Search Results (45,384)

Minor legume crops, including mung bean (Vigna radiata), cowpea, and adzuki bean, are crucial for global food security and sustainable agriculture, yet their genetic improvement has been hindered by narrow germplasm resources and lagging breeding technologies. This article systematically reviews the strategy of integrating polyploid breeding with mutagenic breeding as an innovative pathway to overcome the genetic bottlenecks of minor legumes. It focuses on insights gained from model plants and major legume crops like soybean and alfalfa regarding polyploid advantages and efficient mutagenesis techniques. Furthermore, it provides an in-depth analysis of the unique challenges and adaptation barriers encountered when transferring these paradigms to minor crops. Using mung bean as a representative case study, this review highlights specific challenges, including the creation of stable polyploid germplasm, the elucidation of complex regulatory mechanisms in polyploid genomes, and the technical bottlenecks in gene mapping and functional validation. The review also outlines future directions involving the integration of cutting-edge technologies—such as multi-omics, high-throughput phenomics, and gene editing—to establish a holistic research framework of “germplasm innovation-gene mapping-designer breeding”. This integrated approach aims to advance the breeding practices of minor legumes into a new era of precision design. Full article

In this paper, we propose a novel Width-Adaptive Convolutional Autoencoder (WACAE) that automatically learns the optimal network width. The proposed approach assigns a relevance weight to each channel in the encoder’s hidden layers and leverages these weights to guide architectural adaptation. Based on the learned relevance, the model incrementally introduces new channels when needed and prunes irrelevant ones to achieve an optimal configuration. The WACAE simultaneously trains the network and learns its width in an unsupervised manner. Moreover, a novel cost function is devised to optimize channel relevance weights concurrently with model hyperparameters. Unlike conventional static or widening strategies, the proposed method adaptively enhances feature expressiveness within a single encoder–decoder framework. The model is evaluated on standard benchmark datasets (MNIST and CIFAR-10) and two real-world medical datasets (Brain Tumor MRI and Kvasir-Capsule). Experimental results demonstrate its effectiveness compared to state-of-the-art methods based on empirical tuning and network-width scaling. Furthermore, the proposed inner-product-based relevance weighting mechanism reduces model complexity while achieving high classification accuracy. 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

The common metabolic disorder, lactose intolerance, is often treated with oral lactase enzyme supplements, which can frequently cause gastrointestinal instability. This work utilizes Malbranchea cinnamomea’s AA9 lytic polysaccharide monooxygenase (LPMO) to target β-lactose (β-lactose) in an investigation of a new enzymatic approach for lactose breakdown. Potential possibilities for lactose breakdown are AA9 LPMOs, copper-dependent enzymes that oxidatively cleave glycosidic bonds in polysaccharides. We employed a combined in silico method that incorporated molecular docking, density functional theory (DFT) calculations, and molecular dynamics (MD) simulations. Docking studies revealed that β-lactose formed hydrogen bonds with key residues SER100, ASN54, and ARG56, exhibiting a greater binding affinity (−5.4 kcal/mol) toward LPMO compared to the control citric acid (−4.9 kcal/mol). Upon DFT analysis, (LPMO) showed excellent stability and appropriate reactivity for enzyme interaction. The higher stability of the LPMO-β-lactose complex was highlighted by MD simulation over 100 ns, which showed lower root mean square deviation (RMSD) and root mean square fluctuation (RMSF) values, greater structural compactness, and reduced solvent accessibility when compared to the control. These collective findings suggest that β-lactose interacts efficiently with the AA9 LPMO active site, supporting its potential as a novel enzymatic target for lactose degradation. This computational study provides a theoretical foundation for developing alternative therapeutic strategies for lactose intolerance, though further in vitro and in vivo investigations are required to validate these findings. Full article

The Hough transform (HT) is widely used in computer vision, tomography, and neural networks. Numerous algorithms for HT computation have been proposed, making their systematic comparison essential. However, existing comparative methodologies are either non-universal and limited to certain HT formulations or task-oriented, relying on application-specific criteria that do not fully capture algorithmic properties. This paper introduces a novel unified methodology for the systematic comparison of HT algorithms. It evaluates key characteristics, including computational complexity, accuracy, and auxiliary space complexity, while explicitly accounting for the property of self-adjointness. The methodology integrates both implementation-level and theoretical considerations related to the interpretation of HT as a discrete approximation of the Radon transform. A set of mathematically justified evaluation functions, not previously described in the literature, is proposed to support our methodology. Importantly, the methodology is universal, applicable across diverse HT paradigms, encompasses pattern-based and Fourier-based fast HT (FHT) algorithms, and offers a comprehensive alternative to existing task-specific methodologies. Its application to several state-of-the-art FHT algorithms ($FHT2DT$, $FHT2SP$, $ASD2$, $KHM$, and Fast Slant Stack) yields new experimentally confirmed theoretical insights, identifies $ASD2$ as the most balanced algorithm, and provides practical guidelines for algorithm selection. In particular, the methodology reveals that for image sizes up to 3000, the maximum normalized computational complexity increases as follows: $FHT2DT$ (1.1), $ASD2$ (15.3), and $KHM$ (30.6), while the remaining algorithms exhibit at least 1.1 times higher values. The maximum orthotropic approximation error equals 0.5 for $ASD2$, $KHM$, and Fast Slant Stack; lies between 0.5 and 1.5 for $FHT2SP$; and reaches 2.1 for $FHT2DT$. In terms of worst-case normalized auxiliary space complexity, the lowest values are achieved by $FHT2DT$ (2.0), Fast Slant Stack (4.0, lower bound), and $ASD2$ (6.8), with all other algorithms requiring at least 8.2 times more memory. Full article

In this work, the so-called characteristic function method is proposed as a new approach to describe and interpret the diffusion process with interacting adsorbates in terms of surface coverage. In this context, the intermediate scattering function is identified as a characteristic function that is very well defined in probability theory. From this function, the generating functions of the moments and cumulants of the jump probability distribution are straightforwardly obtained at any order. This analysis is carried out in two stages. First, the dilute limit, corresponding to non-interacting adsorbates or very low surface coverage, is briefly reviewed. Second, the method is extended to low and intermediate coverages, where adsorbate-adsorbate interactions become relevant. A further consequence of the present analysis is that the static structure factor is also a characteristic function of the adsorbate separation distance distribution. This method thus provides a compact and physically transparent route for connecting scattering observables, diffusion coefficients, and coverage-dependent structural correlations. Full article

Microcystin (MC) contamination of surface waters threatens ecosystems and public health. Nature-based solutions such as Multi-Soil-Layering (MSL) systems have been used for MC remediation. However, the biological mechanisms controlling MC degradation remain unclear. The present study investigates microbial community responses in two MSL systems with different clay contents (8% and 54%) exposed to MC-contaminated inputs (well water and eutrophied lake water). Samples were analysed before and after treatment using quantitative PCR (qPCR) to quantify the mlrA gene (encoding microcystinase) and its bacterial hosts. Next-generation sequencing (NGS) was used to assess microbial diversity, while the FAPROTAX database was used to predict functional characteristics. Results showed that MC was mainly adsorbed in pozzolan layers, while mlrA gene abundance and MC-degrading bacteria were higher in soil mixture layers. The presence of mlrA and associated bacteria was most pronounced in lake inflow samples, indicating intrinsic MC Biodegradation potential. Taxonomic analysis revealed dominant phyla including Proteobacteria, Actinobacteriota, Firmicutes, Chloroflexi and Bacteroidota. Functional analysis identified dominant traits such as chemoheterotrophy and aerobic metabolism. These findings provide new insights into microbial interactions in MSL systems and contribute to the optimisation of water treatment strategies for MC-contaminated environments. Full article

In this paper, we revisit the recent result of Luo, Xu, and Raina on an Erdélyi-type integral for Saran’s three-variable hypergeometric function $F_K$. We provide a new proof of this integral and derive an attractive new integral related to Appell’s function $F_2$. A further extension on the L-variable $F_K$ function, which appears in physics, is also discussed. Furthermore, we prove various q-Erdélyi-type integrals for the q-analogue of the $F_K$-function. An interesting discrete analogue is also included. We also provide a valuable compilation of the sources for known Erdélyi-type integrals of many different hypergeometric functions. Full article

Metaheuristic algorithms have become indispensable for solving high-dimensional, non-convex, and constrained optimization problems arising in science and engineering. However, no single method can simultaneously provide strong global exploration, accurate local exploitation, and robust performance across diverse problem classes. This paper proposes JADEFLO, a new hybrid nature-inspired metaheuristic that couples Adaptive Differential Evolution with Optional External Archive (JADE) and Frilled Lizard Optimization (FLO) in a two-stage search framework. In the first stage, JADE drives global exploration using p-best mutation, an external archive, and adaptive control of the mutation factor and crossover rate to maintain population diversity. In the second stage, FLO performs intensive local refinement by mimicking the hunting and tree-climbing behaviors of frilled lizards through dedicated exploration and exploitation moves. The resulting algorithm has linear time complexity with respect to the population size, dimensionality, and number of iterations. JADEFLO is evaluated on the IEEE CEC 2022 single-objective benchmark suite (F1–F12) and three constrained engineering design problems (Pressure Vessel, tension/compression spring, and speed reducer), using 30 independent runs and comparisons against more than thirty state-of-the-art metaheuristics, including GA, PSO, DE variants, GWO, WOA, MFO, and FLO. The results show that JADEFLO attains the best overall rank on the CEC functions, delivers faster convergence and higher accuracy on most test cases, and matches or improves the best-known designs with markedly reduced variance. These findings indicate that JADEFLO is a promising general-purpose optimizer and a flexible foundation for future extensions to multi-objective and large-scale optimization. Full article

Biotissues represent a new technology in tissue regeneration in otolaryngology. Various biomaterials functioning in different combinations are used as bioinks for 3D bioprinting of tissues/tissue fragments. The scaffolds can be populated with several cell categories, each offering distinct advantages and disadvantages, depending on the targeted pathology. Results from in vitro and in vivo studies on animal models are promising, with superior therapeutic potential. The combination of these elements provides promising results, enabling their potential application in personalized medicine. Based on these findings, their application in ENT (ear, nose, and throat) pathology is starting to gain traction. Despite being an emerging field, 3D/4D bioprinting in otolaryngology is rapidly evolving, increasingly replacing conventional inert materials with more sophisticated, bio-integrated alternatives. These alternatives are based on novel bioink formulation involving cells capable of proliferating and integrating the new neo-fragment organ into the host’s endogenous tissues. In this context, this review outlines novel applications that could enhance traditional procedures in ENT reconstructive medicine. Furthermore, biomimetic scaffolds for otolaryngology can be tailored to address factors influencing implant fate during the procedure and in the early and late postoperative periods. Full article

The application of 3D data in pavement inspection represents an emerging trend. Acquiring and measuring the 3D information of pavement distress enables a more comprehensive assessment of severity, thereby allowing for accurate monitoring and evaluation of the pavement’s technical condition. Existing methods face challenges in high-cost pavement scanning and insufficient research on automated 3D distress segmentation. This study employed a consumer-grade action camera for data acquisition and constructed an engineering-aligned 3D point cloud dataset of pavements. Then a long-tail class imbalance mitigation strategy was introduced, integrating adaptive re-sampling with a weighted fusion loss function, effectively balancing minority class representation. The proposed network, named PointPaveSeg, was a dedicated point cloud processing architecture. A dual-stream feature fusion module was designed for the encoder layer, which decoupled geometric and semantic features to improve distress extraction capability. The network incorporated a hierarchical feature propagation structure enhanced by edge reinforcement, global interaction, and residual connections. Experimental results demonstrated that PointPaveSeg achieved an mIoU of 78.45% and an accuracy of 95.43%. In the field evaluation, post-processing and geometric information extraction were performed on the segmented point clouds. The results showed high consistency with manual measurements. Testing confirmed the method’s practical applicability in real-world projects, offering a new lightweight alternative for intelligent pavement monitoring and maintenance systems. Full article

Objectives: Myofascial pain syndrome (MPS) is a common musculoskeletal condition, and while percutaneous needle electrolysis (PNE) and intramuscular electrical stimulation (IMES) are emerging therapies for myofascial pain syndrome and tendinopathies, their effects remain unclear. This systematic review aimed to characterize the methodological features and synthesize the evidence on the clinical improvement and adverse events rates of PNE and IMES in treating MPS and tendinopathies. Data Sources: PubMed, Scopus, Web of Science, ClinicalTrials.gov, the World Health Organization International Clinical Trials Registry Platform, Google Scholar, and reference lists. Searches were carried out on 10 July 2025 and repeated on 16 March 2026, just before final analysis. New results found during final searches were screened for inclusion to ensure currency of the review. Methods: We selected studies based on the PICOS framework and predefined selection criteria: Population: adults with MPS or active myofascial trigger points (TrPs), or tendinopathies; Intervention: PNE or IMES; Comparator: sham procedures, other interventions, or no intervention; Outcomes: pain intensity (e.g., Visual Analogue Scale or Numeric Pain Rating Scale), pressure pain threshold (PPT), and functional measures; and Study Design: experimental studies. Studies focused exclusively on post-surgical or neuropathic pain, studies without a relevant comparator, and studies not reporting clinically meaningful outcomes were excluded. We assessed the risk of bias of included studies and performed a narrative synthesis. Results: From 737 identified records, 30 studies met the selection criteria. PNE was generally effective in reducing pain and improving function in tendinopathies and MPS, although results varied across outcomes and follow-ups. IMES showed moderate evidence for reducing pain and enhancing function, particularly cervical range of motion and PPT. However, both interventions had inconsistent clinical improvement and adverse events rates on disability indices and quality of life. Most studies had a high risk of bias due to challenges in blinding. Reported adverse events were minor and self-limiting, indicating that both therapies are generally safe when performed by trained clinicians. Conclusions: PNE and IMES may improve pain and some functional outcomes in MPS and tendinopathies; however, these findings should be interpreted cautiously because most included studies had a high risk of bias. 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

This study explores the multi-rate sampling-based $H_{\infty }$ load frequency control (LFC) problem for networked power systems by using an area-information-fusion method. This problem is addressed for two reasons: (1) most of networked control methods for LFC are focused on the one-rate sampling scheme and (2) the previous looped function cannot be directly applied within the multi-rate sampling scheme. Here, the multi-rate sampling scheme involves each area sampling rate being reliant on its own sensor. Namely, all area sampling rates are different from each other. In the presence of a multi-rate sampling scheme, a new sampling instants sequence is established by using an area-information-fusion method. It contributes to constructing a corresponding closed-loop model by adding virtual state variables. In addition, a new looped-function approach is devised to capture the sampling information from diverse area sensors. Based on Lyapunov stability theory, less conservative LMI conditions are derived to guarantee the $H_{\infty }$ performance of the multi-rate LFC system. Additionally, a co-designed method for determining the control gain and maximum sampling frequency is established. Finally, simulation studies are conducted to validate the efficacy and features of the proposed control strategy. Full article

As a new class of green functional liquids, deep eutectic solvents (DESs) have attracted increasing attention as alternatives to conventional solvents, such as mineral acids, organic solvents and ionic liquids (ILs), in nuclear chemistry. Owing to their low cost, easy preparation, structural tunability, and adjustable physicochemical properties, DESs provide unique solvation and coordination environments that enable various applications. This review summarizes recent research advances in the application of DESs for the chemical processes of the nuclear fuel cycle. Particular emphasis is focused on dissolution, extraction and separation, electrochemical deposition and redox processes, radionuclide capture, decontamination and detection. This review highlights the fundamental advantages and current limitations of DES-based systems and outlines future trends. Full article