Search Results (281)

The present study aimed to estimate the heritabilities of behavioral, growth, carcass, and growth curve traits and to determine the genetic and phenotypic relationships among these traits in Japanese quail (Coturnix japonica). A total of 500 quail chicks originating from a pedigree population were evaluated for growth performance, carcass characteristics, growth curve parameters, and behavioral traits. Genetic parameters were estimated using multi-trait animal models based on pedigree information. Heritability estimates for body weight traits ranged from 0.19 to 0.71, indicating substantial additive genetic variation for growth performance. Feed conversion ratio traits showed low to moderate heritability estimates (0.06–0.45), whereas growth curve parameters exhibited heritabilities ranging from 0.13 to 0.44. Among behavioral traits, feather pecking (0.35), wing stretching (0.30), and aggressive pecking (0.28) displayed the highest heritability estimates. Favorable genetic and phenotypic relationships were observed between feeding and inactivity behaviors and growth and carcass traits, while walking behavior showed moderate negative genetic associations with BW42. In contrast, welfare-related behavioral traits generally exhibited weak relationships with production traits. Genetic correlations among several behavioral traits suggested the existence of common biological mechanisms influencing behavioral expression. These findings demonstrated that behavioral traits possess exploitable genetic variation and should be considered alongside conventional production traits in future poultry breeding programs aimed at improving both productivity and animal welfare. Full article

Light quality is a pivotal environmental signal governing the morphogenesis and metabolic programming of edible fungi. This study evaluated the effects of eight light qualities—red (R), green (G), blue (B), red-green (RG), red-blue (RB), green-blue (GB), red-green-blue (RGB), and dark control (CK)—on the agronomic traits of Lentinula edodes. Among all treatments, blue light (B) emerged as the most effective regulator, yielding the highest productivity per log (228.12 g), and significantly enhancing pileus diameter (45.17 mm) and stipe thickness (29.45 mm). To elucidate the underlying molecular mechanisms, integrated transcriptomic and metabolomic analyses were performed on primordia and mature fruiting bodies. Transcriptomic profiling identified 4280 differentially expressed genes (DEGs) under blue light, which were significantly enriched in energy metabolism and structural development pathways. Metabolomic analysis revealed 45 differentially expressed metabolites (DEMs), highlighting a 7.64-fold upregulation of 9(S)-HPODE and a marked downregulation of L-arginine at the harvest stage under blue light. Multi-omics integration demonstrated that blue light orchestrates a strategic metabolic shift: it activates arginine and proline metabolism during the primordial stage, maintains linoleic acid metabolism throughout development, and triggers alanine, aspartate, and glutamate metabolism during the transition to maturity. These pathways facilitate the conversion of amino acids into energy precursors and enhance cell membrane fluidity through unsaturated fatty acid synthesis to support rapid growth. Conversely, red light treatment triggered stress-related MAPK signaling, delayed primordium formation, and redirected resources toward stipe elongation via phenylalanine accumulation, resulting in significantly lower yields. In conclusion, this study confirms that blue light is the optimal condition for L. edodes cultivation, providing a robust molecular foundation for precision light-regulation strategies to maximize yield and quality in commercial production. Full article

Background: Platinum-based chemotherapy is the frontline treatment for high-grade serous ovarian cancer (HGSOC); however, the development of therapy resistance greatly limits clinical response. Increasing evidence suggests that platinum agent-driven metabolic programming, particularly within redox-associated pathways, may contribute to chemoresistance. Methods: A syngeneic pair of patient-derived HGSOC cell lines representing cisplatin-sensitive (SE) and cisplatin-resistant (CR) states were evaluated using a multi-omics approach. Differential metabolite abundance and gene expression were assessed, followed by gene set and pathway enrichment analyses to identify coordinated metabolic shifts. In silico analysis of an additional sensitive and resistant HGSOC cell line validated the glutathione pathway upregulation seen in the patient-derived model. The functional contribution of the glutathione pathway on cisplatin resistance was evaluated following glutathione inhibition. Results: Chronic cisplatin exposure induced extensive metabolic rewiring in CR cells, characterized by enrichment of glutathione metabolism at both the metabolite and gene levels. Increased reduced glutathione was observed alongside upregulation of key enzymes involved in its de novo biosynthesis, recycling, and utilization, consistent with enhanced detoxification capacity relating to cisplatin-induced oxidative stress. Additionally, taurine was highly enriched, further highlighting a metabolic shift towards enhanced antioxidant mechanisms. CR cells also demonstrated an increase in NADPH-generating pathways, including amino acid metabolism and fatty acid β oxidation, to support redox balance and biosynthetic demands of increased glutathione metabolism. Transcriptional remodeling of the γ-glutamyl cycle further indicated a shift toward increased glutathione turnover, suggesting that the coordinated changes seen may define a metabolic state enhanced in oxidative stress tolerance and therapeutic resistance. These transcriptional changes were also seen in another model of platinum sensitivity/resistance, indicating a conserved response associated with platinum-induced resistance. Finally, concurrent cisplatin treatment and glutathione inhibition significantly increased sensitivity within the CR cells. Conclusions: These findings suggest that cisplatin-resistant cells, previously exposed to a platinum-based agent, may undergo distinct metabolic rewiring towards antioxidant pathways to survive chronic chemotherapeutic stress. Targeting components of these systems may represent a viable strategy to overcome platinum resistance and improve therapeutic outcomes. Full article

Triple-negative breast cancer (TNBC) lacks effective molecular targets, leading to poor prognosis. Previous computational methods to identify targets have suffered from low druggability, high complexity, and lack of robust validation. We propose a hybrid methodology combining Boolean network modeling with semidefinite programming (SDP) to analyze a TNBC cell line network. The resulting therapeutic pair underwent a multi-level validation framework, including Boolean simulations, statistical uncertainty quantification (bootstrap), sensitivity analysis, and orthogonal computational support from AlphaGenome, a deep learning model from Google DeepMind. Our analysis identified TK1 and VIM as a computationally robust therapeutic pair. Dual inhibition achieved 99.03% similarity to the apoptotic state with a 95% confidence interval of [98.79%, 99.26%], and was statistically superior to alternative pairs ($p<0.001$). The selection remained optimal across all tested model parameters, demonstrating high robustness. Importantly, the pair has full druggability because both targets have available specific inhibitors. Orthogonal computational evidence from AlphaGenome, stratified by mammary compartment, indicated that both targets exhibit moderate baseline expression in normal mammary epithelium (TK1 = 0.159, VIM = 0.143 in normalized RNA-seq units; n = 13 tracks per gene), with VIM showing a 2.2-fold higher expression in mammary stroma than in epithelium—a gradient consistent with its established role as a mesenchymal marker. Promoter-variant proxy analysis indicated near-zero transcriptomic perturbation upon simulated inhibition of either target in normal mammary epithelium (mean $|{log}_2\mathrm{FC}|<0.001$), supporting a favorable therapeutic window. Our methodology identified TK1–VIM as a computationally robust, druggable therapeutic candidate pair with biologically plausible mechanism of action. Gene-variability analysis identified TK1 and VIM as the highest-scoring candidates, with SDP optimization providing complementary, independent confirmation of this selection. This work provides a computationally grounded candidate strategy and a rigorous methodological benchmark for computational drug target identification; experimental validation remains an essential next step before clinical translation. Full article

Artificial intelligence (AI), virtual reality (VR), and exergame technologies have been increasingly used in dance and movement activities. However, this literature remains dispersed across different areas, making it difficult to determine how the field has developed. This study mapped the research landscape and thematic development of AI-, VR-, and exergame-based dance and movement research on psychological outcomes using bibliometric analysis and latent Dirichlet allocation (LDA) topic modeling. A total of 252 records indexed in the Web of Science Core Collection from 2011 to 2025 were included. Five related thematic strands were identified: immersive dance interaction and technology-supported teaching; rehabilitation-oriented dance or rhythm training; school-based exergaming and psychophysiological assessment; behavioral program design and intervention implementation; and AI-based motion or emotion recognition. These strands indicate that the field has developed into a multi-layered research space shaped by technology functions, movement contexts, intervention formats, and psychological constructs, rather than a single dance-intervention or technology-application domain. At the same time, psychological outcomes were not represented with equal clarity across these strands. Participation-related and psychosocial constructs, including enjoyment, motivation, engagement, self-efficacy, social interaction, emotional expression, and quality of life, were more frequently represented, whereas mental-health-related outcomes such as anxiety, depression, stress, loneliness, and psychological well-being were less consistently connected to technology-supported dance or movement interventions. These findings clarify where evidence is concentrated, how major themes are organized, and where psychological outcome measurement requires clearer theoretical and methodological specification. Future studies should use comparative and longitudinal designs to examine whether VR/AI-based feedback-supported movement training offers added value over conventional dance or movement programs for psychological outcomes, participation, exercise experience, and longer-term behavior change. Full article

Disease-modifying therapies (DMT)s) for relapsing-remitting multiple sclerosis (RRMS) act through distinct immunological mechanisms, yet the within-patient molecular response programs associated with these therapies remain incompletely defined. Here, we reanalyzed publicly available peripheral blood mononuclear cell (PBMC) miRNA microarray data (GSE230064) using a longitudinal, robustness-focused framework to compare therapy-associated miRNA response patterns following cladribine versus ocrelizumab treatment. Baseline (t0) and 6-month post-treatment (t1) samples were paired within individuals and technical replicates consolidated prior to analysis, yielding a final paired cohort of four cladribine-treated and six ocrelizumab-treated patients. Within each treatment arm, we quantified per-patient Δ-miRNA (t1 − t0) values and prioritized therapy-associated response features using a multi-evidence framework integrating effect direction, magnitude, directional consistency across individuals, and leave-one-out sensitivity. Cladribine treatment was associated with a highly coordinated, directionally concordant upregulation of five miRNAs including hsa-miR-27a-3p, hsa-miR-27b-3p, hsa-miR-503-5p, hsa-miR-148a-3p, and hsa-miR-26a-5p, all exhibiting 100% directional stability across patients and mean Δ-expression values ranging from +0.77 to +1.38. These miRNAs target pathways relevant to MS pathophysiology, including Th17/Treg balance, Wnt-β-catenin signaling, macrophage polarization, and epigenetic immune regulation. In contrast, ocrelizumab elicited a more selective response pattern, with five miRNAs including hsa-miR-100-5p, hsa-miR-410-3p, hsa-miR-432-5p, hsa-miR-296-5p, and hsa-miR-485-3p showing moderate directional stability (83%) and greater inter-individual heterogeneity, consistent with the more targeted mechanism of CD20+ B-cell depletion. Notably, the two treatment-associated signatures were non-overlapping, with hsa-miR-27b-3p representing the only miRNA shared with prior cross-sectional analyses of this dataset. The identified ocrelizumab-associated miRNAs implicate pathways including mTOR/IGF1R signaling, NF-κB regulation, RNA editing, and mitochondrial biogenesis, several of which are dysregulated in progressive MS. Together, these findings demonstrate that cladribine and ocrelizumab induce distinct, treatment-specific miRNA response architectures that reflect their divergent immunological mechanisms. This work establishes a stability-aware analytic template for extracting reproducible longitudinal miRNA signals from small paired RRMS cohorts and provides a ranked set of biologically plausible candidate miRNAs for prospective validation and mechanistic investigation. Full article

Background: Glioblastoma (GBM) is characterized by pronounced transcriptional plasticity and a highly structured immune microenvironment, yet the molecular features associated with tumor-state transitions and immune remodeling remain incompletely understood. Methods: We used an integrative multi-omics framework to examine how secreted phosphoprotein 1 (SPP1) relates to tumor microenvironment organization in human gliomas. Results: Single-cell analyses associated SPP1 with myeloid populations, mesenchymal-like (MES-like) malignant states, inflammatory regulatory programs, and inferred ligand–receptor co-expression patterns involving SPP1–CD44 and SPP1–integrin pairs. Spatial transcriptomic analyses showed that SPP1-high regions were enriched for estimated myeloid abundance, MES-like tumor signal, and ECM/angiogenic programs, supporting an SPP1-associated spatial mesenchymal–myeloid program in GBM. Computational perturbation analyses provided network-level support for SPP1–CD44-associated stress-responsive programs. HPA immunohistochemistry provided tissue-level protein context for SPP1 and related mesenchymal/receptor-associated components. Ivy GAP analysis showed enrichment of SPP1-associated features in core-like anatomic compartments, and CODEX spatial protein imaging provided antibody-panel-based contextual support for mesenchymal–myeloid-associated features. In the TCGA-GBM cohort, elevated SPP1 expression and an SPP1-associated mesenchymal signature were associated with poorer overall survival. Conclusions: These findings support an inferential model in which SPP1 is associated with spatial mesenchymal–myeloid organization in GBM and nominate SPP1-associated programs as candidate readouts of tumor plasticity, inflammatory myeloid remodeling, and spatial tumor microenvironment organization. Full article

Uterine smooth muscle tumors (USMTs) represent a diverse group of neoplasms arising from the myometrium, ranging from benign uterine fibroids (leiomyomas) to highly aggressive uterine leiomyosarcoma. While genetic alterations contribute to tumor development, growing evidence highlights the crucial role of epigenetic regulation in shaping tumor behavior. Among these mechanisms, histone modification has emerged as a key regulator of chromatin structure and gene expression. Histone modifications, including acetylation, methylation, phosphorylation, ubiquitination, ADP-ribosylation, and SUMOylation, are dynamically controlled by epigenetic regulators known as writers, erasers, and readers, which collectively modulate transcriptional programs involved in cell proliferation, differentiation, and stress responses. Recent studies indicate that dysregulation of histone-modifying enzymes contributes to the pathogenesis of USMTs by altering chromatin accessibility and transcriptional networks. In uterine fibroids, histone modifications are associated with hormone-responsive signaling pathways, extracellular matrix deposition, and abnormal smooth muscle cell proliferation. In contrast, uterine leiomyosarcoma exhibits extensive epigenetic reprogramming characterized by aberrant histone acetylation and methylation patterns, dysregulated chromatin regulators, and activation of oncogenic signaling pathways that promote tumor aggressiveness and genomic instability. Importantly, histone modifications interact with other epigenetic mechanisms, including DNA methylation, non-coding RNA–mediated regulation, and RNA epitranscriptomics, forming complex networks that influence tumor initiation and progression. This narrative review summarizes current knowledge on histone modification pathways and their roles in USMT biology, highlighting the functions of histone-modifying enzymes, their interactions with other epigenetic mechanisms, and their impact on tumor development. In addition, this review discusses emerging therapeutic strategies targeting epigenetic regulators, including inhibitors of histone deacetylases, histone methyltransferases, and readers, as well as potential epigenetic biomarkers for diagnosis and prognosis. Finally, this review outlines future research directions, including multi-omics integration, and advanced epigenomic technologies, which may provide deeper insights into the epigenetic landscape of USMTs and facilitate the development of personalized therapeutic approaches. Full article

Integer Linear Programming (ILP) is a powerful way to formulate sophisticated optimization models for making geospatial decisions in GIS. One of the general modeling constructs in ILP is the multi-level closest assignment (MLCA) constraint in the reliable facility location models with facility failure considerations. Compared with simpler constructs (such as the single-level closest assignment constraint), it involves assigning customers to backup facilities when the closer facility is unavailable. Part of the art of ILP modeling is to find suitable linear constructs to express such complex logic. The desired linear constructs may or may not exist. Even if a model construct is given, whether it can faithfully enforce the intended meaning is unknown. The correctness of the modeling construct is often shown based on informal reasoning or is not verified at all. Consequently, unverified ILP models may be (occasionally) infeasible or give wrong solutions. With the advancement of computerized theorem proving, it is becoming possible to mechanically prove the correctness of modeling constructs in ILP. In this article, we demonstrate that sophisticated model constructs such as MLCA can be proven using induction. This overcomes the inabilities of prior works to handle multiple levels of recursive definitions. Consequently, we are able to provide a first proof (formal or informal) that the specific MLCA form is mathematically correct. Given the generality of the induction method, we expect that it can be applied to prove the correctness of other types of models. Full article

Polycythemia vera (PV) is a JAK2V617F-driven myeloproliferative neoplasm characterized by erythroid expansion, increased thrombotic risk, and heterogeneous clinical outcomes. Although prior studies have described key transcriptional abnormalities—including Janus kinase–signal transducer and activator of transcription (JAK–STAT) hyperactivation and chronic myeloinflammation—most have examined single hematopoietic compartments. A multi-compartment approach may reveal conserved and lineage-specific disease-associated transcriptional programs. Here, an integrated, multi-compartment transcriptomic analysis of publicly available microarray datasets was performed, spanning bone marrow (BM) CD34+ progenitors, peripheral blood (PB) CD34+ progenitors, and whole blood from PV patients and healthy controls, with independent validation in neutrophils. Differential gene expression, pathway enrichment, and protein–protein interaction network analyses were used to delineate conserved versus compartment-specific transcriptional programs and to evaluate persistence of progenitor-derived signatures into mature myeloid cells. Across compartments, PV demonstrated consistent enrichment of inflammatory, interferon, and JAK–STAT-associated pathways despite limited overlap at the individual gene level, indicating that core disease processes are maintained through lineage- and differentiation-stage-specific transcriptional reprogramming. Network analysis identified highly connected hub genes, which were used to derive a single-sample gene set enrichment (ssGSEA) signature. This signature showed strong diagnostic performance across cohorts; remained enriched in PV neutrophils; and correlated with platelet count, indolent disease status, and reduced levels in post-splenectomy patients. Together, these findings support a model in which PV is driven by stable, progenitor-derived inflammatory programs that persist across myeloid differentiation while incorporating compartment-specific adaptations, and highlight the value of multi-compartment, network-based approaches for translational biomarker development. Full article

Economic traits in livestock and poultry arise from the intricate interplay between genetic inheritance and environmental factors, mediated largely by epigenetic regulation. Histone modifications, particularly methylation and acetylation, serve as fundamental epigenetic mechanisms that dynamically remodel chromatin architecture and regulate gene expression in response to developmental and environmental cues. By bridging the gap between static DNA sequences and complex phenotypes, these dynamic marks offer a novel perspective for elucidating trait formation. This review examines the regulatory roles of histone modifications in shaping key economic traits, focusing on skeletal muscle development, fat deposition, and reproductive performance. Furthermore, we highlight two prospective strategies for integrating histone modification data into modern breeding programs: utilizing comprehensive epigenomic maps as novel biomarkers for precision selection, and implementing targeted nutritional regimens to program early phenotypic development. Despite substantial mechanistic advances, critical challenges persist, including high detection costs, inherent tissue specificity, and the necessity to validate transgenerational stability. Looking forward, the integration of multi-omics approaches is anticipated to propel animal breeding beyond traditional genomic selection toward an era of precise epigenomic design. Full article

Targeted inhibition of the MAPK pathway with BRAF and MEK inhibitors (BRAFi/MEKi) produces rapid tumor regressions in BRAF V600-mutant melanoma, yet most patients ultimately develop acquired resistance. Resistance is not solely a tumor-cell-intrinsic phenomenon; it is accompanied by time-dependent remodeling of the tumor immune microenvironment (TIME) that can shape sensitivity to immune checkpoint inhibitors (ICIs) and inform rational combination or sequencing strategies. Early during MAPK inhibition, melanomas often display increased melanoma antigen expression and enhanced CD8⁺ T-cell infiltration, along with reduced immunosuppressive cytokines, suggesting a transient “immune-permissive” window. However, the same period can show induction of PD-L1 and T-cell exhaustion markers, foreshadowing adaptive immune resistance. At progression, immune-favorable features may diminish and immune evasion mechanisms, such as impaired antigen presentation and MHC-I downregulation, can become prominent and associate with resistance to immunotherapy. Here we review the temporal dynamics of TIME under MAPK inhibition, mechanistic links between resistance programs and immune remodeling, including signaling adaptation, focal adhesion/FAK signaling, dendritic cell dysfunction, antigen-presentation defects, and lymphatic/perilymphatic adipose remodeling, and practical biomarker opportunities across baseline, on-treatment, and progression timepoints. We also summarize emerging therapeutic strategies for post-resistance disease, including optimized ICI combinations, triple therapy concepts, and novel approaches such as combining FAK inhibition with RAF-MEK “clamp” therapy. Finally, we highlight key gaps and propose a framework for longitudinal sampling, standardized multi-omics integration, and TIME-informed trial design. The key distinguishing feature of this review is its time-resolved perspective on TIME remodeling, which links baseline immune contexture, early treatment-induced immune permissiveness, and the immune-evasive state that emerges during acquired resistance. Full article

Algae rarely occur as solitary phototrophs in nature or engineering; instead, they are embedded in complex bacterial consortia that control their physiology, productivity and ecological performance. The phycosphere, a microscale niche rich in algal exudates, promotes extensive metabolic exchange and chemical signaling, defining these associations. Bacteria capitalize on the dissolved organic carbon released by algae, providing growth supporting molecules such as vitamins, trace metals, and siderophores, as well as regenerated inorganic nutrients. Bidirectional beneficial interactions range from obligate mutualism to facultative commensalism and antagonism, depending on environmental context and community membership. Bacterial partners can stimulate algal growth, morphogenesis, and stress tolerance, as well as modulating defense and programmed cell death during the decline and bloom succession of algae resulting from algicidal taxa. Metabolic cooperation, QS signaling, extracellular enzyme activity, and chemically induced gene expression produce the exometabolome in the phycosphere, which in turn reprograms gene expression in all partners. Recent advances in multi-omics toolboxes, single-cell isotopic analyses, and microfluidics have greatly enhanced our understanding of the functional and spatiotemporal orientation of algal microbiomes. Ecologically, algal–bacterial interactions manage the phytoplankton community structure, control HABs, and modulate carbon and nutrient fluxes in both marine and freshwater realms. Biotechnologically, engineered algal–bacterial consortia are a promising tool for enhancing biomass production, stabilizing large-scale cultivation, improving wastewater treatment, and upgrading biofuels and fine chemicals. Despite these notable research advances, the context- and species-dependent complexity of multispecies interactions remains a major obstacle to their practical modeling and scalable implementation. Integrative research frameworks that combine molecular, ecological, and bioengineering approaches are urgently needed to unlock the full potential of sustainable applications in the future. Full article

Equality saturation explores equivalent program expressions via e-graphs, and its extraction step selects one representative per equivalence class to form an output tree. Standard extraction minimizes a decomposable per-node cost function that cannot capture multi-node structural patterns arising in SMT preprocessing and compiler instruction selection. We formalize pattern-aware extraction as a weighted pattern cover problem on AND-OR DAGs and establish its correspondence to tree covering in instruction selection. Three challenges arise when migrating tree covering to e-graphs: annotation ambiguity from multiple candidates per class, context-dependent selection from depth-2 templates, and DAG sharing conflict. We show that the coupled selection–tiling problem reduces to a tree DP with three mutually exclusive tile-role states, generalizing BURS tree covering from fixed trees to AND-OR DAGs. A bottom-up pass computes optimal DP values, and a top-down pass traces back decisions to produce the output tree. For template depth at most two, the algorithm computes an exact optimum in $O(N·K·|\mathcal{P}|·C_{\max })$ time. The evaluation targets extraction-level coverage, since end-to-end performance additionally depends on rewrite-rule design and saturation completeness. On SMT-COMP benchmarks, the algorithm achieves up to $31\times$ higher weighted pattern coverage than standard extraction. Depth-2 tiling contributes 45–51% additional improvement, with overhead within $1.5\times$ of standard extraction. Full article

The minimization of Exclusive-OR Sum-of-Products (ESOP) expressions in the case of multi-valued input, multi-output binary, and certain types of multi-valued output functions (MVESOP) is approached in this work through a novel nonlinear integer programming methodology. The devised method is applicable to both completely and incompletely specified functions and has the capacity to provide exact solutions of global optimality. A key general transformation converts the minimization problem from the MVESOP to the classical algebraic domain, resulting in a nonlinear integer program. Notably, for the challenging cases of incompletely specified functions, the problem becomes significantly simpler after this mapping. Several illustrative, analytical, and numerical examples are provided to demonstrate the implementation and performance of the approach. Full article