Abstract: Anxiety disorders represent one of the most prevalent challenges in contemporary mental health, characterized by a hyperactivation of fear circuits. Although conventional pharmacotherapy (benzodiazepines and SSRIs) is effective, it often entails significant adverse effects and a risk of dependence. This article explores the scientific evidence and the functional neuroanatomy underlying clinical phytotherapy as an adjunctive strategy. The pharmacodynamics of key species such as Piper methysticum, Passiflora incarnata, Lavandula angustifolia, and Crocus sativus are analyzed, highlighting their structural interaction with the amygdaloid complex, the hippocampus, and the prefrontal cortex, as well as the modulation of GABAergic receptors and voltage-dependent calcium channels. It is concluded that the integration of standardized extracts offers a neurobiologically viable therapeutic alternative under rigorous professional supervision.

Abstract: Scorpion venom peptides, with their stable disulphide backbone, compact structural framework, and highly selective regulation of ion channels, have long been regarded as important molecular probes in neuropharmacology. However, recent studies have revealed their potential for regulating oxidative stress, anti-inflammation, and neuroprotection, making them a new research frontier. This article focuses on “scorpion venom peptides as drugs,” constructing an integrated knowledge framework from structural classification to clinical translation. First, scorpion venom peptides are systematically classified based on cysteine arrangement patterns and three-dimensional folding topology, and their structure-activity relationships are summarised. Based on this, the molecular mechanisms by which scorpion venom peptides regulate ion channels are systematically analysed. In addition, we review the emerging pharmacological activities of scorpion venom peptides. Of particular note, the representative molecule SVHRSP has shown multi-target synergistic antioxidant and neuroprotective activity in models of Parkinson’s disease. This article also systematically evaluates the application of engineering strategies, including cyclisation modification, nanodelivery, recombinant expression, and AI-assisted optimisation, to overcome the translational bottlenecks in the development of scorpion venom peptides. By integrating molecular structure, redox regulation mechanisms, and translational medicine perspectives, this review aims to provide a theoretical basis and practical pathways for scorpion venom peptides as precision therapeutic molecules for oxidative stress-related diseases.

Abstract: A two-year field experiment was conducted in an organic vineyard in Tuscany (Italy), to evaluate the effects of micronized biochar (0.5% v/v) applied via fertigation on soil fertility/biological quality and Vitis vinifera performance. The biochar, derived from pyrogasified mixed wood, was compared to watered controls (CTR) following a randomized plot design. Soil chemical properties, dehydrogenase (DHA) and alkaline phosphatase (APA) activities, and plant parameters (biomass, leaf area, gas exchange, chlorophyll, flavonols, and foliar nutrients) were assessed in samples collected in July and September (2021 and 2022). Biochar did not significantly alter total and dissolved organic carbon contents or nitrogen fractions but enhanced DHA and APA activities, alongside increased available phosphorous content (+37.5%) and exchangeable potassium content (+7.1 and +19.7% in September 2021 and July 2022, respectively), indicating improved microbial activity and nutrient availability. Conversely, exchangeable calcium and magnesium contents decreased, likely due to biochar adsorption properties. Plant responses included increased leaf area and dry biomass in 2022, elevated net photosynthesis rate (+14.4%) and apparent carboxylation efficiency, and transient increases in foliar nitrogen, phosphorous and potassium contents, with reduced magnesium concentration (–27%) but stable chlorophyll levels. These findings suggest that low doses of micronized biochar may enhance soil quality and vine physiology, supporting its efficient and effective use in organic vineyards.

Abstract: The Riemann Hypothesis (RH) is proved via a new expression of the completed Riemann zeta function $\xi(s)$, obtained through pairing the conjugate zeros $\rho_i$ and $\bar{\rho}_i$ in the Hadamard product while accounting for zero multiplicities (which are uniquely determined, although their specific values remain unknown), i.e. $$\xi(s)=\xi(0)\prod_{\rho}(1-\frac{s}{\rho})=\xi(0)\prod_{i=1}^{\infty}(1-\frac{s}{\rho_i})(1-\frac{s}{\bar{\rho}_i})=\xi(0)\prod_{i=1}^{\infty}\Big{(}\frac{\beta_i^2}{\alpha_i^2+\beta_i^2}+\frac{(s-\alpha_i)^2}{\alpha_i^2+\beta_i^2}\Big{)}^{m_{i}}$$ where $\xi(0)=\frac{1}{2}$, $\rho_i=\alpha_i+j\beta_i$, $\bar{\rho}_i=\alpha_i-j\beta_i$, with $0<\alpha_i<1$, $\beta_i\neq 0$, $0<|\beta_1|\leq|\beta_2|\leq \cdots$, and $m_i\geq 1$ is the multiplicity of $\rho_i/\bar\rho_i$. Then, according to the functional equation $\xi(s)=\xi(1-s)$, we have $$\prod_{i=1}^{\infty}\Big{(}1+\frac{(s-\alpha_i)^2}{\beta_i^2}\Big{)}^{m_{i}}=\prod_{i=1}^{\infty}\Big{(}1+\frac{(1-s-\alpha_i)^2}{\beta_i^2}\Big{)}^{m_{i}}$$ which, owing to the divisibility of entire functions, uniqueness of $m_i$, and the irreducibility of each real quadratic polynomial factor, is finally equivalent to $$\alpha_i=\frac{1}{2}, 0<|\beta_1|<|\beta_2|<|\beta_3|<\cdots, i=1, 2, 3, \dots$$ Thus, we conclude that the RH is true.

Abstract: Oilseed crops are essential for global agriculture, industry, and nutrition, relying on complex pathways governing fatty acid (FA) and triacylglycerol (TAG) synthesis. While the enzymatic steps of TAG formation are well established, mechanisms of lipid trafficking between organelles remain poorly understood. This review summarizes recent advances in identifying lipid transporters involved in intracellular lipid movement and lipid droplet (LD) formation in oilseeds. Key protein families include non-specific lipid transfer proteins (nsLTPs), lipid droplet-associated proteins (LDPs), acyltransferases, ABC transporters, and plant VPS13 paralogs. Manipulating LDPs can enhance oil accumulation, yet the regulatory networks coordinating lipid transport and storage remain unclear. Recent studies highlight membrane contact sites (MCSs), especially between the endoplasmic reticulum (ER) and plastids, as critical hubs for lipid exchange. However, key questions persist regarding transported lipid species, ER–plastid tethering mechanisms, and redundancy among MCS proteins. Future research should prioritize identifying ER-to-LD transporters and expanding characterization of MCS-associated proteins. Integrating multi-omics, advanced imaging, and genome editing will be essential to uncover protein–lipid interactions regulating TAG trafficking and storage, enabling metabolic engineering strategies to improve oil yield and composition in oilseed crops.

Abstract: There are lots of perennial species that enable multiple harvests over years from one planting in nature. These crops require no repeated tillage and can promote root accumulation, thus leaving rural landowners with time for reproduction and further production, but this model is difficult for complex knowledge and operational difficulty. Focusing on the supplementation of distinctive species in rural household agriculture, this paper sorts out existing problems and compiles a biological resource list including perennial crops and self-reproducing animals. Combined with methods such as using bamboo trellises and other climbing structures to block light for non-crops, a household-based perennial agricultural scheme of "one-time work, continuous harvest" is constructed to ease reproductive pressure and accelerate civilizational development. Studies show that perennial, self-propagating, storable crops allow people to run a food company, avoid repetitive labor, and gain stable family food dividends; some resilient perennial species can gain competitive advantages with simple artificial tools, and combining the innate advantages of plants with the acquired strengths of tools can resist various risks; A diversified species lifespan table helps people plan investment according to species longevity and their own needs, allowing some species to form a cycle where longer lifespan is accompanied by larger root tubers and higher fruit yields.

Abstract: The integration of mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy holds great promise for comprehensive biomolecular profiling, yet existing computational approaches are limited to single modality analysis, employ static fusion strategies, and incur prohibitive inference costs. We propose SpectraLLM, a unified large language model-driven framework for multi-modal biological spectrum analysis. SpectraLLM introduces a modality-agnostic spectral encoder that projects both MS and NMR spectra into a shared token space, a contribution-aware dynamic multi-modal balance mechanism that adaptively weights each modality per sample, a flow-based knowledge distillation strategy that compresses the teacher model to a compact student with 4.3× lower latency, and parameter-efficient transfer learning via lightweight adapters for rapid domain adaptation. Evaluated on three large-scale benchmarks—MetaboSpectrum-10K, ProteinSpectra-5K, and CellNMR-3K—SpectraLLM achieves state-of-the-art performance, including an AUC of 0.947 for biomarker identification and 96.1% teacher performance retention after distillation. In a clinical case study on early-stage pancreatic cancer detection, SpectraLLM achieves an AUC of 0.961, substantially outperforming both the clinical standard CA 19-9 and existing computational methods, demonstrating the potential of LLM-driven multi-modal spectral analysis for precision medicine.

Abstract: This technical note discusses the structural limitations of current climate-related Artificial Intelligence (AI) applications due to the lack of standardized and geographically representative in situ monitoring networks. Drawing on the experience of the Salado River Basin monitoring system in Buenos Aires Province, Argentina, the document highlights the risks of training AI models with non-representative data and the urgent need for multilateral investment in physical infrastructure for satellite validation and environmental monitoring. The note argues that AI will only be as reliable as the measurements that support it, emphasizing the importance of certified, continuous, and well-maintained networks to ensure climate resilience and evidence-based water management.

Abstract: This research gives a region-based technology, economic, and environmental assessment of biomass power production in Yalova, Türkiye. The assessment includes an inventory of biomass resource data, how well the materials can be converted to electricity, assumptions regarding the transportation cost of biomass, and a financial analysis of the biomass project. The study will assess if a utility size biomass facility can be constructed in Yalova. In addition to the revised feasibility framework, a MATLAB-based optimization layer was introduced to determine the feedstock blend that minimizes delivered feedstock cost per unit of electricity under regional availability constraints. The compiled inventory indicates a total biomass potential of 610,498 t/year in Yalova, equivalent to 55,040 toe/year, with forestry residues forming the dominant resource class. The configurations of mixed waste stream (forest residues) resulted in the highest yield of electricity. Based in part on this data and optimizing the feedstock allocation by an annual period (to favour chicken litter/forest residues), the potential for generating electricity from a 220,000-tonne-per-annum biomass facility in Yalova is 40.37 GWh/year; approximately 0.184 MWh is produced for each tonne of feedstock delivered at a cost of 286.8 USD/MWh. As evidenced by these results, biomass energy is a technologically feasible means of contributing to emissions reductions in Yalova through implementing data-supported feedstock allocation methodologies that enhance the reliability of investment and operational planning.

Abstract: Osteoarthritis (OA) in companion animals is a progressive and debilitating condition for which current treatments primarily provide symptomatic relief without modifying disease progression. Cell-free regenerative approaches have recently emerged as promising alternatives. This study evaluated the clinical effectiveness of membrane-free stem cell extract (MF-STEM) in canine and feline osteoarthritis in comparison with intra-articular hyaluronic acid in a real-world clinical setting. A total of 271 animals were enrolled, including an MF-STEM group (n = 210) and a control group (n = 61), with treatment allocation based on owner preference. Clinical outcomes were assessed using the Joint Evaluation Index (JEI), along with recovery and recurrence rates. MF-STEM treatment resulted in a marked reduction in JEI scores (12.3 → 2.2), compared with a more limited decrease in the control group (11.3 → 7.0). The recovery rate was significantly higher in the MF-STEM group (93.3% vs. 32.8%), while the recurrence rate within one year was substantially lower (2.4% vs. 60.6%). No treatment-related adverse events were observed (0%). Although the non-randomized design limits causal interpretation, key baseline characteristics were generally comparable between groups, and consistent improvements were observed across multiple outcome measures in a relatively large real-world cohort. In conclusion, MF-STEM demonstrated clinically meaningful improvements in canine and feline osteoarthritis and may suggest a potential to influence disease progression with sustained therapeutic benefits. Further randomized controlled and mechanistic studies are required to confirm these findings.

Abstract: Digital trust in online interactions is commonly established through mechanisms such as decentralized identifiers (DIDs), verifiable credentials (VCs), and digital wallets. While these technologies ensure the correctness of individual components, they do not guarantee that an interaction as a whole is trustworthy. This limitation arises because real-world interactions consist of sequences of dependent steps, where inconsistencies may occur even when each step is locally valid. In this paper, we introduce the concept of executable trust, which models trust as a verifiable property of execution across interaction steps. We formalize interactions as sequences of TrustEvidence objects that capture both step-level validity and cross-step dependencies. Based on this model, we demonstrate that step-level correctness is insufficient to guarantee interaction-level trust, and we derive a minimal and sufficient condition for establishing end-to-end trust through composable verification and consistency constraints. We further present the Executable Trust Architecture (ETA), which operationalizes the proposed model through components for evidence generation, constraint enforcement, secure communication, and auditability. The feasibility and effectiveness of the approach are validated through scenario-based evaluations covering key trust properties, including authenticity, integrity, privacy, and accountability. The proposed approach provides a systematic foundation for verifying trust in complex digital interactions and supports the design of systems in which trust can be explicitly enforced, evaluated, and audited at runtime.

Abstract: This paper presents a Digital Twin (DT)-based framework for the control, monitoring, and intelligent optimization of an Assembly/Disassembly/Repair Mechatronic Production Line (A/D/R MPL), developed as a laboratory platform aligned with Industry/Education 4.0/5.0 paradigms. The A/D/R MPL is assisted by two complementary cyber–physical robotic systems: an Assembly/Disassembly/Replacement Cyber–Physical Robotic System (A/D/R CPRS), and a Mobile Cyber–Physical Robotic System (MCPRS), enabling both fixed and mobile intelligent operations. The CPRS is equipped with an industrial robotic manipulator (IRM) responsible for A/D/R tasks, while the A/D Mechatronic Line (A/D ML) consists of seven interconnected workstations (WS1–WS7) dedicated to storage, transport, quality control, and final product handling. MCPRS includes a wheeled mobile robot (WMR), carrying a robotic manipulator (RM) and Mobile Visual Servoing System (MVSS). Each workstation is connected to a local slave programmable logic controller (PLC), which communicates via Profibus with a master PLC located at the CPRS level. Additional communication infrastructures include LAN Profinet and LAN Ethernet for local integration, and WAN Ethernet connectivity enabled through OPC-UA, ensuring interoperability, scalability, and remote accessibility. Virtual environment supports task planning through Augmented Reality (AR) and real-time monitoring through Virtual Reality (VR). The system behavior is modelled with synchronized hybrid Petri nets (SHPNs) which describe the discrete and hybrid dynamics of A/D/R processes. Artificial Intelligence (AI) techniques are integrated into the DT framework for optimal task scheduling and adaptive decision-making. As a laboratory-scale implementation, the proposed system provides a comprehensive platform for experimentation, validation, and education. It supports Education 4.0/5.0 objectives by facilitating hands-on learning, human–machine interaction, and the integration of emerging technologies such as AI, digital twins, AR/VR, and cyber–physical systems. At the same time, it embodies Industry 4.0/5.0 principles, including interoperability, decentralization, sustainability, robustness, and human-centric design.

Abstract: The microbiota-gut-brain axis (MGBA) represents a bidirectional neuroendocrine system essential for maintaining metabolic and neurological homeostasis. While dietary macronutrients are known modulators of this axis, the cumulative impact of modern industrial xenobiotics remains insufficiently characterized. This review synthesizes contemporary, multidisciplinary evidence to elucidate how four ubiquitous environmental stressors Particulate Matter (PM2.5), Microplastics (MPs), Inorganic Nanoparticles (NPs), and Non-Nutritive Sweeteners (NNS) synergistically perturb this delicate enteric ecosystem. We integrate independent lines of research to propose a unifying pathological framework: these agents induce profound dysbiosis, significantly depleting beneficial, short chain fatty acid (SCFA) producing taxa (e.g., Lachnospiraceae, Faecalibacterium) and sharply diminishing the bioavailability of critical neuroactive mediators, including butyrate, GABA, serotonin, and indole derivatives. Concurrently, NNS-driven bacteriostatic shifts, the MP “plastisphere” phenomenon, and NP-induced oxidative mucosal abrasion critically compromise the intestinal barrier. This “leaky gut” facilitates the unrestricted systemic translocation of lipopolysaccharides (LPS) and trimethylamine-N-oxide (TMAO), driving a peripheral Treg/Th17 immune imbalance that propagates via the gut-liver and gut-heart axes directly to the central nervous system (CNS). Crucially, the synthesis of this data points toward a potential “Dual-Hit” mechanism, suggesting that these xenobiotics aggravate neurological pathology through simultaneous mechanisms: acting as direct neurotoxicants via CNS translocation (e.g., NPs crossing the blood-brain barrier to trigger epigenetic reprogramming and amyloid aggregation) while concurrently driving “bottom-up” systemic neuroinflammation. By linking these disruptions to classic neurodegeneration (Alzheimer’s, Parkinson’s) as well as underexplored pathologies (migraine, epilepsy, restless leg syndrome, and substance use disorders), this review underscores the urgent need for a paradigm shift in environmental neurotoxicology and the development of targeted microbiome-based interventions.

Abstract: In Multi-Criteria Group Decision-Making (MCGDM), the assignment of weights to decision-makers is a crucial but methodologically delicate step, especially when the group includes both human experts and artificial experts such as intelligent agents, Artificial Intelligences (AIs) or Large Language Models (LLMs). Existing weighting strategies are often either difficult to interpret or poorly suited to heterogeneous groups of evaluators. In this paper, we investigate a fuzzy rule-based approach to expert weighting, building on a previously introduced methodological framework and focusing here on its application-oriented validation. The proposed method models expert weighting as a Fuzzy Rule-Based System (FRBS) in which relevant properties of the experts are represented by linguistic variables and combined through interpretable IF–THEN rules. In this way, weighting policies can be expressed transparently and adapted to the requirements of the decision domain. The framework produces normalised weights in the interval [0,1], which can then be incorporated into standard MCGDM aggregation procedures. To assess the operational behaviour of the approach, we consider an application involving the weighting of four LLMs evaluated over multilingual performance, computational requirements, and open-sourceness. The experiments show that the proposed framework is flexible enough to encode different weighting policies and that changes in the rule base produce clear and interpretable changes in the resulting rankings. This confirms both the practical usability of the method and its suitability for contexts in which multiple, potentially competing, objectives must be balanced explicitly. Overall, the paper provides an application-oriented study of an FRBS-based weighting scheme for artificial experts, highlighting its interpretability, adaptability, and potential relevance for contemporary MCGDM settings.

Abstract: Background/Objectives: Treatment-resistant depression (TRD) remains one of the most urgent unmet needs in psychiatry, while its therapeutic pipeline is evolving rapidly. To characterize current development trajectories, we conducted a registry-anchored mapping of interventional trials in adults with major depressive disorder and treatment resistance (MDD-TRD), with the aim of defining the distribution of intervention types, endpoint choices, and key design features across the active trial landscape. Methods: We sys-tematically searched ClinicalTrials.gov, the EU Clinical Trials Information System, and ISRCTN for interventional MDD-TRD trials registered up to September 18, 2025. After data cleaning and cross-registry deduplication, 237 unique trials were retained. Inter-ventions were categorized as pharmacological, device-based, biologic/novel, or digi-tal-combined. Primary endpoints were flagged as standard when they explicitly refer-enced the Montgomery–Åsberg Depression Rating Scale or Hamilton Depression Rating Scale. We also examined developmental phase, sample size, and recurrent methodo-logical features. Results: Digital or hybrid programs accounted for most trials (73.8%), followed by device-based neuromodulation studies (23.6%), pharmacological interven-tions (2.1%), and biologic/novel approaches (0.4%). Standard clinician-rated primary endpoints were used in 63.3% of studies. Trial development was concentrated in mid-phase designs, whereas sample sizes were generally modest (median 49; inter-quartile range, 19-87). Overall, the registered landscape suggested a shift away from novel molecules and toward digital-somatic hybrid models and precision neuromodu-lation. Across modalities, increasing attention was directed to durability of response, functioning, and patient-reported outcomes, with adaptive and enrichment-based designs appearing with greater frequency. Conclusions: The contemporary TRD trial ecosystem appears to be reorganizing around stratified, biomarker-informed, and multimodal care rather than conventional drug development alone. This registry-based mapping provides a near-real-time overview of the field and may support future harmonization of trial endpoints and design standards.

Abstract: Emergency contraception (EC) is a time-sensitive pharmacy service in which consistent, patient-centred counselling is important but can be challenging to deliver in routine practice. This prospective implementation study evaluated the feasibility, real-world use, and patient experience of a digital counselling support tool integrating patient self-reporting with structured, evidence-based support for pharmacist-led EC consultations. All EC consultations using the tool in 10 Swiss community pharmacies were analysed descriptively (n = 3,428), alongside a voluntary anonymous post-consultation survey (n = 148). Median total consultation duration was 11:32 min, including 4:27 min of direct pharmacist counselling, indicating partial transfer of assessment to the digital pre-consultation phase. Ulipristal acetate was dispensed in 71% and levonorgestrel in 26% of consultations. Prior to counselling, 80% of patients reported uncertainty regarding the optimal active ingredient, underscoring the relevance of pharmacist involvement. Survey respondents rated the tool as easy to use (97%), discreet (99%), and trustworthy (98%); 85% preferred it over standard paper-based procedures. These findings demonstrate high feasibility and patient acceptance, supporting the integration of a digital tool into routine EC services while enabling consistent, evidence-based counselling without compromising interpersonal interaction.

Abstract: We propose that the origin of life represents a universal integrable transition governed by Painlevé transcendental equations. Four independent prebiotic subsystems: mineral catalysts, information polymers, energy transducers, and lipid boundaries, undergo autocatalytic coupling that induces a mathematical cascade: PVI → PV → PIII^D6 → PIII^D7 → PIII^D8, culminating in LUCA (Last Universal Common Ancestor). Each degeneration in the Chekhov confluence diagram corresponds to a specific biochemical integration event; the cascade is governed by a universal separation parameter ∆_min ≈ 0.15 between anabolic and catabolic rates, with concentration oscillations scaling as ω ∝ ∆^−1/2. We validate this framework through three independent lines of evidence: (1) retrospective analysis of 26 Belousov-Zhabotinsky oscillating-reaction data points from seven published studies yields ∆_min = 0.135 ± 0.012 (R² = 0.92); (2) six natural extreme environments spanning hydrothermal vents, hot springs, and hypersaline systems (pH 0.5–11, temperatures 0–90◦C) give ∆_min = 0.147 ± 0.020; and (3) prebiotic autocatalytic systems including the formose sugar reaction and clay-catalyzed RNA oligomerization yield ∆_min = 0.152 ± 0.018, consistent with observed oscillation periods of 5–15 minutes. Combined validation across all 33 measurements gives ∆_min = 0.141 ± 0.023, only 6% deviation from the theoretical value. The framework unifies the RNA World, Metabolism-First, and Protocell theories as sequential stages of a single cascade (rather than competing hypotheses), provides a quantitative timeline (4.4–3.5 Ga) anchored to isotopic and phylogenomic data, and makes ten falsifiable predictions testable in laboratory and field settings. This suggests that Painlevé coalescence may represent a general principle of integrative transitions in complex systems.

Abstract: This paper focuses on the problem of anticipating the local occurrence of future large earthquakes. "Local" is defined as the probability of a large earthquake occurring with a defined circle of arbitrary radius surrounding a point of interest. The main (and for that matter, the only) assumption for all these works is that the Gutenberg-Richter (GR) magnitude-frequency relation holds. Here we describe a method for computing calendar time forecasts in a local area for large earthquakes of a target magnitude MT using a count small earthquakes MS < MT in the area. Using the idea that the GR relation is valid throughout the surrounding region, we define an ensemble of earthquakes in larger surrounding regions to be used in computing the forecast. What follows is simple data mining. The method has significant skill, as defined by the Receiver Operating Characteristic (ROC) test, which improves as time since the last major earthquake increases. The probability is conditioned on the number of small earthquakes n(t) that have occurred since the last large earthquake. The probability is computed directly as the Positive Predictive Value (PPV) associated with the ROC curve. The method is validated by comparison to the UCERF3 forecasts for the UCERF3-defined geographic boxes centered on Los Angeles and San Francisco. The method is then applied to a 125-KM radius circular area around Los Angeles, California, following the January 17, 1994 magnitude M6.7 Northridge earthquake, and short term forecasts (1 year and 5 year ) are computed.

Abstract: This manuscript evaluates the electrochemical corrosion resistance of diamond-like car-bon (DLC) coatings deposited via High-Power Impulse Magnetron Sputtering (HiPIMS) on AISI 52100 steel in synthetic seawater. While AISI 52100 steel is valued for its hardness, it is highly susceptible to localized and uniform corrosion in chloride-rich marine environ-ments. In this study, samples were characterized using Raman spectroscopy to analyze sp2/sp3 bonding, and their corrosion behavior was assessed through potentiodynamic po-larization, linear polarization resistance (LPR), and electrochemical impedance spectros-copy (EIS) over 24 hours of immersion. Results demonstrated that the DLC coatings signif-icantly enhanced electrochemical stability, shifting corrosion potentials toward more no-ble values and reducing corrosion current densities by several orders of magnitude com-pared to the uncoated substrate. EIS data revealed high polarization resistance and effec-tive barrier properties, despite a calculated total porosity of 3.06% resulting from intrinsic micro-defects. Although localized subsurface degradation and minor flaking were ob-served at defect sites, the HiPIMS-deposited DLC coatings effectively mitigated the corro-sive impact of synthetic seawater, providing a robust protective barrier for high-precision steel components.

Abstract: The Hsp20 protein family, recognized in all organisms for its chaperone activity in the heat-stress response, is part of the Heat Shock Protein (Hsp) superfamily, defined by a conserved alpha-crystallin domain (ACD). Hsp20s are the smallest proteins in the superfamily (mostly between 15 and 22 kDa) and assist in protein refolding during stress and developmental processes. In this study, we characterize the Hsp20 gene family in Chenopodium quinoa (2n = 4x = 36) using an integrative omic approach. C. quinoa is well known for its global contributions to food production and tolerance to various abiotic stresses. We identified 69 CqHsp20 genes distributed across the nine chromosomes of each subgenome (A and B), organized mainly into homologous pairs, with paralogs on eight chromosomes, likely from tandem duplications, suggesting a well-conserved evolutionary pattern within the species. The phylogenetic analysis grouped CqHsp20 proteins into two main clusters, split into four sub-clusters based on peptides’ cellular localization, consistent with a characteristic gene structure and conserved motif analysis. The integration of transcriptomic data from published experiments enabled us to detect a cluster of putatively ubiquitously expressed CqHsp20, as well as other groups that showed differential responses across abiotic stress conditions. The pattern shows more genes with transcriptional activity under drought and salinity than under heat, key adaptive traits underlying quinoa’s known ecological versatility. Some of these genes, with null or low transcriptional activity under heat stress, encode organelle-targeting peptides, a phenomenon not reported in other model plant studies. Varying expression within CqHsp20 homologous and paralogs supports the idea that gene duplication creates genomic diversity, facilitating adaptation to variable extreme environments. However, while theoretical and in silico analysis provide valuable insight into quinoa Hsp20 response, empirical data are essential to unequivocally understand how these gene expression variations affect quinoa response to abiotic stressors.