搜索结果：Bioinformation

The health and socioeconomic difficulties caused by the COVID-19 pandemic continues to cause enormous tensions around the world. In particular, this extraordinary surge in the number of cases has put considerable strain on health care systems around the world. A critical step in the treatment and management of COVID-19 positive patients is severity assessment, which is challenging even for expert radiologists given the subtleties at different stages of lung disease severity. Motivated by this challenge, we introduce COVID-Net CT-S, a suite of deep convolutional neural networks for predicting lung disease severity due to COVID-19 infection. More specifically, a 3D residual architecture design is leveraged to learn volumetric visual indicators characterizing the degree of COVID-19 lung disease severity. Experimental results using the patient cohort collected by the China National Center for Bioinformation (CNCB) showed that the proposed COVID-Net CT-S networks, by leveraging volumetric features, can achieve significantly improved severity assessment performance when compared to traditional severity assessment networks that learn and leverage 2D visual features to characterize COVID-1

Ultrametric approach to the genetic code and the genome is considered and developed. $p$-Adic degeneracy of the genetic code is pointed out. Ultrametric tree of the codon space is presented. It is shown that codons and amino acids can be treated as $p$-adic ultrametric networks. Ultrametric modification of the Hamming distance is defined and noted how it can be useful. Ultrametric approach with $p$-adic distance is an attractive and promising trend towards investigation of bioinformation.

Network alignment is a problem of finding the node mapping between similar networks. It links the data from separate sources and is widely studied in bioinformation and social network fields. The critical difference between network alignment and exact graph matching is that the network alignment considers node mapping in non-isomorphic graphs with error tolerance. Researchers usually utilize AC (accuracy) to measure the performance of network alignments which comparing each output element with the benchmark directly. However, this metric neglects that some nodes are naturally indistinguishable even in single graphs (e.g., nodes have the same neighbors) and no need to distinguish across graphs. Such neglect leads to the underestimation of models. We propose an unbiased metric for network alignment that takes indistinguishable nodes into consideration to address this problem. Our detailed experiments with different scales on both synthetic and real-world datasets demonstrate that the proposed metric correctly reflects the deviation of result mapping from benchmark mapping as standard metric AC does. Comparing with the AC, the proposed metric effectively blocks the effect of indisting

Recent developments/efforts to understand aspects of the brain function at the {\em sub-neural} level are discussed. MicroTubules (MTs) participate in a wide variety of dynamical processes in the cell, especially in bioinformation processes such as learning and memory, by possessing a well-known binary error-correcting code with 64 words. In fact, MTs and DNA/RNA are unique cell structures that possess a code system. It seems that the MTs' code system is strongly related to a kind of ``Mental Code" in the following sense. The MTs' periodic paracrystalline structure make them able to support a superposition of coherent quantum states, as it has been recently conjectured by Hameroff and Penrose, representing an external or mental order, for sufficient time needed for efficient quantum computing. Then the quantum superposition collapses spontaneously/dynamically through a new, string-derived mechanism for collapse proposed recently by Ellis, Mavromatos, and myself. At the moment of collapse, organized quantum exocytosis occurs, and this is how a ``{\em mental order}" may be translated into a ``{\em physiological action}". Our equation for quantum collapse, tailored to the MT system, p

The coronavirus disease 2019 (COVID-19) pandemic continues to have a tremendous impact on patients and healthcare systems around the world. In the fight against this novel disease, there is a pressing need for rapid and effective screening tools to identify patients infected with COVID-19, and to this end CT imaging has been proposed as one of the key screening methods which may be used as a complement to RT-PCR testing, particularly in situations where patients undergo routine CT scans for non-COVID-19 related reasons, patients with worsening respiratory status or developing complications that require expedited care, and patients suspected to be COVID-19-positive but have negative RT-PCR test results. Motivated by this, in this study we introduce COVIDNet-CT, a deep convolutional neural network architecture that is tailored for detection of COVID-19 cases from chest CT images via a machine-driven design exploration approach. Additionally, we introduce COVIDx-CT, a benchmark CT image dataset derived from CT imaging data collected by the China National Center for Bioinformation comprising 104,009 images across 1,489 patient cases. Furthermore, in the interest of reliability and tran

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Scientists have uncovered a surprising connection between quantum gravity and an exotic quantum state of matter that could explain why the universe isn’t expanding wildly fast。 The study suggests that the very shape of space-time may protect the cosmological constant from disruptive quantum effects

For testing patients infected with COVID-19, along with RT-PCR testing, chest radiology images are being used. For the detection of COVID-19 from radiology images, many organizations are proposing the use of Deep Learning. University of Waterloo and DarwinAI, have designed their own Deep Learning model COVIDNet-CT to detect COVID-19 from infected chest CT images. Additionally, they have introduced a CT image dataset COVIDx-CT, from CT images collected by the China National Center for Bioinformation. COVIDx-CT contains 104,009 CT image slices across 1,489 patient cases. After obtaining remarkable results on the identification of COVID-19 from chest X-ray images by using the COGNEX VisionPro Deep Learning Software 1.0 this time we test the performance of the software on the identification of COVID-19 from CT images. COGNEX Deep Learning Software: VisionPro Deep Learning, is a Deep Learning software that is used across various domains ranging from factory automation to life sciences. In this study, we train the classification model on 82,818 chest CT training and validation images from the COVIDx-CT dataset in 3 classes - normal, pneumonia, and COVID-19 and then test the results of th

Researchers found that a Chinese sodium-ion battery performs far better than expected, with production quality and design features comparable to Tesla’s batteries。 If engineers can improve cold-weather charging and energy density, sodium could become a cheaper and more abundant alternative to lithium for EVs and large-scale energy storage

Ron DeSantis calls it a crackdown on "radical climate policies

A year in, National Design Studio delays plan to update government web standards

What if some black holes aren’t black holes at all。 A new theoretical study suggests that when a massive star collapses, it might not form a singularity hidden behind an event horizon。 Instead, the collapse could trigger the birth of a tiny new universe inside the dying star

New Fire Stick OS helps Amazon block third-party homepage launchers, ad blockers

Small-scale solar helped renewables hit nearly triple coal's generation in the US

Astronomers may be closing in on a long-standing cosmic mystery: why some of the universe’s biggest galaxies seem to have far fewer stars than expected。 Using NASA- and JAXA-supported XRISM observations of a galaxy called NGC 4151, researchers found strong evidence that supermassive black holes can unleash powerful winds that blow away the raw mate

A rare meteorite has revealed evidence of a massive lost world that once orbited the young Sun before being destroyed in a catastrophic collision。 The discovery suggests some early planets formed from dramatically different materials than Earth and Mars, rewriting part of the solar system’s origin story

The race to build data centers in space is gaining momentum as AI drives unprecedented demand for computing power。 Orbital facilities could tap into abundant solar energy and avoid many of the environmental challenges faced on Earth。 Yet space remains a harsh and expensive place to operate, with major hurdles including cooling, maintenance, radiati

A new sunlight-powered material can convert visible light into higher-energy UV light, overcoming a challenge that has frustrated scientists for years。 The breakthrough could enable cleaner air purification, solar-driven chemistry, and advanced manufacturing technologies using nothing more than natural sunlight

Peptide drugs are popular, but FDA scientists warn they're untested, may be harmful