Search Results (2,774)

Quantitative real-time PCR (qPCR) remains a cornerstone method for analyzing gene expression due to its high sensitivity, specificity, and reproducibility. However, for reliable results in relative quantification studies, the choice of an appropriate reference gene is critical to ensure accurate normalization. The expression of commonly used reference genes can vary depending on developmental stage and experimental conditions, making their validation essential. To date, no validated reference genes have been reported for Agrostemma githago L. (corn cockle, Caryophyllaceae). To facilitate research on genes involved in natural product biosynthesis and specialized metabolism regulation, we aimed to identify the most stable reference genes across various plant organs and cultivation conditions of this species. Drawing on previous literature, we have selected seven housekeeping genes widely used for evaluation: actin, β-tubulin, elongation factor 1α, glyceraldehyde-3-phosphate dehydrogenase, histone H3, translation elongation factor 1, and eukaryotic translation initiation factor 5A1 (for which two primer sets were tested). The nucleotide sequences of these potential reference genes were identified from the A. githago transcriptome. Using qRT-PCR, transcript levels of seven potential reference genes were estimated in 40 different A. githago samples, including 25 in vitro samples under various treatment conditions and 15 soil-grown samples representing A. githago organs in different developmental stages. Expression stability of candidate reference genes was assessed using the RefFinder platform, which combines four commonly applied statistical algorithms: geNorm, NormFinder, BestKeeper, and the comparative Δ-Ct method. The results revealed that the selection of optimal reference genes varied based on the particular organ, developmental stage and condition being examined. TIF5A1-2 (one of the two primer pairs tested) and GAPHD consistently exhibited the most stable expression under various conditions in vitro. EF1α and H3 exhibited superior performance across different organs of soil-grown plants. Moreover, our integrated analysis enabled the identification of the two most stable, universal reference genes suitable for normalization in A. githago under all tested conditions—H3 and TIF5A1-2. Our work provides a robust foundation for future transcriptomic and functional studies of the specialized metabolism of A. githago and other related species. Full article

Background: Gastric signet-ring cell carcinoma (GSRCC) is a distinct subtype of gastric cancer characterized by unique biological features, leading to low rates of early diagnosis, poor prognosis, and limited response to chemotherapy and immunotherapy. Effective targeted therapies for GSRCC remain scarce. Given these treatment challenges and the potential efficacy of antibody-drug conjugates (ADCs) in clinical settings, this study focuses on identifying novel ADCs with significant potential to improve the treatment outcomes of GSRCC. Methods: We conducted a comprehensive bioinformatics analysis of GSRCC using multi-omics data (including transcriptomics and proteomics) and identified the poliovirus receptor (PVR) as a potential therapeutic target for GSRCC. We selected deruxtecan (DXd) as an effective carrier for developing an ADC targeting GSRCC. The synthesized PVR monoclonal antibody-DXd complex (PVR-DXd) has a drug-to-antibody ratio (DAR) of 4. Results: PVR-DXd demonstrated potent antitumor activity in a human GSRCC xenograft model, effectively eliminating tumors while sparing normal tissue, highlighting its potential as a novel and impactful targeted therapy for this aggressive subtype of gastric signet ring cell carcinoma. Conclusions: This preliminary study supports the further development of PVR-DXd as a candidate therapy for advanced GSRCC. Full article

Over the past two decades, pharmaceutical peptides have emerged as a powerful alternative to traditional small molecules, offering high potency, specificity, and low toxicity. However, most computational drug discovery tools remain optimized for small molecules and need to be entirely adapted to peptide-based compounds. Molecular docking algorithms, commonly employed to rank drug candidates in early-stage drug discovery, often fail to accurately predict peptide binding poses due to their high conformational flexibility and scoring functions not being tailored to peptides. To address these limitations, we present PepScorer::RMSD, a novel machine learning-based scoring function specifically designed for pose selection and enhancement of docking power (DP) in virtual screening campaigns targeting peptide libraries. The model predicts the root-mean-squared deviation (RMSD) of a peptide pose relative to its native conformation using a curated dataset of protein–peptide complexes (3–10 amino acids). PepScorer::RMSD outperformed conventional, ML-based, and peptide-specific scoring functions, achieving a Pearson correlation of 0.70, a mean absolute error of 1.77 Å, and top-1 DP values of 92% on the evaluation set and 81% on an external test set. Our PLANTS-based workflow was benchmarked against AlphaFold-Multimer predictions, confirming its robustness for virtual screening. PepScorer::RMSD and the curated dataset are freely available in Zenodo Full article

Recent advancements in prosthetic technology have increasingly focused on enhancing dexterity and autonomy through intelligent control systems. Vision-based approaches offer promising results for enabling prosthetic hands to interact more naturally with diverse objects in dynamic environments. Building on this foundation, the paper presents a vision-guided grasping algorithm for a prosthetic hand, integrating perception, planning, and control for dexterous manipulation. A camera mounted on the set up captures the scene, and a Bounding Volume Hierarchy (BVH)-based vision algorithm is employed to segment an object for grasping and define its bounding box. Grasp contact points are then computed by generating candidate trajectories using Rapidly-exploring Random Tree Star (RRT*) algorithm, and selecting fingertip end poses based on the minimum Euclidean distance between these trajectories and the object’s point cloud. Each finger’s grasp pose is determined independently, enabling adaptive, object-specific configurations. Damped Least Square (DLS) based Inverse kinematics solver is used to compute the corresponding joint angles, which are subsequently transmitted to the finger actuators for execution. Our intention in this work was to present a proof-of-concept pipeline demonstrating that fingertip poses derived from a simple, computationally lightweight geometric representation, specifically an AABB-based segmentation can be successfully propagated through per-finger planning and executed in real time on the Linker Hand O7 platform. The proposed method is validated in simulation, and experimental integration on a Linker Hand O7 platform. Full article

This paper proposes an efficiency-optimized multi-parameter collaborative non-dimensional selection method for industrial fans. Based on fan similarity theory, selection parameters are transformed into non-dimensional forms. The fan’s best working area (BWA) is defined according to stall margin, flow range, total pressure rise deviation, and minimum efficiency. The initial model selection uses the boundary equations of the defined BWA as screening criteria. Decision parameters comprise Euclidean distance, design point distance, pressure deviation, and current efficiency. These collectively form a multi-objective evaluation function. The NSGA-II algorithm determines the optimal weight distribution of decision parameters, generating a Pareto-optimal solution set. The initially selected models are subsequently subjected to secondary optimization through a comprehensive evaluation function. Selection case studies demonstrate that this method preliminarily screens 7 models that meet the target parameters from 400 candidate models. Secondary screening determines the model with the optimal efficiency and best comprehensive evaluation performance. The method effectively resolves the mismatch between fan model design points and target operational parameters in selection processes. This method integrates directly into selection software platforms and validation with 100 sets of fan selection parameters demonstrates that selected models achieve 99% accuracy. Achieving the secondary optimization function for fan model selection. Full article

Multiple Myeloma (MM) is an incurable malignancy that progresses from asymptomatic precursor stages—Monoclonal Gammopathy of Undetermined Significance (MGUS) and Smouldering Multiple Myeloma (SMM)—to active disease. Despite ongoing research, the molecular mechanisms driving this progression remain poorly understood. In this study, we aimed to uncover key regulatory factors involved in MM progression by integrating single-cell RNA sequencing (scRNA-seq) data with curated a priori biological knowledge of MM. To this end, we first integrated a priori knowledge from databases in a synthetic gene network map to play the role of an MM-related backbone to project findings from scRNA analysis on CD138⁺ Plasma Cells. This was followed by stage-specific regulatory network construction and analysis using Integrated Value of Influence (IVI) metrics to identify the most influential genes across disease stages. Our findings revealed GSK3B, RELA, CDKN1A, and PCK2 as central regulators shared across multiple stages of the disease. Notably, several of these genes had not previously been included in established MM gene sets, highlighting them as prime candidates for biomarkers and drug targets. Full article

Designing thermal–fluid devices that reduce peak temperature while limiting pressure loss is challenging because high-fidelity (HF) Navier–Stokes–convection simulations make direct HF-driven topology optimization computationally expensive. This study presents a two-dimensional, steady, laminar multifidelity topology design framework for thermal–fluid devices operating in a low-to-moderate Reynolds number regime. A computationally efficient low-fidelity (LF) Darcy–convection model is used for topology optimization, where SEMDOT decouples geometric smoothness from the analysis field to produce CAD-ready boundaries. The LF optimization minimizes a P-norm aggregated temperature subject to a prescribed volume fraction constraint; the inlet–outlet pressure difference and the P-norm parameter are varied to generate a diverse candidate set. All candidates are then evaluated using a steady incompressible HF Navier–Stokes–convection model in COMSOL 6.3 under a consistent operating condition (fixed flow; pressure drop reported as an output). In representative single- and multi-channel case studies, SEMDOT designs reduce the HF peak temperature (e.g., ~337 K to ~323 K) while also reducing the pressure drop (e.g., ~18.7 Pa to ~12.6 Pa) relative to conventional straight-channel layouts under the same operating point. Compared with a conventional RAMP-based pipeline under the tested settings, the proposed approach yields a more favorable Pareto distribution (normalized hypervolume 1.000 vs. 0.923). Full article

A novel fluorinated diamine monomer, 4,4′-((bicyclo[2.2.1]hept- 5-ene-2,3-diylbis (methylene)) bis(oxy))bis(3- (trifluoromethyl) aniline) (NFDA), featuring a tailored alicyclic norbornane core, flexible ether linkages, and pendant trifluoromethyl groups, was successfully synthesized. This monomer was polymerized with six commercial dianhydrides to produce a series of poly(amic acid) precursors, which were subsequently converted into high-performance polyimide (PI) films via a thermal imidization process. The strategic integration of the alicyclic, ether, and fluorinated motifs within the polymer backbone resulted in materials with an exceptional combination of properties. These PI films display outstanding solubility in a wide range of organic solvents, including low-boiling options like chloroform and tetrahydrofuran, highlighting their superior solution processability. The films are amorphous and exhibit remarkable hydrophobicity, evidenced by high water contact angles (up to 109.4°) and minimal water absorption (as low as 0.26%). Furthermore, they possess excellent optical transparency, with a maximum transmittance of 86.7% in the visible region. The materials also maintain robust thermal stability, with 5% mass loss temperatures exceeding 416 °C, and offer a desirable balance of mechanical strength and flexibility. This unique set of attributes, stemming from a rational molecular design, positions these polyimides as highly promising candidates for next-generation flexible electronics and advanced photovoltaics. Full article

Centralized UAV logistics controllers can achieve strong navigation performance in controlled settings, but they do not capture key deployment factors in crowdsourcing-enabled emergency logistics, where heterogeneous UAV owners participate with unreliability and dropout, and incentive expenditure and fairness must be accounted for. This paper presents a decentralized crowdsourcing multi-UAV emergency logistics framework on an edge-orchestrated architecture that (i) performs urgency-aware dispatch under distance/energy/payload constraints, (ii) tracks reliability and participation dynamics under stress (unreliable agents and dropout), and (iii) quantifies incentive feasibility via total payment and payment inequality (Gini). We adopt a hybrid decision design in which PPO/DQN policies provide real-time navigation/control, while GA/ACO act as planning-level route refinement modules (not reinforcement learning) to improve global candidate quality under safety constraints. We evaluate the framework in a controlled grid-world simulator and explicitly report stress-matched re-evaluation results under matched stress settings, where applicable. In the nominal comparison, centralized DQN attains high navigation-centric success (e.g., 0.970 ± 0.095) with short reach steps, but it omits incentives by construction, whereas the proposed crowdsourcing method reports measurable payment and fairness outcomes (e.g., payment and Gini) and remains evaluable under unreliability and dropout sweeps. We further provide a utility decomposition that attributes negative-utility regimes primarily to collision-related costs and secondarily to incentive expenditure, clarifying the operational trade-off between mission value, safety risk, and incentive cost. Overall, the results indicate that navigation-only baselines can appear strong when participation economics are ignored, while a deployable crowdsourcing system must explicitly expose incentive/fairness and robustness characteristics under stress. Full article

Ovarian cancer (OC), the third most common gynecologic malignancy, is characterized by high mortality largely driven by chemotherapy resistance, leading to recurrence and metastasis. Using transcriptomic data from GSE73935, we constructed a weighted gene co-expression network and identified eight hub genes (IGF1R, CDH2, PDGFRA, CDKN1A, SHC1, SPP1, CAV1 and FGF18) associated with cisplatin resistance, among which CDH2 emerged as the most clinically relevant candidate. CDH2 demonstrated moderate diagnostic potential (AUC = 0.792) and was markedly upregulated in cisplatin-resistant A2780/CP70 cells. Independent validation using clinical single-cell RNA-seq data (GSE211956) confirmed its selective enrichment in resistant tumor cell subpopulations. Gene set enrichment analysis linked elevated CDH2 expression to p53 signaling, DNA replication, nucleotide excision repair, and Toll-like receptor pathways, with qPCR supporting upregulation of key downstream genes in resistant cells. Immune deconvolution further indicated that high CDH2 expression correlated with increased infiltration of NK cells, Tregs, macrophages, and neutrophils, and immunohistochemistry verified CDH2 overexpression in cisplatin-resistant tissues. In addition, virtual screening and drug sensitivity profiling identified several FDA-approved agents with potential relevance to CDH2-associated drug response. These findings indicate that CDH2 may serve as a candidate marker associated with cisplatin response in OC, and its association with immune cell infiltration provides further insight into mechanisms potentially underlying chemoresistance. Full article

To address the challenge of effectively extracting early-stage failure features in rolling bearings, this paper proposes a two-stage harmonic optimization-gram method based on spectral amplitude modulation (SAM-TSHOgram). The method first employs amplitude spectra with varying weighting exponents to preprocess the signal, performing nonlinear adjustments to the vibration signal’s spectrum to enhance weak periodic impact characteristics. Subsequently, a two-stage evaluation strategy based on spectral coherence (SCoh) was designed to adaptively identify the optimal frequency band (OFB). The first stage employs the Periodic Harmonic Correlation Strength (PHCS) metric, based on autocorrelation, to coarsely screen candidate bands with strong periodic structures. The second stage utilizes the Sparse Harmonic Significance (SHS) metric, based on spectral negative entropy, to refine the candidate set, selecting bands with the most prominent harmonic features. Finally, SCoh is integrated over the selected OFB to generate an Improved Envelope Spectrum (IES). The proposed method was validated using both simulated and experimental vibration signals from bearings and gearboxes. The results demonstrate that SAM-TSHOgram significantly outperforms conventional approaches such as EES, Fast Kurtogram, and IESFOgram in terms of signal-to-noise ratio (SNR) enhancement, harmonic clarity, and diagnostic robustness. These findings confirm its potential for reliable early fault detection in rolling bearings. Full article

Background/Objectives: DNA methylation is a key epigenetic process that regulates gene expression and is often disrupted in colorectal cancer (CRC). Aberrant methylation of promoter CpG islands can silence tumor suppressor genes and drive tumorigenesis. A subset of CRCs exhibits the CpG Island Methylator Phenotype (CIMP), characterized by widespread hypermethylation and distinct clinical outcomes. Identifying genes whose expression is epigenetically regulated by methylation is important for prioritizing candidate biomarkers and therapeutic targets in CRC. Methods: We developed and compared a series of computational approaches to identify genes whose expression is regulated by DNA methylation in The Cancer Genome Atlas (TCGA) cohort of Colon Adenocarcinoma (COAD) patients. Samples were stratified according to their CpG Island Methylator Phenotype (CIMP) level to capture distinct epigenetic subgroups. The proposed framework integrates methylation and transcriptomic data to systematically detect methylation–expression associations indicative of epigenetic regulation. Results: The best-performing method identified gene sets strongly associated with promoter methylation–expression relationships and enriched for pathways relevant to colorectal cancer progression and patient stratification. To evaluate the robustness and transferability of the approach, it was further validated on independent datasets, including Stomach Adenocarcinoma (STAD), Glioblastoma Multiforme (GBM), and Mesothelioma (MESO), supporting its robustness and potential generalizability across multiple tumor types. Conclusions: Our study highlights the potential of computational pipelines to uncover epigenetically regulated genes in colorectal cancer. The identified candidate genes provide a hypothesis-generating foundation for refining molecular stratification and guiding future studies aimed at epigenetic biomarker discovery and therapeutic hypothesis development. Full article

Norovirus (NoV) and sapovirus (SaV) are major viral pathogens causing acute gastroenteritis (AGE) in both children and adults in developed countries and are also responsible for large-scale outbreaks. However, in Quebec, Canada, there are limited and updated data with respect to the genotypes circulating and implicated in outbreaks, particularly for SaV. This study aimed to investigate the genetic diversity and genotype predominance of NoVs and SaVs associated with AGE outbreaks in Quebec, Canada. Confirmed NoV and SaV outbreaks from long-term care facilities and hospital settings between September 2011 and April 2016 were investigated (n = 252). NoVs and SaVs were genetically diverse: 21 RdRp-capsid combinations were identified, of which 10 are recombinants. NoV GII.4 New Orleans[P4 NewOrleans] was the predominant genotype from 2011 to 2013, and GII.4 Sydney[P31] was the predominant genotype from 2013 to 2015. In 2015–2016, no single genotype predominated; instead, GII.17[P17], GII.4 Sydney[P16], GII.4 Sydney[P31], and SaV GI.2 strains were co-circulating at similar frequencies. Notably, emerging global genotypes including GII.17[P17], GII.4 Sydney[P16], GII.2[P16], and GII.4 San Francisco[P31] were detected for the first time in Quebec. These findings may contribute to an enhanced understanding of NoV and SaV infection and spread, and to the development of candidate vaccines. Full article

Acrolein, a highly reactive environmental toxicant widely present in urban air and tobacco smoke, has been implicated in the development of multiple malignancies. In oral tissues, chronic acrolein exposure induces oxidative stress, inflammation, and genetic mutations, all of which are closely linked to the development of oral squamous cell carcinoma (OSCC). Although accumulating evidence indicates a strong association between acrolein exposure and OSCC, its prognostic significance remains poorly understood. In this study, we analyzed transcriptome data to identify differentially expressed genes (DEGs) between tumor and adjacent normal tissues, and screened acrolein-related candidates by intersecting DEGs with previously identified acrolein-associated gene sets. Functional alterations of these genes were assessed using Gene Set Variation Analysis (GSVA), and a protein–protein interaction (PPI) network was constructed to identify key regulatory genes. A prognostic model was developed using Support Vector Machine–Recursive Feature Elimination (SVM-RFE) combined with LASSO-Cox regression and validated in an independent external cohort. Among the acrolein-related DEGs, four key genes (PLK1, AURKA, CTLA4, and PPARG) were ultimately selected for model construction. Kaplan–Meier analysis showed significantly worse overall survival in the high-risk group (p < 0.0001). Receiver operating characteristic (ROC) curve analysis further confirmed the strong predictive performance of the model, with area under the curve (AUC) values of 0.72 at 1 year, 0.72 at 3 years, and 0.75 at 5 years. Furthermore, the high risk score was significantly correlated with a ‘cold’ immune microenviroment, suggesting that acrolein-related genes may modulate the tumor immune microenvironment. Collectively, these findings highlight the role of acrolein in OSCC progression, suggesting the importance of reducing acrolein exposure for cancer prevention and public health, and call for increased attention to the relationship between environmental toxicants and disease initiation, providing a scientific basis for public health interventions and cancer prevention strategies. Full article

Automated detection of impact craters is essential for planetary surface studies, yet it remains a challenging task due to variable morphology, degraded rims, complex geological settings, and inconsistent illumination conditions. This study presents a novel crater detection methodology designed for large-scale analysis of Lunar Reconnaissance Orbiter Wide-Angle Camera (WAC) imagery. The framework integrates several key components: automatic region-of-interest (ROI) selection to constrain the search space, Canny edge detection to enhance crater rims while suppressing background noise, and a modified Hough transform that efficiently localizes elliptical features by restricting votes to edge points validated through local fitting. Candidate ellipses are then refined through a two-stage adjustment, beginning with L1-norm fitting to suppress the influence of outliers and fragmented edges, followed by least-squares optimization to improve geometric accuracy and stability. The methodology was tested on four representative Wide-Angle Camera (WAC) sites selected to cover a range of crater sizes (between ~1 km and 50 km), shapes, and geological contexts. The results showed detection rates between 82% and 91% of manually identified craters, with an overall mean of 87%. Covariance analysis confirmed significant reductions in parameter uncertainties after refinement, with standard deviations for center coordinates, shape parameters, and orientation consistently decreasing from the L1 to the L2 stage. These findings highlight the effectiveness and computational efficiency of the proposed approach, providing a reliable tool for automated crater detection, lunar morphology studies, and future applications to other planetary datasets. Full article