Search Results (5,964)

The epidemic expansion of maize white leaf spot (WLS) is a substantial threat to the secure and sustained production of maize. Given its diversity and strong environmental adaptability, WLS has the potential to emerge as a globally prevalent disease affecting maize crops. A detailed exploration of genetic segments and genes that are significantly associated with resistance to WLS in maize, an analysis of the genetic mechanisms underlying maize’s response to this disease, as well as the identification and development of resistant germplasm resources and their promotion and application, are of great practical significance for ensuring the safe production of maize. In this study, a genome-wide association study (GWAS) of 11 related traits in 141 maize accessions was conducted, and a total of 1174 significant single-nucleotide polymorphism (SNP) sites were identified. BSA-Seq (Bulked Segregant Analysis Sequencing) identified 6319 sites and 79 candidate genes. Through comprehensive analysis of GWAS and RNA-Seq data, a total of 13 candidate genes associated with maize white spot resistance, including Zm00001eb093900 and Zm00001eb078490, were identified. This study systematically explored genetic regions and genes significantly linked to white spot resistance in maize, thereby providing novel genetic resources for future molecular design-based breeding and improvement of resistance traits. Furthermore, by correlating field disease incidence with the expression of immune response-related gene products, we developed a rapid evaluation system for maize WLS resistance, in which soil plant analysis development (SPAD) value, Fm, SSC, and POD served as key indicators. Using this system, 5 immune germplasm resources such as QB2229 and NP5366 and 89 highly resistant materials such as QB1923 and Chang7-2 were identified. The accurate evaluation of maize WLS resistance will provide essential resistance sources for subsequent breeding programs. RNA-Seq analysis revealed that systemic acquired resistance to maize WLS involves key pathways, including phenylpropanoid metabolism, as well as the synthesis of secondary metabolites such as flavonoids and glutathione. Further analysis of race-specific resistance indicated that the response of highly resistant maize varieties to WLS is primarily characterized by the accumulation of defense-related substances and enhanced activity across multiple energy metabolism pathways. In contrast, highly susceptible maize lines exhibited more pronounced enrichment in hormone signaling, the mitogen-activated protein kinase (MAPK) signaling pathway, and the metabolism of various amino acids. Full article

To address the issue that existing penalty weight update strategies in the augmented Lagrangian multiplier method are disconnected from the optimality conditions and remain relatively sensitive to initial parameters, this paper proposes a complementarity-gap-driven adaptive sequential convex programming algorithm. The algorithm directly incorporates the complementarity slackness information from the KKT conditions into the parameter update laws. The defined complementarity slackness ratio and complementarity gap respectively measure the deviation of the current penalty intensity from the ideal multiplier level and the degree of departure from the complementarity slackness condition. Based on these two quantities, a bidirectional smooth update law for the penalty weight and a normalized gap update law for the multiplier are designed, which decouple the multiplier growth from the current multiplier magnitude. On this basis, a complete theoretical convergence framework is established, in which the monotonic bounded convergence of the Lagrange multiplier and the convergence of the slack variables and the complementarity gap are rigorously proved, and a conditional convergence theorem is given. Taking the reentry trajectory planning problem of a gliding vehicle as an example, numerical simulations are conducted with the initial penalty weight spanning five orders of magnitude. Simulation results demonstrate that the proposed algorithm converges rapidly and stably to the optimal solution satisfying the accuracy requirements under different initial weights, with the terminal position error stabilizing at 0.4–0.5 km, exhibiting favorable convergence accuracy. In addition, the stable convergence exhibited by the complementarity gap and the slackness radius validates the effectiveness and robustness of the complementarity-gap-driven adaptive update mechanism. Full article

Microglia are adaptive immune cells that maintain central nervous system homeostasis and respond dynamically to injury, infection, and other neurological insults. While traditionally classified into resting, pro-inflammatory “M1”, and anti-inflammatory “M2” states, advances in multi-omic profiling technologies have established that microglial phenotypes exist along a multidimensional and context-dependent continuum. The mammalian target of rapamycin (mTOR), a central regulator of cellular metabolism, growth, survival, and protein synthesis, has emerged as a potential central mediator of these state transitions through distinct activities downstream of mTOR complex 1 (mTORC1) and mTOR complex 2 (mTORC2). In this review, we examine current evidence linking mTOR signaling to microglial phenotypic polarization and functional plasticity. Generally, evidence suggests that mTORC1 acts as a context-dependent amplifier of inflammatory responses, whereas mTORC2 promotes anti-inflammatory and neuroprotective programs; however, the effects of either complex vary according to disease context. Understanding the balance and coordination of mTORC1 and mTORC2 signaling programs may clarify mechanisms that underly chronic neuroinflammation and guide the development of targeted therapies for neuroinflammatory disorders including Alzheimer’s disease, stroke, and epilepsy. Full article

Crop models are increasingly incorporated into web applications, but their user and programming interfaces are often developed separately from the model implementation. We extended Cropbox, a crop modeling framework written in Julia, to derive both interfaces from model declarations written in its domain-specific language (DSL). The describe() function extracts model structure and declared metadata. The serve() function uses this description to expose simulation and visualization operations through HTTP routes. The dashboard() function constructs a web user interface from the same contract. The implementation was examined with chilling–forcing phenology, coupled gas exchange, and a whole plant crop model. A Model Context Protocol (MCP) adapter connected the same service to a local large language model (LLM). The applications reproduced direct simulation results through the service and supported interactive model exploration. These results demonstrate a shared basis for building user and programming interfaces from model declarations. Full article

Chronic liver diseases are frequently accompanied by metabolic dysfunction, making precision nutrition relevant for prevention, diagnosis, risk stratification, and management. This review summarizes nutritional omics evidence in hepatology, emphasizing metabolic dysfunction-associated steatotic liver disease as a prevalent model. Nutrigenetics provides information on inherited susceptibility by identifying genetic variation affecting hepatic lipid handling, triglyceride export, phospholipid remodeling, and glucose-driven lipogenesis. Nutrigenomics characterizes transcriptional programs involved in hepatic lipogenesis, inflammation, oxidative stress, and fibrogenesis. Nutriepigenetics captures exposure memory through DNA methylation, histone regulation, and small-RNA signaling, including miR-122, miR-21, miR-34a, and miR-192; diet may modulate this layer through one-carbon metabolism, methyl-donor availability, oxidative stress, acetyl-CoA and NAD+-dependent pathways, and lipid peroxidation. Nutrimetagenomics highlight taxa such as Ruminococcus, Faecalibacterium, Veillonella, Bacteroides, Escherichia, and Klebsiella, but translation requires functional characterization beyond stool taxa. Nutrimetabolomics and lipidomics, including OWL-liver platforms, track dietary adherence, lipid species, bile acids, amino acids, lipoproteins, and biological non-response. Future progress will require AI-supported, phenotype-first integrated models tested in multiethnic longitudinal studies with standardized dietary assessment, biospecimen collection, meaningful liver endpoints, realistic workflows, and adaptive lifestyle-care strategies. Full article

This paper studies the minimization of a bi-quadratic function with a linear term. As both a quartic programming problem and a generalized convex multiplicative programming problem, it poses significant challenges for standard convex optimization methods. We propose an adaptive branch-and-bound algorithmic framework based on explicit relaxation bounds. Theoretically, the proposed framework guarantees a global $ϵ$-approximate solution within $O(1/ϵ)$ worst-case iterations. Numerically, we apply it to compute the Legendre–Fenchel conjugate of the product of two positive definite quadratic forms, demonstrating its advantages over the compared methods. Full article

When does a complex adaptive system (CAS) (like a thermostat, a market, a flock, or a large language model) become a complex adaptive autopoietic system (CAAS), one that actively produces and sustains itself rather than merely adapting to its environment? We argue that this transition requires two simultaneous conditions. The first, established in a companion work, is architectural sufficiency. The system’s decision structure must achieve recursive closure at the third cybernetic order, the so-called fractal-seed point. The second, derived here, is corporeal viability. The system must pay a structural cost (the enactment tension E) large enough to hold its organization in place against perturbation. Working within Fisher-Generative Informational Realism (FGIR), an informational-realist framework in which information, not matter or energy, is the primary generative substrate of reality, we derive a scalar invariant E = I_pc², where I_p is the integrated informational potential of the operative field and c is the global coherence conductance at which informational structure locks into place. This invariant marks the threshold at which a proto-autopoietic system crosses into full autopoiesis. Because FGIR treats physical mass-energy as the result of a freezing projection acting on an incorporeal informational manifold rather than as the foundation of reality, the same invariant that governs autopoietic closure in social and organizational systems also projects, under appropriate bridge conditions, for instance, onto the classical physical law $\widehat{E}=\widehat{m}\hspace{0.17em}{\widehat{c}}^2$, or in a quantum context, the Schrödinger equation. The physical projection is stated as a conditional equivalence (T-BRIDGE), but it is not the focus of this paper. Rather, the central contribution is the cybernetic threshold itself and its operationalization. We show how a systems theorist can assess a system’s distance from critical admissibility, even when the absolute magnitudes of I_p and c are unavailable in domains that lack R(6) closure; we connect the coercivity threshold explicitly to Ashby’s Law of Requisite Variety, supplying the energetic constraint that Ashby’s purely combinatorial criterion leaves implicit. The contemporary case of large language models illustrates the framework. LLMs display transient, externally-scaffolded R(3)-like decision structure but fail the coercivity threshold, and so remain proto-autopoietic. The paper thus offers systems science a derived criterion, additional to architectural closure, for distinguishing full from proto-autopoiesis, with a specified but as yet unexecuted test program across physical, biological, cognitive, and social domains. Full article

This study aims to evaluate young people’s perceptions, attitudes, and behaviors regarding food waste in the North-West region of Romania. It seeks to identify key factors influencing awareness and practices, quantify the attitude–behavior gap, and propose targeted educational and policy interventions. A cross-sectional survey was conducted between July and October 2024 using a 17-item questionnaire. Snowball sampling via social media and educational networks yielded 955 valid responses from participants aged 14–33 across six counties. Descriptive statistics, chi-square tests, Kruskal–Wallis tests with Dunn’s post hoc comparisons, Pearson and Spearman correlation analyses, and effect size calculations were performed, guided by the Theory of Planned Behavior. Respondents showed high knowledge (89.47% aggregated positive responses) and strongly positive attitudes (>90% on all items). However, a significant attitude–behavior gap emerged: while 93.51% intended to reduce food waste, only 50.05% consistently adapted portions to appetite. Family was the primary source of awareness (42.41%), whereas formal schooling contributed minimally (3.35%), despite 92.46% supporting school-based education. Knowledge correlated strongly with attitudes (r = 0.624) but only moderately with practice (r = 0.326). Results highlight the need to integrate food waste education into formal curricula and develop targeted programs bridging the gap between positive attitudes and actual behavior change among Romanian youth. Full article

This paper presents a novel fault-tolerant model predictive control (FTMPC) framework for fuel cell hybrid electric vehicles (FCHEVs) used for postal delivery applications. The main contribution of the proposed FTMPC is the adaptive adjustment of the model predictive control cost function weights based on fault severity. The proposed reformulation incorporates fault characterization across the diverse degradation mechanisms while maintaining reliable vehicle operation. The FTMPC approach dynamically adapts cost function weights and system constraints based on the fault severity index. The resulting control strategy provides fault-aware power allocation between the fuel cell and battery while accounting for the specified operating and safety constraints. To isolate the contribution of the proposed health-dependent adaptation mechanism, a controlled ablation study was performed against a structurally identical fixed-MPC controller under the same vehicle model, driving cycle, initial conditions, prediction and control horizons, solver configuration, and fault scenarios. The adaptive FTMPC achieved a 10.6956% reduction in direct hydrogen consumption relative to the fixed-MPC baseline. Because differences in terminal battery state of charge (SoC) can influence comparisons based solely on hydrogen consumption, a charge-corrected hydrogen-equivalent metric was also evaluated; using this more conservative metric, the adaptive FTMPC retained a 2.7276% improvement. The final quadratic programming implementation achieved a 100% successful optimization rate in the validation run with no fallback-controller activation, while the maximum soft-constraint slack remained on the order of 10−9. Additional sensitivity analyses were conducted to evaluate the influence of relevant vehicle and operating conditions on energy consumption and battery utilization. These results provide direct quantitative evidence of the contribution of the proposed fault-adaptive mechanism and demonstrate its numerical feasibility for FCHEV energy management, while the limitations of the present simulation-based validation are explicitly acknowledged. Full article

NF-κB p65 (RelA) is a key regulator of inflammation, immunity, and stress responses. Recent evidence indicates that alternative p65 isoforms may diversify NF-κB signaling, but their functions remain largely unexplored. We previously identified a novel splice variant, p65 iso5, which contains an additional upstream exon and displays distinct molecular properties, including the ability to interact with dexamethasone in a glucocorticoid receptor-dependent manner. Here, we define the transcriptional programs regulated by p65 iso5 using RNA-seq analysis of HeLa cells expressing either p65 iso5 or canonical p65, with or without dexamethasone treatment. Under basal conditions, p65 iso5 expression was associated with reduced expression of genes involved in type I interferon and antiviral pathways as compared to the effects of canonical p65, while genes involved in translation, ribosome biogenesis, and metabolic activity were not reduced as with p65. Upon dexamethasone stimulation, p65 iso5 expression was associated with extensive transcriptome remodeling characterized by suppression of biosynthetic and proliferative programs and activation of metabolic and stress-adaptive pathways. Comparative analyses reveal that glucocorticoid responses are strongly isoform-dependent, with p65 iso5 expression being associated with distinct gene expression networks supported by distinct protein–protein interaction hubs. p65 iso5 is overexpressed in cirrhotic and hepatocellular carcinoma tissues as well as in high-grade colon tumors, suggesting clinical relevance. Our findings identify p65 iso5 as a context-dependent NF-κB modulator with unique transcriptional and metabolic functions and potential relevance in inflammation-associated diseases and cancer. Full article

Smart systems, the Industrial Internet of Things, and cyber-physical networks increasingly make decisions on the devices where data is generated, on nodes short of memory, compute, energy, and bandwidth, and exposed to real adversaries. Two research currents have grown to meet this: one makes artificial intelligence small and distributed (quantization, pruning, distillation, TinyML, federated and split learning), the other makes it safe (defenses against poisoning, backdoors, inversion, and evasion). This review argues that the two are entangled rather than parallel. Operators that shrink a model or scatter it across nodes also redraw its attack surface, each carrying a security dividend and a security liability, and because a node’s resources are finite and shared, model capacity and defense strength compete for one multi-dimensional budget. We formalize this as an efficiency-decentralization-security (EDS) design tension, explicitly a tension and not an impossibility, and show with published measurements that the coupling is non-monotonic. Around this thesis we build three artifacts, following an explicit design-science research process: an evidence-graded scoring matrix that separates each operator’s security dividend from its liability across seven axes and reports the direction of every effect separately from the confidence in the evidence behind it; a resource-aware threat model that judges attack and defense feasibility against a tiered device, gateway, network, and server budget with stated units; and a co-design framework whose decision workflow terminates in a defense-selection program and a verification step under adaptive attack. We work the framework through an industrial predictive-maintenance scenario with the resource arithmetic computed line by line, and evaluate it retrospectively against six published edge-AI systems. The result is a decision-support guide for building edge AI that is efficient, decentralized, and secure at once. Full article

Parkinson’s disease (PD) is characterized by oxidative stress, mitochondrial dysfunction, and dopaminergic neuron loss, for which effective treatments remain unavailable. Here, we report CL0179, a fungal-derived decahydrofluorene alkaloid with antioxidant-associated neuroprotective properties, and evaluate its effects across cellular and animal PD models. CL0179 exhibited a favorable safety profile and protected SHSY5Y against 6-hydroxydopamine- (6-OHDA), rotenone-, and 1-Methyl-4-phenylpyridinium-iodide (MPP⁺)-induced neurotoxicity by preserving mitochondrial membrane potential and network integrity. Transcriptomic analyses revealed selective restoration of gene-expression programs associated with oxidative phosphorylation, mitochondrial bioenergetics, and stress adaptation disrupted by MPP⁺. CL0179 also enhanced SIRT1 activity under MPP⁺ stress, whereas pharmacological SIRT1 inhibition partially attenuated protection of mitochondrial membrane potential and cell viability. In LRRK2-G2019S astrocytes, CL0179 reduced ROS and α-synuclein accumulation and restored mitochondrial organization, while in human dopaminergic neurons, it attenuated toxin-induced mitochondrial depolarization and preserved neuronal architecture. To overcome the low production of CL0179, we generated the structurally related analogue CL0670. Both compounds crossed the blood–brain barrier and protected mouse primary cortical neurons, while CL0670 improved motor deficits in a 6-OHDA mouse model. Collectively, these compounds promote mitochondrial resilience and stress-adaptive neuroprotection, supporting their potential for PD and related neurodegenerative disorders. Full article

Lysine lactylation (Kla) is a lactate-driven post-translational modification that covalently links lactyl groups to lysine residues, directly coupling cellular metabolic states to gene expression regulation and protein functional remodeling. Since its first report in 2019, extensive studies have confirmed that lactylation is broadly present on histones and thousands of non-histone substrates. In the context of tumor biology, lactylation reinforces glycolysis through positive feedback loops, suppresses oxidative phosphorylation, remodels lipid and glutamine metabolism, and exerts regulatory functions in autophagy, pyroptosis, ferroptosis, and apoptosis, thereby comprehensively participating in tumor cell proliferation, metabolic adaptation, cell death resistance, and invasion and metastasis. Within the tumor microenvironment, lactylation constructs an immune evasion barrier by upregulating immune checkpoints, including programmed death-ligand 1 (PD-L1), driving tumor-associated macrophage polarization toward the M2 phenotype, inducing CD8⁺ T cell exhaustion, and enhancing regulatory T cell suppressive function. Strategies targeting lactate production (lactate dehydrogenase A (LDHA) inhibitors), lactate transport (monocarboxylate transporter (MCT) inhibitors), and the lactylation enzymatic machinery (p300/CBP inhibitors, histone deacetylase (HDAC) inhibitors) have shown promising results in preclinical models, and a limited number of agents, including the MCT1 inhibitor AZD3965 and the p300/CBP inhibitor CCS1477, have entered early-phase clinical trials primarily for safety and tolerability assessment. This review systematically summarizes the molecular mechanisms and enzymatic basis of lactylation, as well as its regulatory functions in core cancer hallmarks and the immune microenvironment, evaluates the translational prospects of targeting the lactate–lactylation axis, and discusses the key scientific questions and future research directions currently facing the field. Full article

Background/Objectives: Operating room nurse scheduling is a complex healthcare optimization problem. Operating room settings are particularly challenging because permanent and subcontracted nurses operate under complex 16 h and 24 h shift structures, with continuous surgical coverage requirements and recovery-period requirements. To our knowledge, few existing models simultaneously integrate nurse preferences, recovery-period requirements, and heterogeneous shift structures within a unified goal programming framework. This study aims to develop and implement a goal programming model that incorporates nurses’ needs and preferences as flexible constraints to optimize shift scheduling in an operating room department. Methods: This single-center case study combined a qualitative component, an analysis of scheduling records, and mathematical optimization modeling. It was conducted at the operating room department of a public hospital in Türkiye employing 37 permanent and 7 subcontracted nurses in the shift rotation, together with a head nurse responsible for the roster. The hospital was selected purposively as a high-volume public center with a dual-tier staffing model and a fully manual scheduling process; semi-structured interviews were then conducted with all 37 permanent nurses and the head nurse (n = 38). The seven subcontracted nurses were not interviewed; the constraints applying to them were derived from national regulatory guidance and operational information provided by the head nurse. Scheduling requirements were formalized as five flexible constraints informed by nurses’ preferences and institutional requirements and incorporated into a goal programming model alongside obligatory coverage and staffing constraints. Penalty weights were calibrated through structured consultation with the head nurse. The model was solved to proven optimality using Python with the OR-Tools CP-SAT solver. Results: The optimized 28-day schedule eliminated direct night-to-day shift transitions, which, under the manual schedule, affected approximately three nurse assignments per week. Weekly night shifts were limited to a maximum of two per nurse in every planning block, a limit that individual nurses exceeded under the manual system. Monthly working days were standardized to a range of 18–20 days (mean = 19.84, SD = 0.49) from an irregular 14–26-day range (mean = 20.57, SD = 3.51), an 86% reduction in the standard deviation. All identified rest-period violations for subcontracted nurses on 16 h and 24 h duties were eliminated in the optimized schedule. Conclusions: A goal programming model integrating flexible constraints informed by nurses’ preferences and institutional requirements generated a schedule with greater equality in the distribution of monthly working days and improved compliance with the predefined scheduling objectives compared with the historical manual schedule. The model offers nurse managers a potentially adaptable decision-support tool that requires prospective validation in other settings. Future work should extend the model to incorporate dynamic patient demand, cost optimization, and multi-department scheduling scenarios. Full article

Growing air traffic and dynamic missions require conflict detection and resolution (CD&R) during the execution phase, when ad hoc airspace must be inserted into an already conflict-free baseline plan without global re-optimization. This paper proposes a grid-based online optimization framework built on Geographical Coordinate Subdivision grid with One dimension integer coding on 2ⁿ-tree (GeoSOT) discretization that transforms four-dimensional spatiotemporal conflict judgment into efficient grid-code matching and interval comparison. Incremental conflict detection restricts pairwise checks to candidate ad hoc-related pairs, reducing detection scale by over 99% relative to full screening. A lexicographic two-stage resolution policy prioritizes ad hoc adjustments—incorporating horizontal, altitude, temporal, and grid-shrinkage operations—and activates limited baseline coordination only when necessary. The The Incremental Ad-hoc Operation—Tiered Priority Time-Sliced Search (IAO-TPTS) algorithm implements this policy under a hard time budget through Phase A (ad-hoc-restricted Dimension-wise Conflict-Driven Assignment, DCDA-Lite) for fast ad hoc-only feasibilization and Phase B (Hybrid Adaptive Large Neighborhood Search, Hybrid-ALNS) for tiered refinement, with dual validation to prevent secondary conflicts in neighboring airspace. Experiments including visualization, ablation, algorithm comparison, and scalability analysis on Small, Medium, and Large scenarios show 100% feasibility within 180 s, median solve times as low as 0.069 s, competitive objective values versus mixed-integer linear programming (MILP) and Adaptive Large Neighborhood Search (ALNS), and sub-linear scalability from 20 to 300 baseline airspaces. The novelty is this integrated execution-phase framework (incremental detection, lexicographic baseline-protective scheduling, and time-budgeted IAO-TPTS with dual validation), rather than a new grid-coding scheme or a standalone MILP. Full article