Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 23
Filter
1.
International Journal of Molecular Sciences ; 23(17):9659, 2022.
Article in English | MDPI | ID: covidwho-2006039

ABSTRACT

COVID-19, caused by the highly transmissible severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has rapidly spread and become a pandemic since its outbreak in 2019. We have previously discovered that aloperine is a new privileged scaffold that can be modified to become a specific antiviral compound with markedly improved potency against different viruses, such as the influenza virus. In this study, we have identified a collection of aloperine derivatives that can inhibit the entry of SARS-CoV-2 into host cells. Compound 5 is the most potent tested aloperine derivative that inhibited the entry of SARS-CoV-2 (D614G variant) spike protein-pseudotyped virus with an IC50 of 0.5 µM. The compound was also active against several other SARS-CoV-2 variants including Delta and Omicron. Results of a confocal microscopy study suggest that compound 5 inhibited the viral entry before fusion to the cell or endosomal membrane. The results are consistent with the notion that aloperine is a privileged scaffold that can be used to develop potent anti-SARS-CoV-2 entry inhibitors.

2.
Front Psychiatry ; 13: 880978, 2022.
Article in English | MEDLINE | ID: covidwho-1952740

ABSTRACT

Background: The novel coronavirus disease 2019 (COVID-19) pandemic causes great disruption to cancer care services, which might bring about psychological problems and further lower both physical and mental life quality in cancer patients. Until now, very few studies focused on the psychological distress of patients with advanced melanoma before or during the epidemic. This study aimed to elucidate the fear of progression (FoP), anxiety, depression, and related independent predictors in patients with advanced melanoma during the COVID-19 outbreak. Methods: Two hundred and seventy-three patients with unresectable stage III or metastatic melanoma were recruited from February 2020 to November 2021, and completed the Fear of Progression Questionnaire-Short Form (FoP-Q-SF), State Trait Anxiety Inventory (STAI-6), and Patient Health Questionnaire (PHQ-9). Results: One hundred and seventy-four (64.7%) patients experienced heighted FoP (FoP-Q-SF: 39.9 ± 11.0), 198 (72.5%) patients reported elevated anxiety (STAI-6: 13.1 ± 3.0), and 62 (22.7%) patients had increased depression (PHQ-9: 6.4 ± 6.1). In multivariate analysis, illness duration (OR = 0.987 for FoP; OR = 0.984 for depression), cancer stage (OR = 14.394 for anxiety) and disease progression (OR = 1.960 for FoP; OR = 23.235 for anxiety; OR = 1.930 for depression) were independent predictors for FoP, anxiety or depression. Additionally, the high levels of FoP, anxiety and depression were significantly positive correlated with each other (r = 0.466 for FoP and anxiety; r = 0.382 for FoP and depression; r = 0.309 for anxiety and depression). Conclusion: Our study indicates that FoP, anxiety and depression are persisting among patients with advanced melanoma in the COVID-19 and post-COVID-19 era. Effective psycho-oncological interventions are needed for melanoma patients with psychological distress during the ongoing COVID-19 pandemic.

3.
IEEE Trans Biomed Eng ; 69(8): 2557-2568, 2022 Aug.
Article in English | MEDLINE | ID: covidwho-1948846

ABSTRACT

OBJECTIVE: The m6A modification is the most common ribonucleic acid (RNA) modification, playing a role in prompting the virus's gene mutation and protein structure changes in the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Nanopore single-molecule direct RNA sequencing (DRS) provides data support for RNA modification detection, which can preserve the potential m6A signature compared to second-generation sequencing. However, due to insufficient DRS data, there is a lack of methods to find m6A RNA modifications in DRS. Our purpose is to identify m6A modifications in DRS precisely. METHODS: We present a methodology for identifying m6A modifications that incorporated mapping and extracted features from DRS data. To detect m6A modifications, we introduce an ensemble method called mixed-weight neural bagging (MWNB), trained with 5-base RNA synthetic DRS containing modified and unmodified m6A. RESULTS: Our MWNB model achieved the highest classification accuracy of 97.85% and AUC of 0.9968. Additionally, we applied the MWNB model to the COVID-19 dataset; the experiment results reveal a strong association with biomedical experiments. CONCLUSION: Our strategy enables the prediction of m6A modifications using DRS data and completes the identification of m6A modifications on the SARS-CoV-2. SIGNIFICANCE: The Corona Virus Disease 2019 (COVID-19) outbreak has significantly influence, caused by the SARS-CoV-2. An RNA modification called m6A is connected with viral infections. The appearance of m6A modifications related to several essential proteins affects proteins' structure and function. Therefore, finding the location and number of m6A RNA modifications is crucial for subsequent analysis of the protein expression profile.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , RNA, Viral/analysis , RNA, Viral/genetics , SARS-CoV-2/genetics , Sequence Analysis, RNA
4.
PLoS One ; 17(7): e0271381, 2022.
Article in English | MEDLINE | ID: covidwho-1933385

ABSTRACT

OBJECTIVE: We used SARS-CoV-2 whole-genome sequencing (WGS) and electronic health record (EHR) data to investigate the associations between viral genomes and clinical characteristics and severe outcomes among hospitalized COVID-19 patients. METHODS: We conducted a case-control study of severe COVID-19 infection among patients hospitalized at a large academic referral hospital between March 2020 and May 2021. SARS-CoV-2 WGS was performed, and demographic and clinical characteristics were obtained from the EHR. Severe COVID-19 (case patients) was defined as having one or more of the following: requirement for supplemental oxygen, mechanical ventilation, or death during hospital admission. Controls were hospitalized patients diagnosed with COVID-19 who did not meet the criteria for severe infection. We constructed predictive models incorporating clinical and demographic variables as well as WGS data including lineage, clade, and SARS-CoV-2 SNP/GWAS data for severe COVID-19 using multiple logistic regression. RESULTS: Of 1,802 hospitalized SARS-CoV-2-positive patients, we performed WGS on samples collected from 590 patients, of whom 396 were case patients and 194 were controls. Age (p = 0.001), BMI (p = 0.032), test positive time period (p = 0.001), Charlson comorbidity index (p = 0.001), history of chronic heart failure (p = 0.003), atrial fibrillation (p = 0.002), or diabetes (p = 0.007) were significantly associated with case-control status. SARS-CoV-2 WGS data did not appreciably change the results of the above risk factor analysis, though infection with clade 20A was associated with a higher risk of severe disease, after adjusting for confounder variables (p = 0.024, OR = 3.25; 95%CI: 1.31-8.06). CONCLUSIONS: Among people hospitalized with COVID-19, older age, higher BMI, earlier test positive period, history of chronic heart failure, atrial fibrillation, or diabetes, and infection with clade 20A SARS-CoV-2 strains can predict severe COVID-19.


Subject(s)
Atrial Fibrillation , COVID-19 , Heart Failure , COVID-19/epidemiology , Case-Control Studies , Electronic Health Records , Heart Failure/epidemiology , Heart Failure/genetics , Humans , SARS-CoV-2/genetics
5.
Frontiers in immunology ; 13, 2022.
Article in English | EuropePMC | ID: covidwho-1787529

ABSTRACT

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of COVID-19, which has broken out worldwide for more than two years. However, due to limited treatment, new cases of infection are still rising. Therefore, there is an urgent need to understand the basic molecular biology of SARS-CoV-2 to control this virus. SARS-CoV-2 replication and spread depend on the recruitment of host ribosomes to translate viral messenger RNA (mRNA). To ensure the translation of their own mRNAs, the SARS-CoV-2 has developed multiple strategies to globally inhibit the translation of host mRNAs and block the cellular innate immune response. This review provides a comprehensive picture of recent advancements in our understanding of the molecular basis and complexity of SARS-CoV-2 protein translation. Specifically, we summarize how this viral infection inhibits host mRNA translation to better utilize translation elements for translation of its own mRNA. Finally, we discuss the potential of translational components as targets for therapeutic interventions.

6.
Aerosol and Air Quality Research ; 21(11), 2021.
Article in English | ProQuest Central | ID: covidwho-1771483

ABSTRACT

We studied the impact of COVID-19 (coronavirus disease 2019) lockdown on the air quality over the Atlanta area using satellite and ground-based observations, meteorological reanalysis data and traffic information. Unlike other cities, we found the air quality has improved slightly over the Atlanta area during the 2020 COVID-19 lockdown period (March 14–April 30, 2020), compared to the analogous period of 2019 (March 14-April 30, 2019). Ground NO2 concentrations have decreased slightly 10.8% and 8.2% over the near-road (NR) and urban ambient (UA) stations, respectively. Tropospheric NO2 columns have reduced 13%-49% over the Atlanta area from space-borne observations of TROPOspheric Monitoring Instrument (TROPOMI). Ground ozone and PM2.5 have decreased 15.7% an ~5%, respectively. This slight air quality improvement is primarily caused by the reduced human activities, as COVID-19 lockdowns have reduced ~50% human activities, measured by traffic volume. Higher wind speed and precipitations also make the meteorological conditions favorable to this slight air quality improvement. We have not found a significant improvement in Atlanta amid the lockdown when human activities have reduced ~50%. Further studies are needed to understand the impacts of reduced human activities on atmospheric chemistry. We also found TROPOMI and ground measurements have disagreements on NO2 reductions, as collocated TROPOMI observations revealed ~23% and ~21% reductions of tropospheric NO2 columns over NR and UA stations, respectively. Several factors may explain this disagreement: First, tropospheric NO2 columns and ground NO2 concentrations are not necessarily the same, although they are highly correlated in the afternoon;Second, meteorological conditions may have different impacts on TROPMI and ground measurements. Third, TROPOMI may underestimate tropospheric NO2 due to uncertainties from air mass factors. Fourth, the uncertainties of chemiluminescence NO2 measurements used by ground stations. Consequently, studies using space-borne tropospheric NO2 column and ground NO2 measurements should take these factors into account.

7.
Int J Public Health ; 67: 1604329, 2022.
Article in English | MEDLINE | ID: covidwho-1731881

ABSTRACT

Objective: The aim of our case-control study was to find the influence of lifestyle and comorbidities on COVID-19 susceptibility, identify risk factors and protective factors, and identify ways to encourage people to adopt a healthy lifestyle. Methods: Patients with COVID-19 were matched with non-COVID-19 participants in a ratio of 1:2. Univariate analysis was performed using the chi-square test, and multivariate analysis was performed using conditional logistic regression. Results: Multivariate analysis using conditional logistic regression found that alcohol consumption (AC) and a bland diet increased the risk of COVID-19, while college degrees and above, smoking, drinking tea, and exercise, especially walking, significantly reduced the risk of COVID-19. Conclusion: After removing the effects of demographic factors, the study demonstrated that AC significantly reduced the ability of the body to resist COVID-19 infection. Moreover, following a bland diet increased the susceptibility to COVID-19. Notably, people who drank tea and performed regular exercises, especially walking, were significantly less likely to be infected with COVID-19. College degree or above relative illiteracy is COVID-19 protective factors of infection.


Subject(s)
COVID-19 , Adult , Alcohol Drinking , Areca/adverse effects , COVID-19/epidemiology , Case-Control Studies , Humans , Life Style , Risk Factors , SARS-CoV-2
8.
Chin Med J (Engl) ; 133(9): 1039-1043, 2020 May 05.
Article in English | MEDLINE | ID: covidwho-1722619

ABSTRACT

BACKGROUND: A patient's infectivity is determined by the presence of the virus in different body fluids, secretions, and excreta. The persistence and clearance of viral RNA from different specimens of patients with 2019 novel coronavirus disease (COVID-19) remain unclear. This study analyzed the clearance time and factors influencing 2019 novel coronavirus (2019-nCoV) RNA in different samples from patients with COVID-19, providing further evidence to improve the management of patients during convalescence. METHODS: The clinical data and laboratory test results of convalescent patients with COVID-19 who were admitted to from January 20, 2020 to February 10, 2020 were collected retrospectively. The reverse transcription polymerase chain reaction (RT-PCR) results for patients' oropharyngeal swab, stool, urine, and serum samples were collected and analyzed. Convalescent patients refer to recovered non-febrile patients without respiratory symptoms who had two successive (minimum 24 h sampling interval) negative RT-PCR results for viral RNA from oropharyngeal swabs. The effects of cluster of differentiation 4 (CD4)+ T lymphocytes, inflammatory indicators, and glucocorticoid treatment on viral nucleic acid clearance were analyzed. RESULTS: In the 292 confirmed cases, 66 patients recovered after treatment and were included in our study. In total, 28 (42.4%) women and 38 men (57.6%) with a median age of 44.0 (34.0-62.0) years were analyzed. After in-hospital treatment, patients' inflammatory indicators decreased with improved clinical condition. The median time from the onset of symptoms to first negative RT-PCR results for oropharyngeal swabs in convalescent patients was 9.5 (6.0-11.0) days. By February 10, 2020, 11 convalescent patients (16.7%) still tested positive for viral RNA from stool specimens and the other 55 patients' stool specimens were negative for 2019-nCoV following a median duration of 11.0 (9.0-16.0) days after symptom onset. Among these 55 patients, 43 had a longer duration until stool specimens were negative for viral RNA than for throat swabs, with a median delay of 2.0 (1.0-4.0) days. Results for only four (6.9%) urine samples were positive for viral nucleic acid out of 58 cases; viral RNA was still present in three patients' urine specimens after throat swabs were negative. Using a multiple linear regression model (F = 2.669, P = 0.044, and adjusted R = 0.122), the analysis showed that the CD4+ T lymphocyte count may help predict the duration of viral RNA detection in patients' stools (t = -2.699, P = 0.010). The duration of viral RNA detection from oropharyngeal swabs and fecal samples in the glucocorticoid treatment group was longer than that in the non-glucocorticoid treatment group (15 days vs. 8.0 days, respectively; t = 2.550, P = 0.013) and the duration of viral RNA detection in fecal samples in the glucocorticoid treatment group was longer than that in the non-glucocorticoid treatment group (20 days vs. 11 days, respectively; t = 4.631, P < 0.001). There was no statistically significant difference in inflammatory indicators between patients with positive fecal viral RNA test results and those with negative results (P > 0.05). CONCLUSIONS: In brief, as the clearance of viral RNA in patients' stools was delayed compared to that in oropharyngeal swabs, it is important to identify viral RNA in feces during convalescence. Because of the delayed clearance of viral RNA in the glucocorticoid treatment group, glucocorticoids are not recommended in the treatment of COVID-19, especially for mild disease. The duration of RNA detection may relate to host cell immunity.


Subject(s)
Betacoronavirus/genetics , Coronavirus Infections/genetics , Pneumonia, Viral/genetics , RNA, Viral/genetics , Adult , Aged , COVID-19 , COVID-19 Testing , Clinical Laboratory Techniques , Coronavirus Infections/diagnosis , Coronavirus Infections/rehabilitation , Female , Humans , Male , Middle Aged , Pandemics , Pneumonia, Viral/rehabilitation , Real-Time Polymerase Chain Reaction , Retrospective Studies , SARS-CoV-2
9.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-325441

ABSTRACT

Background: Coronavirus Disease (COVID-19) causes a sudden turn over to bad at some check-point and thus needs intervention of intensive care unit (ICU). This resulted in urgent and large needs of ICUs posed great risks to the medical system. Estimating the mortality of critical in-patients who were not admitted to the ICU (MI-mortality) will be valuable to optimize the management and assignment of ICU. Methods: . Retrospective, of the 733 in-patients diagnosed with COVD-19 at Huangpi Hospital of Traditional Chinese Medicine (Wuhan, China), as of March 18, 2020. This study aims to estimate the MI-mortality and build a model to identify the critical in-patients. Demographic, clinical and laboratory results were collected and analyzed. The mortality rate for the patients who failed to receive ICU and unfortunately died was analyzed. To this end, the key factors for prognostic of patients who may need ICU care were found. A prognostic classification model using machine learning was built to identify the patient who may need ICU. Results: . Considering the shortage of ICU beds at the beginning of disease emergence, we defined the mortality for those patients who were predicted to be in needing of ICU treatment yet they did not as MI-mortality. Patients who entered the ICU and died were defined as ICU-mortality. To estimate MI-mortality, a prognostic classification model was built to identify the in-patients who may need ICU care based on the medical factors collected in-hospital. Its predictive accuracies on whole patient set (733 [25 708]), training set (586 [20 566]) and testing set (147 [5 142]) dataset were 0.8513, 0.8935 and 0.8288, with the AUC of 0.8844, 0.8941 and 0.9120, respectively. Our analysis had shown that the MI-mortality is 41% and the ICU-mortality is 32%, implying that enough bed of ICU in treating patients in critical conditions. Conclusions: . On our cohort of 733 patients, 25 in-patients were admitted to ICU, among them 8 patients died. 25 in-patients who have been predicted by our model that they should need ICU care, yet they did not enter ICU due to lack of shorting ICU wards. The MI-mortality is 41%.

10.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-325412

ABSTRACT

Deep learning models usually require a large amount of labeled data to achieve satisfactory performance. In multimedia analysis, domain adaptation studies the problem of cross-domain knowledge transfer from a label rich source domain to a label scarce target domain, thus potentially alleviates the annotation requirement for deep learning models. However, we find that contemporary domain adaptation methods for cross-domain image understanding perform poorly when source domain is noisy. Weakly Supervised Domain Adaptation (WSDA) studies the domain adaptation problem under the scenario where source data can be noisy. Prior methods on WSDA remove noisy source data and align the marginal distribution across domains without considering the fine-grained semantic structure in the embedding space, which have the problem of class misalignment, e.g., features of cats in the target domain might be mapped near features of dogs in the source domain. In this paper, we propose a novel method, termed Noise Tolerant Domain Adaptation, for WSDA. Specifically, we adopt the cluster assumption and learn cluster discriminatively with class prototypes in the embedding space. We propose to leverage the location information of the data points in the embedding space and model the location information with a Gaussian mixture model to identify noisy source data. We then design a network which incorporates the Gaussian mixture noise model as a sub-module for unsupervised noise removal and propose a novel cluster-level adversarial adaptation method which aligns unlabeled target data with the less noisy class prototypes for mapping the semantic structure across domains. We conduct extensive experiments to evaluate the effectiveness of our method on both general images and medical images from COVID-19 and e-commerce datasets. The results show that our method significantly outperforms state-of-the-art WSDA methods.

11.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-315713

ABSTRACT

The considerable cessation of human activities during the COVID-19 pandemic has affected global energy use and CO2 emissions. Here we show the unprecedented decrease in global fossil CO2 emissions from January to April 2020 was of 7.8% (938 Mt CO2 with a +6.8% of 2-σ uncertainty) when compared with the period last year. In addition other emerging estimates of COVID impacts based on monthly energy supply or estimated parameters, this study contributes to another step that constructed the near-real-time daily CO2 emission inventories based on activity from power generation (for 29 countries), industry (for 73 countries), road transportation (for 406 cities), aviation and maritime transportation and commercial and residential sectors emissions (for 206 countries). The estimates distinguished the decline of CO2 due to COVID-19 from the daily, weekly and seasonal variations as well as the holiday events. The COVID-related decreases in CO2 emissions in road transportation (340.4 Mt CO2, -15.5%), power (292.5 Mt CO2, -6.4% compared to 2019), industry (136.2 Mt CO2, -4.4%), aviation (92.8 Mt CO2, -28.9%), residential (43.4 Mt CO2, -2.7%), and international shipping (35.9Mt CO2, -15%). Regionally, decreases in China were the largest and earliest (234.5 Mt CO2,-6.9%), followed by Europe (EU-27 & UK) (138.3 Mt CO2, -12.0%) and the U.S. (162.4 Mt CO2, -9.5%). The declines of CO2 are consistent with regional nitrogen oxides concentrations observed by satellites and ground-based networks, but the calculated signal of emissions decreases (about 1Gt CO2) will have little impacts (less than 0.13ppm by April 30, 2020) on the overserved global CO2 concertation. However, with observed fast CO2 recovery in China and partial re-opening globally, our findings suggest the longer-term effects on CO2 emissions are unknown and should be carefully monitored using multiple measures.

12.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-315299

ABSTRACT

Background: Adult patients diagnosed as COVID-19 in Shanghai were accepted in Shanghai Public Health Clinical Center. We found around 4.91% of cases showed non-pneumonia on CT imaging when they were confirmed. Understanding the characteristics of non-pneumonia cases is of great significance to guide clinical treatment and improve prevention and control measures. Methods: All dataset of demography, epidemiology, clinical manifestation, laboratory test, diagnosis, classification, condition change, treatment and outcome were obtained by retrospective investigation. Results: 16 cases were confirmed COVID-19 with non-pneumonia with clear epidemiological history. The median age of patients was 37 years old and 81.25% were female. The median incubation period was 15.25 days. 75% patients were familial clusters. These patients were presented with mild clinical manifestations, such as bronchitis, common cold and asymptomatic infection with or without laboratory abnormalities. 4(25%)cases had underlying diseases. 3 of them had mild pneumonia on chest CT imaging during hospitalization. All of the cases were cured and discharged after support treatment. Conclusions: A few of adult patients after COVID-19 infection had non-pneumonia, with mild clinical manifestations and long incubation time. It usually occurred in young women and history of family aggregation. The mild clinical symptom may be caused by the decreasing pathogenicity after multiple generation of virus replication. However, we should be on alert that the virus is still contagious to human. Therefore, an intensive attention should be paid to these patients to avoid misdiagnosis and overlook, because these patients are potential viral source in infection of other people.

13.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-313344

ABSTRACT

Background: Since December 2019, coronavirus disease 2019 (COVID-19) rapidly spread throughout the whole world , data have been needed on the clinical characteristics of the affected patients. Objectives: A total of 579 adult COVID-19 cases were enrolled in Shanghai from Jan 20 to Apr 15, 2020, in which 95 cases (16.41%) showed non-pneumonia on CT when confirmed. The characteristics of non-pneumonia cases have not been clearly described previously, and this might provide guidance to prevent and treatment of COVID-19. Method: We retrospectively collected the patient clinical dataset including demography, epidemiology, clinical manifestation, laboratory test results, diagnostic classification, treatment and clinical outcomes. Results: : The average age of 95 COVID-19 cases was 31.45 ± 12.89 years old and 95.79% of them were less than 60 years old. They had mild clinical symptoms and/or laboratory abnormalities. 20 of the 95 cases occurred mild pneumonia during hospitalization, accompanied with lower lymphocyte counts, in which 60% cases were complicated with underlying condition and 15% cases were over 60 years old. All cases were cured. 16 of the 95 cases were local residents with clear epidemiological history and long incubation time, and mainly discovered as fever and respiratory symptoms. Other 79 cases were overseas imported, some had initial symptoms of diarrhea, smell or taste disorders and so on. They were mainly found at port of entry. Conclusions: : Non-pneumonia COVID-19 predominantly occurred among young adults with mild clinical symptoms and possible long incubation time. The patients with underlying condition or at older age more likely developed mild pneumonia after diagnosis. Thereby, it is very important to pay attention to these patients and make reasonable diagnostic classification towards better prevention and treatment of COVID-19.

14.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-308916

ABSTRACT

The coronavirus pandemic greatly shocked the global energy market, which could be clearly demonstrated by the recent collapse in crude oil prices. Using a dynamic multi-regional computable general equilibrium model, we explored the influences of the COVID-19 pandemic on energy production and consumption. The associated impacts on the macroeconomy as well as on carbon emissions are also examined. The results of this paper indicate dramatic negative shocks of the COVID-19 pandemic to energy consumption at both global and national levels, particularly for oil and oil products. However, the energy transition to renewables will be paused, as other non-oil fossil fuels can still play significant roles in economic activity. The epidemic may also temporarily terminate the more than ten-year increasing trend of the world’s total CO 2 emissions, despite its limited contribution to the mitigation of global warming. However, there are still many opportunities worthy of use to promote short- or mid-term low-carbon energy transitions.

15.
Transportation Research Record ; : 03611981211039163, 2021.
Article in English | Sage | ID: covidwho-1438201

ABSTRACT

Real-time highly resolved spatial-temporal vehicle energy consumption is a key missing dimension in transportation data. Most roadway link-level vehicle energy consumption data are estimated using average annual daily traffic measures derived from the Highway Performance Monitoring System;however, this method does not reflect day-to-day energy consumption fluctuations. As transportation planners and operators are becoming more environmentally attentive, they need accurate real-time link-level vehicle energy consumption data to assess energy and emissions;to incentivize energy-efficient routing;and to estimate energy impact caused by congestion, major events, and severe weather. This paper presents a computational workflow to automate the estimation of time-resolved vehicle energy consumption for each link in a road network of interest using vehicle probe speed and count data in conjunction with machine learning methods in real time. The real-time pipeline can deliver energy estimates within a couple seconds on query to its interface. The proposed method was evaluated on the transportation network of the metropolitan area of Chattanooga, Tennessee. The volume estimation results were validated with ground truth traffic volume data collected in the field. To demonstrate the effectiveness of the proposed method, the energy consumption pipeline was applied to real-world data to quantify road transportation-related energy reduction because of mitigation policies to slow the spread of COVID-19 and to measure energy loss resulting from congestion.

16.
Non-conventional in English | Transportation Research Board, Grey literature | ID: grc-747438

ABSTRACT

Real-time highly resolved spatial-temporal vehicle energy consumption is a key missing dimension in transportation data. Most roadway link-level vehicle energy consumption data are estimated using average annual daily traffic (AADT) measures derived from the Highway Performance Monitoring System (HPMS). However, this method does not reflect day-to-day energy consumption fluctuations. As transportation planners and operators are becoming more environmentally attentive, they need accurate real-time link-level vehicle energy consumption data to assess energy and emissions, incentivize energy-efficient routing, and estimate energy impact due to congestion, major events, and severe weather. This paper presents a computational workflow to automate the estimation of time-resolved vehicle energy consumption for each link in a road network of interest, utilizing vehicle probe speed and count data in conjunction with machine learning methods in real-time. The real-time pipeline is capable of delivering energy estimates within a couple seconds upon query to its interface. The proposed method was evaluated on the transportation network of the metropolitan area of Chattanooga, TN. The model results were validated with ground truth traffic volume data collected in the field and from AADT. Energy consumption was estimated and compared for three scenarios, including a COVID-19 period, free flow condition, and peak hour, to demonstrate the effectiveness of the proposed method, estimate energy reduction due to mitigation policies to slow COVID-19 spread, and measure energy loss due to congestion.

17.
J Thorac Dis ; 13(6): 3628-3642, 2021 Jun.
Article in English | MEDLINE | ID: covidwho-1296313

ABSTRACT

BACKGROUND: To analyze the clinical characteristics and predictors for mortality of adult younger than 60 years old with severe coronavirus disease 2019 (COVID-19). METHODS: We retrospectively retrieved data for 152 severe inpatients with COVID-19 including 60 young patients in the Eastern Campus of Wuhan University affiliated Renmin Hospital in Wuhan, China, from January 31, 2020 to February 20, 2020. We recorded and analyzed patients' demographic, clinical, laboratory, and chest CT findings, treatment and outcomes data. RESULTS: Of those 60 severe young patients, 15 (25%) were died. Male was more predominant in deceased young patients (12, 80%) than that in recovered young patients (22, 49%). Hypertension was more common among deceased young patients (8, 53%) than that in recovered young patients (7, 16%). Compared with the recovered young patients, more deceased young patients presented with sputum (11, 73%), dyspnea (12, 80%) and fatigue (13, 87%). Only sputum, PSI and neutrophil counts were remained as independent predictors of death in a multivariate logistic regression model. Among ARDS patients, the recovered were administrated with corticosteroid earlier and anticoagulation. The addition of neutrophil counts >6.3×109/L to the SMART-COP score resulted in improved area under the curves. CONCLUSIONS: Severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) infection in young deceased patients appears to cause exuberant inflammatory responses, leading to compromised oxygen exchange, coagulation and multi-organ dysfunction. In addition, young patients with ARDS could benefit from adjuvant early corticosteroid and anticoagulation therapy. The expanded SMART-COP could predict the fatal outcomes with optimal efficiency.

18.
Geophys Res Lett ; 48(4): 2e020GL091265, 2021 Feb 28.
Article in English | MEDLINE | ID: covidwho-1104432

ABSTRACT

Satellite HCHO data are widely used as a reliable proxy of non-methane volatile organic compounds (NMVOCs) to constrain underlying emissions and chemistry. Here, we examine global significant changes in HCHO columns at the early stage of the COVID-19 pandemic (January-April 2020) compared with the same period in 2019 with observations from the TROPOspheric Monitoring Instrument (TROPOMI). HCHO columns decline (11.0%) in the Northern China Plain (NCP) because of a combination of meteorological impacts, lower HCHO yields as NO x emission plunges (by 36.0%), and reduced NMVOC emissions (by 15.0%) resulting from the lockdown. HCHO columns change near Beijing (+8.4%) due mainly to elevated hydroxyl radical as NO x emission decreases in a NO x -saturated regime. HCHO columns change in Australia (+17.5%), Northeastern Myanmar of Southeast Asia (+14.9%), Central Africa (+7.8%), and Central America (+18.9%), consistent with fire activities. Our work also points to other changes related to temperature and meteorological variations.

19.
Socioecon Plann Sci ; 80: 101029, 2022 Mar.
Article in English | MEDLINE | ID: covidwho-1071927

ABSTRACT

In recent years, public health emergencies have occurred frequently, posing a serious threat to the regional economy and the safety of people's lives and property. In particular, the outbreak of the COVID-19 novel coronavirus this year has caused serious losses to the global economy. On this basis, this article attempts to use modern advanced artificial intelligence technology and modern social science and technology to provide technical assistance and support for the prevention and control of major public health incidents, in order to improve the Chinese government's public relations capabilities and response to public health emergencies. Ability and level. This article attempts to use 3S technology closely related to artificial intelligence technology to design and establish a public health emergency response system, so as to improve the government's response and decision-making ability to respond to and deal with public health emergencies, and reduce the occurrence of emergencies. The results showed that among the 298 respondents, 145 believed that public health emergencies depend on human-to-human transmission. Most event information is acceptable, while 169 people who rely on mobile phones for information think that most of them are acceptable, and 89 people who rely on TV media for information think that most of them are acceptable. It shows that the use of artificial intelligence technology can effectively solve and prevent the further development of the situation, and at the same time improve the government's ability and level to respond to major public health emergencies, and increase the government's prestige in the eyes of the public.

SELECTION OF CITATIONS
SEARCH DETAIL