Joint Shape Reconstruction and Registration via a Shared Hybrid Diffeomorphic Flow.

Deep implicit functions (DIFs) effectively represent shapes by using a neural network to map 3D spatial coordinates to scalar values that encode the shape's geometry, but it is difficult to establish correspondences between shapes directly, limiting their use in medical image registration. The recently presented deformation field-based methods achieve implicit templates learning via template field learning with DIFs and deformation field learning, establishing shape correspondence through deformation fields. Although these approaches enable joint learning of shape representation and shape correspondence, the decoupled optimization for template field and deformation field, caused by the absence of deformation annotations lead to a relatively accurate template field but an underoptimized deformation field.

TD-HCN: A trend-driven hypergraph convolutional network for stock return prediction.

Stock data analysis has become one of the most challenging tasks in time series data analysis due to its dynamism, complexity, and nonlinearity. Recently, relational graphs have become popular for describing certain important relationships in data, particularly by mapping indirect and direct relationships between stocks into non-Euclidean spaces. Existing graph-based methods mainly capture simple pairwise and static relationships between stocks, so they cannot effectively identify higher-order relationships and characterize the dynamic trends of stock relationships.

A Graph Convolutional Network-Based Fine-Grained Low-Latency Service Slicing Algorithm for 6G Networks.

The future 6G (sixth-generation) mobile communication technology is required to support advanced network services capabilities such as holographic communication, autonomous driving, and the industrial internet, which demand higher data rates, lower latency, and greater reliability. Furthermore, future service classifications will become more fine-grained. To meet the requirements of these low-latency services with varying granularities, this work investigates fine-grained network slicing for low-latency services in 6G networks.

Driven by textual knowledge: A Text-View Enhanced Knowledge Transfer Network for lung infection region segmentation.

Lung infections are the leading cause of death among infectious diseases, and accurate segmentation of the infected lung area is crucial for effective treatment. Currently, segmentation methods that rely solely on imaging data have limited accuracy. Incorporating text information enriched with expert knowledge into the segmentation process has emerged as a novel approach.

A Novel Dual-Network Approach for Real-Time Liveweight Estimation in Precision Livestock Management.

The increasing demand for automation in livestock farming scenarios highlights the need for effective noncontact measurement methods. The current methods typically require either fixed postures and specific positions of the target animals or high computational demands, making them difficult to implement in practical situations. In this study, a novel dual-network framework is presented that extracts accurate contour information instead of segmented images from unconstrained pigs and then directly employs this information to obtain precise liveweight estimates.

MonoRelief: Recovering 2.5D Relief From a Single Image.

In this article, we introduce MonoRelief, a novel method that combines the strengths of a depth map and a normal map to achieve high-quality relief recovery from a single image. By constructing a large-scale relief dataset that encompasses a diverse range of relief shapes, materials, and lighting conditions, we enable the training of a robust normal estimation network capable of handling various types of relief images. Furthermore, we leverage the state-of-the-art method, DepthAnything v2 (Yang et al.

Xiaoqinglong Decoction Attenuates Inflammatory Response and Mitochondrial Injury by Regulating the MyD88/NF-κB Pathway Dependent of TLR4 in Allergic Rhinitis.

Introduction: Allergic rhinitis (AR) is a systemic immune inflammatory response disease of the nasal mucosa. Current treatment strategies for AR are limited, owing to a lack of safety and the inability to cure the condition completely. Xiaoqinglong decoction (XQLD), a classical chinese medicine prescription, is widely used to treat AR with a good curative effect.

MDWConv:CNN based on multi-scale atrous pyramid and depthwise separable convolution for long time series forecasting.

Long time series forecasting has extensive applications in various fields such as power dispatching, traffic control, and weather forecasting. Recently, models based on the Transformer architecture have dominated the field of time series forecasting. However, these methods lack the ability to handle the correlation of multi-scale information and the interaction of information between variables in model design.

NEIL3 Upregulated by TFAP2A Promotes M2 Polarization of Macrophages in Liver Cancer via the Mediation of Glutamine Metabolism.

Introduction: Tumor-associated macrophages, which are part of the tumor microenvironment, are a major factor in cancer progression. However, a complete understanding of the regulatory mechanism of M2 polarization of macrophages (Mø) in liver cancer is yet to be established. This study aimed to investigate the potential mechanism by which NEIL3 influenced M2 Mø polarization in liver cancer.

Multi-scale convolution enhanced transformer for multivariate long-term time series forecasting.

In data analysis and forecasting, particularly for multivariate long-term time series, challenges persist. The Transformer model in deep learning methods has shown significant potential in time series forecasting. The Transformer model's dot-product attention mechanism, however, due to its quadratic computational complexity, impairs training and forecasting efficiency.

Prognostic significance of pyrimidine metabolism-related genes as risk biomarkers in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC), as a malignancy derived from liver tissue, is typically associated with poor prognosis. Increasing evidence suggests a connection between pyrimidine metabolism and HCC progression. The purpose of this study was to establish a model applied to the prediction of HCC patients' overall survival.

Computing Smooth and Integrable Cross Fields via Iterative Singularity Adjustment.

We propose a new method for computing smooth and integrable cross fields on 2D and 3D surfaces. our approach first computes smooth cross fields by minimizing the Dirichlet energy. Unlike existing optimization-based methods, our technique determines the singularity configuration-i.

Transcription Factor MAZ Potentiates the Upregulated NEIL3-mediated Aerobic Glycolysis, thereby Promoting Angiogenesis in Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (HCC) is characterized by high vascularity and notable abnormality of blood vessels, where angiogenesis is a key process in tumorigenesis and metastasis. The main functions of Nei Like DNA Glycosylase 3 (NEIL3) include DNA alcoholization repair, immune response regulation, nervous system development and function, and DNA damage signal transduction. However, the underlying mechanism of high expression NEIL3 in the development and progression of HCC and whether the absence or silencing of NEIL3 inhibits the development of cancer remain unclear.

Multivariate Time Series Forecasting Using Multiscale Recurrent Networks With Scale Attention and Cross-Scale Guidance.

Multivariate time series (MTS) forecasting is considered as a challenging task due to complex and nonlinear interdependencies between time steps and series. With the advance of deep learning, significant efforts have been made to model long-term and short-term temporal patterns hidden in historical information by recurrent neural networks (RNNs) with a temporal attention mechanism. Although various forecasting models have been developed, most of them are single-scale oriented, resulting in scale information loss.

Pact-Net: Parallel CNNs and Transformers for medical image segmentation.

Background And Objective: The image segmentation of diseases can help clinical diagnosis and treatment in medical image analysis. Because medical images usually have low contrast and large changes in the size and shape of some structures, this will lead to over-segmentation and under-segmentation. These problems are particularly evident in the segmentation of skin damage.

Shape-Aware Joint Distribution Alignment for Cross-Domain Image Segmentation.

We present an unsupervised domain adaptation method for image segmentation which aligns high-order statistics, computed for the source and target domains, encoding domain-invariant spatial relationships between segmentation classes. Our method first estimates the joint distribution of predictions for pairs of pixels whose relative position corresponds to a given spatial displacement. Domain adaptation is then achieved by aligning the joint distributions of source and target images, computed for a set of displacements.

CAT: Constrained Adversarial Training for Anatomically-Plausible Semi-Supervised Segmentation.

Deep learning models for semi-supervised medical image segmentation have achieved unprecedented performance for a wide range of tasks. Despite their high accuracy, these models may however yield predictions that are considered anatomically impossible by clinicians. Moreover, incorporating complex anatomical constraints into standard deep learning frameworks remains challenging due to their non-differentiable nature.

COVID19-MLSF: A multi-task learning-based stock market forecasting framework during the COVID-19 pandemic.

The sudden outbreak of COVID-19 has dramatically altered the state of the global economy, and the stock market has become more volatile and even fallen sharply as a result of its negative impact, heightening investors' apprehension regarding the correlation between unexpected events and stock market volatility. Additionally, internal and external characteristics coexist in the stock market. Existing research has struggled to extract more effective stock market features during the COVID-19 outbreak using a single time-series neural network model.

SEVs-mediated miR-6750 transfer inhibits pre-metastatic niche formation in nasopharyngeal carcinoma by targeting M6PR.

Reliable detection of circulating small extracellular vesicles (SEVs) and their miRNA cargo has been needed to develop potential specific non-invasive diagnostic and therapeutic marker for cancer metastasis. Here, we detected miR-6750, the precise molecular function of which was largely unknown, was significantly enriched in serum-SEVs from normal volunteers vs. patients with nasopharyngeal carcinoma (NPC).

SNHG3 regulates NEIL3 via transcription factor E2F1 to mediate malignant proliferation of hepatocellular carcinoma.

The involvement of small nucleolar RNA host gene 3 (SNHG3) in cancer regulation has been reported. This study attempted to deeply investigate the molecular regulatory mechanism of SNHG3 on malignant progression of hepatocellular carcinoma (HCC). According to TCGA analysis, high SNHG3 expression was a risk factor for poor prognosis of HCC patients.

Neural Modeling of Portrait Bas-Relief From a Single Photograph.

In this paper, we present an end-to-end neural solution to model portrait bas-relief from a single photograph, which is cast as a problem of image-to-depth translation. The main challenge is the lack of bas-relief data for network training. To solve this problem, we propose a semi-automatic pipeline to synthesize bas-relief samples.

Fast Generation of Superpixels With Lattice Topology.

Serving as an essential step for many applications of image processing, superpixel generation has attracted a lot of attentions. Most existing superpixel generation algorithms focus on the boundary adherence and compactness of the superpixels, but ignore the topological consistency between the superpixels, which severely limites their applications in the subsequent tasks, especially in the CNN based image processing tasks. In this paper, we present a fast lattice superpixel generation algorithm, which can generate superpixels with lattice topology like the original pixels.

Mechanism of RNA circHIPK3 Involved in Resistance of Lung Cancer Cells to Gefitinib.

To study the mechanism of circular ribonucleic acid (RNA) circHIPK3 involved in the resistance of lung cancer cells to gefitinib, 110 patients with lung cancer were recruited as the research objects, and the tumor tissue and para-cancerous tissue of each patient's surgical specimens were collected and paraffinized to detect the expression of circHIPK3 in different tissues. Gefitinib drug-resistant cell line of lung cancer was constructed with gefitinib to detect cell apoptosis under different conditions. As a result, the relative expression of circHIPK3 in patients with tumor diameter no less than 3 cm was dramatically inferior to that in patients with tumor diameter less than 3 cm ( < 0.