PLoS Pathog. 2025 Apr 22;21(4):e1013067. doi: 10.1371/journal.ppat.1013067. eCollection 2025 Apr.

NO ABSTRACT

PMID:40261841 | DOI:10.1371/journal.ppat.1013067

JCI Insight. 2025 Apr 22:e188146. doi: 10.1172/jci.insight.188146. Online ahead of print.

ABSTRACT

Pf bacteriophages, lysogenic viruses that infect Pseudomonas aeruginosa (Pa), are implicated in the pathogenesis of chronic Pa infections; phage-infected (Pf+) strains are known to predominate in people with cystic fibrosis (pwCF) who are older and have more severe disease. However, the transmission patterns of Pf underlying the progressive dominance of Pf+ strains are unclear. In particular, it is unknown whether phage transmission commonly occurs horizontally between bacteria via viral particles within the airway or if Pf+ bacteria are mostly acquired via de novo Pseudomonas infections. Here, we studied Pa genomic sequences from 3 patient cohorts totaling 662 clinical isolates from 105 pwCF. We identified Pf+ isolates and analyzed transmission patterns of Pf within patients between genetically similar groups of bacteria called "clone types". We found that Pf was predominantly passed down vertically within Pa clone types and rarely via horizontal transfer between clone types within the airway. Conversely, we found extensive evidence of Pa de novo infection by a new, genetically distinct Pf+ Pa. Finally, we observed that clinical isolates showed reduced activity of the type IV pilus and reduced susceptibility to Pf in vitro. These results cast new light on the transmission of virulence-associated phages in the clinical setting.

PMID:40261708 | DOI:10.1172/jci.insight.188146

JCI Insight. 2025 Apr 22:e187951. doi: 10.1172/jci.insight.187951. Online ahead of print.

ABSTRACT

Pharmacological rescue of F508del-CFTR by the triple combination CFTR modulator therapy elexacaftor/tezacaftor/ivacaftor (ETI) leads to unprecedented clinical benefits in patients with cystic fibrosis (CF), however, previous studies in CF primary human airway epithelial cultures demonstrated that chronic treatment with the potentiator ivacaftor can render the F508del protein unstable thus limiting restoration of CFTR chloride channel function. However, quantitative studies of this unwanted effect of ivacaftor on F508del channel function including dependency on cell culture conditions remain limited and the impact of chronic ivacaftor exposure on restoration of mucociliary clearance that is impaired in patients with CF has not been studied. In patient-derived primary nasal epithelial cultures, we found that different culture conditions (UNC-ALI medium vs. PneumaCult medium) have profound effects on ETI-mediated restoration of F508del-CFTR function. Chronic treatment with ivacaftor as part of ETI triple therapy limited the rescue of F508del-CFTR chloride channel function when CF nasal epithelial cultures were grown in UNC-ALI medium, but not in PneumaCult medium. In PneumaCult medium, both chronic and acute addition of ivacaftor as part of ETI treatment led to constitutive CFTR-mediated chloride secretion in the absence of exogenous cAMP-dependent stimulation. This constitutive CFTR-mediated chloride secretion was essential to improve viscoelastic properties of the mucus layer and to restore mucociliary transport on CF nasal epithelial cultures. Furthermore, nasal potential difference measurements in patients with CF showed that ETI restored constitutive F508del-CFTR activity in vivo. These results demonstrate that ivacaftor as a component of ETI therapy is essential to restore mucociliary clearance and suggest that this effect is facilitated by its constitutive activation of F508del channels following their folding-correction in patients with CF.

PMID:40261705 | DOI:10.1172/jci.insight.187951

Physiother Theory Pract. 2025 Apr 22:1-8. doi: 10.1080/09593985.2025.2494114. Online ahead of print.

ABSTRACT

OBJECTIVE: To characterize physiological responses to a 1-minute sit-to-stand test (STS) and assess correlations with cardiopulmonary exercise test (CPET) variables, nutritional status, pulmonary function, and quadriceps muscle strength in cystic fibrosis (CF) patients.

METHODS: Subjects aged 6-18 years with a genetic diagnosis of CF were enrolled in this cross-sectional study. After collecting demographic, anthropometric, and clinical data the following tests were performed: pulmonary function (spirometry), aerobic fitness (CPET), STS, and isometric quadriceps muscle strength (hand-held dynamometry). Data collection was performed on the same day.

RESULTS: The study sample comprised 17 children (9.8 ± 1.6 years) and adolescents (13.7 ± 1.5 years) with a mean forced expiratory volume in one second (FEV1) of - 0.80 ± 1.61 (z-score). In the CPET, peak exercise oxygen consumption (VO2peak) was 35.1 ± 4.2 mL.kg-1.min-1, while in the STS mean number of repetitions was 32.5 ± 6.2 and total work (repetitions × body mass) was 1326.9 ± 379.6. At peak exercise, CPET elicited higher heart rate (p = .001) and subjective sensation of dyspnea (p = .001) compared to STS, though no significant differences were observed in peripheral oxygen saturation. Moderate and significant correlations were identified between total workload (CPET) and repetitions adjusted for body weight (r = 0.684; p = .002) and between STS repetitions and muscle strength corrected for body weight (r = 0.531; p = .034). No significant correlations were found with nutritional status (BMI), pulmonary function (FEV1), or other aerobic fitness variables (VO2 at ventilatory threshold or VO2peak).

CONCLUSION: In children and adolescents with CF, compared to CPET, the STS test elicits a submaximal cardiorespiratory response that is mostly dependent on quadriceps muscle strength.

PMID:40260956 | DOI:10.1080/09593985.2025.2494114

J Xray Sci Technol. 2025 Apr 22:8953996251325092. doi: 10.1177/08953996251325092. Online ahead of print.

ABSTRACT

BackgroundPathological images play a crucial role in the diagnosis of critically ill cancer patients. Since cancer patients often seek medical assistance when their condition is severe, doctors face the urgent challenge of completing accurate diagnoses and developing surgical plans within a limited timeframe. The complexity and diversity of pathological images require a significant investment of time from specialized physicians for processing and analysis, which can lead to missing the optimal treatment window.PurposeCurrent medical decision support systems are challenged by the high computational complexity of deep learning models, which demand extensive data training, making it difficult to meet the real-time needs of emergency diagnostics.MethodThis study addresses the issue of emergency diagnosis for malignant bone tumors such as osteosarcoma by proposing a Lightened Boundary-enhanced Digital Pathological Image Recognition Strategy (LB-DPRS). This strategy optimizes the self-attention mechanism of the Transformer model and innovatively implements a boundary segmentation enhancement strategy, thereby improving the recognition accuracy of tissue backgrounds and nuclear boundaries. Additionally, this research introduces row-column attention methods to sparsify the attention matrix, reducing the computational burden of the model and enhancing recognition speed. Furthermore, the proposed complementary attention mechanism further assists convolutional layers in fully extracting detailed features from pathological images.ResultsThe DSC value of LB-DPRS strategy reached 0.862, the IOU value reached 0.749, and the params was only 10.97 M.ConclusionExperimental results demonstrate that the LB-DPRS strategy significantly improves computational efficiency while maintaining prediction accuracy and enhancing model interpretability, providing powerful and efficient support for the emergency diagnosis of malignant bone tumors such as osteosarcoma.

PMID:40262109 | DOI:10.1177/08953996251325092

J Chem Inf Model. 2025 Apr 22. doi: 10.1021/acs.jcim.5c00296. Online ahead of print.

ABSTRACT

In chemical reaction network theory, ordinary differential equations are used to model the temporal change of chemical species concentration. As the functional form of these ordinary differential equation systems is derived from an empirical model of the reaction network, it may be incomplete. Our approach aims to elucidate these hidden insights in the reaction network by combining dynamic modeling with deep learning in the form of neural ordinary differential equations. Our contributions not only help to identify the shortcomings of existing empirical models but also assist the design of future reaction networks.

PMID:40262040 | DOI:10.1021/acs.jcim.5c00296

Arch Insect Biochem Physiol. 2025 Aug;118(4):e70060. doi: 10.1002/arch.70060.

ABSTRACT

Goji berry is an important economic crop, yet pest infestations pose a significant threat to its yield and quality. Traditional pest identification mainly relies on manual inspection by experts with specialized knowledge, which is subjective, time-consuming, and labor-intensive. To address these issues, this experiment proposes an improved convolutional neural network (CNN) for accurate identification of 17 types of goji berry pests. Firstly, the original data set is augmented using a multi-graph-occlusion data augmentation method. Subsequently, the augmented data set is imported into the improved CNN for training. Based on the original ResNet18 model, a new CNN, named GojiNet, is constructed by embedding multi-attention fusion modules at appropriate locations. Experimental results demonstrate that GojiNet achieves an average recognition accuracy of 95.35%, representing a 2.60% improvement over the ResNet18 network. Notably, compared to the original network, the training time of this model increases only slightly, while its size is reduced, and the recognition accuracy is enhanced. The experiment verifies the performance of the GojiNet model through a series of evaluation indicators. This study confirms the tremendous potential and application prospects of deep learning in pest identification, providing a referential solution for intelligent and precise pest identification.

PMID:40262026 | DOI:10.1002/arch.70060

PLoS One. 2025 Apr 22;20(4):e0321849. doi: 10.1371/journal.pone.0321849. eCollection 2025.

ABSTRACT

Metallized Ceramic Ring is a novel electronic apparatus widely applied in communication, new energy, aerospace and other fields. Due to its complicated technique, there would be inevitably various defects on its surface; among which, the tiny pinhole defects with complex texture are the most difficult to detect, and there is no reliable method of automatic detection. This Paper proposes a method of detecting micro-pinhole defects on surface of metallized ceramic ring combining Improved Detection Transformer (DETR) Network with morphological operations, utilizing two modules, namely, deep learning-based and morphology-based pinhole defect detection to detect the pinholes, and finally combining the detection results of such two modules, so as to obtain a more accurate result. In order to improve the detection performance of DETR Network in aforesaid module of deep learning, EfficientNet-B2 is used to improve ResNet-50 of standard DETR network, the parameter-free attention mechanism (SimAM) 3-D weight attention mechanism is used to improve Sequeeze-and-Excitation (SE) attention mechanism in EfficientNet-B2 network, and linear combination loss function of Smooth L1 and Complete Intersection over Union (CIoU) is used to improve regressive loss function of training network. The experiment indicates that the recall and the precision of the proposed method are 83.5% and 86.0% respectively, much better than current mainstream methods of micro defect detection, meeting requirements of detection at industrial site.

PMID:40261923 | DOI:10.1371/journal.pone.0321849

PLoS One. 2025 Apr 22;20(4):e0321697. doi: 10.1371/journal.pone.0321697. eCollection 2025.

ABSTRACT

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder leading to an inability to socially communicate and in extreme cases individuals are completely dependent on caregivers. ASD detection at early ages is crucial as early detection can reduce the effect on social impairment. Deep learning models have shown capability to detect ASD earlier compared to traditional detection methods used by clinics and experts. Ensemble models, renowned for their ability to enhance predictive performance by combining multiple models, have emerged as a powerful tool in machine learning. This study harnesses the strength of ensemble learning to address the critical challenge of ASD diagnosis. This study proposed a deep ensemble model leveraging the strengths of VGG16 and Xception net trained on Facial Images for ASD detection overcoming limitations in existing datasets through extensive preprocessing. Proposed model preprocessed the training dataset of facial images by converting side posed images into frontal face images, using Histogram Equalization (HE) to enhance colors, data augmentation techniques application, and using the Hue Saturation Value (HSV) color model. By integrating the feature extraction strengths of VGG16 and Xception with fully connected layers, our model has achieved a notable 97% accuracy on the Kaggle ASD Face Image Dataset. This approach supports early detection of ASD and aligns with Sustainable Development Goal 3, which focuses on improving health and well-being.

PMID:40261913 | DOI:10.1371/journal.pone.0321697

IEEE J Biomed Health Inform. 2025 Apr 22;PP. doi: 10.1109/JBHI.2025.3563433. Online ahead of print.

ABSTRACT

Synergistic drug combinations have shown promising results in treating cancer cell lines by enhancing therapeutic efficacy and minimizing adverse reactions. The effects of a drug vary across cell lines, and cell lines respond differently to various drugs during treatment. Recently, many AI-based techniques have been developed for predicting synergistic drug combinations. However, existing computational models have not addressed this phenomenon, neglecting the refinement of features for the same drug and cell line in different scenarios. In this work, we propose a feature refinement deep learning framework, termed FRSynergy, to identify synergistic drug combinations. It can guide the refinement of drug and cell line features in different scenarios by capturing relationships among diverse drug-drug-cell line triplet features and learning feature contextual information. The heterogeneous graph attention network is employed to acquire topological information-based original features for drugs and cell lines from sampled sub-graphs. Then, the feature refinement network is designed by combining attention mechanism and context information, which can learn context-aware feature representations for each drug and cell line feature in diverse drug-drug-cell line triplet contexts. Extensive experiments affirm the strong performance of FRSynergy in predicting synergistic drug combinations and, more importantly, demonstrate the effectiveness of feature refinement network in synergistic drug combination prediction.

PMID:40261768 | DOI:10.1109/JBHI.2025.3563433

IEEE Trans Ultrason Ferroelectr Freq Control. 2025 Apr 22;PP. doi: 10.1109/TUFFC.2025.3562313. Online ahead of print.

ABSTRACT

Surgical tool tip localization and tracking are essential components of surgical and interventional procedures. The cross sections of tool tips can be considered as acoustic point sources to achieve these tasks with deep learning applied to photoacoustic channel data. However, source localization was previously limited to the lateral and axial dimensions of an ultrasound transducer. In this paper, we developed a novel deep learning-based three-dimensional (3D) photoacoustic point source localization system using an object detection-based approach extended from our previous work. In addition, we derived theoretical relationships among point source locations, sound speeds, and waveform shapes in raw photoacoustic channel data frames. We then used this theory to develop a novel deep learning instance segmentation-based 3D point source localization system. When tested with 4,000 simulated, 993 phantom, and 1,983 ex vivo channel data frames, the two systems achieved F1 scores as high as 99.82%, 93.05%, and 98.20%, respectively, and Euclidean localization errors (mean ± one standard deviation) as low as 1.46±1.11 mm, 1.58±1.30 mm, and 1.55±0.86 mm, respectively. In addition, the instance segmentation-based system simultaneously estimated sound speeds with absolute errors (mean ± one standard deviation) of 19.22±26.26 m/s in simulated data and standard deviations ranging 14.6-32.3 m/s in experimental data. These results demonstrate the potential of the proposed photoacoustic imaging-based methods to localize and track tool tips in three dimensions during surgical and interventional procedures.

PMID:40261767 | DOI:10.1109/TUFFC.2025.3562313

Database (Oxford). 2025 Apr 22;2025:baaf031. doi: 10.1093/database/baaf031.

ABSTRACT

As the importance of precision agriculture grows, scalable and efficient methods for real-time data collection and analysis have become essential. In this study, we developed a system to collect real-time crop images, focusing on physiological disorders in tomatoes. This system systematically collects crop images and related data, with the potential to evolve into a valuable tool for researchers and agricultural practitioners. A total of 58 479 images were produced under stress conditions, including bacterial wilt (BW), Tomato Yellow Leaf Curl Virus (TYLCV), Tomato Spotted Wilt Virus (TSWV), drought, and salinity, across seven tomato varieties. The images include front views at 0 degrees, 120 degrees, 240 degrees, and top views and petiole images. Of these, 43 894 images were suitable for labeling. Based on this, 24 000 images were used for AI model training, and 13 037 images for model testing. By training a deep learning model, we achieved a mean Average Precision (mAP) of 0.46 and a recall rate of 0.60. Additionally, we discussed data augmentation and hyperparameter tuning strategies to improve AI model performance and explored the potential for generalizing the system across various agricultural environments. The database constructed in this study will serve as a crucial resource for the future development of agricultural AI. Database URL: https://crops.phyzen.com/.

PMID:40261733 | DOI:10.1093/database/baaf031

IET Syst Biol. 2025 Apr 22:e12107. doi: 10.1049/syb2.12107. Online ahead of print.

ABSTRACT

Single-cell sequencing (scRNA-seq) allows researchers to study cellular heterogeneity in individual cells. In single-cell transcriptomics analysis, identifying the cell type of individual cells is a key task. At present, single-cell datasets often face the challenges of high dimensionality, large number of samples, high sparsity and sample imbalance. The traditional methods of cell type recognition have been challenged. The authors propose a deep residual generation model based on semi-supervised learning (scRSSL) to address these challenges. ScRSSL creatively introduces residual networks into semi-supervised generative models. The authors take advantage of its semi-supervised learning to solve the problem of sample imbalance. During the training of the model, the authors use a residual neural network to accomplish the inference of cell types so that local features of single-cell data can be extracted. Because of the semi-supervised learning approach, it can automatically and accurately predict individual cell types in datasets, even with only a small number of cell labels. Experimentally, the authors' method has proven to have better performance compared to other methods.

PMID:40261690 | DOI:10.1049/syb2.12107

Radiol Phys Technol. 2025 Apr 22. doi: 10.1007/s12194-025-00905-2. Online ahead of print.

ABSTRACT

This study aimed to develop and evaluate a deep-learning model for attenuation and scatter correction in whole-body 64Cu-based PET imaging. A swinUNETR model was implemented using the MONAI framework. Whole-body PET-nonAC and PET-CTAC image pairs were used for training, where PET-nonAC served as the input and PET-CTAC as the output. Due to the limited number of Cu-based PET/CT images, a model pre-trained on 51 Ga-PSMA PET images was fine-tuned on 15 Cu-based PET images via transfer learning. The model was trained without freezing layers, adapting learned features to the Cu-based dataset. For testing, six additional Cu-based PET images were used, representing 1-h, 12-h, and 48-h time points, with two images per group. The model performed best at the 12-h time point, with an MSE of 0.002 ± 0.0004 SUV2, PSNR of 43.14 ± 0.08 dB, and SSIM of 0.981 ± 0.002. At 48 h, accuracy slightly decreased (MSE = 0.036 ± 0.034 SUV2), but image quality remained high (PSNR = 44.49 ± 1.09 dB, SSIM = 0.981 ± 0.006). At 1 h, the model also showed strong results (MSE = 0.024 ± 0.002 SUV2, PSNR = 45.89 ± 5.23 dB, SSIM = 0.984 ± 0.005), demonstrating consistency across time points. Despite the limited size of the training dataset, the use of fine-tuning from a previously pre-trained model yielded acceptable performance. The results demonstrate that the proposed deep learning model can effectively generate PET-DLAC images that closely resemble PET-CTAC images, with only minor errors.

PMID:40261572 | DOI:10.1007/s12194-025-00905-2

Intern Med J. 2025 Apr 22. doi: 10.1111/imj.70051. Online ahead of print.

ABSTRACT

Pulmonary fibrosis care, affecting both idiopathic pulmonary fibrosis and other forms of interstitial lung disease (ILD) characterised by fibrosis, has transformed with a range of innovations that affect the diagnosis, treatment and prognosis of this condition. Pharmacotherapeutic options have expanded, with increased indications for the application of effective antifibrotic therapy in non-IPF progressive pulmonary fibrosis as a solo treatment or combined with immunosuppression, emerging evidence for immunomodulatory therapy including biologic agents and greater access to clinical trials. The diagnostic approach to unclassifiable ILD now includes transbronchial lung cryobiopsy, a less invasive method to obtain histopathology with reduced morbidity and mortality compared to surgical lung biopsy. A multidisciplinary approach optimises the care of people with ILD and includes non-pharmacological management, addressing significant comorbidities, symptom care and advanced care planning. This review will summarise recent updates in pulmonary fibrosis management.

PMID:40260907 | DOI:10.1111/imj.70051

ACS Synth Biol. 2025 Apr 22. doi: 10.1021/acssynbio.4c00868. Online ahead of print.

ABSTRACT

Researchers have invested much effort into developing kinetic models due to their ability to capture dynamic behaviors, transient states, and regulatory mechanisms of metabolism, providing a detailed and realistic representation of cellular processes. Historically, the requirements for detailed parametrization and significant computational resources created barriers to their development and adoption for high-throughput studies. However, recent advancements, including the integration of machine learning with mechanistic metabolic models, the development of novel kinetic parameter databases, and the use of tailor-made parametrization strategies, are reshaping the field of kinetic modeling. In this Review, we discuss these developments and offer future directions, highlighting the potential of these advances to drive progress in systems and synthetic biology, metabolic engineering, and medical research at an unprecedented scale and pace.

PMID:40262025 | DOI:10.1021/acssynbio.4c00868

Plant Cell. 2025 Apr 2;37(4):koaf056. doi: 10.1093/plcell/koaf056.

ABSTRACT

Plants use environmental cues to orient organ and plant growth, such as the direction of gravity or the direction, quantity, and quality of light. During the germination of Arabidopsis thaliana seeds in soil, negative gravitropism responses direct hypocotyl elongation such that the seedling can reach the light for photosynthesis and autotrophic growth. Similarly, hypocotyl elongation in the soil also requires mechanisms to efficiently grow around obstacles such as soil particles. Here, we identify KIPK (KINESIN-LIKE CALMODULIN-BINDING PROTEIN-INTERACTING PROTEIN KINASE) and the paralogous KIPKL1 (KIPK-LIKE1) as genetically redundant regulators of gravitropic hypocotyl bending. Moreover, we demonstrate that the homologous KIPKL2 (KIPK-LIKE2), which shows strong sequence similarity, must be functionally distinct. KIPK and KIPKL1 are polarly localized plasma membrane-associated proteins that can activate PIN-FORMED auxin transporters. KIPK and KIPKL1 are required to efficiently align hypocotyl growth with the gravity vector when seedling hypocotyls are grown on media plates or in soil, where contact with soil particles and obstacle avoidance impede direct negative gravitropic growth. Therefore, the polar KIPK and KIPKL1 kinases have different biological functions from the related AGC1 family kinases D6PK (D6 PROTEIN KINASE) or PAX (PROTEIN KINASE ASSOCIATED WITH BRX).

PMID:40261964 | DOI:10.1093/plcell/koaf056

Sci Signal. 2025 Apr 22;18(883):eads2547. doi: 10.1126/scisignal.ads2547. Epub 2025 Apr 22.

ABSTRACT

Adaptation to starvation is a multimolecular and temporally ordered process. We sought to elucidate how the healthy liver regulates various molecules in a temporally ordered manner during starvation and how obesity disrupts this process. We used multiomic data collected from the plasma and livers of wild-type and leptin-deficient obese (ob/ob) mice at multiple time points during starvation to construct a starvation-responsive metabolic network that included responsive molecules and their regulatory relationships. Analysis of the network structure showed that in wild-type mice, the key molecules for energy homeostasis, ATP and AMP, acted as hub molecules to regulate various metabolic reactions in the network. Although neither ATP nor AMP was responsive to starvation in ob/ob mice, the structural properties of the network were maintained. In wild-type mice, the molecules in the network were temporally ordered through metabolic processes coordinated by hub molecules, including ATP and AMP, and were positively or negatively coregulated. By contrast, both temporal order and coregulation were disrupted in ob/ob mice. These results suggest that the metabolic network that responds to starvation was structurally robust but temporally disrupted by the obesity-associated loss of responsiveness of the hub molecules. In addition, we propose how obesity alters the response to intermittent fasting.

PMID:40261956 | DOI:10.1126/scisignal.ads2547

PLoS Comput Biol. 2025 Apr 22;21(4):e1012925. doi: 10.1371/journal.pcbi.1012925. eCollection 2025 Apr.

ABSTRACT

Modern biological research is increasingly data-intensive, leading to a growing demand for effective training in biological data science. In this article, we provide an overview of key resources and best practices available within the Bioconductor project-an open-source software community focused on omics data analysis. This guide serves as a valuable reference for both learners and educators in the field.

PMID:40261894 | DOI:10.1371/journal.pcbi.1012925

Database (Oxford). 2025 Mar 21;2025:baaf020. doi: 10.1093/database/baaf020.

ABSTRACT

Ontologies are widely used in databases to standardize data, improving data quality, integration, and ease of comparison. Within ontologies tailored to diverse use cases, post-composing user-defined terms reconciles the demands for standardization on the one hand and flexibility on the other. In many instances of Breedbase, a digital ecosystem for plant breeding designed for genomic selection, the goal is to capture phenotypic data using highly curated and rigorous crop ontologies, while adapting to the specific requirements of plant breeders to record data quickly and efficiently. For example, post-composing enables users to tailor ontology terms to suit specific and granular use cases such as repeated measurements on different plant parts and special sample preparation techniques. To achieve this, we have implemented a post-composing tool based on orthogonal ontologies providing users with the ability to introduce additional levels of phenotyping granularity tailored to unique experimental designs. Post-composed terms are designed to be reused by all breeding programs within a Breedbase instance but are not exported to the crop reference ontologies. Breedbase users can post-compose terms across various categories, such as plant anatomy, treatments, temporal events, and breeding cycles, and, as a result, generate highly specific terms for more accurate phenotyping.

PMID:40261748 | DOI:10.1093/database/baaf020