Abstract: Puntius sophore, a small indigenous fish species, has significance to the nutritional and livelihood security of rural communities in India. This study presents baseline data on the population dynamics, length-weight relationships (LWRs), and condition factors of P. sophore in two major rivers, the Ganga and Punpun in India, while assessing multi-decadal (1990-2023) land use and land cover (LULC) changes. Fish samples (506) were collected monthly from each of three landing sites from the river Ganga and Punpun. The results revealed that populations in both rivers have positive allometric growth, with length-weight relationship (LWR) equations of W = 0.0129 L^3.023 (Ganga) and W = 0.0079 L^3.169 (Punpun). The condition factors (Kc, Kn, Ka) were significantly higher (p < 0.05) than 1 in the Punpun River, indicating a favorable environment, while values < 1 in the Ganga showed higher ecological stress. The growth aspects (L∞ = 134.60 mm, K = 0.71 yr⁻¹ for Ganga; L∞ = 133.42 mm, K = 0.735 yr⁻¹ for Punpun) were analyzed by TropFish-R, showing a fast-growing species. Total mortality (Z) was higher in the Ganga (1.822 yr-1) in comparison to the Punpun (1.797 yr-1), though, exploitation rates (E < 0.5) confirmed under-exploitation. Length-based spawning potential ratio (LB-SPR) analysis revealed recruitment overfishing (SL < Lm), with a sustainable SPR of 0.40 in the Punpun and a depleting SPR of 0.32 in the Ganga. Although current fishing pressure is adequate, spatiotemporal LULC indicated that rapid urbanization and habitat fragmentation exhibit a substantial "squeeze" effect that adversely impacts aquatic ecosystems and poses a significant risk to spawning and feeding grounds of that ecosystem. Effective management approaches, including the execution of seasonal fishing bans during monsoon recruitment and the implementation of strict mesh-size standards, are essential for the conservation of wild populations and the protection of regional food supplies.

Abstract: The growing visibility of psychological services in Kazakhstan is unfolding amid major cultural and digital transformations. Yet psychological distress is still often localized within the individual, even when it emerges from tensions among family obligations, professional norms, material conditions, historically constituted meanings, and algorithmically mediated social comparison. This article develops a cultural-psychological model of psychologist selection based on the distinction between professional competence and problem–practitioner complementarity. Culture is conceptualized as an ongoing semiotic process through which individuals appropriate, combine, contest, and transform heterogeneous historical resources. Kazakhstan provides a theoretically significant case because its cultural environment reflects the long-term interaction of diverse historical, religious, institutional, and global-digital influences. Professional competence includes appropriate education, ethical practice, supervision, evidence-informed reasoning, risk recognition, and referral. Complementarity refers to the fit between the organization of a specific problem and the practitioner’s experience, theoretical repertoire, cultural-semiotic sensitivity, communication style, and digital literacy. Two empirically grounded analytical vignettes show that professional competence may coexist with insufficient complementarity, while cultural proximity and trust do not guarantee comprehensive professional assessment. The article also proposes a digital platform as a participatory semiotic infrastructure for transparent practitioner selection and responsible AI use, together with a preliminary qualitative dialogical protocol requiring further empirical validation.

Abstract: Public art does not enter public life as an autonomous object in a neutral setting; it is a relational encounter embedded in urban life. Its meanings are co-produced through the political, spatial, and visual organisation of the city. Drawing on theories of socially produced space, visual culture, spectacle and aesthetic governmentality, this paper examines a few contemporary Indian public-art situations where curated visual narratives coexist with informality, congestion, contested histories, and intensely lived public realms. The cases are the India Gate–Kartavya Path precinct in New Delhi, the Statue of Unity in the Narmada Valley, the Kempegowda statue at Bengaluru’s international airport, and Kolkata’s Durga Puja pandals. These are embedded within projects of nation-building, infrastructure development, city branding, heritage production and ritual expression. The cases reveal distinct modes of visual-spatial production: reinscription, as political narratives are rewritten through inherited monumental landscapes; territorialisation, as monumentality extends into infrastructure and development; translation, as regional memory is reformulated through metropolitan aspiration; and distributed production, where aesthetic production cannot be attributed to a single institutional author. In state-backed cases, public art may function as an aesthetic instrument through which the postcolonial state stages preferred narratives of development, identity, and aspiration; these are subsequently reframed through interactions with informality, ritual, memory, and everyday use.. This paper develops the concept of the visual-spatial field to examine how public art is read through relations among co-present signs and practices. The paper consequently positions urban design as an active mediator in the politics of public art: through decisions about location, access, visibility, and surrounding development, they structure the conditions of encounter without determining their outcome. The paper distinguishes power over meaning from power over the conditions under which meaning is produced, arguing that co-production should not be confused with equal capacity.

Abstract: Friction modeling in finite element simulations of cold forging is commonly based on analytical formulations ranging from the Coulomb and shear friction laws to advanced models incorporating multiple tribological state variables. However, fundamentally different friction formulations are difficult to compare directly because their parameters refer to different physical quantities. This work presents a systematic benchmark of conventional, extended analytical and machine-learning-based friction models under identical tribological boundary conditions. Sliding compression tests covering a wide range of contact conditions are combined with finite element simulations to establish a time-resolved database of local tribological state variables. To enable a physically consistent and model-independent comparison, all models are evaluated on the common level of frictional shear stress. Conventional friction models exhibit pronounced load-dependent residual structures, while extended analytical formulations reduce but do not eliminate systematic deviations. The neural-network-based formulations introduced in this study, predicting either the coefficient of friction, the friction factor or the frictional shear stress directly, achieve the highest predictive accuracy, with nMAE values of 0.85–0.96% relative to the mean flow stress. Despite their different internal parameterizations, these models converge towards highly similar frictional shear stress predictions. Explainability analyses further show that similar predictive behavior on the stress level is achieved through formulation-dependent relationships on the parameter level. The results therefore reveal a distinction between stress-level convergence and parameter-level divergence in data-driven friction modeling.

Abstract: Background/Objectives: Structured exercise may modulate the myostatin–follistatin (MSTN–FST) axis, but evidence across exercise modalities and doses remains uncertain. Methods: We systematically searched seven bibliographic databases for controlled exercise interventions lasting at least four weeks in adults (PROSPERO CRD420261492548). Multilevel random-effects models with study-clustered CR2 correction accounted for dependent effect sizes; sensitivity, exercise-modality subgroup, and linear duration analyses were exploratory. Results: Thirty-seven independent studies were included. Structured exercise was associated with lower circulating MSTN (16 effects from 10 studies; Hedges’ g = 1.79, 95% confidence interval [CI] 0.62–2.96) and higher circulating FST (12 effects from 7 studies; g = 1.62, 95% CI 0.18–3.06). Heterogeneity was high and both prediction intervals crossed the null. Effect directions generally persisted across assumed within-study correlations and after excluding HIFT/CrossFit-type interventions, although excluding extreme effects attenuated the estimates. Exploratory analyses showed no clear difference between resistance-based and non-resistance-based exercise, and program duration was not linearly associated with either outcome. GRADE certainty was moderate for both outcomes. Conclusions: Structured exercise may favorably alter circulating MSTN and FST, but sparse modality- and dose-specific evidence precludes identifying a superior modality, optimal dose, confirmed intracellular mechanism, or validated clinical surrogate endpoint.

Abstract: This conceptual article examines whether Catholic Religious Education (CRE) and Ethics in Maltese schools can enter a constructive pedagogical dialogue while retaining their distinct disciplinary identities. Rather than proposing a merger, hierarchy, or shared epistemology, it develops the more limited claim that a critically appropriated Philosophy for Children (P4C) pedagogy and its Community of Inquiry (CoI) may provide a shared pedagogical grammar. The Maltese case is significant because Ethics is institutionally associated with P4C, while recent CRE scholarship calls for more student-centred, constructivist, and existentially responsive approaches. The article therefore treats this proximity as a problem of disciplined borrowing: which dialogical practices can travel across subject boundaries, and what must remain discipline-specific? Drawing on Matthew Lipman and engaging in greater depth with John Dewey, Paulo Freire, Hannah Arendt, and Thomas Merton, it identifies complementary but non-identical resources for experience-based inquiry, dialogical agency, judgment in plurality, and contemplative attention. Their tensions are treated as analytically productive rather than harmonised. Recent empirical and theoretical literature is used to qualify the scope of the proposal. The article then develops implications for classroom practice, assessment, collaborative professional development, and a future empirical research agenda. Its contribution is a framework for pedagogical dialogue that seeks to make CRE more dialogical without treating theological sources as interchangeable with philosophical warrants, and Ethics more open to religiously situated moral worlds without compromising philosophical autonomy.

Abstract: Plant diseases pose a serious threat to agriculture, causing yield losses of 20 to 40 percent each year, resulting in more than 220,000 million dollars in economic damage and significantly affecting the global food supply. Traditional plant health monitoring practices involve visual inspection of plant tissue and can only detect the presence of disease once visual symptoms are already evident. This article presents AgriIDIA, an early-detection plant disease recognition system trained on datasets of 24-channel multispectral images derived from six optical filters (BlueIR, Hotmirror, K590, K665, K720, and K850) and six vegetation indices (NDVI, GNDVI, NDRE, EVI, REI, and SAVI). First, an exploratory data analysis is conducted on the diagnostic capability of the described 24-channel data representation, using 1,266 image stacks labeled with six classes (diseased/healthy papaya, diseased/healthy potato, diseased/healthy tomato). Next, using the results of the exploratory data analysis, the manuscript describes the training and cross-validation performance of AgriIDIA, with a macro-F1 score of 83.91 ± 3.42% and an accuracy of 84.00 ± 3.17% on the validation set. Finally, the performance of the trained model is evaluated on the reserved test set (N=190), demonstrating an accuracy of 81.05%, a macro-F1 score of 0.7398, and a weighted ROC-AUC of 0.9383. The results of this study suggest that the 24-channel multispectral representation has significant diagnostic potential for the early detection of plant diseases and can be used to design accessible phytosanitary methods for small-scale farmers.

Abstract: Artificial intelligence is transforming teaching, learning, research, and institutional operations, yet its adoption presents higher education with a distinctive governance challenge. Universities have contributed substantially to the development of modern AI while remaining responsible for protecting academic integrity, validating knowledge, certifying learning, and maintaining public trust. This paper conceptualises this dual responsibility as the AI paradox in higher education, expressed through three interconnected institutional tensions: innovation versus integrity, knowledge creation versus knowledge validation, and education versus credentialing. Drawing on a structured integrative review of scholarly literature, policy analysis, and experiential grounding, the paper examines the opportunities and risks associated with generative AI and proposes an integrated academic governance framework comprising six institutional pillars: foundational principles and institutional commitment, differentiated policy architecture, assessment redesign and integrity by design, AI literacy and capacity building, transparency and disclosure, and continuous evaluation and adaptation. The institutional pillars are operationalised through complementary role-specific frameworks for educators, researchers, and students, supported by institutional enabling mechanisms and a phased implementation roadmap. A multi-stakeholder expert review using a modified Delphi process is proposed as the next stage of framework validation following finalisation of the governance architecture. The framework offers higher education institutions a theoretically grounded and operational approach to balancing technological innovation with human accountability, educational quality, research integrity, equitable access, and authentic student achievement.

Abstract: Invasive fungal diseases (IFD) have become a routine diagnostic and therapeutic challenge in high-acuity hospitals worldwide, and Latin America presents a distinctive scenario in which opportunistic mycoses of tertiary care coexist with endemic dimorphic fungi, heterogeneous diagnostic capacity, and uneven antifungal access. This narrative review organizes a practical, clinically usable pathway for Latin American high-acuity hospitals, from the construction of a “window of suspicion” based on host risk factors, through the diagnostic work-up available in the region (non-culture biomarkers, molecular platforms, imaging, and histopathology), to syndrome-specific clinical pathways for candidemia, invasive aspergillosis, mucormycosis, and cryptococcal meningitis, including antifungal selection, dosing, and stewardship. We highlight where regional diagnostic and therapeutic constraints depart from guidance developed in high-resource settings and where structured clinical suspicion must substitute for advanced biomarkers. We conclude that early, syndrome-directed pathways adapted to local resource availability, rather than uncritical adoption of international algorithms, offer the most realistic route to earlier antifungal therapy and improved outcomes in Latin American high-acuity hospitals.

Abstract: Hyaluronan (HA) has traditionally been viewed as a structural component of the extracellular matrix, valued primarily for its viscoelastic, lubricating, and hydrating properties. However, accumulating evidence suggests that ultra-high molecular weight hyaluronan (UHMW-HA) functions far beyond passive tissue support, acting as a dynamic regulator of inflammation, mechanotransduction, immune homeostasis, cellular senescence, and regeneration. In this narrative review, we examine HA and specifically focus upon UHMW-HA operating as a “living hydrogel”, an adaptive regenerative macromolecule that continuously interacts with its microenvironment to shape tissue function and resilience. We examine evidence spanning evolutionary biology, glycobiology, biomaterials science, and regenerative medicine to examine how polymer size encodes biological activity and how UHMW-HA influences stem cell behaviour, extracellular matrix remodelling, and responses to injury and ageing. Attention is given to unique biological models, including the naked mole-rat, whose exceptional longevity and cancer resistance have been linked to abundant UHMW-HA, offering insights into the relationship between extracellular matrix architecture and organismal health. We further discuss how advances in synthetic biology and biomaterials engineering are enabling the development of next-generation HA-based hydrogels that mimic these adaptive properties for therapeutic applications in tissue engineering, in addition to potential roles in dynamically optimising wound repair, and musculoskeletal rejuvenation, through precision medicine approaches. Finally, we argue that reclassifying UHMW-HA as a living regenerative material rather than an inert scaffold provides a new concept for designing bioinspired therapeutics and understanding extracellular matrix function. This review demonstrates HA-adaptive macromolecules as critical entities in future regenerative strategies and highlights the specific and unique potential of UHMW-HA to combine material science developments with enhancement of preferable biological outcome.

Abstract: This paper addresses the problem of characterizing, in structural rather than purely metaphorical terms, the role that agentic artificial intelligence is coming to occupy within increasingly automated cybersecurity environments. Building on cybernetics, autonomic computing, and the philosophy of information, the paper models agentic AI as an integrative sensing-and-response layer overlaid on pre-existing, reflex-like security automation (intrusion detection, SIEM correlation, SOAR remediation), and proposes that this layer's growing structural centrality — rather than speed alone — motivates treating it as central to cybersecurity specifically. The centrality hypothesis is made operationally explicit through a small, reproducible network model: a six-node security-operations graph in which an agentic node is connected incrementally, and betweenness centrality is recomputed at each step. The model shows that centrality stays low under within-cluster delegation and rises sharply only when the agentic layer bridges two previously disconnected clusters of legacy automation, and shows that removing the agentic node once fully connected disables specifically the cross-domain correlations it uniquely supplied, while legacy automation continues unaffected. Four operational criteria distinguish agentic systems from conventional autonomic and rule-based automation. Drawing on Floridi's method of levels of abstraction, the paper further argues that cybersecurity is reflexive: agent, medium, and protected object can be modeled at a common informational level of abstraction, and the agent is constitutively dependent on, and acts to regulate, the environment it protects — a criterion extending to a second-order concern, the agentic layer's own security. Implications for practitioners, and the scope limits of the argument, are discussed.

Abstract: Tuberculosis (TB) remains a leading global infectious cause of death. While host lipidomics offers a promising frontier for biomarker discovery, the evidence base remains fragmented across heterogeneous study models, biological matrices, and analytical platforms. This scoping review systematically maps the landscape of TB host lipidomics to identify methodological trends, established knowledge clusters, and translational bottlenecks. Guided by the PRISMA-ScR and the PCC JBI (Joanna Briggs Institute) frameworks, a structured search was conducted across PubMed, Europe PMC, OpenAlex, and Google Scholar. The review protocol was prospectively registered on the Open Science Framework (OSF Protocol: 10.17605/OSF.IO/5EGMR). Forty-eight primary empirical studies published between 2014 and 2026 were selected for descriptive numerical analysis and thematic synthesis. Research is highly centralized in low- and middle-income countries (85.4%), with China, India, and South Africa acting as primary hubs. Methodological analysis revealed a heavy reliance on blood-based matrices (64.6% plasma/serum), with truly non-invasive matrices, urine and exhaled breath, accounting for only 14.6% of studies with liquid chromatography-mass spectrometry accounting for (77.1% LC-MS/MS). Glycerophospholipids (66.7%) and sphingolipids (43.8%) emerged as the most consistent systemic metabolic signatures of active disease. However, a severe longitudinal data deficit exists: 64.6% of studies are restricted to cross-sectional diagnostic discovery, while only 12.5% address treatment monitoring or prognosis. Critically, 70.8% of the evidence base lacked independent cohort validation, and no studies correlated host lipid profiles with direct measures of TB infectiousness. TB lipidomics has achieved technical maturity in initial diagnostic discovery but remains poorly optimized for longitudinal patient management and transmission control. A critical knowledge gap is the total absence of data linking host lipid signatures to aerobiologic measures of transmission potential, such as Cough Aerosol Culture (CAC) positivity. As primary disease burden hubs and research volume, LMICs are uniquely positioned to lead this next translational phase of prospective longitudinal validation. Advancing the field requires a deliberate reorientation toward specimen types and study designs capable of capturing the biology of the lung-airway interface, the anatomical origin of infectious aerosols. Prospective studies correlating systemic and localized lipid signatures with CAC-defined infectiousness in transmission cohorts represent an essential and entirely unexplored frontier for host-directed TB biomarker science.

Abstract: The conventional interpretation of Mixolab curves describing wheat dough behavior under simultaneous mechanical and thermal stress was substantially extended by introducing a series of novel kinetic and geometrical descriptors. In addition to the standard Mixolab parameters, new indexes of Mechanical Weakening of Proteins (MWP), Thermo-Mechanical Weakening of Proteins (TMW), several traits aiming at stability of dough consistency (STA_{C1 rel}, C12/STA_C1), Structural Recovery Index (C5/C1), micro-scaled torque change rates (ΔmCij) and physical angular descriptors (slopes omega°, delta°) were developed to characterize individual phases of viscoelastic dough transformation to the solidified bread crumb-like material. The approach was applied to investigate the effects of four chemically distinct essential oils (EOs), namely cinnamon, oregano, lemongrass, and thyme ones, incorporated into wheat flour at five concentration levels (5–80 mg · 100 g^–1 flour). Besides those all innovations, selected standard Mixolab torque-parameters were informatively recalculated to the Brabender units—to be easily compared to the Farinograph proof results, because this rheological test represents the standard in the cereal chemistry and technology branch. Adaptation of the Principal Components Analysis lies in: i) using of the Variance Components Analysis in a role of the input data filter and ii) unfolding of the 3D-space to 2D-surfaces pair of plots along the shared PC2 coordinate. Conventional Mixolab parameters revealed that cinnamon and oregano oils significantly weakened the gluten network, reducing dough stability and lowering the minimum protein-related torque C2, whereas lemongrass oil preserved the protein stability at levels comparable to the control flour. Oregano and thyme oils significantly increased the final torque C5, indicating enhanced starch retrogradation and formation of a firmer structure of the cooled gel. The innovative descriptors provided substantially deeper insight into these transformations. Cinnamon oil generated the highest MWP values (up to 18.5 %), indicating pronounced mechanically induced gluten weakening. In contrast, lemongrass oil maintained MWP values close to the control while accelerating starch gelatinization and hot-gel breakdown, reflected by elevated beta° values and steepened gamma° slopes. Oregano and thyme oils reduced the gelatinization kinetics but markedly supported the retrogradation-related parameters, including C54, slope delta°, and the C5/C1 ratio, indicating enhanced structural recovery during cooling. Correlation analysis confirmed that the innovative descriptors captured complementary information and exhibited substantially lower redundancy than conventional Mixolab variables. Before Principal Components Analysis from 34 variables in total, Variance Components Analysis identified 17 representative parameters with the highest distinguishing power; Principal Component Analysis demonstrated two-component model of PC1 and PC2 explained 72% of total experimental variability and reduced data noise to only 6 % (per contra to 61% and 14%, respectively, in the case of the standard Mixolab parameters). The multivariate models clearly differentiated essential oils according to their chemical composition and dose-dependent rheological effects on water-dough behavior. The results demonstrated chemically distinct essential oils influence on wheat dough rheology through different mechanisms affecting protein weakening, starch gelatinization, hot-gel stability, and starch retrogradation. The proposed kinetic and geometrical descriptors substantially enhance the interpretive capacity of Mixolab analysis and provide a valuable tool for mechanistic–kinetic investigation of functional ingredients in cereal-based systems.

Abstract: Artificial intelligence is rapidly transforming education globally, yet the economic consequences of AI-enabled education remain insufficiently understood. Existing research has largely examined AI in relation to learning outcomes and instructional efficiency, with limited attention to its implications for human capital formation, productivity, and inclusive economic growth. This study develops and empirically examines a framework linking AI-enabled education with economic outcomes through the formation of human capital and the development of labour-market-relevant skills. Using OECD country-level panel data for 38 countries over the period 2015-2025 (N = 350 country-year observations), we construct a composite AI-Enabled Education Index (AIEI) using Principal Component Analysis. The index incorporates indicators of digital educational infrastructure, AI skills, educational technology adoption, teacher digital competence, and AI-related educational policies. We employ two-way fixed effects estimation with country and year fixed effects, mediation analysis, and robustness tests including system GMM and instrumental variables. The AIEI demonstrates a significant positive association with human capital formation (β = 0.28, p < 0.001). Human capital mediates the relationship between AI-enabled education and labour productivity, with the indirect effect accounting for 45.5% of the total effect (Sobel Z = 4.82, p < 0.001). AI-enabled education is also positively associated with inclusive economic growth (β = 0.28, p < 0.001). Heterogeneity analysis reveals that effects are larger in countries with higher digital infrastructure (difference = 0.20, p < 0.001) and stronger institutional quality (difference = 0.20, p < 0.001). AI-enabled education contributes to human capital formation, productivity, and inclusive economic growth, with human capital serving as a key mediating mechanism. Investments in digital infrastructure, teacher development, and institutional quality amplify these benefits. The findings provide evidence for policy interventions that leverage AI to enhance educational and economic outcomes.

Abstract: Triple-negative breast cancer (TNBC) is the most aggressive form of breast cancer and poses significant treatment challenges due to fewer available options. Creating highly specific and precise anticancer therapies continues to be a significant challenge for TNBC. Previous studies showed that combining drugs or adding chemotherapy to other therapies has significantly improved patient prognosis and proved to be an effective strategy for treating TNBC compared to using chemotherapeutic drugs alone targeting TNBC pathways. One such combination is a PARP inhibitor (PARPi) with an immune checkpoint inhibitor (ICI). In this study, we explored the transcriptomic profiles of the combination of Olaparib (a PARP inhibitor) and atezolizumab (a monoclonal antibody targeting PD-L1) in cells derived from African American (AA) and White populations. Our results demonstrated that combining Olaparib (Ola) and Atezolizumab (Atz) significantly reduced cell viability in both AA and White cell lines, compared to either agent alone. Our transcriptional profiling results showed that in both up- and down-regulated genes, there was widespread suppression of proliferation and biosynthetic gene networks, and selective activation of immune and stress-related pathways. Our transcriptional profiling revealed that both up- and down-regulated genes exhibited widespread suppression of proliferation and biosynthesis pathways, along with selective activation of immune and stress-related pathways. Our functional enrichment analysis revealed significant changes in DNA damage response, chromatin organization, RNA regulatory processes, immune signaling, and inflammatory responses. In conclusion, our study’s results provide valuable insights into key features of combination therapy, which may help guide the more targeted development of these regimens.

Abstract: Optical response is commonly regarded as an intrinsic property of a photonic structure. Here we show that, in curved photonic systems, geometry plays a dual role: the photonic architecture defines the optical-state landscape, whereas the illumination–observation geometry determines which optical states become experimentally accessible. Self-assembled, millimeter-scale, free-standing curved silica opals coupled to spherical water droplets on Teflon or planar mirrors reveal simultaneous access to multiple Bragg diffraction bands, together with distance-controlled spectral redistribution, spatially organized halos, and pseudo-collimated beams under different illumination–observation geometries. Natural illumination further reveals optical responses relevant to biological and biomimetic photonic structures. Key aspects of this optical functionality are transferred to a graded curved photonic heterostructure formed by partial polystyrene infiltration of a curved silica opal, demonstrating that the underlying geometrical principle extends beyond liquid-confined systems. Curved photonic architectures therefore provide an experimental platform for geometry-controlled optical-state accessibility.

Abstract: As microbiome research increasingly seeks to identify true ecological shifts, transitioning from associational to causal approaches is essential. However, detecting structural changes across independent networks remains challenging due to the absence of established biological ground truths and the small, imbalanced sample sizes typical of microbiome cohorts. To address this, we extend an existing network comparison framework to enable node-level mechanism-shift detection under the direct linear non-Gaussian acyclic model (DirectLiNGAM). We evaluate the Naive, Bootstrap, and Relative sample size Bootstrap Stability (RSBS) estimators across extensive synthetic discovery runs and a semi-synthetic, batch-corrected human gut microbiome cohort. Our results demonstrate that resampling-based estimation consistently outperforms a single-fit Naive baseline by trading marginal recall for substantial precision gains. On both semi-synthetic and synthetic data, standard Bootstrap is optimal for comparing datasets of equal size, whereas RSBS is the only estimator that reliably handles imbalanced cohorts. This advantage strengthens as network dimensionality increases and persists under authentic compositional noise. Navigating this complex and emerging research area is currently constrained by limitations in data quantity and quality, scarce biological knowledge, and a lack of dedicated software. To address these critical gaps, we provide the complete benchmark pipeline and synthetic data generators as an open-access Python package, causal-comparator, to support node-level mechanism-shift detection across systems biology applications.

Abstract: The secondary dendrite arm spacing (SDAS) is a critical microstructural parameter that directly influences the local mechanical performance of AlSi casting alloys. Although the power-law relationship between cooling rate and SDAS is widely acknowledged, the existing literature lacks a standardized framework for selecting the most appropriate solidification thermal interval for calculating this characteristic cooling rate. This study introduces an optimized methodological approach by evaluating solidification kinetics across two distinct thermal regimes: a standard non-isothermal sand mold (no chill) and an accelerated cast iron insert configuration (with chill). Cooling curves of a primary hypoeutectic AlSi7Mg0.3 alloy were continuously recorded via ten calibrated K-type thermocouples along a wedge-shaped casting profile, and the corresponding local SDAS values were quantified using light optical metallography. By mathematically evaluating six distinct thermal analysis boundaries, the results demonstrate that conventional global intervals (e.g., liquidus-to-eutectic) yield lower predictive accuracy (R² ≈ 0.75) due to the accumulation of thermal "noise" from the early fluidic stage and the final eutectic reaction. In contrast, the localized interval bounded strictly between the Dendrite Coherency Point and the Rigidity Temperature (TDCP-TRigidity) achieved excellent correlation for both the baseline slow-cooling regime (R² = 0.8509) and the accelerated regime (R² = 0.9547). This optimized window successfully isolates the exact kinetic timeframe of secondary dendritic evolution and effectively eliminates localized calculation anomalies across the entire spectrum of cooling rates. Thermodynamic analysis reveals a sharp cooling asymmetry in the wedge geometry, where the local solidification timescale collapses by a factor of 34 at the chill base. This extreme kinetic restriction limits the characteristic solute diffusion distance(L≈D.t)) by a factor of nearly six, providing a quantitative physical basis for the significantly flatter coarsening slope (-14.85) under accelerated cooling compared to the baseline regime (-32.63), while simultaneously shifting the eutectic silicon morphology from coarse acicular plates to a highly refined, fibrous structure. The proposed methodology establishes a physically grounded, highly accurate microstructural prediction tool suitable for advanced foundry engineering and casting simulations.

Abstract: Multiportal video-assisted thoracoscopic surgery (VATS) was introduced in Japan in the 1990s, with uniportal VATS (U-VATS) and robot-assisted thoracic surgery (RATS) following in the 2010s. U-VATS gained significant traction after the 1st Japanese Society of Thoracic Surgeons (JSTS) Fellowship in 2018, leading to the establishment of the Japanese Uniportal VATS Interest Group (JUVIG) in April 2018. JUVIG has since driven wider adoption through extensive activities, including educational programs, publications, and collaborative multicenter studies. As a result, expert surgeons now utilize U-VATS for complex procedures such as bronchoplasty, completion lobectomy, complex segmentectomy, and pediatric surgery. Concurrently, RATS gained insurance coverage in Japan in 2018 and has become more prevalent than U-VATS. Despite the significant costs associated with RATS, its adoption rate surpasses that of U-VATS due to institutional policies, perceived ease of implementation, branding, recruitment incentives, and recent national healthcare insurance reimbursement revisions (add-ons) introduced in 2026, which exert administrative pressure to prioritize robotic cases. Although reduced-port RATS is gaining popularity—blurring the lines with U-VATS—and newly emerging indigenous robotic platforms are expected to further accelerate RATS adoption, multiportal VATS (M-VATS) remains the dominant approach in many institutions. Furthermore, maintaining proficiency in VATS, U-VATS, and open thoracotomy remains indispensable for surgical education, economic viability, and acute intraoperative crisis management. Ultimately, while U-VATS represents a promising minimally invasive option, its widespread adoption for major and complex resections in Japan continues to be restricted by both educational and societal barriers.

Abstract: Driven by the common entropy formalism in statistical physics and information theory here we show that methods developed for the analysis of molecular conformational ensembles may be successfully used for classification. Entropy is estimated using the k-th nearest neighbour method combined with the Maximum Information Spanning Tree method to account for the mutual informations between variables. This formalism is used to classify new samples using their estimated cross-entropy with the training set. The method is tested using diverse datasets and the results obtained are comparable to those obtained by machine learning methods, or better when the number of samples is small. The methods described here have the advantage, compared to other classification methods, of directly linking variables and pairs of variables to the assigned class, a valuable feature when aiming at identifying causative relationships between features and classes (e.g. the relationship between genes and diseases).