Search Results (6,086)

Tooth development or odontogenesis is a complex morphogenetic process that requires tightly regulated interactions between the oral epithelium and mesenchyme of neural crest origin. In this narrative review, we compile existing knowledge regarding gene regulatory networks and epigenetic factors throughout tooth development from initiation to eruption. Signaling between the epithelium and mesenchyme is mediated by four conserved pathways—Wnt/β-catenin, bone morphogenetic protein (BMP), fibroblast growth factor (FGF), and Sonic hedgehog (Shh)—which operate iteratively and interact through extensive crosstalk at each developmental stage. Transcription factors, such as PAX9, MSX1, PITX2, and LEF1, interpret these signals to control cell fate decisions and differentiation. Epigenetic modifications, including DNA methylation, histone modifications, and microRNA-mediated regulation, provide additional layers of control that fine-tune gene expression programs. Unlike existing reviews that address these regulatory mechanisms separately, here we integrate signaling pathways, transcription factor networks, epigenetic regulation, human genetic disorders, dental stem cell biology, and recent single-cell transcriptomic insights into a unified framework. We discuss opportunities to apply developmental biology knowledge towards regenerative dentistry goals, including iPSC-derived dental models and spatially resolved multi-omics approaches, while acknowledging the considerable gap between preclinical findings and clinical applications. Full article

Background: Breast cancer (BC) is one of the most diagnosed malignancies and a leading cause of cancer-related mortality among women worldwide, thereby posing a substantial threat to women’s health worldwide. However, clinically robust diagnostic biomarkers with high sensitivity and specificity, as well as well-validated molecular targets for targeted therapy, remain limited. Methods: BC transcriptomic data from seven GEO datasets and the TCGA-BRCA cohort (n = 1231) were integrated for analysis. After batch-effect correction, candidate genes were screened through DEA, WGCNA, and PPI networks analysis. An ensemble machine learning (ML) framework incorporating 127 algorithmic combinations was constructed, and SHAP analysis was applied to identify hub genes. Further analyses included functional enrichment, immune infiltration, miRNA regulatory network analysis, and SMR analysis. The expression patterns were validated using single-cell transcriptome data. Drug repositioning analysis and AI-assisted virtual screening were performed to prioritize compounds with favorable drug-like properties. The predicted binding modes of candidate compounds with CHEK1 were assessed by molecular docking. Results: Thirty core genes were obtained through differential expression, WGCNA, and PPI screening. Integrated ML (127 algorithms) determined the optimal model (AUC = 0.919), and SHAP identified nine feature genes, among which CHEK1 and KIF23 showed preliminary diagnostic potential across four external cohorts (AUC: 0.625–0.938). Functional enrichment indicated that both are enriched in the cell cycle and p53 pathways, closely associated with BRCA1/ATR; immune infiltration revealed significant correlations with macrophages and CD8⁺ T cells, with hsa-miR-15a-5p and hsa-miR-607 being common upstream regulatory miRNAs. SMR analysis supported a causal relationship between CHEK1 expression and BC genetic susceptibility (p_SMR < 0.05, p_HEIDI > 0.05); single-cell analysis confirms its heterogeneous expression. AI-assisted virtual screening identified 25 A-grade computational candidate compounds from 171 candidates. Molecular docking suggested that Olaparib and LY294002 can form favorable interactions with the CHEK1 active pocket. Conclusions: The study identified CHEK1 as a key diagnostic gene for BC through 127 ML algorithms and SMR causal inference. By combining AI-assisted virtual screening and molecular docking, computational candidate compounds targeting CHEK1 were prioritized. These findings represent hypothesis-generating in silico predictions and require experimental validation before any therapeutic conclusions can be drawn. Full article

The aim of this paper is to test and compare different neuro-evolution methods to train a simulated biped walker to learn to walk. After this step, the best neuro-evolution technique is ported to a real biped, which would train itself to walk. The goal is to reduce the number of falls for the real biped in order to avoid destroying the physical unit. The following four neuro-evolution methods were tested: Deep Q-Learning (DQN), NeuroEvolution of Augmenting Topologies (NEAT), Deep Deterministic Policy Gradients (DDPG), and Augmented Random Search (ARS). The best results from simulations were obtained with the ARS method, but the fastest and easiest to implement on the real biped was the NEAT algorithm. Full article

Daily, monthly, and annual multi-product sales records are stored in databases, but due to the massive amounts of data, they are not used for decision-making when updating product catalogs. Meanwhile, the use of artificial intelligence in business is increasing across all sectors of the economy. Large-scale data handling can be achieved using artificial intelligence techniques. Specifically, ABC inventory classification currently employs artificial intelligence techniques, including neural networks, fuzzy systems, and genetic algorithms. However, a state-of-the-art review has not found any research using vision techniques to classify ABC inventories. To address this gap, this research presents a novel approach to the intelligent classification of a company’s multiple products, using ABC. Recent vision system research often uses the Otsu method or its variants to determine the optimum threshold for binary image segmentation. Unlike this approach, our research does not use a single threshold value; instead, it uses the full binary frequency histogram as an image representation. From this, eight invariant characteristics are extracted from translation, rotation, and scale. The results show that the classification is accurate, clear, and simple as a decision-making tool. The proposed method is general and can be used in any production sector and at any enterprise size. Full article

Both breed and plumage color are considered potential genetic factors influencing meat quality in pigeons, yet their independent effects remain poorly distinguished. This study aimed to disentangle the regulatory roles of breed and plumage color on meat quality, nutritional composition, and flavor-related metabolites in meat pigeons. White-feathered (SQB) and grey-feathered (SQH) Shiqi pigeons were compared with European Mimas white pigeons (MMS) under identical rearing conditions. Slaughter performance, meat quality traits, and flavor profiles were assessed, followed by untargeted metabolomics and transcriptomics sequencing of pectoral muscle tissues. The results demonstrated that breed exerted a significant influence on carcass traits, water-holding capacity, collagen content, as well as the composition of fatty acids and free amino acids. In contrast, no notable disparity in meat quality was observed between the white- and gray-feathered varieties within the same Shiqi pigeon breed. A total of 114 and 205 differentially expressed metabolites (DEMs), and 11 and 327 differentially expressed genes (DEGs) were identified in plumage color and breed comparisons, respectively. Key flavor-associated metabolites, including glutathione, L-histidine, L-carnosine, and cytidine-5′-monophosphate, were identified as candidate biomarkers for breed-specific flavor differentiation. Breed is the dominant genetic factor determining meat quality and flavor in meat pigeons, while plumage color variation within breed has a limited impact. The identified pathways and regulatory networks provide actionable targets for the precision breeding and flavor enhancement of local pigeon breeds. Full article

Acoustic divergence is widely recognized as a key driver of speciation and niche differentiation in vocal animals. In echolocating horseshoe bats (Rhinolophus), the larynx is specialized for producing high-duty-cycle signals used in foraging, navigation, and species recognition. While the ecological role of echolocation is established, the molecular mechanisms regulating laryngeal frequency remain unclear. We compared the laryngeal transcriptomes of three closely related, sympatric Rhinolophus species with distinct resting frequencies (RFs): R. episcopus (~46 kHz), R. siamensis (~66 kHz), and R. osgoodi (~85 kHz). This comparison identified 511 differentially expressed genes. High-frequency species upregulated genes involved in cytoskeletal dynamics and muscle contraction, such as cell adhesion molecules and motor proteins, while low-frequency species upregulated genes related to cellular homeostasis and metabolic maintenance. Weighted gene co-expression network analysis revealed two RF-correlated modules: a high-frequency module enriched in aerobic respiration and carbon metabolism and a low-frequency module enriched in lipid metabolism. Protein–protein interaction analysis identified ACTC1, vital for muscle contraction, as a hub gene. Evolutionary analysis showed that ACTC1 is highly conserved, with no significant positive selection, indicating that transcriptional regulation, rather than coding-sequence divergence, is the primary driver of the observed functional differences. These findings suggest that RF variation likely results from transcriptional remodeling in laryngeal superfast muscles. This study provides the first transcriptomic evidence linking laryngeal gene expression with acoustic divergence and offers new insights into the genetic basis of bat echolocation. Full article

Phosphorus (P) deficiency severely limits the growth and yield of crop plants, and anthocyanin accumulation is a key adaptive physiological response to low-P stress. However, the role of MYB transcription factors in regulating anthocyanin biosynthesis under P-deficient conditions and the application of favorable haplotypes in foxtail millet low-P tolerance breeding remain unclear. Here, we performed genome-wide identification of SiMYB genes, elucidated their evolutionary characteristics, and identified key members regulating anthocyanin accumulation under P deficiency to provide genetic resources and a theoretical basis for foxtail millet molecular breeding aimed at improving nutrient use efficiency. Specifically, a total of 229 SiMYB genes were identified in the foxtail millet genome and classified into three subgroups, with the R2R3-MYB subfamily accounting for 59.8%. Phylogenetic and synteny analyses across 15 plant species revealed diverse divergence times and complex relationships, with 29 R2R3-MYB genes showing conserved collinearity with rice and maize orthologs. Association analysis using 196 foxtail millet accessions showed that 38 single nucleotide polymorphisms (SNPs) from 16 SiMYB genes were significantly associated with leaf anthocyanin content under P deficiency (p < 0.001). Notably, the SiMYB169 gene exhibited differential tissue expression and was highly upregulated in the leaves of a P-tolerant genotype after 24 h of P deficiency treatment. Furthermore, accessions carrying the favorable G allele of SiMYB169 showed significantly higher anthocyanin accumulation under P deficiency (p < 0.01). Network prediction analysis found that SiMYB169 interacted with key genes and multiple transcription factors in the biosynthesis pathway of anthocyanin. These findings highlight SiMYB169 as an evolutionarily conserved regulator that modulated anthocyanin biosynthesis under P-deficient conditions. Full article

Bipolar disorder (BD) is characterized by mood swings between mania and depression, sharing overlapping symptomatic and genetic risk factors with other mood disorders. Long non-coding RNAs (lncRNAs) show specific spatiotemporal precision in distinct cell types in the human brain, and understanding the precise mechanisms of lncRNAs in mood switching in BD is fundamental to deciphering the key molecular networks underlying BD diagnosis and therapy. In this review, we summarize the classification of BD subtypes, the differences between BD and multiple mood disorders, and the functional potential of lncRNAs in BD. Future studies of these lncRNAs will facilitate the development of RNA-based diagnosis for BD. Full article

Drought, a major abiotic stressor affecting global agricultural productivity, significantly reduces crop yields and threatens food security worldwide. As the primary organ for perceiving soil moisture signals and absorbing water, the crop root system architecture plays a pivotal role in plant adaptation to drought conditions. With the development of high-throughput imaging technologies (i.e., 2D/3D image acquisition), high-throughput genotyping platforms, and gene-editing technologies, significant progress has been achieved in the characterization of root traits and the dissection of molecular genetic regulatory networks underlying these traits in crops. This review comprehensively synthesizes recent advances in the phenotypic characterization, underlying molecular regulatory networks, and functional roles of key root architectural traits, including the root length, angle, density, and root hair development, in enhancing drought resilience. Finally, we discuss the existing challenges in the current research and provide an outlook on the future trend of integrating multi-omics, high-throughput phenomics, and genome editing technologies to breed new drought-resistant crop varieties with ideal drought-resistant root architectures. Full article

This study contributes to the modeling and optimization model for reverse osmosis water treatment (ROWT) due to a lack of economic and operational aspects. This study proposes a hybrid modeling and optimization framework using a hybrid artificial neural network (ANN) and genetic algorithm (GA) to enhance the accuracy of economic and operational predictions for ROWT. The ANN model is developed using seventeen key process parameters extracted from various ROWT plants, including flow rate, pH, conductivity, and turbidity. The GA is employed to optimize the network architecture and learning parameters based on the mean absolute percentage error (MAPE) as the fitness function. The findings of this study indicate that the GA-optimized model significantly outperforms the baseline model, reducing MAPE for the economic aspect (84.9% improvement) and the operational aspect (32.2% improvement). The findings from this study indicate that the hybrid ANN–GA approach is a management decision-making tool for reducing expenses without compromising water quality in ROWT management. The practical implications of this study are that predictions not only meet operational parameters but also predict expenses incurred, allowing managers to plan future budgets by optimizing ROWT resources and maintenance activities. Full article

Hair follicles are highly specialized mini-organs within the skin that drive the production of wool and cashmere, traits of major biological and economic importance in sheep and goats. Despite their microscopic size, hair follicles exhibit extraordinary regulatory complexity, integrating genetic programs with seasonal, endocrine, environmental, and epigenetic cues. Although transcriptional networks and signaling pathways underlying follicle morphogenesis and cycling have been extensively investigated, the post-transcriptional mechanisms that fine-tune these processes remain insufficiently understood. MicroRNAs (miRNAs) have emerged as pivotal post-transcriptional regulators that coordinate cell fate determination, lineage commitment, and tissue homeostasis. Growing evidence indicates that miRNAs play essential roles in hair follicle stem cell maintenance, proliferation, differentiation, apoptosis, and organ-level development, functioning through interconnected regulatory networks rather than isolated linear pathways. By modulating the expression of key follicle-determining genes and signaling components, miRNA-mediated regulation shapes follicle formation, cyclic regeneration, and fiber traits. In this review, we synthesize recent advances in miRNA research related to hair follicle biology, with a particular focus on wool- and cashmere-bearing mammals. We integrate findings across species to propose a systems-level framework in which miRNA networks interface with canonical signaling pathways and epigenetic mechanisms to orchestrate follicle development and regeneration. Conserved and species-specific regulatory principles are discussed to bridge fundamental follicle biology with practical applications in fiber production. Overall, this review highlights miRNAs as a critical yet previously underappreciated regulatory layer in hair follicle biology. A deeper understanding of miRNA-mediated control provides new conceptual insights into wool and cashmere development and offers a foundation for future molecular breeding and precision regulation strategies in livestock. Full article

Background: Lipoprotein(a) [Lp(a)] is a genetically determined risk factor for atherosclerotic cardiovascular disease (ASCVD). Proteomic studies suggest that Lp(a)-associated proteins mediate inflammation, thrombosis, and vascular calcification, but methodological variability may influence proteome definition. Methods: Lp(a) was immunoprecipitated from human plasma using an apo(a)-specific monoclonal antibody and analyzed by mass spectrometry following either in-gel digestion or automated in-solution proteolysis. Proteins identified by ≥3 unique peptides and consistently detected across all samples by both methods were considered high confidence. Functional enrichment and interaction networks were assessed using STRING. Results: In-solution proteolysis identified 92 proteins and in-gel digestion identified 55 proteins, with 34 proteins shared between methods. These high-confidence proteins were enriched for pathways involved in lipoprotein remodeling, coagulation regulation, vesicle-mediated transport, lipid binding, and extracellular matrix organization, providing biological insight into mechanisms linking Lp(a) to inflammation, thrombosis, and calcification. Conclusions: Proteome composition of Lp(a) is method-dependent; however, a rigorously defined core proteome of 34 proteins was consistently identified across analytical approaches, highlighting biologically relevant pathways that may underlie Lp(a)-mediated ASCVD risk. Full article

As mobile networks increasingly support sustainable and green Internet of Things (IoT) applications, energy-efficient solutions that address coverage constraints have become paramount. Although backscatter communication (BackCom) offers a low-power option for IoT devices, particularly battery-less IoT nodes, it can suffer from limited coverage. To overcome this, we exploit aerial platforms (UAVs) integrated with non-orthogonal multiple access (NOMA) to enhance both coverage and spectral efficiency. In this paper, we propose a UAV-supported NOMA-enabled BackCom system to serve massive backscatter node (BN) networks. We aim to maximize system throughput by jointly optimizing the power allocation and reflection coefficients of the BNs, along with the trajectory and data collection locations of the UAV. We derive closed-form solutions for the reflection coefficients and the optimal collection locations of the UAV and achieve global optimality in power allocation by utilizing the Karush–Kuhn–Tucker (KKT) optimality conditions in conjunction with the golden-section search (GSS). In addition, we formulate the UAV trajectory optimization problem as a Traveling Salesman Problem (TSP) and propose an efficient low-complexity genetic algorithm (GA)-based solution. The numerical results demonstrate that the proposed scheme outperforms the benchmark schemes in terms of sum-throughput rate and achieves an overall performance enhancement of $8.983$ dB, underscoring the potential of our approach for large-scale battery-less IoT deployments. Full article

Therapeutic termination of pregnancy (TToP) represents an intervention that is performed for medical reasons, such as risks to maternal health or severe fetal anomalies. Advances in prenatal screening and diagnostic tools—including serum markers, ultrasound, cell-free fetal DNA, chorionic villus sampling and amniocentesis—have significantly improved early detection and clinical decision-making. This narrative review synthesizes current knowledge on the genetic, environmental and psychosocial determinants that influence the decision of the patients to pursue TToP. The literature search was performed primarily using PubMed database, while Scopus and Google Scholar were used to identify additional relevant studies. Some of the selected studies, as well as certain sections of this review, address both therapeutic and voluntary termination of pregnancy, whereas others focus exclusively on TToP. Moreover, this review describes the types of abortion (medical or surgical/aspiration) along with their management strategies to prevent or address potential complications. It is well known that demographic, cultural and socio-economic factors continue to influence the access to TToP, as well as the perceptions of it. Psychiatric comorbidities (such as anxiety, affective and psychotic disorders) are observed with a higher prevalence among women undergoing TToP and may influence both the decision and psychological outcomes post-procedure. While most women report emotional relief after TToP, some of them experience depression, post-traumatic stress disorder or substance misuse. Legal and ethical considerations further complicate access to safe abortion, leading to situations where patients may resort to unsafe procedures, which result in higher rates of morbidity and mortality. Data from the EUROCAT network show rising trends in congenital anomalies like trisomy 13, trisomy 18 and caudal regression syndrome (conditions commonly associated with TToP). Therefore, it is mandatory to form a multidisciplinary team in these cases, integrating medical, psychological and ethical dimensions. Ensuring safe, evidence-based and compassionate access to TToP remains a critical component of reproductive healthcare. Full article

WRKY is a crucial transcription factor involved in plant growth, development, and responses to abiotic stress. In the present study, a total of 182 AaWRKY transcription factor members were identified across the Artemisia argyi genome and found to be distributed across 17 chromosomes. Evolutionary analysis revealed that segmental duplication served as the primary driver for family expansion, with the evolutionary trajectory shaped by strong purifying selection (Ka/Ks < 1.0). Phylogenetic classification categorized these members into seven highly conserved subgroups, while physicochemical analysis indicated that most AaWRKYs are unstable, hydrophilic proteins, consistent with the rapid turnover required for transcriptional switches. Transcriptomic profiling unveiled significant tissue-specific expression patterns, with over 50% of the members predominantly enriched in roots and specific genes, such as AaWRKY11, implicated in the regulation of leaf senescence. Protein–protein interaction (PPI) network analysis identified AaWRKY110 as a central regulatory hub linking the MAPK signaling pathway with the isoflavonoid biosynthetic machinery. Furthermore, comparative transcriptomic analysis between aphid-resistant (Ai20K) and susceptible (Ai72G) cultivars demonstrated that resistance is conferred by a priming mechanism involving high basal expression of key candidates, including AaWRKY82, 108, 128, and 71. In contrast, the susceptible genotype exhibited a delayed and ineffective hypersensitive-like response. Collectively, these findings elucidate the evolutionary dynamics of the AaWRKY family and provide critical genetic targets for the concurrent improvement of medicinal metabolite accumulation and biotic stress resilience in Artemisia argyi via molecular breeding. Full article