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.

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.

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.

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.

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.

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.

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)] 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 10 to 10 cells mL and a low analytic limitation of 30 cells mL were achieved for glioma cell.

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.

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.

Anatomical Data Augmentation via Fluid-based Image Registration.

We introduce a fluid-based image augmentation method for medical image analysis. In contrast to existing methods, our framework generates anatomically meaningful images via interpolation from the geodesic subspace underlying given samples. Our approach consists of three steps: 1) given a source image and a set of target images, we construct a geodesic subspace using the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model; 2) we sample transformations from the resulting geodesic subspace; 3) we obtain deformed images and segmentations via interpolation.

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.

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.

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.

Parallel multiple instance learning for extremely large histopathology image analysis.

Background: Histopathology images are critical for medical diagnosis, e.g., cancer and its treatment.