for semi-automated interoperability, although future work must address the lack of explicit spatial reasoning in current architectures.

Abstract: Antarctic ice core data reveal a consistent pattern across glacial cycles: atmospheric CO₂ does not immediately track temperature decline as interglacial conditions give way to glaciation. The most dramatic example occurs during the Last Interglacial (Eemian, MIS 5e), where CO₂ remained essentially constant at 275–280 ppm for approximately 13,000 years while temperature fell 7°C. This paper examines whether similar behavior can be detected during cooling from earlier interglacials. Using harmonic fits to temperature and CO₂ data spanning 350,000 years, phase plots are constructed of CO₂ versus temperature that isolate the warming and cooling branches of each glacial cycle. The analysis reveals that the Eemian is the clearest but not unique example: MIS 9 shows comparable behavior, while the MIS 7 complex presents an instructive exception that may reflect extreme orbital forcing conditions. The asymmetry between rapid CO₂ release during warming and slow CO₂ absorption during cooling suggests rate-limited processes govern the return of atmospheric carbon to oceanic and terrestrial reservoirs. These observations are inconsistent with CO₂ acting as the primary driver of temperature change on glacial-interglacial timescales.

Abstract: Porcine epidemic diarrhea virus (PEDV), particularly the emerging GII genotype, poses a severe threat to the global swine industry. Current vaccines based on classical strains often provide limited cross-protection against these heterogeneous variants. In this study, a novel PEDV GIIc strain, designated as PEDV-HeN2024, was successfully isolated and identified through cell culture, immunofluorescence assay (IFA), genetic sequencing, and phylogenetic analysis. Animal challenge studies demonstrated that this isolation exhibited high pathogenicity, causing severe diarrhea in both 3-5-day-old piglets. Furthermore, an inactivated vaccine was developed by emulsifying the purified virus with ISA 201 VG adjuvant (1:1, v/v). In contrast to the commercial vaccine, the PEDV-HeN2024 inactivated vaccine induced significantly higher titers of neutralizing antibodies and virus-specific total immunoglobulins in immunized animals. Crucially, these antibodies elicited broad cross-neutralizing activity against homologous GIIc, as well as heterologous GIIa and GIIb strains in vitro and in vivo. Our findings indicate that the inactivated vaccine candidate developed from the emerging PEDV-GIIc variant is a promising broad-spectrum vaccine for controlling the prevalent PEDV strains.

Abstract: Traditional log alerting systems suffer from high false positive rates and delayed anomaly diagnosis. This paper proposes an intelligent log analysis framework integrating self-supervised temporal modeling with vectorized semantic retrieval. The system constructs a log collection pipeline using the ELK Stack, employs BERT-derived models for semantic encoding of log fragments, and utilizes a Temporal Contrastive Learning module to capture cross-temporal anomaly patterns. By integrating Cluster-based Outlier Detection and an Attention-based visualization mechanism, it enables interpretable diagnosis of complex system behaviors. Experiments conducted on a production dataset of 120 million logs achieved a 14.7% improvement in F1 score, reduced detection latency by 48%, and attained an average alert accuracy of 92.3%. This framework significantly enhances the intelligent operations and maintenance capabilities of full-stack systems in AIOps environments.

Abstract: Astigmatism, a common refractive error characterized by an irregular corneal or lenticular curvature, represents a significant pediatric public health concern with profound implications for visual development and long-term ocular health. This review synthesizes contemporary evidence on the complex, multifactorial etiology of astigmatism, emphasizing its critical and synergistic relationship with myopia progression. We delineate the substantial genetic component, with heritability estimates of 40-60%, involving polygenic inheritance patterns and specific SNPs in loci such as PDGFRA and CTNNA2. The pathophysiology is further explored through physiological triggers, including extraocular muscle imbalance, dynamic eyelid pressure, and corneal biomechanical weakening. Modern environmental accelerants, notably prolonged near work and digital device usage, are examined for their role in disrupting emmetropization. The core thesis of this manuscript advocates for a paradigm shift towards early infantile screening and personalized, multi-modal intervention strategies. We critically evaluate conventional therapies (spectacles, toric contact lenses, orthokeratology), emerging pharmacological agents (low-dose atropine), and evidence-based lifestyle modifications (increased outdoor exposure, nutritional optimization). Furthermore, we explore the integration of advanced diagnostics (anterior-segment OCT, Scheimpflug tomography, genetic risk profiling) and complementary approaches like Nutritional optimization and traditional medicine systems, such as Ayurvedic Netra Tarpana and yogic eye exercises, within a holistic management framework. The convergence of genetic insights, advanced biometry, and personalized medicine heralds a new era in preventing astigmatism-related amblyopia and mitigating its role in axial elongation, ultimately preserving lifelong visual function.

Abstract: Transcriptomic analyses of public datasets (TCGA and GTEx) revealed that both CD74 and Cathepsin L (CTSL) are significantly overexpressed in diffuse large B-cell lymphoma (DLBCL) compared to normal tissues, and their expression levels are highly correlated (Spearman R = 0.64, p = 3×1046). Kaplan–Meier analysis showed that elevated expression of both genes is associated with reduced disease-free survival (DFS), defining a high-risk CD74+/CTSL+ DLBCL subgroup. This is the first study demonstrating coordinated overexpression of CD74 and CTSL, and proposing their dual targeting via antibody–drug conjugates (ADCs) to improve outcomes in relapsed or refractory DLBCL. Cysteine cathepsins, a family of proteases, are upregulated in many cancers, facilitating tumor invasion and metastasis. Cathepsins are overexpressed and play key roles in DLBCL progression. GB111-NH₂, a potent broad-spectrum cathepsin inhibitor, significantly reduced cathepsin activity in lymphoma cell lines and patient samples. GB111-NH₂ treatment increased apoptosis and caspase-3 activation in DLBCL patient cells and chronic lymphocytic leukemia (CLL) mononuclear cells. Here, we developed a modified cathepsin inhibitor, M-GB, containing a maleimide linker for site-specific antibody conjugation. While M-GB alone has poor cell permeability, when conjugated to an antibody, it forms an ADC (M-GB-ADC) that selectively induces lymphoma cell death. M-GB-ADC demonstrated high specificity for CD74-expressing lymphoma cells while exhibiting minimal toxicity to non-target cells. Our findings highlight the potential of M-GB–ADC as a targeted therapy for overcoming rituximab resistance and treatment failure in DLBCL. This strategy enhances therapeutic efficacy and provides a treatment option by directing a cathepsin inhibitor payload specifically to malignant B cells.

Abstract: Background/Objectives: In recent years, increasing attention has been directed toward the human immune system and strategies to enhance its function. Whole-body cryother-apy (WBC), a short-term therapeutic application of extreme cold of about minus 90 de-grees, seems to show a positive influence on the immune system, physical pain, body composition and other human regulatory systems. Methods: In this one- armed prospec-tive monocentric observational study 20 adult participants underwent 18 sessions of cry-otherapy over 9 weeks (–90°C, 3 – 6 minutes each), followed by a 9-week post-intervention phase. Results: In many parameters of bioimpedance analysis, blood parameters and subjective perception of stress, statistically significant improvement could be found, especially directly after intervention phase. Some improvements persist till end of study time frame. Conclusions: The results of this pilot study underscore the impact of cryotherapy on pathways related to immune function and metabolic regulation. The results will pave the way for further randomized controlled trials that study and confirm the efficacy of WBC.

Abstract: Artificial intelligence (AI) platforms in East Asia often elicit privacy concern yet sustain user participation. This study interprets the pattern as bounded compliance—a satisficing equilibrium in which engagement persists once minimum transparency and reliability thresholds are perceived in platform governance. A symmetric adult survey in Fujian, China (N = 185) and Busan–Gyeongnam, Korea (N = 187) examines how accountability visibility and privacy concern jointly shape platform trust and use. Heat-map diagnostics and logit marginal effects show consistently high willingness (≥0.70) across conditions, with stronger accountability sensitivity in Korea and stronger continuity assurance in China. Under high concern, willingness converges to a “good-enough” zone where participation endures despite discomfort. The findings highlight governance thresholds as practical levers for trustworthy AI: enhancing feedback visibility (e.g., case tracking, resolution proofs) and maintaining institutional continuity (e.g., O&M capacity, incident-response coverage) can sustain public confidence in AI-enabled public-service platforms.