Abstract: Democratic systems rest on institutional counterbalances capable of limiting authority concentration. Historical transitions toward dictatorship often emerge not through abrupt institutional destruction, but progressive weakening of stabilizing mechanisms like parliamentary oversight, judicial autonomy, political pluralism, decentralized governance. Adolf Hitler’s power consolidation following the Weimar Republic’s collapse provides a historical example in which democratic counterbalances lost corrective capacity under economic crisis, institutional fragility, coordinated mass mobilization, etc. While the historical causes of authoritarian transitions have been extensively studied, the dynamics governing the failure of democratic stabilizing mechanisms is less characterized. We introduce a dynamical systems framework aimed at identifying early-warning signals associated with democratic destabilization and executive power concentration. We represented democratic governance as a multidimensional attractor stabilized by negative feedback mechanisms generated by institutional independence, distributed authority, informational plurality, constitutional constraints. Using historical data from Germany between 1928 and 1934, we built a composite systemic stress index integrating economic instability, war trauma, ideological vulnerability, institutional fragility, political polarization and Nazi mobilization. Simulations based on nonlinear response functions and state-space trajectories showed threshold-like transitions in which progressive stress accumulation was followed by accelerated concentration of political authority, once stabilizing feedbacks became insufficient. Democratic collapse could be interpreted as a loss of systemic resilience associated with attractor deformation and feedback amplification. Potential applications of nonlinear approaches include comparative analysis of institutional fragility, quantitative assessment of democratic resilience and development of early-warning frameworks for detecting conditions associated with excessive concentration of political power in contemporary political systems.

Abstract: Background/Objectives: Inflammatory and hematologic indices derived from routine blood tests have been increasingly investigated as prognostic biomarkers in multiple myeloma (MM). However, their clinical utility remains inconsistent, and data on novel composite indices, such as the mean corpuscular volume-to-lymphocyte ratio (MCVL) and the cumulative inflammatory index (IIC), are lacking in MM. Methods: We conducted a retrospective study including 122 patients with newly diagnosed MM. Hematologic and inflammatory indices were evaluated at baseline and after four cycles of induction therapy. Associations with progression-free survival (PFS) and overall survival (OS) were assessed using Kaplan–Meier analysis, Cox regression models, and receiver operating characteristic (ROC) curve analysis. Results: Baseline inflammatory biomarkers, including NLR, PLR, MLR, SII, as well as MCVL and IIC, were not significantly associated with PFS or OS. ROC analysis demonstrated poor discriminative ability for all evaluated markers at both baseline and post-induction timepoints (AUC values close to or below 0.50). In contrast, post-induction inflammatory indices, particularly PLR, MLR, AISI, and SIRI, were significantly associated with PFS in both univariable and multivariable Cox regression analyses. Neither baseline nor post-induction MCVL and IIC showed independent prognostic value. Conclusions: Baseline inflammatory and erythrocyte-derived indices, including the novel composite markers MCVL and IIC, have limited prognostic utility in MM. In contrast, dynamic changes in inflammatory biomarkers during treatment may provide more clinically relevant information regarding disease progression. These findings support the integration of longitudinal biomarker assessment into future risk stratification models in MM.

Abstract: The supercritical CO₂ extraction of essential oils from Origanum vulgare L., Matricaria chamomilla L., and Moringa oleifera Lam. was kinetically interpreted using a logistic mass transfer approach under different combinations of pressure and temperature. Extractions were performed in a fixed-bed SFE system operated for 210 min using high-purity CO₂ under pressures ranging from 100 to 500 bar and temperatures between 30 and 60 °C, depending on the vegetable matrix. The logistic model was parameterized through the total extractable mass (m_t), the characteristic time associated with the maximum extraction rate (t_m), and the kinetic slope parameter b. The highest extraction yields were obtained at 300 bar and 45 °C for oregano (2.807 g), 100 bar and 40 °C for chamomile (5.006 g), and 500 bar and 60 °C for moringa (5.433 g). Simultaneously, increasing pressure and temperature systematically reduced, decreasing from 16.737 to 8.75 min in oregano and from 15.01 to 9.73 min in moringa, indicating an intensification of convective-diffusional transport mechanisms. The model adequately reproduced the experimental extraction curves, particularly in Oregon, where SSD values remained below 0.03 under all evaluated conditions. Unlike highly parameterized phenomenological approaches, the proposed logistic formulation represented the extraction dynamics using kinetically interpretable parameters without requiring experimentally inaccessible internal coefficients. The results demonstrate that logistic modeling constitutes a mathematically simplified but kinetically robust alternative for the comparative analysis and preliminary optimization of supercritical extraction systems applied to aromatic and medicinal plant matrices.

Abstract: Background: Admission-based risk stratification in acute decompensated heart failure (ADHF) remains challenging, particularly in cohorts enriched for cardiorenal syndrome type 1 (CRS1). B-type natriuretic peptide (BNP) is the most extensively validated admission biomarker in ADHF, yet its independent contribution alongside heart failure (HF) phenotype and serum albumin within a prespecified multivariable mortality prediction model has not been formally established in CRS-enriched populations. Methods: In a retrospective cohort of consecutive index ADHF admissions (N=220 complete cases) at a single center enriched for CRS1, we developed a prespecified multivariable logistic regression model to predict in-hospital death using: age, sex, HF phenotype (HFpEF/HFmrEF/HFrEF), systolic blood pressure (SBP), estimated glomerular filtration rate (eGFR), serum albumin, and log-transformed BNP [ln(BNP)]. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC) with 200-iteration bootstrap optimism correction. Calibration was assessed across risk deciles, and clinical utility was evaluated by decision curve analysis. Reporting followed the TRIPOD statement. Results: Seventeen patients (7.7%) died during the index hospitalization. ln(BNP) was the sole statistically significant independent predictor of in-hospital mortality (OR 2.39 per ln-unit; 95% CI 1.25–4.59; p=0.009). Albumin and eGFR showed consistent directional associations with mortality. The model demonstrated good apparent discrimination (AUC 0.81), with an optimism-corrected AUC of 0.73. Decision curve analysis indicated net benefit at threshold probabilities of 5–30%. A prespecified two-variable sensitivity model (albumin + ln[BNP]) yielded AUC 0.77, confirming the robustness of these two markers. Conclusions: This exploratory, internally validated model incorporating BNP, albumin, eGFR, and HF phenotype demonstrated promising discrimination for in-hospital mortality in a CRS-enriched ADHF cohort. The principal contribution is the application of a formally prespecified, TRIPOD-reported admission model in a CRS-enriched population, rather than identifying BNP as a novel prognostic marker. ln(BNP) was the sole statistically significant independent predictor. These findings are hypothesis-generating and require external validation before any clinical deployment.

Abstract: Soil salinization severely limits alfalfa productivity; however, the molecular mechanisms governing cultivar-specific differences in salt tolerance remain largely unclear. In this study, two alfalfa cultivars (Zhongmu No.3 and WL440-HQ) were exposed to 200 mM NaCl stress, followed by integrated transcriptome sequencing, weighted gene co-expression network analysis (WGCNA), and functional validation. In total, 3,517 salt-responsive differentially expressed genes (DEGs) were identified, including 795 shared DEGs and cultivar-specific DEGs (1,336 in Zhongmu No.3 and 1,386 in WL440-HQ). GO and KEGG enrichment revealed conserved stress-response pathways, including flavonoid biosynthesis and starch and sucrose metabolism, as well as cultivar-specific patterns, with Zhongmu No.3 strongly enriched in stimulus-responsive genes. WGCNA further identified phenotype-related modules and core hub genes, notably MsWRKY22 and MsPSK3. Overexpression of MsPSK3 enhanced salt-alkali tolerance in alfalfa by activating antioxidant systems. Dual-luciferase and yeast one-hybrid (Y1H) assays verified that MsMYC2 directly binds to and activates the MsPSK3 promoter. This study reveals the molecular regulatory network underlying alfalfa responses to salt–alkali stress and provides key candidate genes for breeding salt-tolerant alfalfa varieties.

Abstract: This article presents the mathematical foundations of spiking neural networks (SNNs) in a unified formalism, with a deliberate emphasis on derivational provenance. The same neuron model is written one way in computational neuroscience textbooks, another way in machine learning papers, and a third way in the stochastic process literature. Even within a single line of work, papers absorb constants into other constants until two equations from two sources cannot be compared by inspection. We collect the core mathematics in one place, and we attach a status label to every major equation so that the reader sees at a glance whether a given step is a mathematical identity, a parameter limit, a formal approximation under stated conditions, or a useful but unproven heuristic. The labels are exact, reduction, approximation, and heuristic. The substantive content is the following. The reduction chain from Hodgkin-Huxley dynamics through the adaptive exponential integrate-and-fire model down to leaky integrate-and-fire (LIF) is given with status labels at every step, including the spike response model as an exact reformulation under linear subthreshold dynamics. Reset semantics are analyzed in three forms (hard, soft, no reset), with implications for both spike statistics and gradient flow. Network dynamics are written down in a coupled form, and the analytical theory of recurrent SNNs (liquid state machines, the echo state property, balanced excitatory-inhibitory networks) is reviewed with explicit conditions on time constants and weight matrices. The full point process formulation is developed: counting processes, conditional intensities, the time-rescaling theorem, the likelihood for general history-dependent point processes, and the canonical model classes (homogeneous Poisson, inhomogeneous Poisson, Hawkes, point-process generalized linear models). The bridge between state-space SNNs and intensity-based formulations is made explicit, including conditions under which a generalized linear model can be embedded in a finite-dimensional spiking state space. Information-theoretic aspects of spike coding are presented through Fisher information, with a quantitative comparison of rate and time-to-first-spike codes. Computational capacity is treated through three lines of results: the Maass third-generation argument and its noisy temporal-coding strengthening, the Stanojevic exact mapping from feedforward ReLU networks to time-to-first-spike SNNs, and the Date-Schuman Turing-completeness construction. The article closes with a status-labeled taxonomy of the hazard-based H-LIF family and its Liquid extension, drawn from a public, patent-scoped reference implementation with custom CUDA kernel and FPGA validation; the other LIF variant families (multi-spectral, wavelet, fractional, control-theoretic, information-theoretic, and domain-specific gating) are deferred to a companion v2.This article is the second installment in a series on spiking neural networks. The first installment, Spiking Neural Networks: A Tutorial on Models, Coding, and Training [1], introduces the practical side at a tutorial level; the present article develops the underlying mathematics in depth. The two share notation, and a reader who has followed the first installment can read this one essentially in any order; the cross-references between them are explicit. The intended audience is the graduate student or researcher who needs the mathematical underpinnings of SNNs in a single document, rather than reconstructed from a dozen textbooks and review papers.

Abstract: Generally, cancer is responsible for nearly every sixth death worldwide. Early cancer revelation can provide successful and low-cost treatment of cancer, enhancing survival rates of cancer patients. This explains the key importance of development of novel highly sensitive systems for revelation of cancers in humans. Ribonucleic acids (RNAs) of several different types (microRNAs, circular RNAs, and small nucleolar RNAs) represent promising cancer biomarkers. At the same time, nanoribbon biosensors allow one to detect cancer-associated RNAs at ultra-low concentrations. Here we focus at experimental results on the detection of cancer-associated RNAs in human plasma with our nanoribbon biosensor, demonstrating promising capabilities of this nanotechnology-based device as a base of highly efficient diagnostic screening platform for early diagnosis of cancers in humans.

Abstract: The integration of heterogeneous multi-omics data — spanning genomics, transcriptomics,proteomics, epigenomics, and metabolomics — remains one of the central open challenges incomputational biology. Existing approaches either flatten omics layers into feature matrices,losing relational structure, or adopt multilayer network formalisms that treat layers asindependent graphs coupled only by alignment edges. In this position paper we propose afundamentally different data model: a Network of Networks (NoN), in which each node of atop-levelgraphisitselfacompletegraph, definedrecursively. Thisrecursivestructurenaturallyencodes the hierarchical organisation of biological systems — from molecular interactionswithin an omics layer, through pathway-level modules, up to patient-level similarity networks— without collapsing any level of resolution. We formalise the NoN model with a rigorousrecursive graph definition, describe a bioinformatics infrastructure built on top of it, andoutline how heterogeneous Graph Neural Networks (GNNs) can operate across all levels ofthe hierarchy simultaneously. We argue that the NoN paradigm offers a principled, scalable,and biologically interpretable foundation for next-generation multi-omics analysis platforms,and we identify key research directions and open challenges that must be addressed to realisethis vision.

Abstract: Sparganosis is a zoonotic parasitosis associated with freshwater aquatic environments, prevalent in tropical and subtropical regions of the world. Spirometra (S.) mansoni causes sparganosis in humans and spirometrosis in domestic dogs, which is transmitted through the consumption of raw or undercooked meat from fish, frogs or paratenic animals, producing subcutaneous and tissue infections in humans, whereas dogs or cats develop gastrointestinal infections. The purpose of this investigation was to identify S. mansoni in domestic dogs from riverine sectors of the Daule River in Ecuador, using coproparasitological methods: direct examination, flotation and sedimentation with centrifugation using saline solution (as screening); and for confirmation, morphometric methods and PCR were used. Through a descriptive, prospective and cross-sectional study, 402 domestic dogs were analyzed, and Spirometra mansoni were determined in 17% of the collected samples. Clinical and epidemiological characteristics of spirometrosis in dogs and the risk of sparganosis in humans were determined, revealing a profound lack of information and knowledge about the infection; consequently, there is a possibility that cases will spread in pets and that humans will develop sparganosis.

Abstract: Growing evidence suggests that optimized nutritional status and regular physical activity enhance immunotherapy responsiveness by modulating immunometabolism, improving T-cell function, reducing chronic inflammation, and favorably shaping the gut microbiota. Cancer-related metabolic dysfunction and treatment-induced cardiotoxicity converge to impair both skeletal and cardiac muscle energetics, thereby limiting treatment tolerance and effectiveness. Lung cancer (LC) patients frequently present with malnutrition, systemic inflammation, sarcopenia, and pre-existing cardiovascular disease (CVD), conditions that not only compromise functional status and survival but also represent significant competing risks to oncologic outcomes. By counteracting sarcopenia and malnutrition, lifestyle interventions may also reduce immune-related adverse events (irAEs) and mitigate cardiovascular (CV) toxicity, ultimately allowing patients to sustain effective treatment intensity. This narrative review examines the emerging role of targeted nutritional strategies and structured physical exercise as integral components of supportive care in LC, with a specific focus on their impact on cardiac metabolism, CV risk, and response to anticancer therapies, including immunotherapy. In this context, exercise and appropriate dietary interventions emerge as modifiable factors capable of restoring metabolic flexibility, improving mitochondrial function, and reducing systemic inflammation. These effects are particularly relevant in patients receiving immune checkpoint inhibitors (ICIs), where metabolic health and immune competence are tightly interconnected and trained immunity may be a key issue. Finally, the review discusses future challenges and perspectives, emphasizing the impact of CVD on long-term LC survivors’ outcome and of allostatic load and financial toxicity on adherence to lifestyle interventions. The integration of personalized nutrition and exercise programs into cardio-oncology care pathways is proposed as a key strategy to enhance immunotherapy efficacy, improve cardiometabolic resilience, and translate prolonged survival into better quality of life.

Abstract: This study aims to develop decision-making methods for equalizing urban electric vehicle (EV) charging services and apply them to the improvement of Wuhan’s charging infrastructure. Using grid units as the basic analytical units, the study constructs measurement models for two scenarios—daily commuting and weekend travel—including a spatial demand index based on classified population-distribution prediction, a spatial supply index derived from regional charging-facility statistics, and a supply–demand balance index. Grading systems are established for single-scenario demand, layout thresholds, and supply, together with an integrated classification combining both scenarios. According to the suitability of grid units for service improvement, three optimization strategies are proposed: adding charging stations, expanding existing stations, and converting parking lots. Evaluation methods using residential quarters and commercial/service POIs are designed to assess spatial equilibrium pre- and post-optimization. An empirical study of Wuhan’s main urban area shows that service satisfaction reaches 88.68% for residential quarters and 75.93% for commercial/service POIs under current conditions. The proposed scheme recommends 8 new stations, 31 station expansions, and 114 parking-lot conversions, increasing satisfaction to 99.24% and 92.35%, respectively. The model provides a feasible technical framework for urban EV charging-station planning.

Abstract: This article argues that the fragmentation of International Relations (IR) theory is not only a problem of competing schools, but a deeper ontological dispute over social totality. Realism, liberal institutionalism, constructivism, critical theory, post-structuralism, Global IR, and decolonial approaches each assume a different image of world order and of the human subject. Through conceptual genealogy and critical reconstruction, the article revisits Kant, Hegel, Marx, Lukács, Dussel, Quijano, Mariátegui, Zavaleta Mercado, Wynter, Said, Glissant, Wallerstein, and postcolonial IR. It proposes heterogeneous relational totality as a way beyond both closed systemic determinism and pure fragmentation. This framework rethinks power, agency, temporality, recognition, and emancipation through coloniality, planetary interdependence, and relational human existence.

Abstract: Inclusive education has become an important component of educational reform in Kazakhstan, particularly through efforts to align national education policy with international principles of equity and access. However, implementation remains uneven between urban and rural schools. This study explores how teachers implement inclusive education practices in a rural secondary school in Northern Kazakhstan. A qualitative case study design was employed using semi-structured interviews with sixteen teachers working in inclusive classrooms. Data were analyzed through thematic analysis. The findings indicate that teachers demonstrate strong commitment to supporting students with diverse learning needs and regularly adapt instructional practices to promote classroom inclusion. At the same time, participants identified major challenges, including limited professional preparation, shortages of specialized support staff, insufficient instructional resources, and infrastructure constraints affecting rural schools. The findings further suggest that although inclusive education is increasingly emphasized within national educational policy, implementation in rural schools continues to be shaped by structural inequalities and unequal access to institutional support. The study contributes to the limited literature on inclusive education in Central Asia and highlights the importance of strengthening teacher professional development, institutional support systems, and rural educational infrastructure.

Abstract: Alzheimer’s disease (AD) is more common in women than men and the risk of AD increases markedly during and after the menopausal transition. Although a role for estrogen deficiency is well studied, recent reports have revealed the pivotal but under-recognized contribution of follicle-stimulating hormone (FSH) in mediating neurodegenerative risk. In this review, we integrate current understanding of reproductive aging, AD pathobiology, and sex differences with a specific emphasis on endocrine, metabolic and inflammatory processes. FSH increases during reproductive aging and has mechanistic connections to several canonical molecular pathways that are altered in AD. This includes signaling through C/EBPβ-δ-secretase, mitochondria, glucose metabolism, and the autophagic/lysosomal clearance pathway. The convergence of these processes appears to underlie aspects of amyloid-β (Aβ) accumulation, tau pathology, and chronic neuroinflammation. FSH also modulates apolipoprotein biology (e.g., ApoE) by impacting lipid metabolism, protein lipidation, and clearance, which in turn affects Aβ kinetics and neuroinflammation in an ApoE isoform-specific manner. In addition, reproductive aging is associated with changes in vascular health and permeability, blood-brain barrier function, and immunometabolic processes that may drive neurodegenerative risk. Critically, these early upstream events drive disease risk before the onset of the more classical pathological features, which may shift our current perception of Aβ and tau as causes of AD to instead be consequences of upstream failure. Overall, this review provides mechanistic insight into the role of FSH and its downstream signaling pathways in neurodegeneration. As such, modulating FSH signaling and downstream pathways is a promising and mechanistically supported therapeutic strategy for reducing AD risk in women.

Abstract: Rising demand for high-performance battery thermal management systems (BTMS) has rendered single-mode cooling insufficient for advanced lithium-ion batteries (LIBs) in new energy vehicles (NEVs), particularly under high discharge rates. This study proposes a synergistic hybrid BTMS integrating composite phase change material (CPCM)–Aluminum foam with liquid cooling to enhance thermal regulation of cylindrical battery modules under 5 C discharge conditions. Multiple liquid cooled plate (LCP) configurations, including serpentine, straight, and leaf-shaped designs, together with different flow channel topologies (FCTs), were systematically investigated and optimized. The effects of coolant flow speed (CFS) and ambient temperature are also analyzed. Results indicate that the optimized leaf-shaped LCP with FCT #2 delivers superior performance, limiting the maximum temperature to 309.98 K, reducing temperature difference by 7.6 %, and decreasing pressure drop by 88.79 % compared to the serpentine configuration. Increasing CFS improves heat dissipation and delays PCM melting, although it raises pressure losses. Furthermore, the proposed system maintains a cell-to-cell temperature difference below 0.51 K, indicating excellent thermal uniformity. Compared to a CPCM-only system, the hybrid BTMS reduces peak temperature by 8.81 K under elevated ambient conditions (309.15 K), demonstrating strong potential for reliable and efficient thermal management in demanding operating environments.

Abstract: Adhering to physical activity and diet, risk factors for non-communicable diseases, is important in the management of treatment and medications for chronic conditions, such as diabetes and hypertension. With this aim, this study examines the perceived determinants influencing adherence to and maintenance of these two behavioral risk factors while individuals manage their chronic conditions. Within a planetary health equity framework, a phenomenological approach was taken in a qualitative study to explore the perceived determinants and their interlinkages that collectively shape behavioral adherence to walking and dietary practices among individuals diagnosed with diabetes and hypertension in a single neighborhood. A total of twenty in-depth interviews were conducted. This study found that individual, social, economic, and environmental determinants and their interlinkages made adherence to the physical activity and diet advised by treating physicians challenging and complex. This study also found that behavioral adherence goes beyond individual choice; material and spatial circumstances also play a key role in adherence and maintenance of changing behaviors. Therefore, behavior change without improving these underlying determinants is likely to have less impact on adaptation to walking and diet. A planetary health equity approach that addresses the nexus between human health, society, and the environment must be adopted to resolve the critical challenges in adhering to behavioral change and its maintenance. Intervention strategies must act beyond clinic-based medication and counseling to, through a whole-community and whole-systems approach, integrating primary healthcare, urban planning, environmental governance, and socioeconomic protection.

Abstract: Honeybee population decline poses a serious threat to global biodiversity and agricultural productivity, underscoring the need for continuous and non-invasive hive monitoring solutions. In particular, early detection of queen absence is critical for maintaining colony viability. This study investigates the effectiveness of machine learning and deep learning models for acoustic-based queen-presence detection using short-duration hive audio recordings. Audio data collected from multiple sources were processed to extract spectrogram, Mel-spectrogram, and Mel-frequency cepstral coefficient features, which were evaluated using classical ML classifiers and convolutional neural networks. Experimental results indicate that MFCC-based representations consistently outperform spectrogram-based features across segment lengths, achieving higher accuracy and greater stability. The best performance was obtained with Mel features using convolutional neural networks for short segments and gradient-boosted models for longer windows. These findings demonstrate that brief acoustic segments are sufficient for reliable classification, supporting real-time monitoring under noisy field conditions. The proposed approach offers a scalable and low-cost framework for precision beekeeping and contributes to sustainable beekeeping through early, automated anomaly detection. The proposed framework supports real-time, low-cost deployment scenarios, enabling scalable precision apiculture solutions.

Abstract: Pathogenicity islands (PAIs) are regions of bacterial genomes that harbor genes encoding virulence factors. Identifying molecules that enhance pathogenicity is crucial for understanding the mechanisms pathogens employ to cause disease and their evolution. Corynebacterium pseudotuberculosis (C. pseudotuberculosis) is a pathogenic micro-organism that causes caseous lymphadenitis (CLA) in sheep and goats. Despite its prevalence in Mexico, its genetic material has not been analyzed for virulence factors acquired through horizontal gene transfer. Therefore, the objective of this study was to analyze the complete genome of C. pseudotuberculosis strains of Mexican origin to identify genes hosted in PAIs. Seventeen genomes were sequenced using Illumina technology. GIPSY software was used to identify the coordinates of the PAIs, and a positive selection analysis was performed. All genomes corresponded to C. pseudotuberculosis biovar ovis, and fourteen regions harboring virulence factors were identified. Additionally, five coding sequences with mutations under positive selection were identified. A comparative genetic study was conducted between the new Mexican strains and previously reported strains, using whole-genome multilocus sequence typing (wgMLST) to determine phylogenetic relationships. This work provides the complete genetic repertoire of 17 new strains and identifies 51 genes that could serve as targets in future studies.

Abstract: The coagulation cascade depends on the active participation of several elements present in the blood as well as signals arising from the endothelial cells. A platelet plug is a temporary, fast-response seal formed by platelets at the site of a damaged blood vessel to initiate hemostasis. It acts as the first step in primary hemostasis, where platelets stick to exposed collagen, activate, and aggregate to create a plug that temporarily prevents blood loss. Among changes platelets undergo is the degranulation step. Platelet degranulation is the process where activated platelets release stored chemical mediators from their internal alpha and dense granules into the bloodstream to promote hemostasis and immune responses. Platelet degranulation results in the release of substances like ADP, serotonin, fibrinogen, and zinc. In the present work we provide evidence that the high local concentration of zinc is intended to target junctional adhesion molecule A (JAM-A) that remains inactive (inhibited cell-adhesion and cytoskeleton dynamics) when coagulation is not needed and platelets move through the blood stream as single units. Zinc-activated JAM-A leads the platelets to aggregate. Our experimentation includes work with platelets, and a synthetic biology small peptide to quench the effects of zinc. We suggest that further exploring this mechanism of zinc-activated JAM-A can be advantageous for better understanding hemostasis, its role in antithrombotic therapy, coagulation inhibition, or thrombosis prevention.

Abstract: Financial crises are usually identified through drawdowns, volatility and changes in returns, but these indicators do not fully describe changes in the underlying dynamical structure of markets. This study tests whether Laminarity, a measure derived from Recurrence Quantification Analysis, can provide a complementary indicator of financial market stress during the COVID-19 shock. Daily data for the Dow Jones Industrial Average, S&P 500 and NASDAQ Composite from 2018 to 2022 are analyzed using adjusted prices and log returns. Rolling-window Recurrence Quantification Analysis is applied across alternative window lengths and recurrence thresholds, and the resulting Laminarity measures are compared with conventional benchmarks including drawdown and rolling volatility. The results confirm that the COVID-19 crisis is clearly identified by conventional risk indicators, while Laminarity provides a more nuanced and parameter-sensitive signal. Price-based Laminarity generally increases during the COVID-19 stress period, suggesting a more persistent crisis trajectory, whereas return-based Laminarity produces mixed evidence, including some cases of Laminarity loss depending on index and window length. The findings indicate that Laminarity should not be interpreted as a universal or mechanical crash-warning signal, but as a complementary diagnostic measure that can help describe changes in market-regime structure during periods of acute stress.