Search Results (7,471)

Seed coat color in peanut (Arachis hypogaea L.) is a critical agronomic trait that affects both nutritional quality and market appeal. In this study, we identified two bHLH transcription factor genes, AhWSC1a and AhWSC1b, homologues of Arabidopsis TRANSPARENT TESTA 8, as indispensable gatekeepers of basal flavonoid pigmentation. QTL-seq analysis of a recombinant inbred line population derived from a black-testa parent (S3) and a white-testa parent (S2) revealed that recessive loss-of-function mutations in both AhWSC1a/1b abolish proanthocyanidin biosynthesis, resulting in a white testa. Integrated metabolomic and transcriptomic profiling confirmed the absence of proanthocyanidins and a strong repression of late anthocyanin-pathway genes (DFR, LDOX) in the mutants. Molecular assays further demonstrated that AhWSC1 physically interacts with the R2R3-MYB regulator AhTc1 to form a functional MBW complex that activates AhDFR and AhLDOX transcription. In this research, we also found that the black testa phenotype may arise from elevated AhTc1 expression associated with a structural variant (SV); however, in the SV background, the introduction of ahwsc1a/1b mutant leads to a significant suppression of AhTc1 expression. Notably, because AhWSC1 is transcriptionally silent in hairy-root systems, overexpression of AhTc1 alone failed to induce these late-stage anthocyanin biosynthesis genes, highlighting AhWSC1 as an indispensable, rate-limiting hub of anthocyanin biosynthesis pathway regulation. Collectively, our findings establish AhWSC1a and AhWSC1b as master regulators of peanut testa pigmentation, elucidate the molecular basis of classical white testa inheritance, and provide genetic targets for precision-breeding of nutritionally enhanced cultivars. Full article

Background and Objectives: Sentinel lymph node (LN) biopsy (SLNB) remains to be the standard approach for surgical axillary staging of breast cancer (BC) patients. The aim of this study was to investigate the factors that affect axillary LN involvement in early BC patients. Materials and Methods: Clinically node negative early stage (cT1-2N0) BC patients having undergone breast conserving surgery (BCS) between February 2021 and January 2024 were included. During axillary exploration of all cases, sentinel LNs were excised and reserved for permanent section pathological examination (PS) only. Historical records of patients including clinicopathological features, surgical outcomes as well as pathological results were recorded and analyzed retrospectively. p < 0.05 indicated statistically significant results. Results: The study group consisted of 150 women with cT1-2N0 BC having undergone BCS with a median age of 59 (range: 25–81) years. According to the PS results of the sentinel LNs, the need for reoperation to complete axillary lymph node dissection was present in three (2%) patients. Tumors of the Luminal B subtype were significantly associated with increased sentinel LN positivity (p = 0.014). The risk of sentinel LN metastasis was found to be 5.2 times greater in patients with a Ki-67 ≥ %14 [OR: 5.224 (%95 CI:1.73–15.82, p = 0.003)] and the Ki-67 proliferation index was determined as an independent risk factor. Conclusions: In early-stage BC patients, PS of the excised sentinel LN offers sufficient axillary LN staging. On the other hand, a more careful clinical assessment is necessary for early BC patients harboring tumors with an elevated Ki-67 index and/or tumors of the Luminal B subtype. Full article

This study evaluated the early structural and functional effects of combined citicoline and coenzyme Q10 (CoQ10) (CitiQ10) treatment in a laser-induced ocular hypertension (OHT) model in Swiss albino mice, focusing on retinal inflammation and neuroprotection. Sixty male CD-1 mice were assigned to four groups: vehicle, CitiQ10, OHT, and OHT + CitiQ10. OHT was induced by laser photocoagulation of limbal and episcleral veins, and CitiQ10 was administered orally starting 15 days before induction. Intraocular pressure (IOP) was measured by rebound tonometry, retinal structure was assessed by spectral domain optical coherence tomography (SD-OCT), and function was evaluated using full-field electroretinography (ffERG). At 3 days post-induction, OHT eyes exhibited significant retinal nerve fiber layer (RNFL) thickening, increased vitreous particles, and early functional impairment, particularly reduced scotopic b-wave and oscillatory potentials. CitiQ10 treatment mitigated these changes, reducing vitreous particles, moderating RNFL alterations, and not exhibiting significant changes in ERG amplitudes. At 7 days post-induction, structural and functional deficits persisted but were less pronounced in treated eyes. These findings suggest that CitiQ10 treatment may attenuate early retinal damage in glaucoma, with OCT and ffERG serving as reliable monitoring tools, supporting the therapeutic potential of this approach in early stage disease. Full article

Non-small cell lung cancer (NSCLC) accounts for approximately 85% of lung cancers with adenocarcinoma being the most common subtype. Patients with stage IV NSCLC typically have poor prognosis. In these patients, identification of actionable genomic alterations allows for the selection of targeted therapy rather than chemotherapy or chemo-immunotherapy. EGFR mutations are a common oncogenic driver in NSCLC and are targetable by tyrosine kinase inhibitors (TKIs). However, most of the studies primarily focus on common mutations, which are exon 19 deletions (Ex19del) and exon 21 (L858R) point mutations, and there is inconsistent data on efficacy in the treatment of patients with uncommon EGFR mutations. Currently, the first-line treatment for patients with common EGFR mutations involves a third-generation TKI, typically osimertinib. This case describes a 66-year-old gentleman with two uncommon EGFR frameshift deletions (E701fs and L702fs). His tumor staging was denoted as cT3N2M1b, stage IVA. The patient demonstrated a radiological and biochemical response to osimertinib as part of the OCELOT clinical trial (supported by a grant from AstraZeneca), with evidence of tumor marker decline and radiographic improvement within two months of osimertinib treatment initiation. This response has been durable with continued radiological stability and biochemical improvement at 11 months and ongoing. This case will help guide management for patients with this uncommon EGFR mutations and contribute to the scarce literature of EGFR frameshift deletions in advanced NSCLC patients. Full article

Large language models (LLMs) demonstrate outstanding capability in understanding natural language and show great potential in open-domain travel planning. However, when confronted with multi-constraint itineraries, personalized recommendations, and scenarios requiring rigorous external information validation, pure LLM-based approaches lack rigorous planning ability and fine-grained personalization. To address these gaps, we propose the Symbolic LoRA Travel Planner (SLTP) framework—an agent architecture that combines a two-stage symbol-rule LoRA fine-tuning pipeline with a user multi-option heuristic tree search (MHTS) planner. SLTP decomposes the entire process of transforming natural language into executable code into two specialized, sequential LoRA experts: the first maps natural-language queries to symbolic constraints with high fidelity; the second compiles symbolic constraints into executable Python planning code. After reflective verification, the generated code serves as constraints and heuristic rules for an MHTS planner that preserves diversified top-K candidate itineraries and uses pruning plus heuristic strategies to maintain search-time performance. To overcome the scarcity of high-quality intermediate symbolic data, we adopt a teacher–student distillation approach: a strong teacher model generates high-fidelity symbolic constraints and executable code, which we use as hard targets to distill knowledge into an 8B-parameter Qwen3-8B student model via two-stage LoRA. On the ChinaTravel benchmark, SLTP using an 8B student achieves performance comparable to or surpassing that of other methods built on DeepSeek-V3 or GPT-4o as a backbone. Full article

Artificial intelligence technology holds significant importance for building intelligent question-answering systems in the field of coal mine safety and enhancing safety management levels. Currently, there is a lack of specialized large language models and high-quality question-answering datasets in this field. To address this, this study proposes a two-stage fine-tuning method based on Low-Rank Adaptation (LoRA) and Group Sequence Policy Optimization (GSPO) for training a question-answering model tailored to the coal mine safety domain. The research begins by constructing a dedicated question-answering dataset based on domain-specific regulatory documents. Subsequently, using Qwen2.5-7B Instruct as the base model, the study fine-tunes the model through supervised learning with LoRA technology, followed by further optimization of the model’s performance using the GSPO reinforcement learning algorithm. Experiments show that the model trained with this method exhibits significant improvements in coal mine safety-related tasks, achieving superior results on multiple automated evaluation metrics compared to contrast models of similar scale. This study validates the effectiveness of the two-stage fine-tuning method in adapting large language models (LLMs) to specific domains, providing a new technical approach for the intelligentization of coal mine safety. It should be noted that due to the lack of external data, this study relies on a self-constructed dataset and has not yet undergone external independent validation, which constitutes the main limitation of the current work. Full article

In this work, we discuss a method for the QR-factorization of N × N matrices where N ≥ 3 which is based on transformations which are called discrete signal-induced heap transformations (DsiHTs). These transformations are generated by given signals and can be composed by elementary rotations. The data processing order, or the path of the transformations, is an important characteristic of it, and the correct choice of such paths can lead to a significant reduction in the operation when calculating the factorization for large matrices. Such paths are called fast paths of the N-point DsiHTs, and they define sparse matrices with more zero coefficients than when calculating QR-factorization in the traditional path, that is, when processing data in the natural order x₀, x₁, x₂, …. For example, in the first stage of the factorization of a 512 × 512 matrix, a matrix is used with 257,024 zero coefficients out of a total of 262,144 coefficients when using the fast paths. For comparison, the calculations in the natural order require a 512 × 512 matrix with only 130,305 zero coefficients at this stage. The Householder reflection matrix has no zero coefficients. The number of multiplication operations for the QR-factorization by the fast DsiHTs is more than 40 times smaller than when using the Householder reflections and 20 times smaller when using DsiHTs with the natural paths. Examples with the 4 × 4, 5 × 5, and 8 × 8 matrices are described in detail. The concept of complex DsiHT with fast paths is also described and applied in the QR-factorization of complex square matrices. An example of the QR-factorization of a 256 × 256 complex matrix is also described and compared with the method of Householder reflections which is used in programming language MATLAB R2024b. Full article

Mass production in product design typically relies on standardized geometries and dimensions to accommodate a broad user population. However, when products are required to interface directly with the human body, such generalized design approaches often result in inadequate fit and reduced user comfort. This limitation highlights the necessity of fully personalized design methodologies based on individual anthropometric characteristics. This paper presents a novel application that automates the design of custom-fit sunglasses through the integration of Artificial Intelligence (AI) and Computational Design. The system is implemented using both textual (Python™ version 3.10.11) and visual (Grasshopper 3D™ version 1.0.0007) programming environments. The proposed workflow consists of the following four main stages: (a) acquisition of user facial images, (b) AI-based detection of facial landmarks, (c) three-dimensional reconstruction of facial features via an optimization process, and (d) generation of a personalized sunglass frame, exported as a three-dimensional model. The application demonstrates a robust performance across a diverse set of test images, consistently generating geometries that conformed closely to each user’s facial morphology. The accurate recognition of facial features enables the successful generation of customized sunglass frame designs. The system is further validated through the fabrication of a physical prototype using additive manufacturing, which confirms both the manufacturability and the fit of the final design. Overall, the results indicate that the combined use of AI-driven feature extraction and parametric Computational Design constitutes a powerful framework for the automated development of personalized wearable products. Full article

Background: Sarcomas are rare, aggressive malignancies with limited therapeutic options in advanced stages. This is the first real-world study in the MENA region evaluating the clinical utility of Next-Generation Sequencing (NGS) in guiding sarcoma treatment and improving outcomes. Methods: We retrospectively reviewed sarcoma patients who underwent NGS at a major referral center (2021–2024), comparing clinical and molecular outcomes between those who received NGS-based treatment adjustments (NBTA) and those who did not. Results: Seventy-eight patients were included (60% male; median age 44 years). Soft tissue sarcomas accounted for 70.5% of cases (n = 55), while bone sarcomas represented 29.5% (n = 23). Prior to NGS, 64.1% of patients had received a median of one line of systemic therapy. NGS was performed late in the disease course in 73% of cases. At least one mutation was detected in 87% (median 3 mutations). Targetable alterations were identified in 33% at the time of testing, rising to 42% with updated genomic knowledge and therapeutic advances. Overall, 20.5% received NBTA. Among non-NBTA patients, 67% had no actionable targets, 17% had no detectable mutations, and 16% were ineligible due to cost, limited access, or clinical deterioration. Tumor Mutational Burden was low in 79%, intermediate in 19%, and high in 2%, and all tumors were microsatellite stable. Patients receiving NBTA had a longer median Progression-Free Survival (9 vs. 2 months; p = 0.023). Median Overall Survival was longer in the NBTA group (74 vs. 48 months), though not statistically significant (p = 0.207). Genomic alterations were subtype-specific: EWSR1 rearrangements (Ewing and Desmoplastic small round cell tumors), CDK4 and MDM2 amplifications (Liposarcoma and Osteosarcoma), TP53 and RB1 mutations (Leiomyosarcoma), CDKN2A/B deletions (Undifferentiated Pleomorphic Sarcoma and Chondrosarcoma), and SS18 rearrangements (Synovial Sarcoma). Conclusions: Genomics-guided therapy in sarcoma is feasible and impactful. Expanding timely access to molecular profiling is essential for advancing precision oncology in the MENA region. Full article

Background: Head and neck (H&N) cutaneous melanomas have poorer outcomes than melanomas at other sites, yet sentinel lymph node biopsy (SLNB)—a key prognostic tool in clinically node-negative disease—is less frequently performed, particularly outside tertiary centers. We evaluated the feasibility and prognostic relevance of SLNB in a medium-volume regional institution. Methods: We retrospectively reviewed patients with primary H&N cutaneous melanoma who underwent SLNB at the Department of Oral and Maxillofacial Surgery, University of Szeged, between 2010 and 2022. Clinicopathological features, nodal outcomes, recurrence patterns, recurrence-free survival (RFS), and overall survival (OS) were analyzed using Kaplan–Meier methods and univariate Cox regression. Results: Thirty-eight patients underwent SLNB, with a 100% sentinel lymph node identification rate and no major complications. Positive sentinel lymph nodes were identified in 8 patients (21.1%). Two false-negative events occurred, resulting in a false-omission rate of 6.7% and a negative predictive value of 93.3%. SLN-negative patients demonstrated longer RFS and OS, although differences were not statistically significant. Among patients with intermediate-risk melanoma (pT1b–pT3a), 18.5% had a positive SLN. Conclusions: SLNB is a safe and clinically meaningful staging procedure for H&N melanoma in a medium-volume regional center. Sentinel node status provides important prognostic information and supports appropriate patient selection for contemporary adjuvant therapy. Full article

Maize is one of the top three crops globally, ranking only behind rice and wheat, making it an important crop of interest. Aboveground biomass is a key indicator for assessing maize growth and its yield potential. This study developed an efficient and stable biomass prediction model to estimate the aboveground biomass (AGB) of spring maize (Zea mays L.) under subsurface drip irrigation in arid regions, based on UAV multispectral remote sensing and machine learning techniques. Focusing on typical subsurface drip-irrigated spring maize in arid Xinjiang, multispectral images and field-measured AGB data were collected from 96 sample points (selected via stratified random sampling across 24 plots) over four key phenological stages in 2024 and 2025. Sixteen vegetation indices were calculated and 40 texture features were extracted using the gray-level co-occurrence matrix method, while an integrated feature-selection strategy combining Elastic Net and Random Forest was employed to effectively screen key predictor variables. Based on the selected features, six machine learning models were constructed, including Elastic Net Regression (ENR), Gradient Boosting Decision Trees (GBDT), Gaussian Process Regression (GPR), Partial Least Squares Regression (PLSR), Random Forest (RF), and Extreme Gradient Boosting (XGB). Results showed that the fused feature set comprised four vegetation indices (GRDVI, RERVI, GRVI, NDVI) and five texture features (R_Corr, NIR_Mean, NIR_Vari, B_Mean, B_Corr), thereby retaining red-edge and visible-light texture information highly sensitive to AGB. The GPR model based on the fused features exhibited the best performance (test set R² = 0.852, RMSE = 2890.74 kg ha⁻¹, MAE = 1676.70 kg ha⁻¹), demonstrating high fitting accuracy and stable predictive ability across both the training and test sets. Spatial inversions over the two growing seasons of 2024 and 2025, derived from the fused-feature GPR optimal model at four key phenological stages, revealed pronounced spatiotemporal heterogeneity and stage-dependent dynamics of spring maize AGB: the biomass accumulates rapidly from jointing to grain filling, slows thereafter, and peaks at maturity. At a constant planting density, AGB increased markedly with nitrogen inputs from N0 to N3 (420 kg N ha⁻¹), with the high-nitrogen N3 treatment producing the greatest biomass; this successfully captured the regulatory effect of the nitrogen gradient on maize growth, provided reliable data for variable-rate fertilization, and is highly relevant for optimizing water–fertilizer coordination in subsurface drip irrigation systems. Future research may extend this integrated feature selection and modeling framework to monitor the growth and estimate the yield of other crops, such as rice and cotton, thereby validating its generalizability and robustness in diverse agricultural scenarios. Full article

Background: Clear cell renal cell carcinoma (ccRCC) is the most prevalent kidney cancer subtype and, in most cases, it is incidentally diagnosed, as early-stage disease is often asymptomatic. Therefore, the identification of stable, noninvasive biomarkers is a major unmet clinical need. Urinary microRNAs (miRNAs) have emerged as promising candidates since they are extraordinarily stable in urine and show a close relationship with tumour biology. Methods: In this study, urinary expression levels of five miRNAs (miR-15a, miR-15b, miR-16, miR-210, and miR-let-7b) were analysed in RCC patients before surgery, 5 days after, and one month after surgery, and compared to healthy controls. Results: Non-parametric analyses revealed significant postoperative decreases for miR-15a (p = 0.002), miR-16 (p = 0.025), miR-210 (p = 0.030), and in the overall miRNA Sum (p = 0.002), suggesting that these miRNAs are directly linked to tumour presence. In the comparison between preoperative and one-month postoperative samples, miR-let-7b (p = 0.049) and the global miRNA Sum (p = 0.037) remained significantly reduced after intervention, indicating a partial normalisation of urinary miRNA profiles. Correlation analyses demonstrated positive associations between specific miRNAs and clinical parameters such as age, ischemia time, and surgical time, reinforcing their potential relevance to tumour biology and treatment response. Conclusions: These findings support urinary miRNAs as promising, minimally invasive biomarkers for ccRCC diagnosis and postoperative monitoring. Full article

The basic/helix-loop-helix (bHLH) transcription factors are crucial regulators of plant development and stress responses. In this study, we conducted a genome-wide analysis of the bHLH family in Rosa roxburghii, an economically important fruit crop. A total of 89 non-redundant RrbHLHs were identified and unevenly distributed across the seven chromosomes. Phylogenetic analysis classified them into 23 subfamilies and 7 Arabidopsis subfamilies were absent, indicating lineage-specific evolutionary trajectories. Conserved motif and gene structure analyses showed that members within the same subfamily generally shared similar architectures, yet subfamily-specific variations were evident, suggesting potential functional diversification. Notably, key residues involved in DNA-binding and dimerization were highly conserved within the bHLH domain. Promoter analysis identified multiple cis-acting elements related to hormone response, stress adaptation, and tissue-specific regulation, hinting at broad regulatory roles. Expression profiling across fruit developmental stages and in response to GA₃ treatment revealed dynamic expression patterns. Furthermore, 21 duplicated gene pairs (17 segmental and 4 tandem duplicated pairs) were identified, with most evolving under purifying selection. Detailed analysis of these pairs revealed that segmental duplication, coupled with structural variations such as exon indels, dissolution/joining, and exonization/pseudoexonization, substantially contributed to their functional divergence during evolution. Our results provide a basis for understanding the evolution and potential functions of the RrbHLHs. Full article

Mobile edge-AI plant monitoring systems enable automated disease control in greenhouses and open fields, reducing dependence on manual inspection and the variability of visual diagnostics. This paper proposes a lightweight two-stage edge-AI system for strawberries, in which a YOLOv10n detector on board a mobile agricultural robot locates leaves affected by seven common diseases (including Leaf Spot) with real-time capability on an embedded platform. Patches are then automatically extracted for leaves classified as Leaf Spot and transmitted to the second module—a compact MobileViT-S-based classifier with ordinal output that assesses the severity of Leaf Spot on three levels (S1—mild, S2—moderate, S3—severe) on a specialised set of 373 manually labelled leaf patches. In a comparative experiment with lightweight architectures ResNet-18, EfficientNet-B0, MobileNetV3-Small and Swin-Tiny, the proposed Ordinal MobileViT-S demonstrated the highest accuracy in assessing the severity of Leaf Spot (accuracy ≈ 0.97 with 4.9 million parameters), surpassing both the baseline models and the standard MobileViT-S with a cross-entropy loss function. On the original image set, the YOLOv10n detector achieves an mAP@0.5 of 0.960, an F1 score of 0.93 and a recall of 0.917, ensuring reliable detection of affected leaves for subsequent Leaf Spot severity assessment. The results show that the “YOLOv10n + Ordinal MobileViT-S” cascade provides practical severity-aware Leaf Spot diagnosis on a mobile agricultural robot and can serve as the basis for real-time strawberry crop health monitoring systems. Full article

Stem mustard (Brassica juncea var. tumida Tsen et Lee) is an important economic vegetable in China. Premature bolting induced by temperature fluctuations has become a major cultivation constraint. Photoreceptors (PHRs) serve as critical photosensor proteins that interpret light signals and regulate physiological responses in plants. In this study, five core PHR families, namely F-box-containing flavin binding proteins (ZTL/FKF1/LKP2), phytochrome (PHY), cryptochrome (CRY), phototropin (PHOT) and UV RESISTANCE LOCUS 8 (UVR8) were identified in Brassica species. RNA-seq analysis revealed their expression patterns during organogenesis in B. juncea. Seven candidate PHRs were validated by qRT-PCR in B. juncea early-bolting (‘YA-1’) and late-bolting (‘ZT-1’) cultivars. Agrobacterium-mediated BjuFKF1_1 overexpression (OE) lines resulted in significantly earlier flowering under field conditions. Histochemical GUS staining indicated that BjuFKF1_1 was expressed in seedlings, leaves, flower buds and siliques. Transcript analysis revealed that the expression level of BjuFKF1_1 was up-regulated in all tissues at both the vegetative and reproductive stages, whereas the expression of BjuFKF1_1 interacting protein-encoding genes were down-regulated in flowers. Under blue light, genes encoding interacting proteins (BjuCOL5, BjuSKP1, BjuCOL3, BjuAP2, BjuAP2-1 and BjuLKP2) were up-regulated in flower buds, whereas BjuCOL and BjuPP2C52 were down-regulated in flowers. Developmental stage analysis revealed the up-regulation of five (BjuAP2, BjuCOL3, BjuCOL5, BjuAP2-1 and BjuLKP2) and four (BjuCOL, BjuCOL5, BjuAP2 and BjuLKP2) interaction protein-encoding genes during the reproductive stage under white and blue light, respectively. These findings elucidate the role of BjuFKF1_1 in flowering regulation and provide molecular targets for B. juncea bolting-resistant variety breeding. Full article