The Short-Term Effect of COVID-19 Pandemic on China’s Crude Oil Futures Market: A Study Based on Multifractal Analysis

The ongoing COVID-19 shocked financial markets globally, including China’s crude oil future market, which is the third-most traded crude oil futures after WTI and Brent. As China’s first crude oil futures are accessible to foreign investors, the Shanghai crude oil futures (SC) have attracted significant interest since launch at the Shanghai International Energy Exchange. The impact of COVID-19 on the new crude oil futures is an important issue for investors and policy makers. Therefore, this paper studies the short-term influence of COVID-19 pandemic on SC via multifractal analysis. We compare the market efficiency of SC before and during the pandemic with the multifractal detrended fluctuation analysis and other commonly used random walk tests. Then, we generate shuffled and surrogate data to investigate the components of multifractal nature in SC. And we examine cross-correlations between SC returns and other financial assets returns as well as SC trading volume changes by the multifractal detrended cross-correlation analysis. The results show that market efficiency of SC and its cross-correlations with other assets increase significantly after the outbreak of COVID-19. Besides that, the sources of its multifractal nature have changed since the pandemic. The findings provide evidence for the short-term impacts of COVID-19 on SC. The results may have important implications for assets allocation, investment strategies and risk monitoring. [ FROM AUTHOR]

A Class of Shark-Derived Single-Domain Antibodies can Broadly Neutralize SARS-Related Coronaviruses and the Structural Basis of Neutralization and Omicron Escape.

The identification of a novel class of shark-derived single domain antibodies, named vnarbodies that show picomolar affinities binding to the receptor binding domain (RBD) of Wuhan and Alpha, Beta, Kappa, Delta, Delta-plus, and Lambda variants, is reported. Vnarbody 20G6 and 17F6 have broad neutralizing activities against all these SARS-CoV-2 viruses as well as other sarbecoviruses, including Pangolin coronavirus and Bat coronavirus. Intranasal administration of 20G6 effectively protects mice from the challenges of SARS-CoV-2 Wuhan and Beta variants. 20G6 and 17F6 contain a unique "WXGY" motif in the complementary determining region 3 that binds to a hidden epitope on RBD, which is highly conserved in sarbecoviruses through a novel ß-sheet interaction. It is found that the S375F mutation on Omicron RBD disrupts the structure of ß-strand, thus impair the binding with 20G6. The study demonstrates that shark-derived vnarbodies offer a prophylactic and therapeutic option against most SARS-CoV-2 variants and provide insights into antibody evasion by the Omicron variant.

The short-term effect of COVID-19 pandemic on China's crude oil futures market: A study based on multifractal analysis (preprint)

The ongoing COVID-19 shocked financial markets globally, including China's crude oil future market, which is the third most traded crude oil futures after WTI and Brent. As China's first crude oil futures accessible to foreign investors, the Shanghai crude oil futures (SC) have attracted significant interest since launch at the Shanghai International Energy Exchange. The impact of COVID-19 on the new crude oil futures is an important issue for investors and policy makers. Therefore this paper studies the short-term influence of COVID-19 pandemic on SC via multifractal analysis. We compare market efficiency of SC before and during the pandemic with the multifractal detrended fluctuation analysis and other commonly-used random walk tests. Then we generate shuffled and surrogate data to investigate the components of multifractal nature in SC. And we examine cross-correlations between SC returns and other financial assets returns as well as SC trading volume changes by the multifractal detrended cross-correlation analysis. The results show that market efficiency of SC and its cross-correlations with other assets increase significantly after the outbreak of COVID-19. Besides that, the sources of its multifractal nature have changed since the pandemic. The findings provide evidence for the short-term impacts of COVID-19 on SC. The results may have important implications for assets allocation, investment strategies and risk monitoring.

Aorta in Pathologies May Function as an Immune Organ by Upregulating Secretomes for Immune and Vascular Cell Activation, Differentiation and Trans-Differentiation-Early Secretomes may Serve as Drivers for Trained Immunity.

To determine whether aorta becomes immune organ in pathologies, we performed transcriptomic analyses of six types of secretomic genes (SGs) in aorta and vascular cells and made the following findings: 1) 53.7% out of 21,306 human protein genes are classified into six secretomes, namely, canonical, caspase 1, caspase 4, exosome, Weibel-Palade body, and autophagy; 2) Atherosclerosis (AS), chronic kidney disease (CKD) and abdominal aortic aneurysm (AAA) modulate six secretomes in aortas; and Middle East Respiratory Syndrome Coronavirus (MERS-CoV, COVID-19 homologous) infected endothelial cells (ECs) and angiotensin-II (Ang-II) treated vascular smooth muscle cells (VSMCs) modulate six secretomes; 3) AS aortas upregulate T and B cell immune SGs; CKD aortas upregulate SGs for cardiac hypertrophy, and hepatic fibrosis; and AAA aorta upregulate SGs for neuromuscular signaling and protein catabolism; 4) Ang-II induced AAA, canonical, caspase 4, and exosome SGs have two expression peaks of high (day 7)-low (day 14)-high (day 28) patterns; 5) Elastase induced AAA aortas have more inflammatory/immune pathways than that of Ang-II induced AAA aortas; 6) Most disease-upregulated cytokines in aorta may be secreted via canonical and exosome secretomes; 7) Canonical and caspase 1 SGs play roles at early MERS-CoV infected ECs whereas caspase 4 and exosome SGs play roles in late/chronic phases; and the early upregulated canonical and caspase 1 SGs may function as drivers for trained immunity (innate immune memory); 8) Venous ECs from arteriovenous fistula (AVF) upregulate SGs in five secretomes; and 9) Increased some of 101 trained immunity genes and decreased trained tolerance regulator IRG1 participate in upregulations of SGs in atherosclerotic, Ang-II induced AAA and CKD aortas, and MERS-CoV infected ECs, but less in SGs upregulated in AVF ECs. IL-1 family cytokines, HIF1α, SET7 and mTOR, ROS regulators NRF2 and NOX2 partially regulate trained immunity genes; and NRF2 plays roles in downregulating SGs more than that of NOX2 in upregulating SGs. These results provide novel insights on the roles of aorta as immune organ in upregulating secretomes and driving immune and vascular cell differentiations in COVID-19, cardiovascular diseases, inflammations, transplantations, autoimmune diseases and cancers.

29 m6A-RNA Methylation (Epitranscriptomic) Regulators Are Regulated in 41 Diseases including Atherosclerosis and Tumors Potentially via ROS Regulation - 102 Transcriptomic Dataset Analyses.

We performed a database mining on 102 transcriptomic datasets for the expressions of 29 m6A-RNA methylation (epitranscriptomic) regulators (m6A-RMRs) in 41 diseases and cancers and made significant findings: (1) a few m6A-RMRs were upregulated; and most m6A-RMRs were downregulated in sepsis, acute respiratory distress syndrome, shock, and trauma; (2) half of 29 m6A-RMRs were downregulated in atherosclerosis; (3) inflammatory bowel disease and rheumatoid arthritis modulated m6A-RMRs more than lupus and psoriasis; (4) some organ failures shared eight upregulated m6A-RMRs; end-stage renal failure (ESRF) downregulated 85% of m6A-RMRs; (5) Middle-East respiratory syndrome coronavirus infections modulated m6A-RMRs the most among viral infections; (6) proinflammatory oxPAPC modulated m6A-RMRs more than DAMP stimulation including LPS and oxLDL; (7) upregulated m6A-RMRs were more than downregulated m6A-RMRs in cancer types; five types of cancers upregulated ≥10 m6A-RMRs; (8) proinflammatory M1 macrophages upregulated seven m6A-RMRs; (9) 86% of m6A-RMRs were differentially expressed in the six clusters of CD4+Foxp3+ immunosuppressive Treg, and 8 out of 12 Treg signatures regulated m6A-RMRs; (10) immune checkpoint receptors TIM3, TIGIT, PD-L2, and CTLA4 modulated m6A-RMRs, and inhibition of CD40 upregulated m6A-RMRs; (11) cytokines and interferons modulated m6A-RMRs; (12) NF-κB and JAK/STAT pathways upregulated more than downregulated m6A-RMRs whereas TP53, PTEN, and APC did the opposite; (13) methionine-homocysteine-methyl cycle enzyme Mthfd1 downregulated more than upregulated m6A-RMRs; (14) m6A writer RBM15 and one m6A eraser FTO, H3K4 methyltransferase MLL1, and DNA methyltransferase, DNMT1, regulated m6A-RMRs; and (15) 40 out of 165 ROS regulators were modulated by m6A eraser FTO and two m6A writers METTL3 and WTAP. Our findings shed new light on the functions of upregulated m6A-RMRs in 41 diseases and cancers, nine cellular and molecular mechanisms, novel therapeutic targets for inflammatory disorders, metabolic cardiovascular diseases, autoimmune diseases, organ failures, and cancers.

Endothelial Immunity Trained by Coronavirus Infections, DAMP Stimulations and Regulated by Anti-Oxidant NRF2 May Contribute to Inflammations, Myelopoiesis, COVID-19 Cytokine Storms and Thromboembolism.

To characterize transcriptomic changes in endothelial cells (ECs) infected by coronaviruses, and stimulated by DAMPs, the expressions of 1311 innate immune regulatomic genes (IGs) were examined in 28 EC microarray datasets with 7 monocyte datasets as controls. We made the following findings: The majority of IGs are upregulated in the first 12 hours post-infection (PI), and maintained until 48 hours PI in human microvascular EC infected by middle east respiratory syndrome-coronavirus (MERS-CoV) (an EC model for COVID-19). The expressions of IGs are modulated in 21 human EC transcriptomic datasets by various PAMPs/DAMPs, including LPS, LPC, shear stress, hyperlipidemia and oxLDL. Upregulation of many IGs such as nucleic acid sensors are shared between ECs infected by MERS-CoV and those stimulated by PAMPs and DAMPs. Human heart EC and mouse aortic EC express all four types of coronavirus receptors such as ANPEP, CEACAM1, ACE2, DPP4 and virus entry facilitator TMPRSS2 (heart EC); most of coronavirus replication-transcription protein complexes are expressed in HMEC, which contribute to viremia, thromboembolism, and cardiovascular comorbidities of COVID-19. ECs have novel trained immunity (TI), in which subsequent inflammation is enhanced. Upregulated proinflammatory cytokines such as TNFα, IL6, CSF1 and CSF3 and TI marker IL-32 as well as TI metabolic enzymes and epigenetic enzymes indicate TI function in HMEC infected by MERS-CoV, which may drive cytokine storms. Upregulated CSF1 and CSF3 demonstrate a novel function of ECs in promoting myelopoiesis. Mechanistically, the ER stress and ROS, together with decreased mitochondrial OXPHOS complexes, facilitate a proinflammatory response and TI. Additionally, an increase of the regulators of mitotic catastrophe cell death, apoptosis, ferroptosis, inflammasomes-driven pyroptosis in ECs infected with MERS-CoV and the upregulation of pro-thrombogenic factors increase thromboembolism potential. Finally, NRF2-suppressed ROS regulate innate immune responses, TI, thrombosis, EC inflammation and death. These transcriptomic results provide novel insights on the roles of ECs in coronavirus infections such as COVID-19, cardiovascular diseases (CVD), inflammation, transplantation, autoimmune disease and cancers.

Computing infection distributions and longitudinal evolution patterns in lung CT images.

BACKGROUND: Spatial and temporal lung infection distributions of coronavirus disease 2019 (COVID-19) and their changes could reveal important patterns to better understand the disease and its time course. This paper presents a pipeline to analyze statistically these patterns by automatically segmenting the infection regions and registering them onto a common template. METHODS: A VB-Net is designed to automatically segment infection regions in CT images. After training and validating the model, we segmented all the CT images in the study. The segmentation results are then warped onto a pre-defined template CT image using deformable registration based on lung fields. Then, the spatial distributions of infection regions and those during the course of the disease are calculated at the voxel level. Visualization and quantitative comparison can be performed between different groups. We compared the distribution maps between COVID-19 and community acquired pneumonia (CAP), between severe and critical COVID-19, and across the time course of the disease. RESULTS: For the performance of infection segmentation, comparing the segmentation results with manually annotated ground-truth, the average Dice is 91.6% ± 10.0%, which is close to the inter-rater difference between two radiologists (the Dice is 96.1% ± 3.5%). The distribution map of infection regions shows that high probability regions are in the peripheral subpleural (up to 35.1% in probability). COVID-19 GGO lesions are more widely spread than consolidations, and the latter are located more peripherally. Onset images of severe COVID-19 (inpatients) show similar lesion distributions but with smaller areas of significant difference in the right lower lobe compared to critical COVID-19 (intensive care unit patients). About the disease course, critical COVID-19 patients showed four subsequent patterns (progression, absorption, enlargement, and further absorption) in our collected dataset, with remarkable concurrent HU patterns for GGO and consolidations. CONCLUSIONS: By segmenting the infection regions with a VB-Net and registering all the CT images and the segmentation results onto a template, spatial distribution patterns of infections can be computed automatically. The algorithm provides an effective tool to visualize and quantify the spatial patterns of lung infection diseases and their changes during the disease course. Our results demonstrate different patterns between COVID-19 and CAP, between severe and critical COVID-19, as well as four subsequent disease course patterns of the severe COVID-19 patients studied, with remarkable concurrent HU patterns for GGO and consolidations.

High concentration and high dose of disinfectants and antibiotics used during the COVID-19 pandemic threaten human health.

The issue of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has created enormous threat to global health. In an effort to contain the spread of COVID-19, a huge amount of disinfectants and antibiotics have been utilized on public health. Accordingly, the concentration of disinfectants and antibiotics is increasing rapidly in various environments, including wastewater, surface waters, soils and sediments. The aims of this study were to analyze the potential ecological environment impacts of disinfectants and antibiotics by summarizing their utilization, environmental occurrence, distribution and toxicity. The paper highlights the promoting effects of disinfectants and antibiotics on antibiotic resistance genes (ARGs) and even antibiotic resistant bacteria (ARB). The scientific evidences indicate that the high concentration and high dose of disinfectants and antibiotics promote the evolution toward antimicrobial resistance through horizontal gene transformation and vertical gene transformation, which threaten human health. Further concerns should be focused more on the enrichment, bioaccumulation and biomagnification of disinfectants, antibiotics, antibiotic resistance genes (ARGs) and even antibiotic resistant bacteria (ARB) in human bodies.

Hypergraph learning for identification of COVID-19 with CT imaging.

The coronavirus disease, named COVID-19, has become the largest global public health crisis since it started in early 2020. CT imaging has been used as a complementary tool to assist early screening, especially for the rapid identification of COVID-19 cases from community acquired pneumonia (CAP) cases. The main challenge in early screening is how to model the confusing cases in the COVID-19 and CAP groups, with very similar clinical manifestations and imaging features. To tackle this challenge, we propose an Uncertainty Vertex-weighted Hypergraph Learning (UVHL) method to identify COVID-19 from CAP using CT images. In particular, multiple types of features (including regional features and radiomics features) are first extracted from CT image for each case. Then, the relationship among different cases is formulated by a hypergraph structure, with each case represented as a vertex in the hypergraph. The uncertainty of each vertex is further computed with an uncertainty score measurement and used as a weight in the hypergraph. Finally, a learning process of the vertex-weighted hypergraph is used to predict whether a new testing case belongs to COVID-19 or not. Experiments on a large multi-center pneumonia dataset, consisting of 2148 COVID-19 cases and 1182 CAP cases from five hospitals, are conducted to evaluate the prediction accuracy of the proposed method. Results demonstrate the effectiveness and robustness of our proposed method on the identification of COVID-19 in comparison to state-of-the-art methods.

Development of a SERS-based lateral flow immunoassay for rapid and ultra-sensitive detection of anti-SARS-CoV-2 IgM/IgG in clinical samples.

The accurate and rapid screening of serum antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the key to control the spread of 2019 coronavirus disease (COVID-19). In this study, we reported a surface-enhanced Raman scattering-based lateral flow immunoassay (SERS-LFIA) for the simultaneous detection of anti-SARS-CoV-2 IgM/IgG with high sensitivity. Novel SERS tags labeled with dual layers of Raman dye were fabricated by coating a complete Ag shell on SiO2 core (SiO2@Ag) and exhibited excellent SERS signals, good monodispersity, and high stability. Anti-human IgM and IgG were immobilized onto the two test lines of the strip to capture the formed SiO2@Ag-spike (S) protein-anti-SARS-CoV-2 IgM/IgG immunocomplexes. The SERS signal intensities of the IgM and IgG test zones were easily recorded by a portable Raman instrument and used for the high-sensitivity analysis of target IgM and IgG. The limit of detection of SERS-LFIA was 800 times higher than that of standard Au nanoparticle-based LFIA for target IgM and IgG. The SERS-LFIA biosensor was tested on 19 positive serum samples from COVID-19 patients and 49 negative serum samples from healthy people to demonstrate the clinical feasibility of our proposed assay. The results revealed that the proposed method exhibited high accuracy and specificity for patients with SARS-CoV-2 infection.