A comparative analysis of near-infrared image colorization methods for low-power NVIDIA Jetson embedded systems

The near-infrared (NIR) image obtained by an NIR camera is a grayscale image that is inconsistent with the human visual spectrum. It can be difficult to perceive the details of a scene from an NIR scene; thus, a method is required to convert them to visible images, providing color and texture information. In addition, a camera produces so much video data that it increases the pressure on the cloud server. Image processing can be done on an edge device, but the computing resources of edge devices are limited, and their power consumption constraints need to be considered. Graphics Processing Unit (GPU)-based NVIDIA Jetson embedded systems offer a considerable advantage over Central Processing Unit (CPU)-based embedded devices in inference speed. For this study, we designed an evaluation system that uses image quality, resource occupancy, and energy consumption metrics to verify the performance of different NIR image colorization methods on low-power NVIDIA Jetson embedded systems for practical applications. The performance of 11 image colorization methods on NIR image datasets was tested on three different configurations of NVIDIA Jetson boards. The experimental results indicate that the Pix2Pix method performs best, with a rate of 27 frames per second on the Jetson Xavier NX. This performance is sufficient to meet the requirements of real-time NIR image colorization

Cerebellum and hippocampus abnormalities in patients with insomnia comorbid depression: a study on cerebral blood perfusion and functional connectivity

Chronic insomnia disorder and major depressive disorder are highly-occurred mental diseases with extensive social harm. The comorbidity of these two diseases is commonly seen in clinical practice, but the mechanism remains unclear. To observe the characteristics of cerebral blood perfusion and functional connectivity in patients, so as to explore the potential pathogenesis and biological imaging markers, thereby improving the understanding of their comorbidity mechanism. 44 patients with chronic insomnia disorder comorbid major depressive disorder and 43 healthy controls were recruited in this study. The severity of insomnia and depression were assessed by questionnaire. The cerebral blood perfusion and functional connectivity values of participants were obtained to, analyze their correlation with questionnaire scores. The cerebral blood flow in cerebellum, vermis, right hippocampus, left parahippocampal gyrus of patients were reduced, which was negatively related to the severity of insomnia or depression. The connectivities of left cerebellum-right putamen and right hippocampus-left inferior frontal gyrus were increased, showing positive correlations with the severity of insomnia and depression. Decreased connectivities of left cerebellum-left fusiform gyrus, left cerebellum-left occipital lobe, right hippocampus-right paracentral lobule, right hippocampus-right precentral gyrus were partially associated with insomnia or depression. The connectivity of right hippocampus-left inferior frontal gyrus may mediate between insomnia and depression. Insomnia and depression can cause changes in cerebral blood flow and brain function. Changes in the cerebellar and hippocampal regions are the result of insomnia and depression. They reflect abnormalities in sleep and emotion regulation. That may be involved in the pathogenesis of comorbidity

Machine Learning for Prediction of Sudden Cardiac Death in Heart Failure Patients With Low Left Ventricular Ejection Fraction: Study Protocol for a Retrospective Multicentre Registry in China

Introduction: Left ventricular ejection fraction (LVEF) ≤35%, as current significant implantable cardioverter-defibrillator (ICD) indication for primary prevention of sudden cardiac death (SCD) in heart failure (HF) patients, has been widely recognised to be inefficient. Improvement of patient selection for low LVEF (≤35%) is needed to optimise deployment of ICD. Most of the existing prediction models are not appropriate to identify ICD candidates at high risk of SCD in HF patients with low LVEF. Compared with traditional statistical analysis, machine learning (ML) can employ computer algorithms to identify patterns in large datasets, analyse rules automatically and build both linear and non-linear models in order to make data-driven predictions. This study is aimed to develop and validate new models using ML to improve the prediction of SCD in HF patients with low LVEF. Methods and analysis: We will conduct a retroprospective, multicentre, observational registry of Chinese HF patients with low LVEF. The HF patients with LVEF ≤35% after optimised medication at least 3 months will be enrolled in this study. The primary endpoints are all-cause death and SCD. The secondary endpoints are malignant arrhythmia, sudden cardiac arrest, cardiopulmonary resuscitation and rehospitalisation due to HF. The baseline demographic, clinical, biological, electrophysiological, social and psychological variables will be collected. Both ML and traditional multivariable Cox proportional hazards regression models will be developed and compared in the prediction of SCD. Moreover, the ML model will be validated in a prospective study. Ethics and dissemination: The study protocol has been approved by the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University (2017-SR-06). All results of this study will be published in international peer-reviewed journals and presented at relevant conferences

A Survey of Chinese Pig Farms and Human Healthcare Isolates Reveals Separate Human and Animal Methicillin-Resistant Staphylococcus aureus Populations.

There has been increasing concern that the overuse of antibiotics in livestock farming is contributing to the burden of antimicrobial resistance in people. Farmed animals in Europe and North America, particularly pigs, provide a reservoir for livestock-associated methicillin-resistant Staphylococcus aureus (LA-MRSA ST398 lineage) found in people. This study is designed to investigate the contribution of MRSA from Chinese pig farms to human infection. A collection of 483 MRSA are isolated from 55 farms and 4 hospitals in central China, a high pig farming density area. CC9 MRSA accounts for 97.2% of all farm isolates, but is not present in hospital isolates. ST398 isolates are found on farms and hospitals, but none of them formed part of the "LA-MRSA ST398 lineage" present in Europe and North America. The hospital ST398 MRSA isolate form a clade that is clearly separate from the farm ST398 isolates. Despite the presence of high levels of MRSA found on Chinese pig farms, the authors find no evidence of them spilling over to the human population. Nevertheless, the ST398 MRSA obtained from hospitals appear to be part of a widely distributed lineage in China. The new animal-adapted ST398 lineage that has emerged in China is of concern

Generative Table Pre-training Empowers Models for Tabular Prediction

Recently, the topic of table pre-training has attracted considerable research interest. However, how to employ table pre-training to boost the performance of tabular prediction remains an open challenge. In this paper, we propose TapTap, the first attempt that leverages table pre-training to empower models for tabular prediction. After pre-training on a large corpus of real-world tabular data, TapTap can generate high-quality synthetic tables to support various applications on tabular data, including privacy protection, low resource regime, missing value imputation, and imbalanced classification. Extensive experiments on 12 datasets demonstrate that TapTap outperforms a total of 16 baselines in different scenarios. Meanwhile, it can be easily combined with various backbone models, including LightGBM, Multilayer Perceptron (MLP) and Transformer. Moreover, with the aid of table pre-training, models trained using synthetic data generated by TapTap can even compete with models using the original dataset on half of the experimental datasets, marking a milestone in the development of synthetic tabular data generation. The codes are available at https://github.com/ZhangTP1996/TapTap

Progress in Mass Spectrometry-based Metabolomics Data Analysis Techniques

Metabolomics technique, as an important part of systems biology, aims to identify and quantify all endogenous small molecule metabolites in organisms at certain condition. The continuous iteration of mass spectrometry and nuclear magnetic resonance system facilitate great progress in metabolomics technologies. Among them, mass spectrometry and related metabolomic techniques have been the most widely used due to their ability to detect thousands of metabolites in biological fluids, cells and tissues simultaneously, without complex pre-processing steps for sample preparation. Therefore, the development of tools for mass spectrometry-based metabolomics data analysis has been a hot topic in metabolomics research in the past decade. In this review, we systematically summarized the research progress in four main aspects of gas/liquid chromatography tandem mass spectrometry (GC/LC-MS)-based metabolomics data analysis, including metabolomics data preprocessing, statistical analysis of metabolomics data, metabolic pathway enrichment analysis, and identification of unknown metabolites. We mainly introduced the commonly used analysis strategies and software related with MS-based metabolomic data analysis; and highlighted the cutting-edge innovation about molecular networking-, artificial intelligence-and databases-based metabolite identification. Finally we gave a brief future perspective about MS-based metabolomic data analysis, and believe that new developed strategies, which integrate the known biochemical reactions, molecular networking tools, and genetic loci information regulating the metabolite biosynthesis, will promote the number and accuracy of identified metabolites. This review will provide new ideas for deeper exploration of new methods for metabolomic data analysis and biological significance from metabolomic data

MPFINet: A Multilevel Parallel Feature Injection Network for Panchromatic and Multispectral Image Fusion

The fusion of a high-spatial-resolution panchromatic (PAN) image and a corresponding low-resolution multispectral (MS) image can yield a high-resolution multispectral (HRMS) image, which is also known as pansharpening. Most previous methods based on convolutional neural networks (CNNs) have achieved remarkable results. However, information of different scales has not been fully mined and utilized, and still produces spectral and spatial distortion. In this work, we propose a multilevel parallel feature injection network that contains three scale levels and two parallel branches. In the feature extraction branch, a multi-scale perception dynamic convolution dense block is proposed to adaptively extract the spatial and spectral information. Then, the sufficient multilevel features are injected into the image reconstruction branch, and an attention fusion module based on the spectral dimension is designed in order to fuse shallow contextual features and deep semantic features. In the image reconstruction branch, cascaded transformer blocks are employed to capture the similarities among the spectral bands of the MS image. Extensive experiments are conducted on the QuickBird and WorldView-3 datasets to demonstrate that MPFINet achieves significant improvement over several state-of-the-art methods on both spatial and spectral quality assessments