Potential regulator of meat quality in geese: C1QTNF1 implications on cell proliferation and muscle growth.

Goose creates important economic value depending on their enrich nutrients of meat. Our previous study investigates potential candidate genes associated with variations in meat quality between Xianghai Flying (XHF) Goose and Zi Goose through genomic and transcriptome integrated analysis. Screening of 5 differential expression candidate genes related to muscle development identified by the F XP-EHH and RNA-seq in breast muscle from various geese.

A dataset for fine-grained seed recognition.

The research of plant seeds has always been a focus of agricultural and forestry research, and seed identification is an indispensable part of it. With the continuous application of artificial intelligence technology in the field of agriculture, seed identification through computer vision can effectively promote the development of agricultural and forestry wisdom. Data is the foundation of computer vision, but there is a lack of suitable datasets in the agricultural field.

Habitual salt preference worsens blood pressure in hospitalized hypertensive patients with omicron infection under epidemic-related stress.

Background: We investigated the synergistic effect of stress and habitual salt preference (SP) on blood pressure (BP) in the hospitalized Omicron-infected patients.

Methods: From 15,185 hospitalized Omicron-infected patients who reported having high BP or hypertension, we recruited 662 patients. All patients completed an electronic questionnaire on diet and stress, and were required to complete morning BP monitoring at least three times.

Undifferentiated carcinoma of the pancreas with osteoclast like giant cells: Literature review with CT/MR imaging findings in 3 cases.

Undifferentiated carcinoma with osteoclast-like giant cells of pancreas (UCOGCP) is a relatively rare tumor worldwide. Its accurate preoperative diagnosis is extremely difficult. Because the mass is usually large and closely related to neighboring structures, it is difficult to locate the tumor and it is often misdiagnosed as pancreatic cancer, neuroendocrine tumor or gastrointestinal stromal tumor.

View-Consistent Heterogeneous Network on Graphs With Few Labeled Nodes.

Performing transductive learning on graphs with very few labeled data, that is, two or three samples for each category, is challenging due to the lack of supervision. In the existing work, self-supervised learning via a single view model is widely adopted to address the problem. However, recent observation shows multiview representations of an object share the same semantic information in high-level feature space.

Evaluation of Modified Calcium Removal Algorithm in dual energy CT of Internal Carotid Artery.

Purpose: To evaluate the performance of a dual-energy (DE) calcium removal software based on a modified three-material decomposition algorithm in assessing the stenosis of the internal carotid artery (ICA) in comparison with mixed images using digital subtraction angiography (DSA) as the reference standard.

Methods: Forty-six patients (38 men; 67±8 years old), including 154 calcified ICA segments C1-C2 (59), C3-C5 (63), C6 (24), and C7 (8), were recruited in this retrospective study. Mixed images and virtual non-calcium (VNCa) images using the modified dual-energy computed tomography (DECT) algorithm were reconstructed.

Making Historical Gyroscopes Alive-2D and 3D Preservations by Sensor Fusion and Open Data Access.

The preservation of cultural heritage assets of all kind is an important task for modern civilizations. This also includes tools and instruments that have been used in the previous decades and centuries. Along with the industrial revolution 200 years ago, mechanical and electrical technologies emerged, together with optical instruments.

A Low Temperature Drifting Acoustic Wave Pressure Sensor with an Integrated Vacuum Cavity for Absolute Pressure Sensing.

In this paper we demonstrate a novel acoustic wave pressure sensor, based on an aluminum nitride (AlN) piezoelectric thin film. It contains an integrated vacuum cavity, which is micro-fabricated using a cavity silicon-on-insulator (SOI) wafer. This sensor can directly measure the absolute pressure without the help of an external package, and the vacuum cavity gives the sensor a very accurate reference pressure.

Tuning of Persulfate Activation from a Free Radical to a Nonradical Pathway through the Incorporation of Non-Redox Magnesium Oxide.

Nonradical-based advanced oxidation processes for pollutant removal have attracted much attention due to their inherent advantages. Herein we report that magnesium oxides (MgO) in CuOMgO/FeO not only enhanced the catalytic properties but also switched the free radical peroxymonosulfate (PMS)-activated process into the O based nonradical process. CuOMgO/FeO catalyst exhibited consistent performance in a wide pH range from 5.

Comparison of image quality between split-filter twin beam dual energy and single energy images in abdominal CT.

Purpose: To compare the objective and subjective image quality between composed images from split-filter twin beam dual energy (TBDE) and single-energy computed tomography (SECT) in abdominal CT.

Methods: In this prospective study, 103 patients were imaged using TBDE (n = 51) or SECT (n = 52). The CT number and noise were measured for the following six abdominal structures: liver, spleen, fat, muscle, aorta and portal vein.

Preparation of Yolk-Shell-Structured Co Fe P with Enhanced OER Performance.

The design and development of low-cost, highly efficient, and stable electrocatalysts to take the place of noble-metal catalysts for the oxygen evolution reaction (OER) remain a significant challenge. Herein, the synthesis of yolk-shell-structured binary transition metal phosphide Co Fe P with different Co/Fe ratios by phosphidation of a cobalt ferrite precursor is reported. The as-synthesized Co Fe P catalysts were used for the OER.

Multiview Consensus Graph Clustering.

A graph is usually formed to reveal the relationship between data points and graph structure is encoded by the affinity matrix. Most graph-based multiview clustering methods use predefined affinity matrices and the clustering performance highly depends on the quality of graph. We learn a consensus graph with minimizing disagreement between different views and constraining the rank of the Laplacian matrix.

Adaptive Structure Discovery for Multimedia Analysis Using Multiple Features.

Multifeature learning has been a fundamental research problem in multimedia analysis. Most existing multifeature learning methods exploit graph, which must be computed beforehand, as input to uncover data distribution. These methods have two major problems confronted.

Adaptive Structure Concept Factorization for Multiview Clustering.

Most existing multiview clustering methods require that graph matrices in different views are computed beforehand and that each graph is obtained independently. However, this requirement ignores the correlation between multiple views. In this letter, we tackle the problem of multiview clustering by jointly optimizing the graph matrix to make full use of the data correlation between views.

Graph Learning for Multiview Clustering.

Most existing graph-based clustering methods need a predefined graph and their clustering performance highly depends on the quality of the graph. Aiming to improve the multiview clustering performance, a graph learning-based method is proposed to improve the quality of the graph. Initial graphs are learned from data points of different views, and the initial graphs are further optimized with a rank constraint on the Laplacian matrix.

Diverse Non-Negative Matrix Factorization for Multiview Data Representation.

Non-negative matrix factorization (NMF), a method for finding parts-based representation of non-negative data, has shown remarkable competitiveness in data analysis. Given that real-world datasets are often comprised of multiple features or views which describe data from various perspectives, it is important to exploit diversity from multiple views for comprehensive and accurate data representations. Moreover, real-world datasets often come with high-dimensional features, which demands the efficiency of low-dimensional representation learning approaches.

Feature-Linking Model for Image Enhancement.

Inspired by gamma-band oscillations and other neurobiological discoveries, neural networks research shifts the emphasis toward temporal coding, which uses explicit times at which spikes occur as an essential dimension in neural representations. We present a feature-linking model (FLM) that uses the timing of spikes to encode information. The first spiking time of FLM is applied to image enhancement, and the processing mechanisms are consistent with the human visual system.

Local sparsity enhanced compressed sensing magnetic resonance imaging in uniform discrete curvelet domain.

Background: Compressed sensing(CS) has been well applied to speed up imaging by exploring image sparsity over predefined basis functions or learnt dictionary. Firstly, the sparse representation is generally obtained in a single transform domain by using wavelet-like methods, which cannot produce optimal sparsity considering sparsity, data adaptivity and computational complexity. Secondly, most state-of-the-art reconstruction models seldom consider composite regularization upon the various structural features of images and transform coefficients sub-bands.

New spiking cortical model for invariant texture retrieval and image processing.

Based on the studies of existing local-connected neural network models, in this brief, we present a new spiking cortical neural networks model and find that time matrix of the model can be recognized as a human subjective sense of stimulus intensity. The series of output pulse images of a proposed model represents the segment, edge, and texture features of the original image, and can be calculated based on several efficient measures and forms a sequence as the feature of the original image. We characterize texture images by the sequence for an invariant texture retrieval.