Explainable survival analysis with uncertainty using convolution-involved vision transformer.

Image-based precision medicine research is able to help doctors make better decisions on treatments. Among all kinds of medical images, a special form is called Whole Slide Image (WSI), which is used for diagnosing patients with cancer, aiming to enable more accurate survival prediction with its high resolution. However, One unique challenge of the WSI-based prediction models is processing the gigabyte-size or even terabyte-size WSIs, which would make most models computationally infeasible.

Highly thermal conductive shape-stabilized composite phase change materials based on boron nitride and expanded graphite for solar thermal applications.

Phase change heat storage technology is a good way to solve the problem that the temperature of solar hot water outlet is affected by the time domain. A stearic acid (SA)-benzamide (BA) eutectic mixture is a potential phase change material (PCM), but it still has the disadvantages of low thermal conductivity and liquid leakage. In this work, a new high thermal conductive shape-stabilized composite PCM was prepared by adding boron nitride (BN) and expanded graphite (EG) to a melted SA-BA eutectic mixture using an ultrasonic and melt adsorption method, and its phase change temperature, latent heat, crystal structure, morphology, thermal conductivity, chemical stability, thermal stability, cycle stability and leakage characteristics were characterized.

A convolutional neural network with pixel-wise sparse graph reasoning for COVID-19 lesion segmentation in CT images.

The COVID-19 pandemic has extremely threatened human health, and automated algorithms are needed to segment infected regions in the lung using computed tomography (CT). Although several deep convolutional neural networks (DCNNs) have proposed for this purpose, their performance on this task is suppressed due to the limited local receptive field and deficient global reasoning ability. To address these issues, we propose a segmentation network with a novel pixel-wise sparse graph reasoning (PSGR) module for the segmentation of COVID-19 infected regions in CT images.

Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model.

Recently, brain networks have been widely adopted to study brain dynamics, brain development, and brain diseases. Graph representation learning techniques on brain functional networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. However, current graph learning techniques have several issues on brain network mining.

TNFAIP6 Promotes Gastric Carcinoma Cell Invasion via Upregulating PTX3 and Activating the Wnt/-Catenin Signaling Pathway.

Tumor metastasis is a fundamental cause of the poor prognosis of gastric carcinoma (GC). In order to study the problems affecting metastasis and recurrence of gastric cancer, the paper expose that TNF alpha induced protein 6 (TNFAIP6) is aberrantly overexpressed in GC, and patients with high-TNFAIP6 levels exhibited inferior overall survival. Mechanistically, overexpression of TNFAIP6 raised -catenin ectopic nuclear distribution and activated the Wnt/-catenin signal pathway.

A gas-pressure-assisted ratiometric atomic flame assay for the point-of-care testing of tumor-cell-derived exosomes.

The multicolor-based point-of-care testing (POCT) of tumor cell-derived exosomes is of vital importance for understanding tumor growth and metastasis. Multicolor-based ratiometric signals most often rely on molecular optics, such as fluorescence resonance energy transfer (FRET)-dependent molecular fluorescence and localized surface plasmon resonance (LSPR)-related molecular colorimetry. However, finding acceptable FRET donor-acceptor fluorophore pairs and the kinetically slow color responses during size-related molecular colorimetry have greatly impeded POCT applications.

CommPOOL: An interpretable graph pooling framework for hierarchical graph representation learning.

Recent years have witnessed the emergence and flourishing of hierarchical graph pooling neural networks (HGPNNs) which are effective graph representation learning approaches for graph level tasks such as graph classification. However, current HGPNNs do not take full advantage of the graph's intrinsic structures (e.g.

[The study of hBMP-4 gene modified tissue-engineered bone].

Objective: Bone marrow stromal cells (bMSCs) of rabbits transferred with mammalian hBMP-4 expression plasmid were used to construct tissue-engineered bone. Gene therapy combined with tissue-engineering technique was explored to further improve osteogenesis.

Methods: pEGFP-hBMP-4 plasmid was constructed by subcloning technique.