ABSTRACT
Basic local alignment search tool (BLAST) is one of the popular sequence similarity analysis tools. However, some students and researchers just blindly use the default parameters. Moreover, some students are confused about how to choose the right program. In a word, it is prone to be misused and researchers often draw conclusions incorrectly. In view of this, we traced back the internet hot topic in early 2020 - "MORDERATELY STRONG CONFIRMATION OF A LABORATORY ORIGIN OF COVID-19", and took it as teaching materials to guide the student to use BLAST currently through reanalyzing and reproducing the source of errors. Then we arranged an interesting experiment about fabricating dinosaur genes through modifying a chicken gene. In the experimental design to make the students grasp the BLAST tools better, one group fabricated the dinosaur gene and the other group decrypted the added bases. This instructional design could be conducive to cultivate students ' ability about distinguishing different viewpoints correctly, and we hope it can be enlightening and helpful to the teaching of BLAST tools.Copyright © 2022 by the authors.
ABSTRACT
The virus has a big impact on the whole world. The new Coronavirus has a great impact on everyone's life and will even lead to changes in the world pattern. Because of the virus, society is not functioning properly, the recession, people's expectations of economic development are falling. Trains and planes were suspended in some areas. In this paper, computer is used to simulate SIR model, based on system dynamics, to study the spread of infectious diseases. The SIR model passes reality and limit tests. On the basis of the original model, supplementing the original model, isolation and vaccination can effectively stop the spread of the virus. It can slow the outbreak of the virus and reduce the number of infected people. Panic comes from the unknown, and our confidence in defeating the 2019-nCoV virus comes from our scientific base. © 2022 IEEE.
ABSTRACT
Introduction: Poor outcomes in COVID-19 patients (pt) are associated with C5a-C5aR axis activation. A C5a-specific monoclonal antibody, vilobelimab (VILO), improves outcomes in critically ill COVID-19 pt in a Phase 3 randomized, double-blind, placebo (PLC)- controlled study [1]. Method(s): COVID-19 pt within 48 h of intubation were randomly assigned to receive 6, 800 mg infusions of VILO or PLC at a 1:1 ratio on top of standard of care. Predefined subgroup analyses by region and country were performed. Result(s): Forty-six (46) hospitals on 4 continents randomized 369 pt: VILO (n = 178), PLC (n = 191). VILO significantly reduced 28- (HR 0.67;95% CI 0.48-0.96;p = 0.027) and 60-Day mortality (HR 0.67;95% CI 0.48-0.93, p = 0.0163) using a predefined, unstratified per protocol analysis. Mortality rates at 28- and 60-days and VILO treatment effects, however, differed substantially between regions: Western Europe HR for 60-day mortality 0.59 [0.37-0.95], South Africa plus Russian Federation HR 0.62 [0.28-1.38] and South America HR 0.80 [0.46-1.39] (Fig. 1). The weak signal in South America is predominately driven by Brazil (n = 74), which showed a significant age imbalance with a median 9-years younger PLC group (44.5-years-old vs 53.5-years-old) with low 60-day mortality of ~ 32.5% in the PLC group versus ~ 43.3% in Western Europe. Adjusting for age group categories (<= 30, 31-40, 41-50, 51-60, > 60;Cox regression) for 60-day mortality changed the HR in Brazil (0.96 [0.44-2.10] for continuous age-adjustment) to values near the estimate for the entire study population (HR 0.77 [0.35-1.69] for age in categories), suggesting a by chance imbalance and not a statistically evident weaker effect in Brazil. Conclusion(s): Regional efficacy differences between the rest of the world and South America were driven by age imbalances between treatment groups, which do not diminish the robust efficacy signal for VILO in severe COVID-19.
ABSTRACT
Introduction: C5a-C5aR axis activation is associated with increased mortality in severe COVID-19. Vilobelimab (VILO), a C5a-specific monoclonal antibody, improved mortality in severe COVID-19 patients (pts) in a Phase 3 multicenter, randomized, double-blind, placebo (PLC)- controlled study [1]. A pharmacokinetic/pharmacodynamic (PK/PD) analysis was undertaken to assess VILO and C5a as well as antidrug antibodies (ADA) levels in the study. Method(s): Forty-six (46) hospitals on four continents randomized 369 COVID-19 pts (VILO [n = 178], PLC [n = 191]) within 48 h of being mechanically ventilated to receive 6, 800 mg infusions of VILO or PLC at a 1:1 ratio on top of standard of care. Blood samples were taken at screening, Day 8 and at hospital discharge for VILO and C5a and at screening and hospital discharge for ADA. Enzyme-linked immunosorbent assays were used to analyze levels. Result(s): Screening blood samples for VILO and C5a were available for VILO (n = 93) and PLC (n = 99) from sites in Western Europe. On Day 8 after 3 infusions, mean VILO trough concentrations were 21799.3- 302972.1 ng/mL (geometric mean 137881.3 ng/mL) (Fig. 1). At screening, C5a was highly elevated and comparable between groups: VILO median 118.3 ng/mL, mean 130.3 ng/mL, PLC median 104.6 ng/mL, mean 123.2 ng/mL. By Day 8, C5a levels were reduced by 84.6% in the VILO group (median 14.5 ng/mL [mean 16.8 ng/mL], p < 0.001) versus a 19.6% increase in the PLC group (median, 119.2 ng/mL, mean 129.8 ng/ mL). Beyond Day 8, though PD sampling was sparse, C5a levels remained elevated for PLC whereas C5a slowly rose but did not reach screening levels for VILO. Treatment-induced ADA were observed in 1 pt in the VILO group (Day 40 discharge) and 1 pt in the PLC group (Day 25 discharge), both appeared independent of treatment. Conclusion(s): The PK/PD analysis shows that VILO efficiently inhibits C5a in pts with severe COVID-19 resulting in a robust clinical effect on mortality reduction without inducing ADA.
ABSTRACT
The Corona Virus Disease 2019 (COVID-19) pandemic is still imposing a devastating impact on public health, the economy, and society. Predicting the development of epidemics and exploring the effects of various mitigation strategies have been a research focus in recent years. However, the spread simulation of COVID-19 in the dynamic social system is relatively unexplored. To address this issue, considering the outbreak of COVID-19 at Nanjing Lukou Airport in 2021, we constructed an artificial society of Nanjing Lukou Airport based on the Artificial societies, Computational experiments, and Parallel execution (ACP) approach. Specifically, the artificial society includes an environmental model, population model, contact networks model, disease spread model, and intervention strategy model. To reveal the dynamic variation of individuals in the airport, we first modeled the movement of passengers and designed an algorithm to generate the moving traces. Then, the mobile contact networks were constructed and aggregated with the static networks of staff and passengers. Finally, the complex dynamical network of contacts between individuals was generated. Based on the artificial society, we conducted large-scale computational experiments to study the spread characteristics of COVID-19 in an airport and to investigate the effects of different intervention strategies. Learned from the reproduction of the outbreak, it is found that the increase in cumulative incidence exhibits a linear growth mode, different from that (an exponential growth mode) in a static network. In terms of mitigation measures, promoting unmanned security checks and boarding in an airport is recommended, as to reduce contact behaviors between individuals and staff. © 2021 TUP.
ABSTRACT
Background: Blocking the C5a-C5aR axis in COVID-19 patients could improve outcomes by limiting myeloid cell infiltration in damaged organs and preventing excessive lung inflammation and endothelialitis. Aims and Objectives: Vilobelimab (VILO), an anti-C5a mAb that preserves the membrane attack complex (MAC), was tested in a Phase III adaptively designed multicenter, double-blind placebo (P)-controlled study for survival in critically ill COVID-19 patients. Method(s): COVID-19 pneumonia patients (N=369;VILO n=178, P n=191) within 48 hrs of intubation were randomly assigned to receive 6, 800 mg infusions of VILO or P on top of standard of care. Primary outcome was 28-day allcause mortality. Result(s): 28-day all-cause mortality was 31.7% VILO vs 41.6% P (Kaplan-Meier estimates;Cox regression site stratified, HR 0.73;95%CI:0.50-1.06;P=0.094) with a 22.7% relative mortality reduction to Day 60. In predefined primary outcome analysis without site stratification, VILO significantly reduced 28-day mortality (HR 0.67;95%CI:0.48-0.96;P=0.027);needed to treat number, 10 to save 1. VILO significantly reduced 28-day mortality in severe patients with baseline WHO ordinal scale score of 7 (n=237, HR 0.62;95%CI:0.40-0.95;P=0.028) or severe ARDS/PaO2/FiO2<=100 mmHg (n=98, HR 0.55;95%CI:0.30-0.98;P=0.044) or eGFR<60 mL/min/1.73m2 (n=108, HR 0.55;95%CI:0.31-0.96;P=0.036). Treatment emergent AEs were 90.9% VILO vs 91.0% P. Infections were comparable;VILO (62.9%), P (59.3%). Serious AEs were 58.9% VILO, 63.5% P. Conclusion(s): VILO reduced mortality at 28 to 60 days in severe COVID-19 pneumonia patients with no increase in infections suggesting the importance of targeting C5a while preserving MAC.
ABSTRACT
After the outbreak of COVID-19, Taiwan has implemented rigorous border control and taken specific measures such as virus detection, contact tracing, and quarantine since 2020. Its epidemic prevention performance has been quite outstanding. Even in May 2021, when the epidemic situation worsens, the people in Taiwan fully cooperate with the government's control measures so as to successfully alleviate and control the epidemic in less than three months. Among them, the detection policy has played a pivotal role. We analyze and discuss the false positive and false negative problems from rapid antigen and PCR detection in the screening policy as well as the timing of using these two instruments. This paper provides theoretical verification of the appropriateness of screening policy in Taiwan, offering a few feasible suggestions for related policies in other countries or regions at different stages of this and other potential epidemics. (c) 2022 by the authors;licensee Growing Science, Canada.
ABSTRACT
Background. SARS-CoV-2 induces endothelial damage and activates the complement system. In severe COVID-19 patients, complement split factor C5a is highly elevated leading to inflammation that contributes to multiorgan failure. The anti-C5a monoclonal antibody, Vilobelimab (Vilo), which preserves the membrane attack complex (MAC), was investigated in an adaptively designed, randomized doubleblind, placebo (P)-controlled Phase 3 international multicenter study for survival in critically ill COVID-19 patients (pts). Methods. COVID-19 pneumonia pts (N=368;Vilo n=177, P n=191), mechanically ventilated within 48 hrs before treatment, received up to 6, 800 mg infusions of Vilo or P on top of standard of care. The primary and main secondary endpoints were 28-day (d) and 60-d all-cause mortality. Results. Pts enrolled in the study were on corticosteroids (97%) and anticoagulants (98%) as standard of care. A smaller proportion (20%) were either continuing or had taken immunomodulators such as tocilizumab and baricitinib prior to receiving Vilo. The 28-d all-cause mortality was 31.7% with Vilo vs 41.6% with P (Kaplan-Meier estimates;Cox regression site-stratified, HR 0.73;95% CI:0.50-1.06;P=0.094), representing a 23.8% relative mortality reduction. In predefined primary outcome analysis without site stratification, however, Vilo significantly reduced mortality at 28 (HR 0.67;95% CI:0.48-0.96;P=0.027) and 60 days (HR 0.67;95% CI:0.48-0.92;P=0.016). Vilo also significantly reduced 28-d mortality in more severe pts with baseline WHO ordinal scale score of 7 (n=237, HR 0.62;95% CI:0.40-0.95;P=0.028), severe ARDS/PaO2/FiO2 <= 100 mmHg (n=98, HR 0.55;95% CI:0.30-0.98;P=0.044) and eGFR < 60 mL/min/1.73m2 (n=108, HR 0.55;95% CI:0.31-0.96;P=0.036). Treatment-emergent AEs were 90.9% Vilo vs 91.0% P. Infections were comparable: Vilo 62.9%, P 59.3%. Infection incidence per 100 Pt days were equal. No meningococcal infections were reported. Serious AEs were 58.9% Vilo, 63.5% P. Conclusion. Vilo significantly reduced mortality at 28 and 60 days in critically ill COVID-19 pts with no increase in infections suggesting the importance of targeting C5a while preserving MAC. Vilo targets inflammation which may represent an approach to treat sepsis and ARDS caused by other respiratory viruses. (Figure Presented).
ABSTRACT
In recent years, with the impact of COVID-19 epidemic, Taiwan's food manufacturing industry needs digital transformation. Faced with such a dynamic enterprise environment, the construction of accurate R&D system becomes very important and urgent. By discussing the market analysis methods and the design of research and development process in literatures, this study further used a combination of natural semantic analysis technology and QFD method to build an integrated model, so as to help food processing companies understand the consumer demand and some issues in product research and development in the terminal market. Proof of Concept(POC) showed that, first, the marketing supervisor or R&D supervisor accurately evaluate the consumer needs of online users on the e-commerce platform, successfully develop products that consumers are satisfied with, and strengthen R&D decisions;second, by analyzing the characteristics of consumer demand through machine learning model, aspects of demands that consumers care the most could further help to formulate product improvement strategies with application value, which would be extremely helpful to the methodology research of incremental innovation;and third, an integrated model of market and R&D analysis would effectively assist the food industry to develop products on the spot to be successfully deliver to Amazon platform, in which this methodology could be applied in the entire food manufacturing industry. © 2022 PICMET.
ABSTRACT
Since face masks may help slow the spread of diseases, a patient may wear a face mask for an MRI exam during the COVID-19 pandemic. However, metal parts, like nose or face clips within the mask, may burn the patient during an MRI. In this numerical study, we investigated the two-channel RF shimming effect on the RF-induced local SAR of a face mask with a metal strip. With the parallel transmission RF field exposure to the virtual adult male model with a face mask, the RF-induced local SAR1g is calculated for each excitation condition. Under the exposure limit of a whole-body averaged SAR of 2 W/kg and head averaged SAR of 3.2 W/kg, the peak SAR1g is 178 W/kg and 62 W/kg occurs at the nose touching the metal strip. The SAR1g value is higher on the skin area close to the metal strip than at other locations. The metal strip within the face mask could cause a potential RF-induced heating hazard. © 2022 IEEE.
ABSTRACT
Social media have been awash with news and discussions of the COVID-19 pandemic. It is phenomenal to observe that social media has been the focal venue for people to express their reactions, opinions, and interpretations of the pandemic, given the presence of mixed sources of real information and misinformation. Thus, it is essential to conduct professional assessments of the public views and their evolving nature. Our study aims to extract and assess insights into the reflections of sentiments and topics of the public on Twitter and their dynamics along the timeline of the Delta variant. It highlights the extraordinary influence Twitter, or similar major social media, would have on people to comprehend and decide how to cope with the pandemic. We present findings of extracted sentiments and topics from a large-scale dataset of COVID-related tweets collected for the recent phase of the Delta variant of the pandemic (July-September 2021). We utilized a variety of machine learning algorithms for topic modeling and testing the accuracy of sentiment analysis. Our study shows the dramatic dominance of a positive and objective sentiment rather than a negative and subjective sentiment as well as the shift of prevalent topics during the period of study. The findings indicate the importance of conveying real, rational, and accurate information instead of misinformation on social media to foster the public's awareness and preparedness for a major public emergency incident such as the pandemic. © 2022 IEEE.
ABSTRACT
Background: Disparities in cancer screening have been well documented during the Covid-19 pandemic. However, there are limited patient-reported data describing the prevalence and drivers of patient hesitancy towards cancer screening and willingness to resume screening. As health systems continue to experience pandemic-related capacity strain, there is an urgent need for innovative models of re-engaging patients in preventive screening. To address this issue, we developed a medical student-led, high-touch outreach model to re-engage primary care patients at Brookside Community Health Center in cancer screening. Methods: We iteratively optimized semi-structured call scripts and surveys in English and Spanish to contact patients overdue for mammography screening. Student callers included medical and pre-medical students with native Spanish fluency. Using the call script, students identified patient-reported barriers and facilitated mammogram scheduling for consenting patients. For consenting patients, student callers placed a telephone encounter with a pended screening mammogram order in the electronic medical record. PCP confirmation of the order triggered outreach by the radiology department for mammogram scheduling. Patients also received reminder calls from students the week of their appointment. Primary outcomes include screening consent rates, mammogram scheduling and completion rates, and screening results. Patient survey responses were securely recorded using the REDCap survey platform. Results: 198 patients were eligible for the intervention. 60% are primarily Spanish-speaking and 81% are insured by Medicaid. 145 patients (73%) have successfully been contacted, of which 129 (89%) consented for mammogram screening. 74 (57%) of the consenting patients have scheduled their mammogram and 38 (29%) have completed their mammogram. 36% of consenting Spanish-speaking patients with active mammogram orders did not have a mammogram scheduled, compared to 9% of consenting English-speaking. To date, 6 patients had abnormal mammograms requiring subsequent diagnostic imaging, and 1 patient was diagnosed with ductal carcinoma in situ requiring oncologic care. Qualitative analysis of patient surveys found that primary barriers to screening included factors associated with the Covid-19 pandemic (32.9% of contacted patients), lack of awareness of overdue status (25.9%) and patient unavailability (e.g. outside of country) (20%). Conclusions: In this single-center quality improvement study, we found that patients had a high willingness to engage in cancer screening during the pandemic and that trainees can play a vital role in re-engaging patients in preventative care. The disparity between Spanish and English-speaking patients' ability to schedule a mammogram after the consent process suggests that patients with limited English proficiency face additional challenges in accessing screenings.
ABSTRACT
STATEMENT OF PROBLEM/QUESTION: The COVID-19 pandemic has caused marked declines in cancer screenings and exacerbated preexisting disparities in cancer screening among vulnerable patient populations. DESCRIPTION OF PROGRAM/INTERVENTION: Despite the availability of robust quantitative data reporting disparities in cancer screening during the COVID-19 pandemic, there is a dearth of patient-reported data available describing prevalence and drivers of patient hesitancy towards cancer screening and patient willingness to resume cancer screening. Additionally, as health systems continue to experience pandemic-related bandwidth strain, there is an urgent need to develop innovative models of re-engaging patients in preventive screening that can successfully be implemented in the current healthcare environment. To address this issue, we developed a medical student-led, high- touch outreach model to re-engage primary care patients of the Brookside Community Health Center (BCHC) in cancer screening. We iteratively optimized semi-structured call scripts and surveys in English and Spanish to contact patients overdue for mammography screening. Student callers consisted of medical students and premedical students with native Spanish fluency. Call script language allows students to identify patient-reported barriers and facilitates re-scheduling of mammograms for consenting patients. For consenting patients, student callers input a telephone encounter with a pended screening mammogram order in the electronic medical record;the note is then routed to the patient's PCP for signing. Patients additionally receive reminder calls from students the week of their mammography appointment. MEASURES OF SUCCESS: Primary outcomes include screening consent rates, rates of mammogram scheduling and completion, and screening results. Patient response to survey prompts and student call summaries were securely recorded and analyzed utilizing the REDCap survey platform. FINDINGS TO DATE: 198 patients eligible for the intervention have been identified, of which 60% are primarily Spanish-speaking and 81% are enrolled in MassHealth (MA Medicaid). 145 patients (73%) have successfully been contacted, of which 129 (89%) consented for mammogram screening. 74 (57%) of the consenting patients have scheduled their mammogram, and 38 (29%) have completed their mammogram. Of note, 6 patients had abnormal mammograms requiring subsequent diagnostic imaging, and one patient was diagnosed with ductal carcinoma in situ requiring establishment of oncologic care. A preliminary qualitative analysis of patient surveys has found that primary barriers to screening included factors associated with the COVID-19 pandemic, lack of awareness of overdue status, and patient unavailiability (e.g. temporarily out of the country), and miscommunication between patients and the clinic. KEY LESSONS FOR DISSEMINATION: In this single-center quality improvement study, we found willingness to engage in cancer screening during the pandemic remains high and trainees can play a vital role in mitigating screening disparities during the pandemic.
ABSTRACT
BACKGROUND: The COVID-19 pandemic drove burnout and turnover among healthcare workers (HCWs), but working environments may have differentially buffered or exacerbated the pandemic's effects. Primary care HCWs faced pandemic-related challenges, like changes in staffing and space requirements, and a rapid shift to telehealth care. HCWs who were engaged at their workplace may have had better well-being, despite these challenges. Our aims were to measure the prevalence of burnout and turnover intent among HCWs in VA primary care during the COVID-19 pandemic and;(2) to understand the association between individual-level burnout, turnover intent, and employee engagement, and facility-level COVID-19 burden, prior year burnout, and telehealth. METHODS: We obtained data on burnout, turnover intent, employee engagement, and individual demographics from the 2020 VA All Employee Survey (AES) for 19,909 primary care HCWs (providers;registered nurses;clinical associates;administrative associates) in 141 facilities. We linked these data at the facility-level to burnout from the 2019 AES, COVID test and death rates from the 2020 VA COVID Shared Data Resource, the proportion of telehealth primary care visits from the 2020 VA Corporate Data Warehouse, and facility complexity levels from the 2020 VHA Support Service Center. We modeled relationships between burnout, turnover intent, employee engagement, demographics, and facility-level characteristics using logistic regressions with standard errors clustered by facility. RESULTS: Thirty-seven percent of primary care HCWs reported burnout, and 31% reported their intent to leave their job within two years. From March to September 2020, by facility average, COVID tests were 56.5 per 1000 unique patients, COVID deaths were 0.46 per 1000 unique patients, and approximately 29% of primary care visits were conducted by phone, video, or other telehealth medium. Highly engaged employees were less likely to be burned out (odds ratio [OR] 0.29;95% confidence interval [CI] 0.28-0.33) and had a lower intention to leave their job (OR 0.35;95% CI 0.32-0.38). Greater than average facility-level burnout in 2019 was related to higher HCW burnout in 2020 (OR 8.19, 95% CI 2.11-31.82), but 2019 and 2020 turnover intent did not have similar relationship. High COVID tests and deaths, and telehealth measures were not associated with burnout or potential turnover. CONCLUSIONS: While COVID-19 burden and use of telehealth were not associated with worse primary care HCW burnout or turnover intent, our results suggest that interventions to improve employee engagement might mitigate both outcomes. Burnout and turnover intent were high, but similar to pre-pandemic levels, indicating the persistent influence of non-COVID drivers of these outcomes. Future research should focus on understanding elements of the working environment that contribute to burnout and turnover, and interventions should be developed to improve working environments, and therefore HCW well-being, in primary care.
ABSTRACT
With the rapid progress in nanomaterials and biochemistry, there has been an explosion of interest in biomolecule-modified quantum dots (QDs) for biomedical applications. Metal chalcogenide quantum dots (MCQDs), as the most widely studied QDs, have attracted tremendous attention in the biomedical field on account of their unique and excellent optical properties and the ease of biomolecular modifications. Herein, important advances in MCQDs over recent years are reviewed, from materials design to biomedical applications. Especially, this review focuses on the challenges encountered in the applications of MCQDs in biomedical fields and how these problems can be solved by rational design of synthesis methods and modifications, which have opened a universal route to develop the functionalized MCQDs. Moreover, recent processes in bioimaging, biosensing, and cancer therapy based on MCQDs are examined, including the rapid detection and diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This review provides broad insights into MCQDs in the biomedical field and will inspire material researchers to develop MCQDs in the future.
ABSTRACT
One of the common motor symptoms of Parkinson’s disease (PD) is bradykinesia. Automated bradykinesia assessment is critically needed for helping neurologists achieve objective clinical diagnosis and hence provide timely and appropriate medical services. This need has become especially urgent after the outbreak of the coronavirus pandemic in late 2019. Currently, the main factor limiting the accurate assessment is the difficulty of mining the fine-grained discriminative motion features. Therefore, we propose a novel contrastive graph convolutional network for automated and objective toe-tapping assessment, which is one of the most important tests of lower-extremity bradykinesia. Specifically, based on joint sequences extracted from videos, a supervised contrastive learning strategy was followed to cluster together the features of each class, thereby enhancing the specificity of the learnt class-specific features. Subsequently, a multi-stream joint sparse learning mechanism was designed to eliminate potentially similar redundant features of joint position and motion, hence strengthening the discriminability of features extracted from different streams. Finally, a spatial-temporal interaction graph convolutional module was developed to explicitly model remote dependencies across time and space, and hence boost the mining of fine-grained motion features. Comprehensive experimental results demonstrate that this method achieved remarkable classification performance on a clinical video dataset, with an accuracy of 70.04% and an acceptable accuracy of 98.70%. These results obviously outperformed other existing sensor- and video-based methods. The proposed video-based scheme provides a reliable and objective tool for automated quantitative toe-tapping assessment, and is expected to be a viable method for remote medical assessment and diagnosis. IEEE
ABSTRACT
Personal protective equipment (PPE) donning detection for medical staff is a key link of medical operation safety guarantee and is of great significance to combat COVID-19. However, the lack of dedicated datasets makes the scarce research on intelligence monitoring of workers??? PPE use in the field of healthcare. In this paper, we construct a dress codes dataset for medical staff under the epidemic. And based on this, we propose a PPE donning automatic detection approach using deep learning. With the participation of health care personnel, we organize 6 volunteers dressed in different combinations of PPE to simulate more dress situations in the preset structured environment, and an effective and robust dataset is constructed with a total of 5233 preprocessed images. Starting from the task???s dual requirements for speed and accuracy, we use the YOLOv4 convolutional neural network as our learning model to judge whether the donning of different PPE classes corresponds to the body parts of the medical staff meets the dress codes to ensure their self-protection safety. Experimental results show that compared with three typical deep -learning-based detection models, our method achieves a relatively optimal balance while ensuring high detection accuracy (84.14%), with faster processing time (42.02 ms) after the average analysis of 17 classes of PPE donning situation. Overall, this research focuses on the automatic detection of worker safety protection for the first time in healthcare, which will help to improve its technical level of risk management and the ability to respond to potentially hazardous events.
ABSTRACT
Observed daily changes in CO2 emissions from across the globe reveal the sectors and countries where pandemic-related emissions declines were most pronounced in 2020. Day-to-day changes in CO2 emissions from human activities, in particular fossil-fuel combustion and cement production, reflect a complex balance of influences from seasonality, working days, weather and, most recently, the COVID-19 pandemic. Here, we provide a daily CO2 emissions dataset for the whole year of 2020, calculated from inventory and near-real-time activity data. We find a global reduction of 6.3% (2,232 MtCO(2)) in CO2 emissions compared with 2019. The drop in daily emissions during the first part of the year resulted from reduced global economic activity due to the pandemic lockdowns, including a large decrease in emissions from the transportation sector. However, daily CO2 emissions gradually recovered towards 2019 levels from late April with the partial reopening of economic activity. Subsequent waves of lockdowns in late 2020 continued to cause smaller CO2 reductions, primarily in western countries. The extraordinary fall in emissions during 2020 is similar in magnitude to the sustained annual emissions reductions necessary to limit global warming at 1.5 degrees C. This underscores the magnitude and speed at which the energy transition needs to advance.