Abstract: Rheumatoid arthritis (RA) is a chronic inflammatory disease of autoimmune background and unknown etiology. The importance of genetic factors in the RA development is well established. Environmental factors have also been extensively researched in relation to risk of RA and managing its symptoms. Smoking, physical activity, diet and gut microbiota are considered to be the most essential modifiable factors in RA. Among dietary interventions the most researched is Mediterranean diet, monounsaturated fatty acids, fish consumption and fish oil (EPA, eicosapentaenoic acid and DHA, docosahexaenoic acid). Others concerned gluten-free and vegan or vegetarian diet, salt intake, supplementation with vitamin D, antioxidants, prebiotics and probiotics. Diet modifications can alter the gut environment and the association between RA development or severity and composition of gut bacteria has already been shown. This review focuses on effectiveness and usefulness of various dietary approaches and supplements in RA prevention and management, including influence on disease activity and inflammatory status. Composition of gut microbiota and its changes in response to dietary factors are also considered. There is a great need for further research into mutual dependencies of diet, microbiome and RA activity. The current state of knowledge provides promising evidence for future nutrition and microbial therapies.

Abstract: Background. The preoperative differential diagnosis of myometrial lesions remains a significant challenge when using conventional imaging techniques, such as ultrasound (US) and magnetic resonance imaging (MRI). Radiomics and machine learning, which leverage quantitative features beyond human visual perception, are increasingly recognized as promising tools for improving differential diagnosis in gynecology. Methods. This retrospective study included patients who underwent surgery for uterine masses and had preoperative MR. A machine learning model was developed to analyze radiomic features extracted from T2-weighted and diffusion-weighted MR images. Results. 44 subjects were included: 19 (43.2%) classified as "sarcoma" and 25 (56.8%) as "fibroid" based on histology after surgery. This dataset was used for training and cross-validation of different models. Three models, comprising ensembles of machine learning classifiers (random forests, support vector machines, and k-nearest neighbors), were developed for binary classification using histological diagnosis as reference standard. The best-performing model achieved the following results: AUC 90%, accuracy 82%, sensitivity 95%, specificity 72%, PPV 72%, and NPV 95%. Conclusions. Our model demonstrated high sensitivity and moderate accuracy, suggesting its potential as a valuable tool for assisting clinicians in the preliminary assessment of myometrial lesions and guiding decision-making toward conservative management in cases of non-suspicious masses.

Abstract: Urban Embodied Agents (UrbanEAs) are emerging to actively interact with complex, large-scale city environments and generate vast, heterogeneous data streams, moving beyond the single-vehicle of existing autonomous driving. However, urban environments present distinct challenges, including environmental variability, limited observability, and interaction complexity. These challenges hinder the effectiveness of existing embodied agents, which have focused on controlled indoor environments, and expose the inherent limitations of relying on single-domain data. Therefore, establishing a comprehensive data lifecycle to fuse multidomain data from terrain, aerial, and space is a strategy for developing actionable embodied capabilities from raw urban streams. Distinct from existing surveys that follow a model-centric paradigm for urban computing or autonomous driving, we systematically propose and review a comprehensive Data Lifecycle from a multidomain data perspective, which is critical for the UrbanEA. First, we propose a unified framework containing four key stages of this lifecycle: Data Perception, Data Management, Data Modeling, and Task Application. Next, we establish a taxonomy for each stage of the lifecycle. Specifically, we detail the evolution from static data storage to active agent memory, and analyze integration strategies designed to bridge multidomain gaps. We demonstrate how UrbanEAs empower downstream tasks, including Urban Scene Question-Answering (SQA), Vision-Language Navigation (VLN), and Human-Agent Collaboration (HAC). Finally, we outline the social impact of the data lifecycle of UrbanEA and open research problems with the future directions. Our survey provides a roadmap for designing the robust, high-performance data frameworks essential for these UrbanEAs.

Abstract: Velika Pasica Cave, 105 m long, 12 m deep, at an elevation of 670 m, situated in the central Slovenia (Europe) has only two to seven meters of thick roof and four permanent trickles from the epikarst zone. From this cave was described the second troglobitic beetle, Anophthalmus hirtus Sturm, 1853. It was about twenty years after the first species, Leptodirus hochenwarti Schmidt, 1832, was described from the cave Postojnska Jama (Slovenia). In the next decades nine more terrestrial species and subspecies were described from the cave belonging to groups Mollusca, Pseudoscorpiones, Collembola and Coleoptera. After 2000, intensive research of the pools and trickles revealed rich aquatic fauna, resulting in the description of four new species of Copepoda and two not yet determined epibiotic protozoans invading them. A complete list of terrestrial and aquatic fauna from the cave has never been published. To fill the gap, data from the literature as well as data from intensive field work in 2019 are presented here. Ninety three terrestrial and 36 aquatic taxa were recorded from the cave so far. Twenty nine aquatic (including two epibionts) and 18 terrestrial species are strict cave-dwelling organisms.

Abstract: Raising the turbine inlet gas temperature is an effective strategy for improving turbomachinery efficiency; however, it imposes severe thermal loads on turbine blades. To enhance blade cooling performance, this study employs computational fluid dynamics (CFD) to investigate the influence of sinusoidal ribs on turbulent flow and heat transfer in rectangular internal cooling channels. Numerical simulations demonstrate that sinusoidal rib configurations achieve superior heat transfer enhancement with reduced pressure losses across a wide Reynolds number range (Re = 20,000–90,000) compared to conventional transverse rib geometries. This improvement is quantified by higher normalized Nusselt numbers (N_u/N_u0) and lower normalized friction factors (f/f₀). Through systematic parametric analysis, the study elucidates how key geometric parameters—amplitude, wave number, and rib height—regulate flow and heat transfer performance. The study ranks nine pre-specified sinusoidal rib configurations under uniform heat flux conditions and identifies SR-I and SR-C as top performers for different design criteria, providing quantitative guidance for the preliminary design of turbine blade cooling channels.

Abstract:

This study evaluated the in vitro anti-inflammatory and antidiabetic activities of methanolic leaf extracts of Ximenia caffra (sour plum), a medicinal plant widely used in traditional healthcare systems across tropical Africa. Medicinal plants remain an important source of bioactive phytochemicals, and growing interest in phytopharmaceuticals has intensified the search for natural compounds with therapeutic potential. The present investigation aimed to scientifically validate the ethnomedicinal use of X. caffra leaves by assessing their enzyme inhibitory and anti-inflammatory properties. Fresh leaves of X. caffra were collected, authenticated, air-dried, pulverized, and extracted using methanol through maceration. Anti-inflammatory activity was determined using protein denaturation inhibition and membrane stabilization assays, while antidiabetic potential was evaluated through α-amylase and α-glucosidase enzyme inhibition assays. The extract exhibited concentration-dependent biological activities across all experimental models. Anti-inflammatory evaluation showed significant inhibition of protein denaturation and membrane stabilization, with IC₅₀ values of 129.83 µg/mL and 288.11 µg/mL, respectively. Similarly, the extract demonstrated appreciable antidiabetic activity, inhibiting α-amylase and α-glucosidase enzymes with IC₅₀ values of 227.01 µg/mL and 179.35 µg/mL, respectively, indicating stronger inhibition of α-glucosidase. These findings suggest that X. caffra leaves contain bioactive compounds capable of modulating inflammatory responses and carbohydrate-digesting enzymes, thereby supporting their traditional medicinal use. The study highlights the potential of X. caffra as a promising natural source for the development of plant-based anti-inflammatory and antidiabetic therapeutic agents.

Abstract: Background: Childhood obesity is associated with important alterations in body composition that may impair muscular strength and functional capacity. While higher body mass is often accompanied by greater absolute strength, the independent impact of adiposity on muscular strength after accounting for lean tissue remains insufficiently understood. The aim of this study was to examine the associations between adiposity, body composition, and muscular strength in children and adolescents, with particular focus on the independent effects of fat mass after adjustment for growth- and maturation-related factors. Methods: This cross-sectional study included 84 children and adolescents aged 5–18 years. Anthropometric measurements were used to calculate body mass index, waist-to-hip ratio, and waist-to-height ratio, with weight status classified according to World Health Organization BMI-for-age criteria. Body composition was assessed using bioelectrical impedance analysis (Tanita), providing estimates of body fat percentage and Tanita-derived muscle mass. Pubertal stage was assessed using Tanner classification. Muscular strength was evaluated using dominant handgrip strength, and habitual physical activity was recorded as hours per week. Associations between adiposity-related indices and muscular strength were explored using correlation and multiple linear regression analyses, with adjustment for age and Tanita-derived muscle mass. Results: Body mass index showed a positive association with handgrip strength, reflecting the contribution of overall body mass. Central adiposity indices demonstrated weak to modest associations with muscular strength. Body fat percentage showed only a limited association with handgrip strength in unadjusted analyses. However, in multivariable regression models adjusting for age and Tanita-derived muscle mass, higher body fat percentage emerged as an independent negative predictor of handgrip strength. Age did not show an independent association with muscular strength in adjusted models. Conclusions: Excess adiposity is independently and negatively associated with muscular strength in children and adolescents, even after accounting for age and Tanita-derived estimates of muscle mass. These findings suggest that increased fat mass may impair neuromuscular performance beyond its effects on body size or lean tissue. Pediatric obesity interventions should therefore focus not only on weight reduction but also on improving body composition and preserving functional strength.

Abstract: Background: Human cadaveric models provide a controlled experimental setting to investigate the anatomical basis and mechanical behavior underlying musculoskeletal ultrasound findings. In recent years, both B-mode ultrasound and shear wave elastography have been applied in cadaveric studies to explore muscle architecture, aponeurotic structures, and passive mechanical properties under standardized conditions [3]. Objective: The aim of this scoping review was to map and synthesize cadaveric studies using ultrasound and shear wave elastography to investigate lower-limb muscles and their aponeurotic structures, with emphasis on methodological applications, anatomical insights, and limitations relevant to clinical interpretation. Material and Methods: A scoping review was conducted according to PRISMA-ScR principles. Studies included if ultrasound imaging (B-mode and/or shear wave elastography) was applied directly to human cadaveric lower-limb muscles or aponeurotic structures. Data were extracted and synthesized descriptively by anatomical region and ultrasound technique [8]. Results: A total of 11 studies met the inclusion criteria and were included in the final qualitative synthesis, all of which applied ultrasound imaging, with or without shear wave elastography, directly to human cadaveric muscle tissue (Table 1). Among these, seven studies specifically investigated lower-limb skeletal muscles and their aponeurotic structures using ultrasound-based techniques to describe muscle architecture, internal connective tissue anatomy, or passive mechanical behavior [5]. These studies focused on the quadriceps femoris, hamstrings, adductor longus, and the gastrocnemius–soleus complex [1]. The remaining four studies were considered relevant and therefore included in the scoping review because, although they did not focus on a specific lower-limb muscle group, they addressed key methodological factors influencing ultrasound and elastography derived measurements in cadaveric muscle tissue [2,4]. These investigations examined the effects of tissue layering, specimen-related characteristics, and measurement conditions, thereby providing essential methodological context for the interpretation of ultrasound-based outcomes across different anatomical regions. Conclusion: Cadaveric ultrasound studies provide essential anatomical context for interpreting musculoskeletal ultrasound, while cadaveric shear wave elastography supports controlled exploration of passive muscle mechanics. At the same time, these studies highlight important methodological sensitivities that should be acknowledged before translating elastography findings to clinical decision-making [2].

Abstract:

Urban freight transport is responsible for creating negative transport externalities in the form of noise and congestion and has a significant environmental impact. One solution is to establish a freight consolidation centre, which could offer benefits such as shorter delivery distances, and fewer delivery routes. However, this would require collaboration between actors with conflicting interests and goals. In this study, we propose a collaborative business model framework for freight consolidation centres. This framework was tested through a pilot project in Gothenburg, using the principles of engaged scholarship. Our results show that last-mile consolidation significantly increases efficiency and enables sustainability gains to be achieved. However, a number of structural, economic and organisational barriers need to be addressed in order to realise the full benefit of the collaborative business model. There is a need for a deeper institualisation of new norms, procedures and policies in the business models of the individual actors involved.

Abstract: The mobile industry has experienced long-run changes in its knowledge structure, including identifiable transition points observable through meaning-based analysis. Using abstracts from 86,674 mobile-industry publications published between 2005 and 2024, we embed documents with SPECTER2, build year-specific embedding distributions, and derive knowledge regimes by combining change-point detection with inter-year distribution distances. We then extract regime-specific topics via clustering and reconstruct topic lineages by aligning topic similarities to classify inheritance, differentiation, convergence, and disappearance. The analysis delineates three regimes spanning 2005 to 2012, 2013 to 2019, and 2020 to 2024, with pronounced transitions around 2012 to 2013 and 2019 to 2020. Regime 1 centers on foundational technologies such as wireless communication, power, sensors, and reliability. Regime 2 expands toward platforms, apps, and data analytics alongside cross-domain convergence. Regime 3 is characterized by strengthened 5G operations and data-driven services, together with the independent rise of policy, governance, and regulation topics. Transitions reflect recombination built on inherited knowledge rather than abrupt replacement, and post-transition topics display distinct growth typologies by network position and growth pattern. By integrating embedding-based change-point detection with topic-lineage reconstruction, we provide a reproducible account of regime transitions and quantitative evidence to inform the timing of corporate R&D, standard and platform strategies, and policy and regulatory design.

Abstract: Background: Cellulite is a highly prevalent aesthetic concern characterized by structural remodeling of subcutaneous adipose tissue and fibrous septa, resulting in visible skin irregularities. Despite the availability of many injectable treatments with documented efficacy, most standard approaches adopt uniform protocols that overlook interindividual anatomical variability, potentially limiting treatment precision and clinical outcomes. This retrospective case–control study evaluated the Modulated Insertion of Regenerative Activation (MIRA), a technique that individualizes needle length and injection angle according to ultrasound findings, modulating insertion parameters to stimulate regen-erative responses within dermal and subcutaneous layers. Methods: Clinical and ul-trasonographic data from 120 women with stage 3 cellulite were analyzed. Stage 3a pa-tients received carbon dioxide therapy (CDT), whereas stage 3b patients underwent in-jectable solution therapy (IST). Within each treatment, patients were allocated to MIRA or control groups. Results: Compared with controls, MIRA showed greater reductions in adipose tissue thickness (CDT: −1.6 mm; IST: −1.5 mm; padj = 0.002), nodules, pain, edema, and fibrosis, with improved fascia regularity. Patient satisfaction was higher in MIRA (CDT: 8.1 ± 1.6; IST: 8.5 ± 1.4; padj = 0.002), and over 76% reported improved skin quality. Conclusion: Ultrasound-guided modulation of needle parameters with MIRA may en-hance structural and esthetic outcomes compared with standard approaches.

Abstract: Against the backdrop of accelerating low-carbon transformation in the global energy system and decarbonization in the transportation sector, the widespread adoption of electric vehicles has intensified grid load imbalances and highlighted challenges in integrating intermittent renewable energy generation. Vehicle-to-Grid (V2G) technology has emerged as a key solution to these challenges. This paper systematically traces the global evolution of V2G technology from conceptualization to large-scale deployment, focusing on localized practices in China's scaled V2G applications. It dissects the logic behind policy evolution, identifies three distinct Chinese V2G models-centralized, distributed and battery-swapping, and validates the practical outcomes of representative pilot projects. Research reveals three core constraints hindering China's large-scale V2G adoption: the absence of battery capacity degradation management mechanisms, fragmented standardization systems, and rigid market mechanisms. Based on this, the paper proposes recommendations for scaling V2G in China across three dimensions: power battery second-life utilization, standardization system construction, and market mechanism optimization. Furthermore, aligning with the global demand for large-scale V2G implementation, this paper proactively proposes innovative market models. These include establishing a coordinated trading mechanism between green power and V2G, developing a digitally driven distributed trust and transaction system, and exploring financialization and risk hedging models for battery assets. These concepts provide theoretical foundations and decision-making references for achieving high-quality, large-scale V2G applications worldwide.

Abstract: Objective: To propose Posterior Sinonasal Syndrome (PSS) as the etiological precursor to a defined subset of chronic rhinosinusitis (CRS), establish pepsin as a field carcinogen across the upper aerodigestive mucosal surface, and define the biological imperative for mucosal-preserving surgery in PSS-CRS patients. Methods: Synthesis of peer-reviewed evidence across four domains: pepsin endocytosis mechanisms in upper airway epithelium; pepsin detection in sinonasal, nasopharyngeal, and middle ear tissue; epidemiological trends in pediatric upper airway disease; and clinical outcomes in refractory CRS. Evidence is stratified as established, strongly inferred, or proposed requiring confirmatory study. Results: Pepsin, delivered via laryngopharyngeal reflux along a defined anatomical concentration gradient, produces receptor-mediated intracellular injury in posterior nasal epithelium — a mechanism established in laryngeal cells and strongly inferred in nasal cells. This injury lowers the posterior nasal mucosal inflammatory threshold, creating PSS as a priming state preceding clinical CRS. Pepsin has been detected within malignant tissue at two anatomically distinct sites: laryngeal and hypopharyngeal carcinoma, and nasopharyngeal carcinoma in 85.7% of cases versus 17.2% of controls — the two-site molecular fingerprint of a field carcinogen across the full upper aerodigestive surface. Pepsin detection in 83% of pediatric middle ear effusions and its correlation with adenoid hypertrophy grade establish that this process begins in childhood. PSS represents a third inflammatory driver of CRS, independent of allergy and anatomy, unrecognized by the 2025 AAO-HNS guideline. Five confirmatory studies and a nasal lavage pepsin assay validation pathway are defined. Conclusion: PSS is the etiological precursor to a misidentified subset of treatment-resistant CRS. Pepsin is both the primary driver of posterior nasal mucosal priming and a field carcinogen across the upper aerodigestive surface. Aggressive tissue-resecting FESS in this population is biologically counterproductive. The confirmatory studies are named, the clinical tools are within reach, and the patients are in rhinology practices now.

Abstract: Fipronil is a phenylpyrazole agrochemical widely used in agriculture and livestock production, posing persistent challenges of environmental contamination due to its toxicity and the formation of stable transformation products. Genome-based analyses provide a powerful framework for exploring the biotechnological potential of environmental microorganisms. The G2.8 isolate, obtained from fipronil-contaminated soil, was initially classified as Enterobacter chengduensis; however, taxonomic reassessment based on whole-genome sequencing combined with average nucleotide identity and digital DNA-DNA hybridization (ANI/dDDH ≈97%) reclassified this strain as Enterobacter pseudoroggenkampii. The occurrence of this species in a contaminated environmental niche highlights its relevance beyond previously reported clinical or plant-associated contexts and supports its potential role in bioremediation. The draft genome of E. pseudoroggenkampii G2.8 was assembled and subjected to rigorous quality assessment and functional annotation using genome-scale approaches. Functional analyses revealed 14 biosynthetic gene clusters, including non-ribosomal peptide synthetases, hybrid NRPS/polyketide synthases, and siderophore-related clusters, indicating potential for secondary metabolite production. In addition, genes encoding oxidoreductases, hydrolases, and esterases associated with xenobiotic transformation were identified, supporting the experimentally observed capacity of this strain to degrade fipronil and its toxic metabolites. Within a One Health framework, the genome exhibited only intrinsic antimicrobial resistance determinants, mainly related to efflux systems and chromosomal β-lactamases, with no evidence of mobile resistance elements, supporting an environmental safety profile. Overall, genome-guided functional and comparative analyses provide a robust foundation for identifying metabolic pathways involved in both biosynthesis and biodegradation, positioning E. pseudoroggenkampii G2.8 as a promising genome-guided candidate for metabolite-driven environmental biotechnology and reinforcing the value of microbial genomics in the development of sustainable bioprocesses.

Abstract: The income inequality hypothesis (IIH) posits that greater income dispersion harms indi-vidual health through psychosocial pathways. Yet decades of empirical re-search—especially in cross-national settings—have yielded inconsistent findings. This study revisits the IIH by distinguishing three temporal dimensions of inequality: immedi-ate (current levels), cumulative (long-run averages), and comparative (recent change). Us-ing harmonized Gini series linked to repeated cross-sections of the World Values Survey (1981-2016) across more than 90 countries and regions, we estimate multilevel models that adjust for individual and national covariates. Results reveal a consistent negative as-sociation between worsening inequality over the prior decade and self-rated health—supporting a comparative, time-sensitive specification of the IIH. In contrast, im-mediate and cumulative inequality often show null or even positive associations, particu-larly in less developed contexts and under random-effects estimation. These patterns suggest that inequality’s health consequences are temporally contingent, and that long-run deterioration in distributional conditions poses a particular threat to population health and should be closely monitored in future research and policy.

Abstract: In 1972, computer scientist Richard Karp made a remarkable discovery: twenty-one very different problems—from routing networks and planning schedules to packing items efficiently—are all equally difficult in a deep mathematical sense. These problems are now called NP-complete, and for more than fifty years researchers have shown their connection by carefully transforming one problem into another step by step. While this approach proves that the problems are related, it often hides the bigger picture of why they share the same level of difficulty. This paper proposes a new way of understanding these problems through geometry. We introduce the Karp Algebraic Reduction Manifold Architecture (KARMA), aframework that places all 21 problems inside a single mathematical “landscape.” In this landscape, each problem describes a different region of the same terrain of computational difficulty, and moving from one problem to another becomes like traveling smoothly across this terrain. The framework naturally groups the problems into three families—graph-theoretic, set-theoretic, and number-theoretic problems. In this geometric interpretation, distances represent how difficult it is to transform one problem into another, while the curvature of the landscape reflects their inherent computational hardness. By revealing this hidden geometric structure, the KARMA framework provides a new perspective on computational complexity. Instead of studying hard problems individually, researchers can explore the entire landscape of computational difficulty at once, potentially inspiring new algorithms, better hardness predictions, and intelligent systems that can automatically reason about problem transformations.

Abstract: Background: Accurate prediction of outcomes in colorectal cancer (CRC) is essential for personalized treatment. Conventional prognostic tools, including TNM staging, have limited accuracy. Machine learning (ML) may better capture complex prognostic patterns. Methods: In a retrospective multicenter cohort of 7,253 non-metastatic CRC patients after radical surgery, we compared prognostic accuracy for predicting recurrence and mortality using: a baseline TNM stage model; a logistic regression model with six clinicopatho-logical variables; and ML algorithms (Logistic Regression, Random Forest, XGBoost, LightGBM, CatBoost) with hyperparameter optimization (Optuna) and iterative feature selection. Binary outcomes (recurrence and all-cause mortality at 1 and 3 years) were used for ML training. Performance was assessed using area under the ROC curve (AUC). Results: The stage-only model showed poor discrimination (weighted AUC: 0.541 for mortality, 0.528 for recurrence). Logistic regression improved predictions (AUC: 0.759 and 0.645, respectively). Among ML models, CatBoost achieved the best performance. After iterative feature selection, the optimized CatBoost model utilizing 17 clinical var-iables demonstrated superior cross-validated AUCs of 0.81 for mortality and 0.84 for recurrence, consistently outperforming both baseline models across all time horizons. External validation on 1,452 held-out patients confirmed robustness with AUCs of 0.83 for mortality and 0.91 for recurrence. Conclusion: An optimized CatBoost model significantly outperforms traditional TNM staging and logistic regression in predicting recurrence and mortality in CRC using 17 routinely available variables. This parsimonious, data-driven tool offers improved individualized risk assessment for guiding post-operative man-agement. Prospective validation is warranted.

Abstract: Background: Cancer cells exhibit metabolic reprogramming characterized by increased dependence on glutamine to sustain rapid proliferation and biosynthetic demands. Kidney-type glutaminase (KGA), which catalyzes the first and rate-limiting step of glutamine metabolism, represents a promising therapeutic target, particularly in triple-negative breast cancer (TNBC), an aggressive subtype lacking effective targeted therapies. This study evaluated 2-amino-4-boronobutyric acid (ABBA), a boronic acid-containing glutamine analog, as a potential KGA inhibitor with anticancer activity. Methods: KGA inhibition was assessed using a fluorometric enzymatic assay. Cytotoxic effects were examined in multiple TNBC cell lines. Covalent docking analysis was performed to characterize interactions between ABBA and the KGA active site. Results: ABBA potently inhibited KGA activity, with an IC₅₀ of approximately 1.0 μM, demonstrating greater efficacy than several non-proteinogenic amino acid analogs. ABBA induced dose-dependent cytotoxicity across multiple TNBC cell lines, with pronounced sensitivity observed in basal subtype cells. and Cellular sensitivity correlated with KGA expression levels. Expression of γ-glutamyl transpeptidase 1 (GGT1) was negligible, indicating that the observed anticancer effects are primarily mediated through KGA inhibition. Docking analysis predicted that ABBA forms a reversible covalent adduct with the catalytic Ser286 residue of KGA, adopting a boronate tetrahedral geometry consistent with transition-state mimics and stabilized by hydrogen bonding and electrostatic interactions. Conclusion: ABBA is a potent boron-based glutaminase inhibitor with therapeutic potential for targeting glutamine metabolism in TNBC. Further structural optimization and in vivo evaluation are warranted to advance ABBA toward therapeutic development.

Abstract: To address the accuracy divergence problem of the integrated navigation system caused by drilling slippage and mismatch between the tail cable encoder and the robot's motion when a seafloor drilling robot operates in deep-sea soft sedimentary layers, this paper proposes a robust navigation method based on robust square root ductile Kalman filter (RSRCKF). Considering the large deformation mechanical characteristics of the seabed under drilling conditions, a unified state-space model including the time-varying odometer scaling factor error is first established. To solve the numerical instability of the nonlinear system under non-Gaussian noise interference, the square root ductile Kalman filter (SRCKF) framework is introduced, and the positive definiteness of the error covariance matrix is dynamically maintained using QR decomposition. Based on this, an online fault detection mechanism based on the novel chi-square test is designed, and an adaptive variance expansion factor is constructed by combining a two-segment IGG weight function to realize the real-time identification and weight reduction processing of abnormal observations caused by slippage. Field drilling and turning tests on the mudflats off the coast of Zhoushan show that, under typical soft clay slippage conditions, this method can effectively identify "false displacement" interference. Compared with the traditional EKF and standard SRCKF, the position error is reduced by approximately 82.4%, and the heading angle error is controlled within±0.5∘Within a certain range, the high robustness and engineering practicality of the algorithm under complex seabed topography were verified.

Abstract: The study was based on a large database of morphometric measurements of the ground beetle Carabus granulatus. It was compiled between 2006 and 2025 and includes over 10,000 individuals of this species, captured in 14 major regions of Russia and Western Europe. Beetles were captured with Barber traps across a spectrum of anthropogenic impacts—urban areas, suburbs, agricultural lands, and natural biotopes. They were then transported to the Institute of Ecology of the Academy of Sciences of the Republic of Tatarstan, where they were measured using a unified method for six linear traits. SSD was assessed using two methods. Using the standard Lovich formula, SSD for all traits was significantly higher, on average in all six traits, in beetle populations from suburban areas. Application of the second method, RMAII, showed that the slope of the regression curve is generally higher in females, indicating greater sensitivity of Carabus granulatus females to environmental factors. At the same time, a comparison of the results obtained by the aforementioned methods did not support the thesis that SSD increases with beetle size. The curves for SSD variability in both urban and non-urban populations were sawtooth-shaped. This conclusion may be due to the fact that the variability of both structural traits and SSD for them is not described by a monotonic curve. This necessitates studying the variability of SSD in other ground beetle species (or genera) using the same data set and a unified methodology.