A burns and COVID-19 shared stress responding gene network deciphers CD1C-CD141- DCs as the key cellular components in septic prognosis (preprint)

Differential body responses to various stresses, infectious or noninfectious, govern clinical outcomes ranging from asymptoma to death. However, the common molecular and cellular nature of the stress responsome across different stimuli is not described. In this study, we compared the expression behaviors between burns and COVID-19 infection by choosing the transcriptome of peripheral blood from related patients as the analytic target since the blood cells reflect the systemic landscape of immune homeostasis. We identified an immune co-stimulator (CD86)-centered network, named stress-response core (SRC), which coordinated multiple immune processes and was robust in membership and highly related to the clinical traits in both burns and COVID-19. An independent whole blood single-cell RNA sequencing of COVID-19 patients demonstrated that the monocyte-dendritic cell (Mono-DC) wing was the major cellular source of the SRC, among which the higher expression of the SRC in the monocyte was associated with the asymptomatic COVID-19 patients, while the quantity-restricted and function-defected CD1C-CD141- DCs were recognized as the key signature which linked to bad consequences in COVID-19. Specifically, the proportion of the CD1C-CD141- DCs and their SRC expression levels were step-wise reduced along with worse clinic conditions while the sub-cluster of CD1C-CD141- DCs of the critical COVID-19 patients was characterized of IFN signaling quiescence, high mitochondrial metabolism and immune-communication inactivation. Thus, our study identified an expression-synchronized and function-focused gene network which was decreased under burns and COVID-19 stress and argued the CD1C-CD141- DC as the prognosis-related cell population which might serve as a new target of diagnosis and therapy.

Remote Sensing Evidence for Significant Variations in the Global Gross Domestic Product during the COVID-19 Epidemic

Coronavirus disease 2019 (COVID-19) has been spreading rapidly and is still threatening human health currently. A series of measures for restraining epidemic spreading has been adopted throughout the world, which seriously impacted the gross domestic product (GDP) globally. However, details of the changes in the GDP and its spatial heterogeneity characteristics on a fine scale worldwide during the pandemic are still uncertain. We designed a novel scheme to simulate a 0.1°×0.1°resolution grid global GDP map during the COVID-19 pandemic. Simulated nighttime-light remotely sensed data (SNTL) was forecasted via a GM(1, 1) model under the assumption that there was no COVID-19 epidemic in 2020. We constructed a geographically weighted regression (GWR) model to determine the quantitative relationship between the variation of nighttime light (ΔNTL) and the variation of GDP (ΔGDP). The scheme can detect and explain the spatial heterogeneity of ΔGDP at the grid scale. It is found that a series of policies played an obvious role in affecting GDP. This work demonstrated that the global GDP, except for in a few countries, represented a remarkably decreasing trend, whereas the ΔGDP exhibited significant differences.

GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics. (preprint)

Our work seeks to transform how new and emergent variants of pandemic causing viruses, specially SARS-CoV-2, are identified and classified. By adapting large language models (LLMs) for genomic data, we build genome-scale language models (GenSLMs) which can learn the evolutionary landscape of SARS-CoV-2 genomes. By pre-training on over 110 million prokaryotic gene sequences, and then finetuning a SARS-CoV-2 specific model on 1.5 million genomes, we show that GenSLM can accurately and rapidly identify variants of concern. Thus, to our knowledge, GenSLM represents one of the first whole genome scale foundation models which can generalize to other prediction tasks. We demonstrate the scaling of GenSLMs on both GPU-based supercomputers and AI-hardware accelerators, achieving over 1.54 zettaflops in training runs. We present initial scientific insights gleaned from examining GenSLMs in tracking the evolutionary dynamics of SARS-CoV-2, noting that its full potential on large biological data is yet to be realized.

Plasma proteomics of SARS-CoV-2 infection and severity reveals impact on Alzheimer and coronary disease pathways (preprint)

Identification of the plasma proteomic changes of Coronavirus disease 2019 (COVID-19) is essential to understanding the pathophysiology of the disease and developing predictive models and novel therapeutics. We performed plasma deep proteomic profiling from 332 COVID-19 patients and 150 controls and pursued replication in an independent cohort (297 cases and 76 controls) to find potential biomarkers and causal proteins for three COVID-19 outcomes (infection, ventilation, and death). We identified and replicated 1,449 proteins associated with any of the three outcomes (841 for infection, 833 for ventilation, and 253 for death) that can be query on a web portal (https://covid.proteomics.wustl.edu/). Using those proteins and machine learning approached we created and validated specific prediction models for ventilation (AUC>0.91), death (AUC>0.95) and either outcome (AUC>0.80). These proteins were also enriched in specific biological processes, including immune and cytokine signaling (FDR < 3.72x10-14), Alzheimer's disease (FDR < 5.46x10-10) and coronary artery disease (FDR < 4.64x10-2). Mendelian randomization using pQTL as instrumental variants nominated BCAT2 and GOLM1 as a causal proteins for COVID-19. Causal gene network analyses identified 141 highly connected key proteins, of which 35 have known drug targets with FDA-approved compounds. Our findings provide distinctive prognostic biomarkers for two severe COVID-19 outcomes (ventilation and death), reveal their relationship to Alzheimer's disease and coronary artery disease, and identify potential therapeutic targets for COVID-19 outcomes.

Reflections on development of rapid and living evidence-based Chinese medicine guidelines for Coronavirus disease 2019

As evidence is rapidly accumulated and updated during the coronavirus disease 2019 (COVID-19) pandemic, rapid and living guidelines are needed to guide the clinical practice of Chinese medicine (CM), for which the WHO handbook of rapid guideline development should be referred to, and the characteristics of CM should be addressed. When constructing the body of evidence, we need to systematically search the studies related to COVID-19 (direct) and indirect diseases, and to collect the experience evidence from ancient documents and expert consensus, thereby maximumly presenting the advantages of CM. When the recommendations are developed, the co-existing direct and indirect evidence, as well as the co-existing research and experience evidence should be fully considered and synthesized by taking priority on whichever higher level evidence it is. The development of the rapid and living CM guidelines meets the ever-changing clinical needs during COVID-19 pandemic, and can provide CM evidence supports for decision making during public health emergencies.

The initial attempt at home hemodialysis in mainland China (preprint)

Background Observational studies showed home hemodialysis (HHD) to be associated with better survival than facility hemodialysis (HD) and peritoneal dialysis (PD). Patients on HHD have reported higher quality of life and independence. HHD is considered to be an economical way to manage end-stage kidney disease (ESKD). The coronavirus disease 2019 pandemic has had a significant impact on patients with ESKD. Patients on HHD may have an advantage over in-center HHD patients because of a lower risk of exposure to infection.Participants and Methods: We enrolled hemodialysis patients from our dialysis center. We estabilished HHD training center at first. The training center was approved by Chinese government. Doctors, nurses and engineers train and assess patients separately. There are three forms of patient monitoring: home visit, internet remote monitoring, and out-patient service. Demographic and medical data included age, gender, blood pressure, dialysis related data. Laboratory tests were conducted in our central testing laboratory including hemoglobin (Hgb), serum creatinine (Cr), urea nitrogen (BUN), uric acid (UA), albumin (Alb), calcium (Ca), phosphorus (P), parathyroid hormone (PTH), brain natriuretic peptide (BNP).Results Six patients who underwent regular dialysis in the hemodialysis center of our hospital were selected for HHD training. We enrolled 6 patients, including 4 males and 2 females. The mean age of the patients was 47.5 (34.7–55.7) years, the mean dialysis age was 33.5 (11.2–41.5) months. After an average of 16.0 (11.2–25.5) months of training, Alb, P and BNP were improved compared with baseline values. After training, three patients returned home to begin independent hemodialysis. During the follow-up, there are no serious adverse events leading to hospitalization or death, but there are several adverse events. They have been solved quickly by extra home visits of the technicians or online by remote monitoring. During the time, laboratory indicators of all the patients including Hgb, Alb, Ca, P, PTH, BNP, β2-MG remained stable before and after HHD treatment.Conclusion HHD is feasible and safe for ESRD in China, but larger-scale and longer-term studies are needed to further confirm.

Study of the Inactivated COVID-19 Vaccine Contamination in Vaccination Sites — Zigong City, Sichuan Province, China, January, 2021

Vaccines are a crucial weapon in combating the global coronavirus disease 2019 (COVID-19) pandemic. At present, China is in a critical period of COVID-19 vaccination, and most of the approved vaccines are developed by inactivated vaccine technology, which contains the complete nucleic acid sequence of the virus (1-2). The inactivated COVID-19 vaccine may contaminate people and environments during the vaccination process, thus triggering a false alarm of the COVID-19 surveillance system. In this study, we selected some vaccination sites to assess the intensity and distribution of vaccine contamination.;;Before field study, we used Reverse Transcription-Polymerase Chain Reaction (RT-PCR) method with kits that produced by Da An Gene and ZJ Bio-Tech to estimate the signal strength of inactivated COVID-19 vaccine (SinovacBiotech). The average Cycle threshold (Ct) value of ORF1Ab /N gene of the vaccine solution was 15.30±0.77, while the Ct value of the kit’s positive control was 28.01±2.38.

Thank You for Your Support [President’s Message]

By the time this issue of IEEE Intelligent Transportation Systems Magazine is published, I will have completed my term as president of the IEEE Intelligent Transportation Systems (ITS) Society. My two-year service coincided with an unprecedented time—the COVID-19 crisis. During the past two years, the pandemic has dramatically changed the lives of everyone on Earth and, most certainly, greatly impacted how the ITS Society operates. Fortunately, our colleagues have made substantial efforts to adapt to the new reality of the pandemic and created opportunities and environments for the Society to innovate and grow.

Optimization of extreme learning machine model with biological heuristic algorithms to estimate daily reference crop evapotranspiration in different climatic regions of China

• Hybrid ELM models (PSO-ELM, GA-ELM and ABC-ELM) were proposed for estimating ET 0 in different climate zones of China. • PSO-ELM model had the highest accuracy, followed by GA-ELM and ABC-ELM. • Hybrid ELM models outperformed standalone ELM and empirical models in different climate zones. • PSO-ELM model with T max , T min and RH obtained accurate ET 0 estimates in TCZ, SMZ and TMZ. • PSO-ELM model with only T max and T min was better performance on ET 0 estimates in MPZ. Accurate prediction of reference crop evapotranspiration (ET 0) is important for regional water resources management and optimal design of agricultural irrigation system. In this study, three hybrid models (PSO-ELM, GA-ELM and ABC-ELM) integrating the extreme learning machine model (ELM) with three biological heuristic algorithms, i.e., PSO, GA and ABC, were proposed for predicting daily ET 0 based on daily meteorological data from 2000 to 2019 at twelve representative stations in different climatic zones of China. The performances of the three hybrid ELM models were further compared with the standalone ELM model and three empirical models (Hargreaves, Priestley-Talor and Makkink models). The results showed that the hybrid ELM models (R 2 = 0.973–0.999) all performed better than the standalone ELM model (R 2 = 0.955–0.989) in four climatic regions in China. The estimation accuracy of the empirical models was relatively lower, with R2 of 0.822–0.887 and RMSE of 0.381–1.951 mm/d. The R 2 values of PSO-ELM, GA-ELM and ABC-ELM models were 0.993, 0.986 and 0.981 and the RMSE values were 0.266 mm/d, 0.306 mm/d and 0.404 mm/d, respectively, indicating that the PSO-ELM model had the best performance. When setting T max , T min and RH as the model inputs, the PSO-ELM model presented better performance in the temperate continental zone (TCZ), subtropical monsoon region (SMZ) and temperate monsoon zone (TMZ) climate zones, with R 2 of 0.892, 0866 and 0.870 and RMSE of 0.773 mm/d, 0.597 mm/d and 0.832 mm/d, respectively. The PSO-ELM model also performed in the mountain plateau region (MPZ) when only T max and T min data were available, with R2 of 0.808 and RMSE of 0.651 mm/d. All the three biological heuristic algorithms effectively improved the performance of the ELM model. Particularly, the PSO-ELM was recommended as a promising model realizing the high-precision estimation of daily ET 0 with fewer meteorological parameters in different climatic zones of China. [ FROM AUTHOR] Copyright of Journal of Hydrology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Thank You to All Volunteers [President’s Message]

The IEEE Intelligent Transportation Systems (ITS) Society’s flagship conferences take place in summer and fall each year. In July, the 32nd IEEE Intelligent Vehicle Symposium (IV) was successfully organized in Nagoya, Japan, through virtual means with great support from Nagoya University and a number of Japanese partners and organizations. The conference committee, led by Kazuya Takeda, Nagoya University, planned a large-scale demonstration of connected and automated vehicle technologies. Many of us were excited for the opportunity to experience cutting-edge intelligent vehicle technologies showcased at the Tokyo Olympics. Unfortunately, with the COVID-19 pandemic persisting, the demonstration could not happen. The Organizing Committee worked hard to transition the conference into a virtual event that offered engaging interactive experiences. IV’21 received 446 papers;220 papers plus additional invited talks were given. The presentations are available on YouTube for easy access.

Medical-VLBERT: Medical Visual Language BERT for COVID-19 CT Report Generation With Alternate Learning (preprint)

Medical imaging technologies, including computed tomography (CT) or chest X-Ray (CXR), are largely employed to facilitate the diagnosis of the COVID-19. Since manual report writing is usually too time-consuming, a more intelligent auxiliary medical system that could generate medical reports automatically and immediately is urgently needed. In this article, we propose to use the medical visual language BERT (Medical-VLBERT) model to identify the abnormality on the COVID-19 scans and generate the medical report automatically based on the detected lesion regions. To produce more accurate medical reports and minimize the visual-and-linguistic differences, this model adopts an alternate learning strategy with two procedures that are knowledge pretraining and transferring. To be more precise, the knowledge pretraining procedure is to memorize the knowledge from medical texts, while the transferring procedure is to utilize the acquired knowledge for professional medical sentences generations through observations of medical images. In practice, for automatic medical report generation on the COVID-19 cases, we constructed a dataset of 368 medical findings in Chinese and 1104 chest CT scans from The First Affiliated Hospital of Jinan University, Guangzhou, China, and The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China. Besides, to alleviate the insufficiency of the COVID-19 training samples, our model was first trained on the large-scale Chinese CX-CHR dataset and then transferred to the COVID-19 CT dataset for further fine-tuning. The experimental results showed that Medical-VLBERT achieved state-of-the-art performances on terminology prediction and report generation with the Chinese COVID-19 CT dataset and the CX-CHR dataset. The Chinese COVID-19 CT dataset is available at https://covid19ct.github.io/.

Tissue Specific Age Dependence of the Cell Receptors Involved in the SARS-CoV-2 Infection (preprint)

The coronavirus disease 2019 (COVID-19) pandemic has affected tens of millions of individuals and caused hundreds of thousands of deaths worldwide. Due to its rapid surge, there is a shortage of information on viral behavior and host response after SARS-CoV-2 infection. Here we present a comprehensive, multiscale network analysis of the transcriptional response to the virus. We particularly focus on key-regulators, cell-receptors, and host-processes that are hijacked by the virus for its advantage. ACE2 -controlled processes involve a key-regulator CD300e (a TYROBP receptor) and the activation of IL-2 pro-inflammatory cytokine signaling. We further investigate the age-dependency of such receptors and identify the adipose and the brain as potentially contributing tissues for the disease’s severity in old patients. In contrast, several other tissues in the young population are more susceptible to SARS-CoV-2 infection. In summary, this present study provides novel insights into the gene regulatory organization during the SARS-CoV-2 infection and the tissue-specific age dependence of the cell receptors involved in COVID-19.

Impacts of worldwide individual non-pharmaceutical interventions on COVID-19 transmission across waves and space (preprint)

Governments worldwide have rapidly deployed non-pharmaceutical interventions (NPIs) to mitigate the COVID-19 pandemic. However, the effect of these individual NPI measures across space and time has yet to be sufficiently assessed, especially with the increase of policy fatigue and the urge for NPI relaxation in the vaccination era. Using the decay ratio in the suppression of COVID-19 infections, we investigated the changing performance of different NPIs across waves from global and regional levels (in 133 countries) to national and subnational (in the United States of America [USA]) scales before the implementation of mass vaccination. The synergistic effectiveness of all NPIs for reducing COVID-19 infections declined along waves, from 95.4% in the first wave to 56.0% in the third wave recently at the global level and similarly from 83.3% to 58.7% at the USA national level, while it had fluctuating performance across waves on regional and subnational scales. Regardless of geographical scale, gathering restrictions and facial coverings played significant roles in epidemic mitigation before the vaccine rollout. Our findings have important implications for continued tailoring and implementation of NPI strategies, together with vaccination, to mitigate future COVID-19 waves, caused by new variants, and other emerging respiratory infectious diseases.

Microbial signatures in the lower airways of mechanically ventilated COVID19 patients associated with poor clinical outcome (preprint)

Mortality among patients with COVID-19 and respiratory failure is high and there are no known lower airway biomarkers that predict clinical outcome. We investigated whether bacterial respiratory infections and viral load were associated with poor clinical outcome and host immune tone. We obtained bacterial and fungal culture data from 589 critically ill subjects with COVID-19 requiring mechanical ventilation. On a subset of the subjects that underwent bronchoscopy, we also quantified SARS-CoV-2 viral load, analyzed the microbiome of the lower airways by metagenome and metatranscriptome analyses and profiled the host immune response. We found that isolation of a hospital-acquired respiratory pathogen was not associated with fatal outcome. However, poor clinical outcome was associated with enrichment of the lower airway microbiota with an oral commensal ( Mycoplasma salivarium ), while high SARS-CoV-2 viral burden, poor anti-SARS-CoV-2 antibody response, together with a unique host transcriptome profile of the lower airways were most predictive of mortality. Collectively, these data support the hypothesis that 1) the extent of viral infectivity drives mortality in severe COVID-19, and therefore 2) clinical management strategies targeting viral replication and host responses to SARS-CoV-2 should be prioritized.

Microbial signatures in the lower airways of mechanically ventilated COVID19 patients associated with poor clinical outcome (preprint)

Mortality among patients with COVID-19 and respiratory failure is high and there are no known lower airway biomarkers that predict clinical outcome. We investigated whether bacterial respiratory infections and viral load were associated with poor clinical outcome and host immune tone. We obtained bacterial and fungal culture data from 589 critically ill subjects with COVID-19 requiring mechanical ventilation. On a subset of the subjects that underwent bronchoscopy, we also quantified SARS-CoV-2 viral load, analyzed the microbiome of the lower airways by metagenome and metatranscriptome analyses and profiled the host immune response. We found that isolation of a hospital-acquired respiratory pathogen was not associated with fatal outcome. However, poor clinical outcome was associated with enrichment of the lower airway microbiota with an oral commensal (Mycoplasma salivarium), while high SARS-CoV-2 viral burden, poor anti-SARS-CoV-2 antibody response, together with a unique host transcriptome profile of the lower airways were most predictive of mortality. Collectively, these data support the hypothesis that 1) the extent of viral infectivity drives mortality in severe COVID-19, and therefore 2) clinical management strategies targeting viral replication and host responses to SARS-CoV-2 should be prioritized.