Search Results (24,926)

Quantum machine learning (QML) has emerged as a promising approach for enhancing image classification by exploiting quantum computational principles such as superposition and entanglement. However, practical applications on complex datasets like CIFAR-100 remain limited due to the low expressivity of shallow circuits and challenges in circuit optimization. In this study, we propose HQCNN–REGA—a novel hybrid quantum–classical convolutional neural network architecture that integrates data re-uploading and genetic algorithm optimization for improved performance. The data re-uploading mechanism allows classical inputs to be encoded multiple times into quantum states, enhancing the model’s capacity to learn complex visual features. In parallel, a genetic algorithm is employed to evolve the quantum circuit architecture by optimizing gate sequences, entanglement patterns, and layer configurations. This combination enables automatic discovery of efficient parameterized quantum circuits without manual tuning. Experiments on the MNIST and CIFAR-100 datasets demonstrate state-of-the-art performance for quantum models, with HQCNN–REGA outperforming existing quantum neural networks and approaching the accuracy of advanced classical architectures. In particular, we compare our model with classical convolutional baselines such as ResNet-18 to validate its effectiveness in real-world image classification tasks. Our results demonstrate the feasibility of scalable, high-performing quantum–classical systems and offer a viable path toward practical deployment of QML in computer vision applications, especially on noisy intermediate-scale quantum (NISQ) hardware. Full article

To address the inefficiency of traditional concrete crack detection methods and the heavy reliance of supervised learning on extensive labeled data, in this study, an intelligent assessment method of concrete damage based on pseudo-label semi-supervised learning and fractal geometry theory is proposed to solve two core tasks: one is binary classification of pixel-level cracks, and the other is multi-category assessment of damage state based on crack morphology. Using three-channel RGB images as input, a dual-path collaborative training framework based on U-Net encoder–decoder architecture is constructed, and a binary segmentation mask of the same size is output to achieve the accurate segmentation of cracks at the pixel level. By constructing a dual-path collaborative training framework and employing a dynamic pseudo-label refinement mechanism, the model achieves an F1-score of 0.883 using only 50% labeled data—a mere 1.3% decrease compared to the fully supervised benchmark DeepCrack (F1 = 0.896)—while reducing manual annotation costs by over 60%. Furthermore, a quantitative correlation model between crack fractal characteristics and structural damage severity is established by combining a U-Net segmentation network with the differential box-counting algorithm. The experimental results demonstrate that under a cyclic loading of 147.6–221.4 kN, the fractal dimension monotonically increases from 1.073 (moderate damage) to 1.189 (failure), with 100% accuracy in damage state identification, closely aligning with the degradation trend of macroscopic mechanical properties. In complex crack scenarios, the model attains a recall rate (Re = 0.882), surpassing U-Net by 13.9%, with significantly enhanced edge reconstruction precision. Compared with the mainstream models, this method effectively alleviates the problem of data annotation dependence through a semi-supervised strategy while maintaining high accuracy. It provides an efficient structural health monitoring solution for engineering practice, which is of great value to promote the application of intelligent detection technology in infrastructure operation and maintenance. Full article

In time series analysis, the ability to effectively handle data with varying sampling rates is crucial for accurate modeling and analysis. This paper presents the MR-BoF (Multi-Resolution Bag of Functions) framework, which leverages sampling-rate-independent techniques to decompose time series data while accommodating signals with differing resolutions. Unlike traditional methods that require uniform sampling frequencies, the BoF framework employs a flexible encoding approach, allowing for the integration of multi-resolution time series. Through a series of experiments, we demonstrate that the BoF framework ensures the precise reconstruction of the original data while enhancing resampling capabilities by utilizing decomposed components. The results show that this method offers significant advantages in scenarios involving irregular sampling rates and heterogeneous acquisition systems, making it a valuable tool for applications in fields such as finance, healthcare, industrial monitoring, IoT networks, and sensor networks. Full article

It is believed that caspases may play a significant role in the development of cancer, and the expression levels of genes encoding these proteins may influence the prognosis and clinical course of cancer. Taking into account the information presented, we examined the expression profiles of 11 genes from the caspase family in patients diagnosed with triple-negative breast cancer (TNBC). We qualified 29 patients with TNBC. A fragment of the tumor and a fragment of normal tissue surrounding the tumor were collected from each patient. Then, RNA was isolated, and the reverse transcription process was performed. The expression levels of caspase family genes were determined using the real-time PCR method. The obtained data were correlated with clinical data and compared with data from the Cancer Genome Atlas database using the Breast Cancer Gene Expression Miner v4.8 and Ualcan. Based on the results of the conducted research, it can be assumed that the levels of expression of caspase family genes may be correlated with the clinical course of cancer in patients with TNBC, and further research may indicate that profiling the expression levels of these genes may be used in selecting personalized treatment methods. Full article

Genomic islands (GIs) including integrative and conjugative elements (ICEs), prophages, and integrative plasmids are central drivers of horizontal gene transfer in bacterial plant pathogens. These elements often carry cargo genes encoding virulence factors, antibiotic and metal resistance determinants, and metabolic functions that enhance environmental adaptability. In plant-pathogenic species such as Pseudomonas syringae, GIs contribute to host specificity, immune evasion, and the emergence of novel pathogenic variants. ICEclc and its homologs represent integrative and mobilizable elements whose tightly regulated excision and transfer are driven by a specialized transcriptional cascade, while ICEs in P. syringae highlight the ecological impact of cargo genes on pathogen virulence and fitness. Pathogenicity islands further modulate virulence gene expression in response to in planta stimuli. Beyond P. syringae, GIs in genera such as Erwinia, Pectobacterium, and Ralstonia underpin critical traits like toxin biosynthesis, secretion system acquisition, and topoisomerase-mediated stability. Leveraging high-throughput genomics and structural biology will be essential to dissect GI regulation and develop targeted interventions to curb disease spread. This review synthesizes the current understanding of GIs in plant-pathogenic gammaproteobacteria and outlines future research priorities for translating mechanistic insights into sustainable disease control strategies. Full article

Powdery mildew (PM), caused by Sphaerotheca phaseoli, severely threatens mungbean (Vigna radiata) productivity and quality, yet the molecular basis of resistance remains poorly defined. This study employed transcriptome profiling to compare defense responses in a resistant genotype, SUPER5, and a susceptible variety, CN84-1, following pathogen infection. A total of 1755 differentially expressed genes (DEGs) were identified, with SUPER5 exhibiting strong upregulation of genes encoding pathogenesis-related (PR) proteins, disease resistance proteins, and key transcription factors. Notably, genes involved in phenylpropanoid and flavonoid biosynthesis, pathways associated with antimicrobial compound and lignin production, were markedly induced in SUPER5. In contrast, CN84-1 showed limited activation of defense genes and downregulation of essential regulators such as MYB14. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses highlighted the involvement of plant–pathogen interaction pathways, MAPK signaling, and reactive oxygen species (ROS) detoxification in the resistant response. Quantitative real-time PCR validated 11 candidate genes, including PAL3, PR2, GSO1, MLO12, and P21, which function in pathogen recognition, signaling, the biosynthesis of antimicrobial metabolites, the production of defense proteins, defense regulation, and the reinforcement of the cell wall. Co-expression network analysis revealed three major gene modules linked to flavonoid metabolism, chitinase activity, and responses to both abiotic and biotic stresses. These findings offer valuable molecular insights for breeding PM-resistant mungbean varieties. Full article

Members of the heat shock protein 20 (HSP20) family of proteins play an important role in responding to various forms of stress. Here, the expression of ClaHSP17.4 was induced by heat stress in watermelon. Then, a floral dipping approach was used to introduce the pCAMBIA1391b-GFP overexpression vector encoding the heat tolerance-related gene ClaHSP17.4 from watermelon into Arabidopsis thaliana, and we obtained ClaHSP17.4-overexpressing Arabidopsis plants. Under normal conditions, the phenotypes of transgenic and wild-type (WT) Arabidopsis plants were largely similar. Following exposure to heat stress, however, the germination rates (96%) of transgenic Arabidopsis plants at the germination stages were significantly higher than those of wild-type idopsis (17%). Specifically, the malondialdehyde (MDA) content of transgenic Arabidopsis was half that of the control group, while the activities of peroxidase (POD) and superoxide dismutase (SOD) were 1.25 times those of the control group after exposure to high temperatures for 12 h at the seedling stages. The proline content in ClaHSP17.4-overexpressing transgenic Arabidopsis increased by 17% compared to WT plants (* p < 0.05), while the soluble sugar content rose by 37% (* p < 0.05). These results suggest that ClaHSP17.4 overexpression indirectly improves the antioxidant capacity and osmotic regulatory capacity of Arabidopsis seedlings, leading to improved survival and greater heat tolerance. Meanwhile, the results of this study provide a reference for further research on the function of the ClHSP17.4 gene and lay a foundation for breeding heat-tolerant watermelon varieties and advancing our understanding of plant adaptation to environmental stress. Full article

This study presents a cross-cultural comparison of two fortified residences—Palazzo Farnese in Italy and Dong’s Fortified Compound in China—through a triadic analytical framework encompassing architectural form, symbolic ornamentation, and public perception. By combining field observation, iconographic interpretation, and digital ethnography, the research investigates how heritage meaning is constructed, encoded, and reinterpreted across distinct sociocultural contexts. Empirical materials include architectural documentation, decorative analysis, and a curated dataset of 4947 user-generated images and 1467 textual comments collected from Chinese and international platforms between 2020 and 2024. Methods such as CLIP-based visual clustering and BERTopic-enabled sentiment modelling were applied to extract patterns of perception and symbolic emphasis. The findings reveal contrasting representational logics: Palazzo Farnese encodes dynastic authority and Renaissance cosmology through geometric order and immersive frescoes, while Dong’s Compound conveys Confucian ethics and frontier identity via nested courtyards and traditional ornamentation. Digital responses diverge accordingly: international users highlight formal aesthetics and photogenic elements; Chinese users engage with symbolic motifs, family memory, and ritual significance. This study illustrates how historically fortified residences are reinterpreted through culturally specific digital practices, offering an interdisciplinary approach that bridges architectural history, symbolic analysis, and digital heritage studies. Full article

Membrane transporters play a crucial role in any cell. Identifying the substrates they translocate across membranes is important for many fields of research, such as metabolomics, pharmacology, and biotechnology. In this study, we leverage recent advances in deep learning, such as amino acid sequence embeddings with protein language models (pLMs), highly accurate 3D structure predictions with AlphaFold 2, and structure-encoding 3Di sequences from FoldSeek, for predicting substrates of membrane transporters. We test new deep learning features derived from both sequence and structure, and compare them to the previously best-performing protein encodings, which were made up of amino acid k-mer frequencies and evolutionary information from PSSMs. Furthermore, we compare the performance of these features either using a previously developed SVM model, or with a regularized feedforward neural network (FNN). When evaluating these models on sugar and amino acid carriers in A. thaliana, as well as on three types of ion channels in human, we found that both the DL-based features and the FNN model led to a better and more consistent classification performance compared to previous methods. Direct encodings of 3D structures with Foldseek, as well as structural embeddings with ProstT5, matched the performance of state-of-the-art amino acid sequence embeddings calculated with the ProtT5-XL model when used as input for the FNN classifier. Full article

Accurate and robust spatial perception is fundamental for dynamic 3D city modeling and urban environmental sensing. High-resolution remote sensing data, particularly LiDAR point clouds, are pivotal for these tasks due to their lighting invariance and precise geometric information. However, processing and aligning sequential LiDAR point clouds in complex urban environments presents significant challenges: traditional point-based or feature-matching methods are often sensitive to urban dynamics (e.g., moving vehicles and pedestrians) and struggle to establish reliable correspondences. While deep learning offers solutions, current approaches for point cloud alignment exhibit key limitations: self-supervised losses often neglect inherent alignment uncertainties, and supervised methods require costly pixel-level correspondence annotations. To address these challenges, we propose UnMinkLO-Net, an end-to-end self-supervised LiDAR odometry framework. Our method is as follows: (1) we efficiently encode 3D point cloud structures using voxel-based sparse convolution, and (2) we model inherent alignment uncertainty via covariance matrices, enabling novel self-supervised loss based on uncertainty modeling. Extensive evaluations on the KITTI urban dataset demonstrate UnMinkLO-Net’s effectiveness in achieving highly accurate point cloud registration. Our self-supervised approach, eliminating the need for manual annotations, provides a powerful foundation for processing and analyzing LiDAR data within multi-sensor urban sensing frameworks. Full article

Glutamate dehydrogenase (GDH) is ubiquitous in organisms and crucial for amino acid metabolism, energy production, and redox balance. The gdhA and gudB genes encoding GDH were identified in Bacillus altitudinis AS19 and shown to be regulated by iron. However, their functions remain unclear. In this study, gdhA and gudB were analyzed using bioinformatics tools, such as MEGA, Expasy, and SWISS-MODEL, expressed with a prokaryotic expression system, and the induction conditions were optimized to increase the yield of soluble proteins. Phylogenetic analysis revealed that GDH is evolutionarily conserved within the genus Bacillus. GdhA and GudB were identified as hydrophobic proteins, not secreted or membrane proteins. Their structures were primarily composed of irregular coils and α-helices. SWISS-MODEL predicts GdhA to be an NADP-specific GDH, whereas GudB is an NAD-specific GDH. SDS-PAGE analysis showed that GdhA was expressed as a soluble protein after induction with 0.2 mmol/L IPTG at 24 °C for 16 h. GudB was expressed as a soluble protein after induction with 0.1 mmol/L IPTG at 16 °C for 12 h. The proteins were confirmed by Western blot and mass spectrometry. The enzyme activity of recombinant GdhA was 62.7 U/mg with NADPH as the coenzyme. This study provides a foundation for uncovering the functions of two GDHs of B. altitudinis AS19. Full article

Many speech coding strategies have been developed over the years, but comparing them has been convoluted due to the difficulty in disentangling brand-specific and patient-specific factors from strategy-specific factors that contribute to speech understanding. Here, we present a comparison with a ‘virtual’ patient, by comparing two strategies from two different manufacturers, Advanced Combination Encoder (ACE) versus HiResolution Fidelity 120 (F120), running on two different implant systems in a computational model with the same anatomy and neural properties. We fitted both strategies to an expected T-level and C- or M-level based on the spike rate for each electrode contact’s allocated frequency (center electrode frequency) of the respective array. This paper highlights neural and electrical differences due to brand-specific characteristics such as pulse rate/channel, recruitment of adjacent electrodes, and presence of subthreshold pulses or interphase gaps. These differences lead to considerably different recruitment patterns of nerve fibers, while achieving the same total spike rates, i.e., loudness percepts. Also, loudness growth curves differ significantly between brands. The model is able to demonstrate considerable electrical and neural differences in the way loudness growth is achieved in CIs from different manufacturers. Full article

Aluminum (Al) stress is an important factor that inhibits crop growth in acidic soils and poses a threat to pumpkin (Cucurbita moschata) production. In this study, we investigated the effect of endophyte (endophyte) strain J01 of blueberry (Vaccinium uliginosum) on the growth, development, and transcriptional regulatory mechanisms of pumpkin under aluminum stress. The results showed that the blueberry endophyte strain J01 significantly increased the root length of pumpkin under aluminum stress, promoted the growth of lateral roots, and increased root vigor; strain J01 reduced the content of MDA and the relative conductivity in the root system; strain J01 enhanced the activities of superoxide dismutase and catalase in the root system but inhibited ascorbate peroxidase activity. Transcriptome analysis further revealed that strain J01 significantly regulated the expression of key genes associated with aluminum tolerance, including the upregulation of transporter protein genes (aluminum-activated malate transporter and aquaporin), affecting the gene expression levels of genes encoding antioxidant enzymes (ascorbate peroxidase and glutathione S-transferase) and cell wall modification genes (xyloglucan endotransglucosylase/hydrolase and pectin methylesterase). This study provides a theoretical basis and practical guidance for using microbial resources to improve aluminum tolerance in cucurbit crops. Full article

Soil degradation resulting from intensive agricultural practices, the excessive use of agrochemicals, and climate-induced stresses has significantly impaired soil fertility, disrupted microbial diversity, and reduced crop productivity. Plant growth-promoting bacteria (PGPB) represent a sustainable biological approach to restoring degraded soils by modulating plant physiology and soil function through diverse molecular mechanisms. PGPB synthesizes indole-3-acetic acid (IAA) to stimulate root development and nutrient uptake and produce ACC deaminase, which lowers ethylene accumulation under stress, mitigating growth inhibition. They also enhance nutrient availability by releasing phosphate-solubilizing enzymes and siderophores that improve iron acquisition. In parallel, PGPB activates jasmonate and salicylate pathways, priming a systemic resistance to biotic and abiotic stress. Through quorum sensing, biofilm formation, and biosynthetic gene clusters encoding antibiotics, lipopeptides, and VOCs, PGPB strengthen rhizosphere colonization and suppress pathogens. These interactions contribute to microbial community recovery, an improved soil structure, and enhanced nutrient cycling. This review synthesizes current evidence on the molecular and physiological mechanisms by which PGPB enhance soil restoration in degraded agroecosystems, highlighting their role beyond biofertilization as key agents in ecological rehabilitation. It examines advances in nutrient mobilization, stress mitigation, and signaling pathways, based on the literature retrieved from major scientific databases, focusing on studies published in the last decade. Full article

The Kindlin family of scaffold proteins plays key roles in integrin-mediated processes. Kindlin-1 and -2, encoded by the FERMT1 and FERMT2 genes, respectively, are expressed in the epidermis. Kindlin-1 plays protective roles against the development of cutaneous squamous cell carcinomas (cSCCs) in epidermal keratinocytes. However, the role of Kindlin-2 in transformed epidermal keratinocytes has remained virtually unexplored. In this study, we used siRNA approaches to generate Kindlin-2-depleted cells in three isogenic transformed keratinocyte lines. PM1, MET1, and MET4 cells model, respectively, a precancerous lesion, a primary cSCC, and a metastatic lesion of the latter. MET1 cells express both Kindlin-1 and -2. However, Kindlin-1 was not detectable in PM1 and MET4 cells. FERMT2 silencing in PM1 and MET4, but not in MET1 cells, reduced proliferation and the ability to adhere to culture surfaces and spreading. Furthermore, Kindlin-2-depleted PM1 and MET4, but not MET1 cells, exhibited decreased numbers of focal adhesions, as well as an altered F-actin and microtubule cytoskeletal organization. Significantly, FERMT2 silencing reduced the directional migration in all three cell types. These findings are consistent with the concept that, in the absence of other Kindlin orthologues, Kindlin-2 plays a prominent role in the modulation of the proliferation, spreading, focal adhesion assembly, and motility of transformed keratinocytes, as exemplified by PM1 and MET4 cells. Full article