Identification of candidate genes associated with primary feathers of tianfu nonghua ducks based on Genome-wide association studies.

The primary feathers of ducks have important economic value in the poultry industry. This study quantified the primary feather phenotype of Nonghua ducks, including the primary feathers' length, area, distribution of black spots, and feather symmetry. And genome-wide association analysis was used to screen candidate genes that affect the primary feather traits. The genome-wide association study (GWAS) results identified the genetic region related to feather length (FL) on chromosome 2. Through Linkage disequilibrium (LD) analysis, candidate regions (chr2: 115,246,393-116,501,448 bp) were identified and were further annotated to 5 genes: MRS2, GPLD1, ALDH5A1, KIAA0319, and ATP9B. Secondly, candidate regions related to feather black spots were identified on chromosome 21. Through LD analysis, the candidate regions (chr21: 163,552-2,183,853 bp) were screened and further annotated to 47 genes. Among them, STK4, CCN5, and YWHAB genes were related to melanin-related pathways or pigment deposition, which may be key genes affecting the distribution of black spots on feathers. In addition, we also screened 125 genes on multiple chromosomes that may be related to feather symmetry. Among them, significant SNPs on chromosome 1 were further identified as candidate regions (chr1: 142,118,209-142,223,605 bp) through LD analysis and annotated into 2 genes, TGFBRAP1 and LOC113839965. These results reported the genetic basis of the primary feather from multiple phenotypes, and offered valuable insights into the genetic basis for the growth and development of duck feathers and feather color pattern.

Transcriptome-metabolome reveals the molecular changes in meat production and quality in the hybrid populations of Sichuan white goose.

Hybrid breeding has proven to enhance meat quality and is extensively utilized in goose breeding. Nevertheless, there is a paucity of research investigating the molecular mechanisms that underlie the meat quality of hybrid geese. In this study, we employed the Sichuan White Goose as the maternal line for hybridization with the Zhedong White Goose and Tianfu Meat Goose P3 line. We assessed the growth and slaughter meat quality performance of 10-wk-old hybrid offspring in comparison to Sichuan white goose purebred offspring. The results indicate that hybrid geese have significantly improved performance in growth and slaughter meat quality. Furthermore, we conducted a comprehensive analysis of the chest muscles of hybrid offspring through transcriptomics and metabolomics to unravel the effects of hybrid breeding on growth and meat quality. A total of 673 differentially expressed genes (DEGs), and 93 differentially expressed metabolites were identified. The joint analysis highlighted the significant enrichment of DEGs AMPD1, AMPD3, RRM2, ENTPD3, and the metabolite UMP in the nucleotide metabolism pathway. These findings underscore the crucial role of these genetic and metabolic factors in regulating muscle growth and meat quality in hybrid populations.

Efficient learning of Scale-Adaptive Nearly Affine Invariant Networks.

Recent research has demonstrated the significance of incorporating invariance into neural networks. However, existing methods require direct sampling over the entire transformation set, notably computationally taxing for large groups like the affine group. In this study, we propose a more efficient approach by addressing the invariances of the subgroups within a larger group. For tackling affine invariance, we split it into the Euclidean group E(n) and uni-axial scaling group US(n), handling invariance individually. We employ an E(n)-invariant model for E(n)-invariance and average model outputs over data augmented from a US(n) distribution for US(n)-invariance. Our method maintains a favorable computational complexity of O(N2) in 2D and O(N4) in 3D scenarios, in contrast to the O(N6) (2D) and O(N12) (3D) complexities of averaged models. Crucially, the scale range for augmentation adapts during training to avoid excessive scale invariance. This is the first time nearly exact affine invariance is incorporated into neural networks without directly sampling the entire group. Extensive experiments unequivocally confirm its superiority, achieving new state-of-the-art results in affNIST and SIM2MNIST classifications while consuming less than 15% of inference time and fewer computational resources and model parameters compared to averaged models.

Charting Aging Trajectories of Knee Cartilage Thickness for Early Osteoarthritis Risk Prediction: An MRI Study from the Osteoarthritis Initiative Cohort.

Knee osteoarthritis (OA), a prevalent joint disease in the U.S., poses challenges in terms of predicting of its early progression. Although high-resolution knee magnetic resonance imaging (MRI) facilitates more precise OA diagnosis, the heterogeneous and multifactorial aspects of OA pathology remain significant obstacles for prognosis. MRI-based scoring systems, while standardizing OA assessment, are both time-consuming and labor-intensive. Current AI technologies facilitate knee OA risk scoring and progression prediction, but these often focus on the symptomatic phase of OA, bypassing initial-stage OA prediction. Moreover, their reliance on complex algorithms can hinder clinical interpretation. To this end, we make this effort to construct a computationally efficient, easily-interpretable, and state-of-the-art approach aiding in the radiographic OA (rOA) auto-classification and prediction of the incidence and progression, by contrasting an individual's cartilage thickness with a similar demographic in the rOA-free cohort. To better visualize, we have developed the toolset for both prediction and local visualization. A movie demonstrating different subtypes of dynamic changes in local centile scores during rOA progression is available at https://tli3.github.io/KneeOA/. Specifically, we constructed age-BMI-dependent reference charts for knee OA cartilage thickness, based on MRI scans from 957 radiographic OA (rOA)-free individuals from the Osteoarthritis Initiative cohort. Then we extracted local and global centiles by contrasting an individual's cartilage thickness to the rOA-free cohort with a similar age and BMI. Using traditional boosting approaches with our centile-based features, we obtain rOA classification of KLG ≤ 1 versus KLG = 2 (AUC = 0.95, F1 = 0.89), KLG ≤ 1 versus KLG ≥ 2 (AUC = 0.90, F1 = 0.82) and prediction of KLG2 progression (AUC = 0.98, F1 = 0.94), rOA incidence (KLG increasing from < 2 to ≥ 2; AUC = 0.81, F1 = 0.69) and rOA initial transition (KLG from 0 to 1; AUC = 0.64, F1 = 0.65) within a future 48-month period. Such performance in classifying KLG ≥ 2 matches that of deep learning methods in recent literature. Furthermore, its clinical interpretation suggests that cartilage changes, such as thickening in lateral femoral and anterior femoral regions and thinning in lateral tibial regions, may serve as indicators for prediction of rOA incidence and early progression. Meanwhile, cartilage thickening in the posterior medial and posterior lateral femoral regions, coupled with a reduction in the central medial femoral region, may signify initial phases of rOA transition.

Patterns of variation among baseline femoral and tibial cartilage thickness and clinical features: Data from the osteoarthritis initiative.

Objective: To employ novel methodologies to identify phenotypes in knee OA based on variation among three baseline data blocks: 1) femoral cartilage thickness, 2) tibial cartilage thickness, and 3) participant characteristics and clinical features. Methods: Baseline data were from 3321 Osteoarthritis Initiative (OAI) participants with available cartilage thickness maps (6265 knees) and 77 clinical features. Cartilage maps were obtained from 3D DESS MR images using a deep-learning based segmentation approach and an atlas-based analysis developed by our group. Angle-based Joint and Individual Variation Explained (AJIVE) was used to capture and quantify variation, both shared among multiple data blocks and individual to each block, and to determine statistical significance. Results: Three major modes of variation were shared across the three data blocks. Mode 1 reflected overall thicker cartilage among men, those with higher education, and greater knee forces; Mode 2 showed associations between worsening Kellgren-Lawrence Grade, medial cartilage thinning, and worsening symptoms; and Mode 3 contrasted lateral and medial-predominant cartilage loss associated with BMI and malalignment. Each data block also demonstrated individual, independent modes of variation consistent with the known discordance between symptoms and structure in knee OA and reflecting the importance of features such as physical function, symptoms, and comorbid conditions independent of structural damage. Conclusions: This exploratory analysis, combining the rich OAI dataset with novel methods for determining and visualizing cartilage thickness, reinforces known associations in knee OA while providing insights into the potential for data integration in knee OA phenotyping.

Transcriptomic analysis of the thyroid and ovarian stroma reveals key pathways and potential candidate genes associated with egg production in ducks.

The importance of thyroid-related genes has been repeatedly mentioned in the transcriptome studies of poultry with different laying performance, yet there are few systematic studies to unravel the regulatory mechanisms of the thyroid-ovary axis in the poultry egg production process. In this study, we compared the transcriptome profiles in the thyroid and ovarian stroma between high egg production (GP) and low egg production (DP) ducks, and then revealed the pathways and candidate genes involved in the process. We identified 1,114 and 733 differentially expressed genes (DEGs) in the thyroid and ovarian stroma, separately. The Gene Ontology (GO) analysis showed that a total of 504 and 189 GO terms were identified in the thyroid and ovarian stroma (P < 0.05). Three common GO terms were identified from the top 5 GO terms with the highest significant level in two tissues, including extracellular space, calcium ion binding, and integral component of plasma membrane. The enrichment analysis of the Kyoto Encyclopedia of Genes and Genomes (KEGG) showed that 15 and 14 KEGG pathways were significantly (P < 0.05) enriched in the thyroid and ovarian stroma, respectively. And, there were 8 common pathways, including neuroactive ligand-receptor interaction, calcium signaling pathway, ECM-receptor interaction, PPAR signaling pathway, melanogenesis, wnt signaling pathway, vascular smooth muscle contraction, and cytokine-cytokine receptor interaction. Notably, the neuroactive ligand-receptor interaction pathway was the most significantly enriched by the DEGs both in the thyroid and ovarian stroma. The interaction among DEGs enriched in the neuroactive ligand-receptor interaction and ECM-receptor interaction suggested that the thyroid may regulate ovarian development by these genes. Through integrated analysis of the protein-protein interaction (PPI) network and KEGG pathway maps, 9 key DEGs (PTH, THBS2, THBS4, CD36, ADIPOQ, ACSL6, PRKAA2, CRH, and PCK1) were identified, which could play crucial roles in the thyroid to regulate ovarian function and then affect egg-laying performance between GP and DP. This study serves as a basis to explore the molecular mechanism of the thyroid affecting ovarian function and egg production in female ducks and may help to identify molecular markers that can be used for duck genetic selection.

Simple and free-labeled voltammetry determination of glioma cell.

Glioma is one of the most frequent brain tumors with substantial mortality and morbidity, thus designing a simple sensor for achieving highly efficient determination of glioma cell is of great importance. In this work, by preparing 3,4,9,10-perylene tetracarboxylic acid (PTCA) non-covalently functionalized carbon black (CB) nanohybrids (CB-PTCA) as matrix and using angiopep-2 peptide (Ang-2) as receptor to recognize selectively glioma cell, a simple and free-labeled voltammetry sensor was developed for the first time to detect glioma cell by using Ang-2 and CB-PTCA modified glassy carbon electrode (Ang-2/CB/GCE): via introducing typical [Fe(CN)6]4-/3- as the signal probe, its electrochemical signal would be suppressed when glioma cells were recognized by Ang-2, and the values of peak current difference varied along with the concentrations of glioma cells. After optimizing the related testing conditions (the amounts of CB-PTCA, concentration of Ang-2 and recognition time of Ang-2 towards glioma cells), a wide linearity from 102 to 106 cells mL-1 and a low analytic limitation of 30 cells mL-1 were achieved for glioma cell. Furthermore, the application of the proposed immunosensor in human serum was also studied, revealing that the results are reliable and the designed proposal offers a significant clinical application for glioma detection.

DADP: Dynamic abnormality detection and progression for longitudinal knee magnetic resonance images from the Osteoarthritis Initiative.

Osteoarthritis (OA) is the most common disabling joint disease. Magnetic resonance (MR) imaging has been commonly used to assess knee joint degeneration due to its distinct advantage in detecting morphologic cartilage changes. Although several statistical methods over conventional radiography have been developed to perform quantitative cartilage analyses, little work has been done capturing the development and progression of cartilage lesions (or abnormal regions) and how they naturally progress. There are two major challenges, including (i) the lack of building spatial-temporal correspondences and correlations in cartilage thickness and (ii) the spatio-temporal heterogeneity in abnormal regions. The goal of this work is to propose a dynamic abnormality detection and progression (DADP) framework for quantitative cartilage analysis, while addressing the two challenges. First, spatial correspondences are established on flattened 2D cartilage thickness maps extracted from 3D knee MR images both across time within each subject and across all subjects. Second, a dynamic functional mixed effects model (DFMEM) is proposed to quantify abnormality progression across time points and subjects, while accounting for the spatio-temporal heterogeneity. We systematically evaluate our DADP using simulations and real data from the Osteoarthritis Initiative (OAI). Our results show that DADP not only effectively detects subject-specific dynamic abnormal regions, but also provides population-level statistical disease mapping and subgroup analysis.

A Deep Network for Joint Registration and Reconstruction of Images with Pathologies.

Registration of images with pathologies is challenging due to tissue appearance changes and missing correspondences caused by the pathologies. Moreover, mass effects as observed for brain tumors may displace tissue, creating larger deformations over time than what is observed in a healthy brain. Deep learning models have successfully been applied to image registration to offer dramatic speed up and to use surrogate information (e.g., segmentations) during training. However, existing approaches focus on learning registration models using images from healthy patients. They are therefore not designed for the registration of images with strong pathologies for example in the context of brain tumors, and traumatic brain injuries. In this work, we explore a deep learning approach to register images with brain tumors to an atlas. Our model learns an appearance mapping from images with tumors to the atlas, while simultaneously predicting the transformation to atlas space. Using separate decoders, the network disentangles the tumor mass effect from the reconstruction of quasi-normal images. Results on both synthetic and real brain tumor scans show that our approach outperforms cost function masking for registration to the atlas and that reconstructed quasi-normal images can be used for better longitudinal registrations.

Networks for Joint Affine and Non-parametric Image Registration.

We introduce an end-to-end deep-learning framework for 3D medical image registration. In contrast to existing approaches, our framework combines two registration methods: an affine registration and a vector momentum-parameterized stationary velocity field (vSVF) model. Specifically, it consists of three stages. In the first stage, a multi-step affine network predicts affine transform parameters. In the second stage, we use a U-Net-like network to generate a momentum, from which a velocity field can be computed via smoothing. Finally, in the third stage, we employ a self-iterable map-based vSVF component to provide a non-parametric refinement based on the current estimate of the transformation map. Once the model is trained, a registration is completed in one forward pass. To evaluate the performance, we conducted longitudinal and cross-subject experiments on 3D magnetic resonance images (MRI) of the knee of the Osteoarthritis Initiative (OAI) dataset. Results show that our framework achieves comparable performance to state-of-the-art medical image registration approaches, but it is much faster, with a better control of transformation regularity including the ability to produce approximately symmetric transformations, and combining affine as well as non-parametric registration.

Region-specific Diffeomorphic Metric Mapping.

We introduce a region-specific diffeomorphic metric mapping (RDMM) registration approach. RDMM is non-parametric, estimating spatio-temporal velocity fields which parameterize the sought-for spatial transformation. Regularization of these velocity fields is necessary. In contrast to existing non-parametric registration approaches using a fixed spatially-invariant regularization, for example, the large displacement diffeomorphic metric mapping (LDDMM) model, our approach allows for spatially-varying regularization which is advected via the estimated spatio-temporal velocity field. Hence, not only can our model capture large displacements, it does so with a spatio-temporal regularizer that keeps track of how regions deform, which is a more natural mathematical formulation. We explore a family of RDMM registration approaches: 1) a registration model where regions with separate regularizations are pre-defined (e.g., in an atlas space or for distinct foreground and background regions), 2) a registration model where a general spatially-varying regularizer is estimated, and 3) a registration model where the spatially-varying regularizer is obtained via an end-to-end trained deep learning (DL) model. We provide a variational derivation of RDMM, showing that the model can assure diffeomorphic transformations in the continuum, and that LDDMM is a particular instance of RDMM. To evaluate RDMM performance we experiment 1) on synthetic 2D data and 2) on two 3D datasets: knee magnetic resonance images (MRIs) of the Osteoarthritis Initiative (OAI) and computed tomography images (CT) of the lung. Results show that our framework achieves comparable performance to state-of-the-art image registration approaches, while providing additional information via a learned spatio-temporal regularizer. Further, our deep learning approach allows for very fast RDMM and LDDMM estimations. Code is available at https://github.com/uncbiag/registration.

Contextual Additive Networks to Efficiently Boost 3D Image Segmentations.

Semantic segmentation for 3D medical images is an important task for medical image analysis which would benefit from more efficient approaches. We propose a 3D segmentation framework of cascaded fully convolutional networks (FCNs) with contextual inputs and additive outputs. Compared to previous contextual cascaded networks the additive output forces each subsequent model to refine the output of previous models in the cascade. We use U-Nets of various complexity as elementary FCNs and demonstrate our method for cartilage segmentation on a large set of 3D magnetic resonance images (MRI) of the knee. We show that a cascade of simple U-Nets may for certain tasks be superior to a single deep and complex U-Net with almost two orders of magnitude more parameters. Our framework also allows greater flexibility in trading-off performance and efficiency during testing and training.

Parallel multiple instance learning for extremely large histopathology image analysis.

BACKGROUND: Histopathology images are critical for medical diagnosis, e.g., cancer and its treatment. A standard histopathology slice can be easily scanned at a high resolution of, say, 200,000×200,000 pixels. These high resolution images can make most existing imaging processing tools infeasible or less effective when operated on a single machine with limited memory, disk space and computing power. RESULTS: In this paper, we propose an algorithm tackling this new emerging "big data" problem utilizing parallel computing on High-Performance-Computing (HPC) clusters. Experimental results on a large-scale data set (1318 images at a scale of 10 billion pixels each) demonstrate the efficiency and effectiveness of the proposed algorithm for low-latency real-time applications. CONCLUSIONS: The framework proposed an effective and efficient system for extremely large histopathology image analysis. It is based on the multiple instance learning formulation for weakly-supervised learning for image classification, segmentation and clustering. When a max-margin concept is adopted for different clusters, we obtain further improvement in clustering performance.