Search Results (912)

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a complex metabolic disorder with a diverse spectrum. This study aimed to classify patients with MASLD into molecular subtypes based on the underlying pathophysiology. Methods: We performed high-throughput RNA sequencing on 164 liver tissue samples from healthy controls and patients with MASLD. The clustering was based on individual genes or pathways that showed high variation across the samples. Second, the clustering was based on single-sample gene set enrichment analysis. Results: Optimal homogeneity was achieved by dividing the samples into four clusters (one healthy control and three MASLD clusters I–III) based on the top genes or pathways with differences across the samples. No significant differences were observed in waist circumference, blood pressure, glucose, alanine transaminase (ALT), or aspartate transferase (AST) levels between cluster I patients with MASLD and the healthy controls. Cluster I showed the clinical features of lean MASLD. Cluster III of MASLD demonstrated hypertension and a T2DM prevalence of 57.1% and 50.0%, respectively, with a significantly higher fibrosis burden (stage of fibrosis, liver stiffness, and FIB-4 value) than clusters I and II. Cluster III was associated with severe fibrosis and abnormal glucose homeostasis. In MASLD cluster I, the sphingolipid and GPCR pathways were upregulated, whereas the complement and phagocytosis pathways were downregulated. In MASLD cluster II, the cell cycle and NOTCH3 pathways increased, whereas the PI3K and insulin-related pathways decreased. In MASLD cluster III, increased activity occurred in the interleukin-2 and -4 and extracellular matrix pathways, coupled with decreased activity in the serotonin 2A and B pathways. Conclusions: MASLD can be divided into three distinct molecular phenotypes, wherein each is characterized by a specific molecular pathway. Full article

Phenotypic traits of fungi and their automated extraction are crucial for evaluating genetic diversity, breeding new varieties, and estimating yield. However, research on the high-throughput, rapid, and non-destructive extraction of fungal phenotypic traits using 3D point clouds remains limited. In this study, a smart phone is used to capture multi-view images of shiitake mushrooms (Lentinula edodes) from three different heights and angles, employing the YOLOv8x model to segment the primary image regions. The segmented images were reconstructed in 3D using Structure from Motion (SfM) and Multi-View Stereo (MVS). To automatically segment individual mushroom instances, we developed a CP-PointNet++ network integrated with clustering methods, achieving an overall accuracy (OA) of 97.45% in segmentation. The computed phenotype correlated strongly with manual measurements, yielding R² > 0.8 and nRMSE < 0.09 for the pileus transverse and longitudinal diameters, R² = 0.53 and RMSE = 3.26 mm for the pileus height, R² = 0.79 and nRMSE = 0.12 for stipe diameter, and R² = 0.65 and RMSE = 4.98 mm for the stipe height. Using these parameters, yield estimation was performed using PLSR, SVR, RF, and GRNN machine learning models, with GRNN demonstrating superior performance (R² = 0.91). This approach was also adaptable for extracting phenotypic traits of other fungi, providing valuable support for fungal breeding initiatives. Full article

A high-throughput platform for assessing the activity of synthetic or natural compounds on the motility and development of Haemonchus contortus larvae has been established for identifying new anthelmintic compounds active against strongylid nematodes. This study evaluated the impact of serum supplementation on larval development, motility and survival in vitro and its implications for phenotypic compound screening. Of five blood components assessed, 7.5% sheep serum significantly enhanced larval development, motility and survival compared to the original medium (LB*), leading to the formulation of an improved medium (LBS*). Proteomic analysis revealed marked differences in protein expression in larvae cultured in LBS* versus LB*, including molecules associated with structural integrity and metabolic processes. The phenotypic screening of 240 compounds (“Global Priority Box” from Medicines Malaria Venture) using LBS* yielded results distinct from those in LB*, highlighting the effect of culture conditions on screening assessments. These findings indicate/emphasise the critical need to evaluate and optimise culture media for physiologically relevant conditions in screening platforms, improving the reliability of anthelmintic discovery. Full article

Plant genomics have progressed significantly due to advances in information technology, but phenotypic measurement technology has not kept pace, hindering plant breeding. As maize is one of China’s three main grain crops, accurately measuring plant height is crucial for assessing crop growth and productivity. This study addresses the challenges of plant segmentation and inaccurate plant height extraction in maize populations under field conditions. A three-dimensional dense point cloud was reconstructed using the structure from motion–multi-view stereo (SFM-MVS) method, based on multi-view image sequences captured by an unmanned aerial vehicle (UAV). To improve plant segmentation, we propose a column space approximate segmentation algorithm, which combines the column space method with the enclosing box technique. The proposed method achieved a segmentation accuracy exceeding 90% in dense canopy conditions, significantly outperforming traditional algorithms, such as region growing (80%) and Euclidean clustering (75%). Furthermore, the extracted plant heights demonstrated a high correlation with manual measurements, with R² values ranging from 0.8884 to 0.9989 and RMSE values as low as 0.0148 m. However, the scalability of the method for larger agricultural operations may face challenges due to computational demands when processing large-scale datasets and potential performance variability under different environmental conditions. Addressing these issues through algorithm optimization, parallel processing, and the integration of additional data sources such as multispectral or LiDAR data could enhance its scalability and robustness. The results demonstrate that the method can accurately reflect the heights of maize plants, providing a reliable solution for large-scale, field-based maize phenotyping. The method has potential applications in high-throughput monitoring of crop phenotypes and precision agriculture. Full article

Maize leaf area offers valuable insights into physiological processes, playing a critical role in breeding and guiding agricultural practices. The Azure Kinect DK possesses the real-time capability to capture and analyze the spatial structural features of crops. However, its further application in maize leaf area measurement is constrained by RGB–depth misalignment and limited sensitivity to detailed organ-level features. This study proposed a novel approach to address and optimize the limitations of the Azure Kinect DK through the multimodal coupling of RGB-D data for enhanced organ-level crop phenotyping. To correct RGB–depth misalignment, a unified recalibration method was developed to ensure accurate alignment between RGB and depth data. Furthermore, a semantic information-guided depth inpainting method was proposed, designed to repair void and flying pixels commonly observed in Azure Kinect DK outputs. The semantic information was extracted using a joint YOLOv11-SAM2 model, which utilizes supervised object recognition prompts and advanced visual large models to achieve precise RGB image semantic parsing with minimal manual input. An efficient pixel filter-based depth inpainting algorithm was then designed to inpaint void and flying pixels and restore consistent, high-confidence depth values within semantic regions. A validation of this approach through leaf area measurements in practical maize field applications—challenged by a limited workspace, constrained viewpoints, and environmental variability—demonstrated near-laboratory precision, achieving an MAPE of 6.549%, RMSE of 4.114 cm², MAE of 2.980 cm², and R² of 0.976 across 60 maize leaf samples. By focusing processing efforts on the image level rather than directly on 3D point clouds, this approach markedly enhanced both efficiency and accuracy with the sufficient utilization of the Azure Kinect DK, making it a promising solution for high-throughput 3D crop phenotyping. Full article

Acute lymphoblastic leukaemia is the most common childhood malignancy that remains a leading cause of death in childhood. It may be characterised by multiple known recurrent genetic aberrations that inform prognosis, the most common being hyperdiploidy and t(12;21) ETV6::RUNX1. We aimed to assess the applicability of a new imaging flow cytometry methodology that incorporates cell morphology, immunophenotype, and fluorescence in situ hybridisation (FISH) to identify aneuploidy of chromosomes 4 and 21 and the translocation ETV6::RUNX1. We evaluated this new “immuno-flowFISH” platform on 39 cases of paediatric ALL of B-lineage known to have aneuploidy of chromosomes 4 and 21 and the translocation ETV6::RUNX1. After identifying the leukaemic population based on immunophenotype (i.e., expression of CD34, CD10, and CD19 antigens), we assessed for copy numbers of loci for the centromeres of chromosomes 4 and 21 and the ETV6 and RUNX1 regions using fluorophore-labelled DNA probes in more than 1000 cells per sample. Trisomy 4 and 21, tetrasomy 21, and translocations of ETV6::RUNX1, as well as gains and losses of ETV6 and RUNX1, could all be identified based on FISH spot counts and digital imagery. There was variability in clonal makeup in individual cases, suggesting the presence of sub-clones. Copy number alterations and translocations could be detected even when the cell population comprised less than 1% of cells and included cells with a mature B-cell phenotype, i.e., CD19-positive, lacking CD34 and CD10. In this proof-of-principle study of 39 cases, this sensitive and specific semi-automated high-throughput imaging flow cytometric immuno-flowFISH method has been able to show that alterations in ploidy and ETV6::RUNX1 could be detected in the 39 cases of paediatric ALL. This imaging flow cytometric FISH method has potential applications for diagnosis and monitoring disease and marrow regeneration (i.e., distinguishing residual ALL from regenerating haematogones) following chemotherapy. Full article

In vitro models play a pivotal role in advancing our understanding of neurodegenerative diseases (NDs) such as Parkinson’s and Alzheimer’s disease (PD and AD). Traditionally, 2D cell cultures have been instrumental in elucidating the cellular mechanisms underlying these diseases. Cultured cells derived from patients or animal models provide valuable insights into the pathological processes at the cellular level. However, they often lack the native tissue environment complexity, limiting their ability to fully recapitulate their features. In contrast, 3D models offer a more physiologically relevant platform by mimicking the 3D brain tissue architecture. These models can incorporate multiple cell types, including neurons, astrocytes, and microglia, creating a microenvironment that closely resembles the brain’s complexity. Bioengineering approaches allow researchers to better replicate cell–cell interactions, neuronal connectivity, and disease-related phenotypes. Both 2D and 3D models have their advantages and limitations. While 2D cultures provide simplicity and scalability for high-throughput screening and basic processes, 3D models offer enhanced physiological relevance and better replicate disease phenotypes. Integrating findings from both model systems can provide a better understanding of NDs, ultimately aiding in the development of novel therapeutic strategies. Here, we review existing 2D and 3D in vitro models for the study of PD and AD. Full article

Modern breeding technologies and the development of quantitative trait locus (QTL) mapping have brought about a new era in peach breeding. This study examines the complex genetic structure that underlies the morphology of peach fruits, paying special attention to the interaction between genome editing, genomic selection, and marker-assisted selection. Breeders now have access to precise tools that enhance crop resilience, productivity, and quality, facilitated by QTL mapping, which has significantly advanced our understanding of the genetic determinants underlying essential traits such as fruit shape, size, and firmness. New technologies like CRISPR/Cas9 and genomic selection enable the development of cultivars that can withstand climate change and satisfy consumer demands with unprecedented precision in trait modification. Genotype–environment interactions remain a critical challenge for modern breeding efforts, which can be addressed through high-throughput phenotyping and multi-environment trials. This work shows how combining genome-wide association studies and machine learning can improve the synthesis of multi-omics data and result in faster breeding cycles while preserving genetic diversity. This study outlines a roadmap that prioritizes the development of superior cultivars utilizing cutting-edge methods and technologies in order to address evolving agricultural and environmental challenges. Full article

Zinc is an essential microelement of enzymes and proteins in wheat grains and humans. A deficiency in zinc content can lead to decreased wheat yield and low zinc content in grains, which in turn leads to insufficient dietary zinc intake. One recombinant inbred line (RIL) population derived from crosses Avocet/Huites (AH population) was used to map QTL for grain zinc content (GZnC) using diversity array technology (DArT). Nine QTLs were identified on chromosomes 2D, 3B, 4A, 4D, 5A, 5B, 6A, 7A, and 7D. Among them, QGZn.haust-AH-2D was detected in multiple environments, accounting for 5.61% to 11.27% of the phenotypic variation with a physical interval of 13.62 Mb to 17.82 Mb. Meanwhile, a genome-wide association study (GWAS) (CH population) comprising 243 cultivars or advanced lines revealed some genetic loci associated with zinc content in the wheat 660K single-nucleotide polymorphism (SNP) array. This was also identified within the physical interval of 13.61 Mb to 15.12 Mb of chromosome 2D, which accounted for 8.99% to 11.58% of the phenotypic variation in five models. A high-throughput competitive allele specific PCR (KASP) marker was developed, which verified the wheat natural population (NA population). QGZn.haust-AH-2D was fine mapped into a narrow region named TaZn-2DS between KAZn-2D-3 and 1111273 at a physical distance of 2.70 Mb, and the genetic effect of TaZn-2DS was 11.43%. This study shows that TaZn-2DS is associated with zinc content, and develops KAZn-2D-3 markers for the genetic improvement of nutritional quality in wheat. Full article

High-throughput measurements of phenotypic parameters in plants generate substantial data, significantly improving agricultural production optimization and breeding efficiency. However, these measurements face several challenges, including environmental variability, sample heterogeneity, and complex data processing. This study presents a method applicable to stem and leaf segmentation and parameter extraction during the tomato seedling stage, utilizing three-dimensional point clouds. Focusing on tomato seedlings, data was captured using a depth camera to create point cloud models. The RANSAC, region-growing, and greedy projection triangulation algorithms were employed to extract phenotypic parameters such as plant height, stem thickness, leaf area, and leaf inclination angle. The results showed strong correlations, with coefficients of determination for manually measured parameters versus extracted 3D point cloud parameters being 0.920, 0.725, 0.905, and 0.917, respectively. The root-mean-square errors were 0.643, 0.168, 1.921, and 4.513, with absolute percentage errors of 3.804%, 5.052%, 5.509%, and 7.332%. These findings highlight a robust relationship between manual measurements and the extracted parameters, establishing a technical foundation for high-throughput automated phenotypic parameter extraction in tomato seedlings. Full article

The maize tassel represents one of the most pivotal organs dictating maize yield and quality. Investigating its phenotypic information constitutes an exceedingly crucial task within the realm of breeding work, given that an optimal tassel structure is fundamental for attaining high maize yields. High-throughput phenotyping technologies furnish significant tools to augment the efficiency of analyzing maize tassel phenotypic information. Towards this end, we engineered a fully automated multi-angle digital imaging apparatus dedicated to maize tassels. This device was employed to capture images of tassels from 1227 inbred maize lines falling under three genotype classifications (NSS, TST, and SS). By leveraging the 3D reconstruction algorithm SFM (Structure from Motion), we promptly obtained point clouds of the maize tassels. Subsequently, we harnessed the TreeQSM algorithm, which is custom-designed for extracting tree topological structures, to extract 11 archetypal structural phenotypic parameters of the maize tassels. These encompassed main spike diameter, crown height, main spike length, stem length, stem diameter, the number of branches, total branch length, average crown diameter, maximum crown diameter, convex hull volume, and crown area. Finally, we compared the GFC (Gaussian Fuzzy Clustering algorithm) used in this study with commonly used algorithms, such as RF (Random Forest), SVM (Support Vector Machine), and BPNN (BP Neural Network), as well as k-Means, HCM (Hierarchical), and FCM (Fuzzy C-Means). We then conducted a correlation analysis between the extracted phenotypic parameters of the maize tassel structure and the genotypes of the maize materials. The research results showed that the Gaussian Fuzzy Clustering algorithm was the optimal choice for clustering maize genotypes. Specifically, its classification accuracies for the Non-Stiff Stalk (NSS) genotype and the Tropical and Subtropical (TST) genotype reached 67.7% and 78.5%, respectively. Moreover, among the materials with different maize genotypes, the number of branches, the total branch length, and the main spike length were the three indicators with the highest variability, while the crown volume, the average crown diameter, and the crown area were the three indicators with the lowest variability. This not only provided an important reference for the in-depth exploration of the variability of the phenotypic parameters of maize tassels but also opened up a new approach for screening breeding materials. Full article

High-throughput proteomic platforms are crucial to identify novel Alzheimer’s disease (AD) biomarkers and pathways. In this study, we evaluated the reproducibility and reliability of aptamer-based (SomaScan^® 7k) and antibody-based (Olink^® Explore 3k) proteomic platforms in cerebrospinal fluid (CSF) samples from the Ace Alzheimer Center Barcelona real-world cohort. Intra- and inter-platform reproducibility were evaluated through correlations between two independent SomaScan^® assays analyzing the same samples, and between SomaScan^® and Olink^® results. Association analyses were performed between proteomic measures, CSF biological traits, sample demographics, and AD endophenotypes. Our 12-category metric of reproducibility combining correlation analyses identified 2428 highly reproducible SomaScan CSF measures, with over 600 proteins well reproduced on another proteomic platform. The association analyses among AD clinical phenotypes revealed that the significant associations mainly involved reproducible proteins. The validation of reproducibility in these novel proteomics platforms, measured using this scarce biomaterial, is essential for accurate analysis and proper interpretation of innovative results. This classification metric could enhance confidence in multiplexed proteomic platforms and improve the design of future panels. Full article

Semantic segmentation of three-dimensional (3D) plant point clouds at the stem-leaf level is foundational and indispensable for high-throughput tomato phenotyping systems. However, existing semantic segmentation methods often suffer from issues such as low precision and slow inference speed. To address these challenges, we propose an innovative encoding-decoding structure, incorporating voxel sparse convolution (SpConv) and attention-based feature fusion (VSCAFF) to enhance semantic segmentation of the point clouds of high-resolution tomato seedling images. Tomato seedling point clouds from the Pheno4D dataset labeled into semantic classes of ‘leaf’, ‘stem’, and ‘soil’ are applied for the semantic segmentation. In order to reduce the number of parameters so as to further improve the inference speed, the SpConv module is designed to function through the residual concatenation of the skeleton convolution kernel and the regular convolution kernel. The feature fusion module based on the attention mechanism is designed by giving the corresponding attention weights to the voxel diffusion features and the point features in order to avoid the ambiguity of points with different semantics having the same characteristics caused by the diffusion module, in addition to suppressing noise. Finally, to solve model training class bias caused by the uneven distribution of point cloud classes, the composite loss function of Lovász-Softmax and weighted cross-entropy is introduced to supervise the model training and improve its performance. The results show that mIoU of VSCAFF is 86.96%, which outperformed the performance of PointNet, PointNet++, and DGCNN, respectively. IoU of VSCAFF achieves 99.63% in the soil class, 64.47% in the stem class, and 96.72% in the leaf class. The time delay of 35ms in inference speed is better than PointNet++ and DGCNN. The results demonstrate that VSCAFF has high performance and inference speed for semantic segmentation of high-resolution tomato point clouds, and can provide technical support for the high-throughput automatic phenotypic analysis of tomato plants. Full article

In the context of bone fractures, the influence of the mechanical environment on the healing outcome is widely accepted, while its influence at the cellular level is still poorly understood. This study explores the influence of mechanical load on naïve mesenchymal stem cell (MSC) differentiation, focusing on hypertrophic chondrocyte differentiation. Unlike primary bone healing, which involves the direct differentiation of MSCs into bone-forming cells, endochondral ossification uses an intermediate cartilage template that remodels into bone. A high-throughput uniaxial bioreactor system (StrainBot) was used to apply varying percentages of strain on naïve MSCs encapsulated in GelMa hydrogels. This research shows that cyclic uniaxial compression alone directs naïve MSCs towards a hypertrophic chondrocyte phenotype. This was demonstrated by increased cell volumes and reduced glycosaminoglycan (GAG) production, along with an elevated expression of hypertrophic markers such as MMP13 and Type X collagen. In contrast, Type II collagen, typically associated with resting chondrocytes, was poorly detected under mechanical loading alone conditions. The addition of chondrogenic factor TGFβ1 in the culture medium altered these outcomes. TGFβ1 induced chondrogenic differentiation, as indicated by higher GAG/DNA production and Type II collagen expression, overshadowing the effect of mechanical loading. This suggests that, under mechanical strain, hypertrophic differentiation is hindered by TGFβ1, while chondrogenesis is promoted. Biochemical analyses further confirmed these findings. Mechanical deformation alone led to a larger cell size and a more rounded cell morphology characteristic of hypertrophic chondrocytes, while lower GAG and proteoglycan production was observed. Immunohistology staining corroborated the gene expression data, showing increased Type X collagen with mechanical strain. Overall, this study indicates that mechanical loading alone drives naïve MSCs towards a hypertrophic chondrocyte differentiation path. These insights underscore the critical role of mechanical forces in MSC differentiation and have significant implications for bone healing, regenerative medicine strategies and rehabilitation protocols. Full article

Large-scale phenotyping using unmanned aerial vehicles (UAVs) has been considered an important tool for plant selection. This study aimed to estimate the correlations between agronomic data and vegetation indices (VIs) obtained at different flight heights and to select prediction models to evaluate the potential use of aerial imaging in cassava breeding programs. Various VIs were obtained and analyzed using mixed models to derive the best linear unbiased predictors, heritability parameters, and correlations with various agronomic traits. The VIs were also used to build prediction models for agronomic traits. Aerial imaging showed high potential for estimating plant height, regardless of flight height (r = 0.99), although lower-altitude flights (20 m) resulted in less biased estimates of this trait. Multispectral sensors showed higher correlations compared to RGB, especially for vigor, shoot yield, and fresh root yield (−0.40 ≤ r ≤ 0.50). The heritability of VIs at different flight heights ranged from moderate to high (0.51 ≤ HCullis2 ≤ 0.94), regardless of the sensor used. The best prediction models were observed for the traits of plant vigor and dry matter content, using the Generalized Linear Model with Stepwise Feature Selection (GLMSS) and the K-Nearest Neighbor (KNN) model. The predictive ability for dry matter content increased with flight height for the GLMSS model (R2 = 0.26 at 20 m and R2 = 0.44 at 60 m), while plant vigor ranged from R2 = 0.50 at 20 m to R2 = 0.47 at 40 m in the KNN model. Our results indicate the practical potential of implementing high-throughput phenotyping via aerial imaging for rapid and efficient selection in breeding programs. Full article