Image captioning using deep learning bridges computer vision and natural language processing, enabling machines to generate human-like textual descriptions for images. While significant progress has been made in English, in Arabic, the image captioning field remains under-explored due to the language’s morphological complexity, right-to-left script, and scarcity of annotated datasets. This paper addresses this gap by adapting the BLIP-2 (Bootstrapped Language—Image Pre-training) model for Arabic caption generation, leveraging machine-translated datasets, like Flickr 30k, to overcome resource limitations. BLIP-2 combines a vision transformer (ViT) for image encoding and a CamelBERT large language model (LLM) for text generation, enhanced by a lightweight Querying Transformer (Q-Former) for cross-modal alignment. Despite challenges such as translation artifacts and linguistic nuances, our experiments demonstrate promising results in generating coherent Arabic captions. In short, this study highlights the potential of BLIP-2 for multilingual applications while underscoring the need for native Arabic datasets and further optimization. Ultimately, this work contributes to advancing inclusive artificial intelligence technologies for Arabic-speaking communities, with applications in assistive tools, education, and content creation. Full article

(This article belongs to the Section Computing and Artificial Intelligence)

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17 pages, 948 KB

Open AccessArticle

Rapid Screening Method to Assess Formation Damage During Injection of Metal Oxide Nanoparticles in Sandstone

by Craig Klevan, Bonnie A. Marion, Jae Jin Han, Taeyoung Chang, Shuhao Liu, Keith P. Johnston, Linda M. Abriola and Kurt D. Pennell

Nanomaterials 2026, 16(7), 402; https://doi.org/10.3390/nano16070402 (registering DOI) - 26 Mar 2026

Abstract

Many advances in enhanced oil recovery (EOR) take advantage of the unique properties of nanomaterials to improve characterization of formation properties, achieve conformance control during flood operations, and extend the controlled release time of polymers. Magnetite nanoparticles (nMag) have been employed in these processes due to their low cost, low toxicity, and ability to be engineered to meet desired needs, especially with the application of a magnetic field. Similarly, silica dioxide (SiO₂) and aluminum oxide (Al₂O₃) nanoparticles have been evaluated for the delivery of scale and asphaltene inhibitors. However, the injection of nanoparticles into porous media comes with the risk of formation damage due to particle deposition, which can lead to increased injection pressures and reductions in permeability. The goal of this study was to develop a method to evaluate and assess nanoparticle formulations for their potential to cause formation damage. A screening apparatus was constructed to hold small sandstone discs (~2 mm) or cores (~2.5 cm) for rapid testing with minimal material use and the capability to be used with either aqueous brine solutions or non-polar solvents as the mobile phase. Image analysis of the disc and pressure measurements demonstrated increasing deposition of nMag and face-caking when the salinity was increased from 500 mg/L NaCl (8.56 mM) to API brine (2.0 M). Similarly, when the injected concentration of silica nanoparticles in 500 mg/L NaCl was increased from 1 to 10 wt%, the back pressure increased by 55 psi, and face-caking was observed. The screening test results were consistent with traditional core-flood tests and was able to be modified to accommodate organic liquid mobile phases. The screening test results closely matched nanoparticle transport and retention measured in sandstone cores, confirming the ability of the system to rapidly screen nanoparticle formulations for potential formation damage. Full article

(This article belongs to the Section Energy and Catalysis)

25 pages, 6434 KB

Open AccessReview

Ultrasonic Nondestructive Evaluation of Welded Steel Infrastructure: Techniques, Advances, and Applications

by Elsie Lappin, Bishal Silwal, Saman Hedjazi and Hossein Taheri

Appl. Sci. 2026, 16(7), 3206; https://doi.org/10.3390/app16073206 - 26 Mar 2026

Abstract

Welding is a critical joining process in civil and transportation infrastructure, enabling the fabrication of complex steel structural systems used in bridges, buildings, and other essential infrastructures. Despite strict adherence to established welding codes and standards, such as AWS D1.1 and AASHTO/AWS D1.5, welding flaws and service-induced defects can occur in welded components. Cause of defects and their structural impact, along with detection, sizing, and localization of these anomalies and flaws, are crucial for adequate maintenance, repair, or replacement planning without compromising the functionality of in-service components. Among available NDT techniques, ultrasonic testing (UT) remains one of the most widely adopted methods of weld inspection due to its depth of penetration, sensitivity to internal defects, and suitability for field deployment. Recent advancements in ultrasonic technologies, particularly Phased Array Ultrasonic Testing (PAUT), along with its emerging approaches such as Full Matrix Capture (FMC) and the Total Focusing Method (TFM), have significantly enhanced inspection accuracy, repeatability, and interpretability. These techniques enable flexile beam steering, multi-angle interrogation, and improved imaging of complex geometries. This paper presents a comprehensive review of PAUT for the inspection of welded steel infrastructure adhering to the recommendations and requirements of the relevant codes and standards, synthesizing the current literature on PAUT principles, wave modes, probe configurations, and data acquisition strategies. Emphasis is placed on the practical implementation of PAUT in civil infrastructure inspection, its advantages over conventional NDT methods, and its potential to support informed decisions related to quality acceptance, repair, and long-term maintenance planning. This paper concludes by identifying current challenges and future research directions for advanced ultrasonic inspection of welded steel structures. Full article

(This article belongs to the Special Issue Application of Ultrasonic Non-Destructive Testing—Second Edition)

30 pages, 2796 KB

Open AccessArticle

Rational Design and Evaluation of Novel TGR5 Agonists for Diabetes

by Rachana S. Bhimanwar, Zachary Detwiler, Jinge G. Zhu, Samuel T. Saghafi, Carolyn A. Winder, Dawn Belt Davis, Amit Mittal, Vikas Sharma, David A. Harris and Snehal N. Chaudhari

Molecules 2026, 31(7), 1093; https://doi.org/10.3390/molecules31071093 - 26 Mar 2026

Abstract

Agonists of the G protein-coupled receptor TGR5 have long been sought-after for their metabolic benefits. Intestinal TGR5 activation induces secretion of the antidiabetic hormone GLP-1, which can systemically improve diabetes phenotypes in multiple organs. However, no TGR5 agonist drug candidate has succeeded in clinical trials due to their low potency and unwanted side effects. A challenge in the field has been the development of TGR5 agonists that are non-toxic, long-acting, and have functional selectivity for G protein-biased agonism. In this study, we propose a systematic pipeline for engineering optimal TGR5 agonists with antidiabetic properties. This pipeline is interdisciplinary, combining in silico, in vitro, and in vivo assays to design and validate drug candidates. We identify 2 lead compounds that outline the necessary beneficial properties for a successful TGR5 agonist against diabetes. We uncover the molecular mechanisms that allow TGR5 agonists to induce the transcription of their target, TGR5, in intestinal enteroendocrine cells. Lastly, we investigate the molecular interactions of our lead candidates in the TGR5 binding pocket to identify optimal parameters for stability and biological activity. Our strategy for TGR5 agonist design and evaluation has the potential to guide the discovery process for targeted TGR5 therapeutics for metabolic diseases. Full article

(This article belongs to the Special Issue 30th Anniversary of Molecules—Recent Advances in Chemical Biology)

29 pages, 16603 KB

Open AccessArticle

Hierarchical Neural-Guided Navigation with Vortex Artificial Potential Field for Robust Path Planning in Complex Environments

by Boyi Xiao, Lujun Wan, Jiwei Tian, Yuqin Zhou, Sibo Hou and Haowen Zhang

Drones 2026, 10(4), 240; https://doi.org/10.3390/drones10040240 - 26 Mar 2026

Abstract

Existing autonomous navigation systems for Unmanned Aerial Vehicles (UAVs) face the dual challenges of local minima entrapment and computational complexity that scales with environmental density. This paper proposes a hierarchical navigation architecture integrating deep representation learning with an improved Vortex Artificial Potential Field (APF). At the decision layer, a Convolutional Neural Network (CNN) encodes the environment as a fixed-dimensional tensor and generates global waypoints with constant-time inference, independent of obstacle count. At the control layer, a Vortex APF resolves the Goal Non-Reachable with Obstacles Nearby (GNRON) problem and limit-cycle oscillations through tangential rotational potentials, achieving significant improvement in trajectory smoothness compared to traditional APF methods. A closed-loop replanning mechanism further ensures robust performance under execution drift. Experiments across varying obstacle densities demonstrate that the combined system achieves high navigation success rates in dense environments with substantially reduced computation time compared to sampling-based planners such as Rapidly exploring Random Tree star (RRT*), while maintaining superior trajectory quality. This architecture provides a computationally efficient solution for resource-constrained UAV platforms operating in GPS-denied or obstacle-rich environments such as warehouses, forests, and disaster sites. Full article

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26 pages, 4449 KB

Open AccessReview

Beyond Reality—How Are Virtual Reality and the Metaverse Shaping Tourism?

by Adelina Zeqiri, Issam Mejri and Adel Ben Youssef

Platforms 2026, 4(2), 6; https://doi.org/10.3390/platforms4020006 - 26 Mar 2026

Abstract

This study aims to systematically analyze scholarly research on virtual reality (VR), augmented reality (AR), and the metaverse in the tourism and hospitality sectors, offering insights into publication patterns, key contributors, thematic evolution, and potential research directions from 2016 to mid-2025. It maps how the literature evolved in response to technological maturation and changing tourism constraints. A systematic literature review and comprehensive bibliometric analysis were conducted using the Scopus database. The analysis encompassed bibliographic metrics, thematic clustering, and content analysis techniques to identify influential journals, authors, and evolving research themes. The results reveal a pronounced acceleration in research activity post-2020, reflecting heightened interest due to the COVID-19 pandemic’s push towards digital and immersive solutions. Core journals identified include Tourism Management, Current Issues in Tourism, and Journal of Travel Research. Influential contributors such as Timothy H. Jung, M. Claudia tom Dieck, and Dimitrios Buhalis significantly shaped the field. The thematic trajectory demonstrates a shift from initial exploration and application of VR and AR technologies toward comprehensive integration into metaverse ecosystems, with emerging themes such as digital twins, synthetic experiences, immersive storytelling, and growing emphasis on ethical and sustainability considerations. By synthesizing nearly a decade of research, this study provides valuable insights into immersive technologies’ evolution in tourism and hospitality, identifying critical areas for future investigation aligned with enterprise information management strategies. Full article

(This article belongs to the Special Issue Exploring Digital Transformation and Sustainability)

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9 pages, 387 KB

Open AccessReview

Desmosine in Aortic Disease: Biology, Measurement, and Clinical Applications in Aortic Pathologies

by Alexander Gombert, Saurav Ranjan Mohapatra, Jelle M. Frankort, Christian Uhl and Panagiotis Doukas

J. Clin. Med. 2026, 15(7), 2540; https://doi.org/10.3390/jcm15072540 - 26 Mar 2026

Abstract

Thoracoabdominal aortic aneurysms (TAAAs) are uncommon and usually silent until rupture, causing a substantial burden to the health care system. Aneurysm growth and rupture prediction is mainly based on aneurysm diameter measurement by imaging modalities, meaning that the biology of aneurysm growth is not part of a potentially more adequate surveillance of aortic aneurysm patients. Alternatives or complementary options for aortic aneurysm surveillance are an ongoing, non-addressed open issue of vascular medicine. The application of different biomarkers has been discussed, yet so far, an adequate candidate for aortic aneurysm surveillance, if it comes to the thoracic or thoracoabdominal aorta, preferably without radiation exposure, has not been named. Elastin breakdown, as a component of aortic wall degeneration primarily driven by matrix metalloproteinases (MMPs), is a core element of aneurysm development. Desmosine is an elastin-specific cross-link increasingly studied as a circulating or urinary biomarker of compromised aortic wall integrity and disease activity. Accordingly, this review investigated whether plasma desmosine (pDES), a highly specific marker of elastin degradation, could be used as a non-invasive biomarker for detecting aortic aneurysms and assessing their risk profile. The existing literature of desmosine in fields of aortic pathologies in the acute and chronic setting will be assessed based on the current literature; furthermore, future perspectives of desmosine as a biomarker of aortic pathologies, such as aortic aneurysm dynamics, will be discussed. Full article

(This article belongs to the Special Issue Aortic Diseases: Modern Strategies in Diagnosis, Management, and Prevention of Complications)

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29 pages, 5682 KB

Open AccessArticle

Vortex-Induced Vibration Energy Harvesting for Road Vehicle Suspensions: Modeling, Prototyping, and Experimental Validation

by Fei Wang, Jiang Liu, Haoyu Sun, Mingxing Li, Hao Yin, Xilong Zhang and Bilong Liu

Energies 2026, 19(7), 1636; https://doi.org/10.3390/en19071636 - 26 Mar 2026

Abstract

To address the demand for a micro-power supply for vehicle suspension control, a novel harvester is proposed to recover vortex-induced vibration energy in the wake of a shock absorber. A suspension dynamic model was established to simulate the spring compression process and identify the wind-shielding condition. The spring-shock absorber assembly was then simplified as a stepped cylinder with two cross-sections. Flow-field analysis showed that the size, shape, and rising angle of the wake vortices were affected by the bluff-body geometry, Reynolds number, and boundary conditions. The downwash motion was found to directly influence vortex development, and two new vortex-connection modes were identified. These results provided guidance for harvester optimization. A two-way fluid–structure interaction model was developed to describe the electromechanical conversion behavior of the proposed harvester under flow excitation. Numerical results showed that the output voltage increased with vehicle speed. An average peak voltage of 1.82 V was obtained when the piezoelectric patches were installed two larger-cylinder diameters downstream. The optimal patch length was 120 mm, and further increasing the length did not significantly improve the harvesting performance. Finally, a full-scale prototype was tested, and the measured voltage agreed well with the simulation results. The proposed harvester can therefore serve as a potential micro-power source for low-power suspension electronics. Full article

(This article belongs to the Special Issue Innovations and Applications in Piezoelectric Energy Harvesting)

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36 pages, 1048 KB

Open AccessReview

Patient-Specific 3D-Printed Porous Metal Implants in Orthopedics: A Narrative Review of Current Applications and Future Prospects

by Connor P. McCloskey, Anoop Sunkara, Siddhartha Kalala, Jack T. Peterson, Michael O. Sohn, Austin R. Chen, Arun K. Movva and Albert T. Anastasio

Appl. Sci. 2026, 16(7), 3192; https://doi.org/10.3390/app16073192 - 26 Mar 2026

Abstract

Atypical joint spaces, such as those encountered in complex segmental bone loss and large structural defects, remain challenging to manage with conventional implants within divisions across orthopedics, including arthroplasty, tumor reconstruction, trauma, and spine. Additive manufacturing advances have made patient-specific implants a possibility, and this promising solution has enabled the creation of implants with customized geometry and controlled surface porosity to enhance osseointegration, reduce rejection rates, optimize biomechanics, and promote longevity. Despite its potential, patient-specific implants are still eclipsed in use by conventional, “off-the-shelf” implants due to their lower cost, documented long-term durability, insurance coverage, and the strength of available clinical evidence supporting their use. This narrative review summarizes current materials and manufacturing approaches for additively manufactured metal porous implants, including imaging and design workflows, lattice and pore architecture, and how the printing process influences implant stiffness, fatigue strength, surface roughness, and porosity. We also discuss the experimental and preclinical data on mechanical performance, fatigue resistance, and osseointegration for new developments in the field. Emerging trends such as material innovation, streamlined digital planning-to-implant workflows, 4D printing and other advanced additive manufacturing concepts, and cost-reduction efforts are examined in the context of clinical practicality. In this review, the integration of engineering principles with early clinical outcomes will provide orthopedic surgeons with a realistic understanding of the benefits and limitations of the future utilization of additive manufacturing in clinical practice. Full article

(This article belongs to the Special Issue Design, Development, and Characterization of Advanced Materials for Modern Industry)

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20 pages, 17978 KB

Open AccessArticle

Research on the Temperature Variation Characteristics of Large-Scale Concrete Pouring in Open-Cut Railway Stations

by Haitao Zhang, Chenyang Tang, Ruoyan Cai, Yapeng Wang and Yonghua Su

Buildings 2026, 16(7), 1312; https://doi.org/10.3390/buildings16071312 - 26 Mar 2026

Abstract

In recent years, China’s rapid economic development has driven the improvement of infrastructure, with mass concrete widely applied in engineering for its unique structural functions. However, mass concrete is prone to temperature stress and thermal cracks due to its low thermal conductivity, huge volume, complex construction conditions, and frequent environmental changes, which pose potential structural safety risks. The hydration heat of mass concrete can also cause structural deformation, so targeted measures must be taken based on actual engineering conditions to minimize cracks. Real-time temperature monitoring during pouring is of crucial significance to ensure the quality and safety of mass concrete in practical projects. Taking the Phase I Project of Qingdao Metro Line 9 as the research object, this paper explores the temperature variation characteristics of mass concrete during pouring and forming on-site. It analyzes the temperature changes in mass concrete based on field temperature-monitoring data and laboratory test results, plots temperature measurement curves, and identifies the temperature variation trend of mass concrete caused by hydration heat. A numerical model is established via ANSYS to study the effects of ventilation temperature and velocity by simulation. Results show that the temperature of mass concrete pouring blocks rises rapidly to a peak and then decreases to room temperature, which is analyzed from the perspectives of hydration heat reaction mechanism and heat transfer. Laboratory test data are highly consistent with field data, verifying the temperature variation characteristics of concrete pouring. The numerical simulation of heat transfer-influencing factors reveals that the optimal ventilation velocity is 4 m/s for sufficient air circulation in the foundation pit; when the ventilation temperature is below 25 °C, the surface temperature of concrete decreases significantly with an obvious cooling effect. Full article

(This article belongs to the Section Building Structures)

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15 pages, 967 KB

Open AccessArticle

A Retrieval-Augmented Generation with Dual-Similarity Monitoring for Nuclear Energy Knowledge Q&A

by Cheng-Hsing Chiang and Kun-Chou Lee

Appl. Sci. 2026, 16(7), 3182; https://doi.org/10.3390/app16073182 - 26 Mar 2026

Abstract

We present a Retrieval-Augmented Generation (RAG)-based question-answering system for nuclear energy science communication, characterizing retrieval quality in generated responses. The system introduces a dual-similarity analysis that jointly measures (i) question-to-context (Q→C) and (ii) answer-to-context (A→C) semantic consistency, serving as “retrieval-side semantic alignment signal” and “post-generation semantic alignment indicator” respectively. Built with LangChain, FAISS retrieval, and a large language model, our pipeline separates offline indexing from online inference and is grounded on authoritative Taiwanese Nuclear Safety Commission documents. We evaluate two settings: (a) in-domain prompts derived from the corpus and (b) out-of-domain, randomly generated nuclear energy questions. Results show that generated answers are, on average, more semantically similar to retrieved contexts than the original questions under the present setup, while the overall association between retrieval-side and answer-side signals remains stronger in the in-domain setting. Out-of-domain questions show weaker but still observable answer-to-context alignment patterns, contingent on corpus overlap. These findings suggest that combining RAG with dual-similarity analysis offers a practical and audit-oriented approach for educational Q&A, and we discuss potential improvements in versioned regulations, re-ranking, and abstention strategies. In this study, the RAG technique and dual-similarity analysis are combined together to promote nuclear energy knowledge. The research flow chat of this study can be applied to many other fields of scientific knowledge. Full article

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26 pages, 1908 KB

Open AccessReview

Recent Advances in Graphene-Based Field-Effect Transistor Biosensors for Disease Biomarker Detection and Clinical Prospects

by Deeksha Nagpal, Anup Singh, John Link, Abijeet Singh Mehta, Ashok Kumar and Vinay Budhraja

Biosensors 2026, 16(4), 190; https://doi.org/10.3390/bios16040190 - 26 Mar 2026

Abstract

Field-effect transistor (FET) biosensors using graphene have become one of the most promising biosensing platforms for the early diagnosis of diseases with featu21res such as high sensitivity, label-free detection and application compatibility with point-of-care systems. Herein, we critically discuss recent advances in graphene FET (GFET) biosensor development toward clinically relevant biomarkers associated with representative diseases including cancer, neurodegenerative disease, infectious disease, and inflammatory conditions. Recent progress was reviewed to evaluate GFET architectures, surface functionalization methods, and detection quality. The biomarkers explored were clusterin in Alzheimer’s disease, thrombin in coagulopathy, estrogen receptor α (ER-α) in breast cancer, Carcinoembryonic antigen in lung cancer, microRNAs for malignant tumors, exosomes derived from HepG2 for the hepatocellular carcinoma (HCC) cell line, interleukin-6 (IL-6) for chronic obstructive pulmonary disease (COPD), Polyclonal antibodies and antigens (P24) for HIV and prostate-specific antigen for prostate cancer. The developed devices demonstrate ultralow detection limits at femtomolar to attomolar concentrations with the aid of designed antibodies, aptamers and nanomaterials. Herein, this review presents the sensing mechanisms and biomedical application of various GFET platforms, focusing on their emerging potential as next-generation platforms for rapid, non-invasive and point-of-care diagnostics. Full article

(This article belongs to the Special Issue Organic Materials and Devices for Biosensing and Neuromorphic Computing)

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32 pages, 1329 KB

Open AccessReview

Deep Learning-Based Gaze Estimation: A Review

by Ahmed A. Abdelrahman, Basheer Al-Tawil and Ayoub Al-Hamadi

Robotics 2026, 15(4), 69; https://doi.org/10.3390/robotics15040069 - 25 Mar 2026

Abstract

Gaze estimation, a critical facet of understanding user intent and enhancing human–computer interaction, has seen substantial advancements with the integration of deep learning technologies. Despite the progress, the application of deep learning in gaze estimation presents unique challenges, notably in the adaptation and optimization of these models for precise gaze tracking. This paper conducts a thorough review of recent developments in deep learning-based gaze estimation, with a particular focus on the evolution from traditional methods to sophisticated appearance-based techniques. We examine the key components of successful gaze estimation systems, including input feature processing, neural network architectures, and the importance of data preprocessing in achieving high accuracy. Our analysis extends to a comprehensive comparison of existing methods, shedding light on their effectiveness and limitations within various implementation contexts. Through this systematic review, we aim to consolidate existing knowledge in the field, identify gaps in current research, and suggest directions for future investigation. By providing a clear overview of the state-of-the-art in gaze estimation and discussing ongoing challenges and potential solutions, our work seeks to inspire further innovation and progress in developing more accurate and efficient gaze estimation systems. Full article

(This article belongs to the Topic Advances in Robot Vision Perception and Control Technology)

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21 pages, 3335 KB

Open AccessArticle

Effects of Combined Application of Nitrogen Fertilizer and Multiple Soil Amendments on Soil Properties and Bacterial Community Structure in Arid-Zone Jujube Orchards

by Yuxuan Wei, Yunqi Ma, Jinwei Sun, Haoyang Liu, Shuangquan Jing, Cuiyun Wu and Yuyang Zhang

Agronomy 2026, 16(7), 694; https://doi.org/10.3390/agronomy16070694 - 25 Mar 2026

Abstract

Jujube (Ziziphus jujuba Mill.) cultivation in arid regions of China faces severe soil constraints, including high alkalinity, low organic matter content, and poor water retention. Although soil amendments have demonstrated potential for improving soil quality, their combined effects on soil–plant–microbe interactions in desert agroecosystems remain poorly understood. This study conducted a three-year field experiment in a desert jujube orchard in southern Xinjiang, China, to evaluate six nitrogen fertilizer management strategies: urea alone (CK) or combined with biochar (NB), bentonite (NP), graphene (NS), biochar plus bentonite (NBP), or microbial inoculants (NW). Soil physicochemical properties, enzyme activities, bacterial community structure, and jujube yield were analyzed. Structural equation modeling (SEM) was employed to elucidate the pathways linking soil amendments to crop productivity. Results showed that NBP was the most effective in improving soil physical structure, significantly reducing bulk density and enhancing water retention capacity compared to the control. The NBP treatment also enhanced soil organic matter (30% increase), available phosphorus (119% increase), and urease activity (44% increase), resulting in the highest jujube yield (7.14 kg per tree). Bacterial community analysis revealed that NBP significantly increased Shannon diversity and enriched Actinobacteriota and Proteobacteria. SEM analysis indicated that urease activity served as a significant mechanistic pathway linking soil organic matter improvements to enhanced crop productivity. These findings demonstrate that combined application of biochar and bentonite with nitrogen fertilizer represents an effective strategy for improving soil quality, enhancing microbial functionality, and increasing crop yield in desert jujube orchards, providing a practical and synergistic amendment combination for sustainable soil management and productivity enhancement in arid agroecosystems. Full article

(This article belongs to the Section Soil and Plant Nutrition)

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18 pages, 1536 KB

Open AccessArticle

Stable qw12-1 Locus Across Environments: High-Resolution QTL Mapping for Sustainable Southern Soybean Crinkle Leaf Disease Resistance Control

by Wenjie Chen, Chunting Zhang, Qian Shi, Xiaohong Guo, Xiayan Qin, Shufang Chen, Kai Sun, Qingyuan Wei, Fuyue Tang, Jiang Liang, Tuanjie Zhao and Yuan Chen

Plants 2026, 15(7), 1010; https://doi.org/10.3390/plants15071010 - 25 Mar 2026

Abstract

Severe southern soybean crinkle leaf disease (SSCLD) reduces soybean seed yield by approximately 40%. Identifying the genes that control SSCLD is crucial for breeding resistant varieties and elucidating the molecular mechanisms underlying SSCLD infection. In this study, recombinant inbred lines (RILs, n = 236) derived from a cross between Nannong1138-2 (NN1138-2) and Zhengxiaodou (ZXD) were used as experimental materials. A field trial employing a randomized block design was conducted in four environments across two locations, Nanning (2019–2021) and Du’an (2020) in Guangxi, to identify the disease severity grades of SSCLD in the field. QTLs controlling SSCLD were detected via a genetic map constructed using 3255 SLAF (specific locus amplified fragment) markers from the recombinant inbred lines. RT‒qPCR was used to analyze candidate gene expression at major effect loci. The results revealed that eight SSCLD-associated QTLs were identified on chromosomes 3, 6, 12, and 17. Notably, the qw12-1 locus on chromosome 12 was detected across three developmental stages in three of the four environments, explaining 10.18–58.20% of the phenotypic variation. RT‒qPCR analysis of 12 disease resistance-related genes within the qw12-1 interval revealed that GLYMA_12G233000 and GLYMA_12G239200 presented significantly higher expression in crinkled leaf lines than in normal leaf lines during the V5 (fifth trifoliolate stage), R2 (full bloom stage), and R6 (full seed stage) stages. These genes were prioritized as potential prime candidates for SSCLD resistance genes. This research provides foundational data for the fine mapping of qw12-1 and cloning SSCLD-related genes, advancing our understanding of the molecular mechanisms underlying SSCLD. Full article

(This article belongs to the Section Plant Genetics, Genomics and Biotechnology)

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