Abstract: Reliable public-sector labour-market forecasting requires models that can be updated as data sources, AI tools, and labour-market signals evolve. This paper proposes a provider-independent multi-agent framework for dynamic predictive evaluation of national and regional labour markets in Bulgaria. Implemented as a Model Context Protocol (MCP) server, the system coordinates specialised agents for data ingestion, preprocessing, semantic extraction, AI-adjusted transformation modelling, automated model evaluation, and reporting through stable input-output contracts. The empirical application integrates Bulgarian Employment Agency administrative registered-unemployment indicators, Eurostat labour-market data, World Bank macroeconomic data, and text/audio/video evidence on AI, skills, and employment change. The study period covers 2015–2030, combining observed official inputs for 2015–2025 with forecast/scenario outputs for 2026–2030. For youth unemployment under 25, the semantic-enhanced model achieves the best predictive accuracy (RMSE = 0.2033; MAE = 0.1457), representing a small improvement over the structured baseline (RMSE = 0.2057; MAE = 0.1462) and a substantial RMSE reduction relative to the persistence benchmark (RMSE = 0.4750; MAE = 0.2891). Regional forecasts indicate persistent spatial inequality, with the Northwest remaining the highest-risk region and the Southwest the lowest-risk region.

Abstract: Localized lower respiratory tract infection including unilobar and round pneumonia can be associated with hypoxia and oxygen requirements. Previous explanations include shunting of deoxygenated blood, a systemic inflammatory response syndrome and vasoconstriction.This is unexplained.The alternative hypothesis is that spread of fluid absorption inhibiting cytokines in the alveolar spaces of the inflamed lung is cause of hypoxia in localized lower respiratory tract infection by spread of Cystic Fibrosis Transmembrane Conductance (CFTR) dysfunction in alveolar epithelial cells to more areas including those not infected. There is no evidence of pulmonary shunting to explain hypoxia in localized pneumonia. Systemic inflammatory response syndrome (SIRS) related generalized increase in alveolar capillary barrier or pulmonary vasoconstriction not visible on a chest x-ray cannot explain the hypoxia detected in most patients. Confirmation of the hypothesis could be achieved using pulmonary MRI or high resolution CT to confirm spread of alveolar fluid accumulation from the localized pneumonia focus as opposed to generalized SIRS related pulmonary oedema together with cytokine and chloride measurement in bronchoalveolar lavage samples from the lung segments near the affected lung segment and unaffected contralateral lung. Ventilation/perfusion scintigraphy could investigate for involvement of vasoconstriction or micro-emboli from intravascular coagulation.Should the posed hypothesis be confirmed adjuvant strategies including small molecule CFTR activators, CFTR activating combination of beta-agonists, phosphodiesterase inhibitors and steroids could be used to treat hypoxia and CFTR activating low-intensity ultrasound explored.

Abstract: The competitive ground of digital visibility has moved twice in eighteen months. The first move was from the ten blue links to the AI-generated answer; the second, still in progress, is from the answer to the autonomous action. This article develops a framework that integrates three optimization disciplines emerging in response to that shift—Answer Engine Optimization (AEO), Generative Engine Optimization (GEO), and Agentic Optimization (AgO)—under a single theoretical lens: delegated consumer–AI agency. Building on Puntoni et al.’s (2021) experiential perspective on consumer AI and Davenport et al.’s (2020) account of AI in marketing, we treat the AI assistant not as a channel but as a delegated decision-maker whose choices are governedby retrievability, attribution cost, and embedded brand associations. We argue that the central strategic risk of this transition is brand erasure—the systematic elimination of brand identity from synthesized answers and completed agentic actions—and we develop six propositions that link content and infrastructure choices to brand outcomes in this environment. We close with managerial implications for brands operating across the dual audience of human readers andmachine intermediaries, and a research agenda focused on measurement, audit methods, and governance.

Abstract: Poxviruses are large double-stranded DNA (dsDNA) viruses that cause important human and animal diseases, including smallpox and mpox. Poxviruses have also been identified in diverse bat populations; however, their potential for zoonotic transmission and adaptation to other mammalian hosts remains poorly understood. Poxviruses encode numerous immunomodulatory proteins that contribute to virulence, immune evasion, and host range. In this study, we performed a comparative genomic analysis of two bat-associated poxviruses belonging to the genus Vespertilionpoxvirus: hypsugopox virus (HYPV) and eptesipox virus (EPTV). We identified 24 and three previously unannotated putative open reading frames (ORFs) in HYPV, and EPTV, respectively, substantially expanding the predicted coding capacity of these viruses. Comparative analyses further revealed gene duplication and fragmentation events affecting several virulence and host range factors, as well as other unusual genomic features, including the presence of two divergent E3L homologs in EPTV. Together, our findings provide new insights into the genome evolution and potential host adaptation of bat-associated poxviruses and establish a foundation for future functional studies of Vespertilionpoxvirus biology, host-virus interactions, and zoonotic potential.

Abstract: In pregnancy physiologically based pharmacokinetic (PBPK) modelling within PK-Sim/Mobi, pregnancy stage is canonically parameterised on the fertilisation-age (FA) timescale, yet PK-Sim’s physiology database is indexed by chronological “Age”. The Open Systems Pharmacology (OSP) pregnancy framework therefore encodes FA on a dummy “Age” axis (30.00 years = FA 0 weeks; 30.75 years = FA 38 weeks) and generates pregnancy physiology vectors from FA 0 – 38 weeks discretised at 1-day intervals for database ingestion. Although the anchor points are publicly documented, an explicit closed-form transform and week-resolved lookup suitable for deterministic reproduction of daily physiology grids has not been routinely foregrounded in the literature or repository instructions. A unique affine mapping is derived, implied by the anchors, that provide forward and inverse equations in week- and day-space, quantifying rounding error in terms of FA-day misalignment, and supply a fertilization week (1-38) table for implementation-ready pregnancy virtual population creation in PK-Sim.

Abstract: Metabolomics is a powerful approach for monitoring metabolic effects in a particular situation by qualitatively or quantitatively analyzing metabolites related to specific physiological or pathological responses within a biological process. Rapeseed is a major source of vegetable oil and contains a wide variety of metabolites. Recent advances, particularly the integration of metabolomics with other omics approaches, now allow us not only to obtain a comprehensive overview but also to perform detailed analyses of key metabolites that respond to specific conditions. In this review, we summarize recent progresses in rapeseed metabolomics study, introduce the key metabolites uncovered by this approach, and discuss those associated with growth & development, and abiotic and biotic stresses, including macronutrient availability, temperature, water stress, salt stress, and cadmium toxicity. Future perspectives and current challenges in metabolomics are also discussed, along with its potential for breeding applications aimed at developing new rapeseed varieties with stable, high-yield, and high-quality traits.

Abstract: We introduce the Information Lattice Model (ILM), a theoretical framework in the braneworld tradition in which the brane–bulk system is represented as a stratified informational graph whose inter-layer link capacity is governed by a permeability function \mathrm{\Pi}\left(y\right)=1+\varepsilonexp\left(-\lambda\left|y\right|/\ell_{Pl}\right). The dimensionless parameter \varepsilon is identified with the ratio of bulk-to-brane entanglement entropy flux via the Ryu–Takayanagi formula, connecting the model to the Bekenstein–Hawking entropy bound and the AdS/CFT correspondence. This identification is presented as a conceptual proposal ahead of full formal derivation, in the tradition of framework papers from Kaluza–Klein to Verlinde’s entropic gravity. The permeability function modifies the Randall–Sundrum warp factor, introducing an additional entropic dilution of the effective gravitational coupling beyond geometric warping alone. Within this framework, the ILM suggests a unified phenomenological perspective on the hierarchy problem, the black hole information paradox, and the possible role of sub-brane entropic degrees of freedom as an effective dark matter component. The model also predicts frequency-dependent corrections to gravitational-wave propagation arising from bulk-mediated entropic coupling. The model generates two testable predictions. The primary prediction is a positive correlation between the dimensionless radiated energy excess \mathrm{\Delta A} and total merger mass M_{total} in binary black hole systems, arising from the mass dependence of the integrated horizon permeability. Under a quadratic area-law scaling, \mathrm{\Delta A} may approach \sim10-3 for M_{total}\sim{10}^6\thinsp M_\odot events accessible to LISA, while the sign and slope of the \mathrm{\Delta A}–M_{total} correlation is independently falsifiable via stacked regression analysis of the GWTC-3 catalogue; a full Bayesian treatment will be presented in future work. The secondary prediction is a non-linear deviation in quantum decoherence rates near lattice saturation, potentially testable with ion traps, superconducting qubits, and Bose–Einstein condensates.

Abstract: Background/Objectives: Artificial intelligence (AI) is integrated into diagnostic, thera-peutic, administrative, and communicative healthcare domains in Italy under regulations requiring human oversight. Empirical evidence on AI attitudes, acceptance, and per-ceptions in Italian healthcare is rapidly accumulating but not systematically mapped. This scoping review aimed to (i) map empirical evidence on AI attitudes, acceptance, and perceptions in Italy by population and domain; (ii) identify measurement instruments used in studies and their origins; and (iii) characterize determinants, themes, and methodological gaps in the Italian evidence base. Methods: The review used Joanna Briggs Institute methodology, reported via PRISMA-ScR (protocol Open Science Framework doi: 10.17605/OSF.IO/TZRVF). PubMed and Embase were searched on 27 April 2026 from January 2018 in English, Italian, or German, combining controlled vo-cabulary and free-text terms across AI, attitudes-acceptance, and healthcare delivery, with an Italian-context qualifier. Eligibility criteria used the Population–Concept–Context mnemonic. Results: Of 1,510 unique records screened, 35 empirical studies were retained, comprising seven studies of Italian patients and the general population, 22 studies of healthcare professionals, three psychometric validation studies of AI-acceptance instru-ments, one mixed-population study and two international comparator studies with sub-stantial Italian sub-samples. Acceptance was consistently positive but conditional on physician oversight, training and regulatory clarity. A recurrent optimism–knowledge gap and an absence of probabilistic, population-representative evidence were identified as principal gaps. Conclusions: Italian evidence on AI attitudes is expanding but methodologically narrow. Three Italian-validated acceptance instruments are now available. Population-representative, multilingual and longitudinal evidence is required.

Abstract: Maintaining food systems in the face of climate change (CC) is a major concern. Palm oil is included in many commodities and this food system will be affected detri-mentally by inclement future climate, when oil palm (OP) will experience increased disease and declining growth. The OP diseases considered are basal stem rot (BSR), bud rot (BR) and fusarium wilt (FW), where the attempts to control them have been un-successful. An approach may be to replace compromised OP with different crops better suited to future climate: These plants will have less disease because of the “Parasites Lost” phenomenon. Maintaining a vegetable oil product is an important advantage. How CC will affect OP and associated ailments has been determined previously by CLIMEX modelling. The modelling of future suitable climate (FSC) has also been car-ried out for soybean, maize and the common bean (CB) using the same modelling pa-rameters. This enables direct comparisons in the OP producing countries of Colombia, Nigeria and Papua New Guinea (PNG). Limited data for rapeseed are also discussed. The FSC for OP was much reduced in these countries and that for soybean was higher. Soybeans will have less disease as it would be an introduced and annual crop. Maize had much fewer advantages and the CB and rapeseed had none. Maize had potential advantages in Nigeria until 2050. A novel method for adapting to the serious diseases of OP and poor growth would be to grow soybeans in similar regions to where OP grows currently. Plans could be made for replacing OP with soybeans which could be modified when real time data becomes available. The paper provides a novel method for mitigating future diseases and poor growth of OP, which are otherwise unavailable, whilst maintaining a valuable oil product.

Abstract: Fourier Neural Operators have become a central tool for learning solution operators of partial differential equations, but their spectral layers remain entirely classical and rely on digital Fourier processing. In this work, we introduce the Continuous-Variable Quantum Fourier Neural Operator (CV-QFNO), a Gaussian photonic formulation of the FNO spectral layer. The proposed architecture maps the essential operations of Fourier-domain operator learning, Fourier transformation, mode selection, and channel mixing, onto native continuous-variable optical primitives. In this way, CV-QFNO provides a photonic quantum analogue of the truncated spectral mechanism underlying the classical FNO, while avoiding the compilation overhead and spectral mismatch that arise in qubit-based Quantum FNO constructions. We extend the framework to both one- and two-dimensional operator learning and validate it on standard PDE benchmarks, including Burgers’ equation, heat equation, Navier–Stokes dynamics, and Darcy flow. The results show that the proposed model preserves the predictive accuracy, resolution generalisation, and spectral inductive bias of Fourier neural operators while using a structurally constrained photonic parameterisation. Since all experiments are performed as classical simulations, the contribution should be understood as an architectural and algorithmic blueprint for photonic neural operators, rather than as a demonstration of quantum computational advantage.

Abstract: Background: Gastrectomy for gastric cancer is associated with substantial metabolic, nutritional, and immunological disturbances that may significantly influence postoperative recovery. Increasing evidence suggests that perioperative immunonutritional status, particularly as assessed by the Controlling Nutritional Status (CONUT) score, represents an important predictor of surgical outcomes. However, prospective data evaluating sex-related differences in postoperative nutritional recovery after gastrectomy remain limited. Methods: This prospective observational cohort study included 150 consecutive patients undergoing curative-intent gastrectomy for gastric adenocarcinoma at a tertiary referral center between 2021 and 2024. Nutritional and immune status were longitudinally assessed using the CONUT score at predefined perioperative timepoints: preoperatively (T0), early postoperatively (T1), and at 3-month follow-up (T3). Functional recovery outcomes, postoperative complications, and mid-term functional parameters were compared between male and female patients. Multivariable logistic regression analysis was performed to identify independent predictors of delayed postoperative recovery. Results: The study population included 91 male patients (60.7%) and 59 female patients (39.3%). Significant postoperative deterioration of albumin level, lymphocyte count, total cholesterol, and CONUT score was observed in the entire cohort (p-time < 0.001 for all comparisons), followed by partial recovery during follow-up. No significant sex-related differences were identified regarding longitudinal immunonutritional evolution, postoperative complications, gastrointestinal recovery, or functional outcomes (p > 0.05). Overall postoperative complications occurred in 31.3% of patients, while 90-day mortality was 2.7%. Elevated baseline CONUT score ≥5 (OR 2.74, 95% CI 1.48–5.09, p = 0.001), postoperative CONUT score T1 ≥5 (OR 3.36, 95% CI 1.82–6.19, p < 0.001), ASA class III (OR 2.08, 95% CI 1.19–3.63, p = 0.010), and anastomotic leakage (OR 4.91, 95% CI 1.74–13.88, p = 0.003) independently predicted delayed functional recovery. Male sex was not independently associated with adverse postoperative recovery (OR 1.18, 95% CI 0.74–1.89, p = 0.44). Conclusions: Gastrectomy induces significant postoperative immunonutritional deterioration irrespective of sex. Although biological sex did not independently influence postoperative recovery trajectories, impaired perioperative immunonutritional status—particularly elevated postoperative CONUT score—was strongly associated with delayed functional recovery. Serial perioperative CONUT assessment may represent a valuable tool for individualized postoperative risk stratification and nutritional management in gastric cancer patients undergoing gastrectomy.

Abstract: Chronic wounds are a persistent clinical and public health challenge. Natural polyphenols such as curcumin and resveratrol, alongside mesenchymal stem cell (MSC) secretome, have demonstrated complementary anti-inflammatory, antioxidant, and pro-angiogenic properties with potential for wound healing. This study reports two complementary in vitro investigations evaluating the release profiles of curcumin and resveratrol from two polymeric platforms: poly(vinyl alcohol)/sodium alginate/carboxymethylcellulose films (Study 1) and an acrylate copolymer-based hydrogel incorporating MSC secretome (Study 2). UV-Vis spectrophotometric analysis confirmed analytical selectivity with no interference from excipients. In Study 1, films containing curcumin alone exhibited low structural stability and early disintegration in aqueous medium, whereas resveratrol-only films (2% w/w) demonstrated sustained and reproducible release profiles. Combined formulations showed that curcumin compromised polymer matrix integrity and reduced resveratrol release efficiency. In Study 2, resveratrol exhibited progressive and consistent release from the hydrogel, reaching 14.31 µg/mL (isolated control) and 12.60 µg/mL (combined with curcumin) at 120 min. Curcumin showed unsatisfactory release in both systems, attributed to its low aqueous solubility. These results support resveratrol-loaded polymeric matrices as promising sustained-release platforms for bioactive wound dressings and highlight the need for nanoencapsulation strategies to improve curcumin bioavailability.

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.