Quantitative analysis of the impact of COVID-19 on ship visiting behaviors to ports- A framework and a case study.

Corona Virus Disease 2019 (COVID-19) outbreak leads to a significant downturn in the global economy and supply chain. In the maritime sector, trade volume slumped by 3.8% in 2020 compared with 2019. To explore the impacts of COVID-19 on ship visiting behaviors, a framework is proposed to analyze the impact of COVID-19 on port traffic using Automatic Identification System (AIS) data. Firstly, a ship travel behavior-based model is proposed to identify the vessel anchoring and berthing. Then, the diversity in vessel anchoring and berthing time are analyzed, reflecting the impact of COVID-19. The port congestion caused by COVID-19 is quantified by accounting for the number of visiting ships and their residence time. Finally, a case study is carried out on vessels in the Beibu Gulf, China, operating from 2019 to 2020. The results show that the average anchoring time and berthing time increase by 62% and 11% for cargo ships and by 112% and 63% for oil tankers after the outbreak of COVID-19 compared with that before COVID-19. And the density of ships increases in the port area in 2020. Accordingly, the relevant improvements and countermeasures are proposed to reduce the adverse impact of the epidemic on the port navigation system. The paper has the potential to provide a reference for port management and improving port navigation efficiency in the post-pandemic era.

Quantitative analysis of the impact of COVID-19 on ship visiting behaviors to ports- A framework and a case study

Corona Virus Disease 2019 (COVID-19) outbreak leads to a significant downturn in the global economy and supply chain. In the maritime sector, trade volume slumped by 3.8% in 2020 compared with 2019. To explore the impacts of COVID-19 on ship visiting behaviors, a framework is proposed to analyze the impact of COVID-19 on port traffic using Automatic Identification System (AIS) data. Firstly, a ship travel behavior-based model is proposed to identify the vessel anchoring and berthing. Then, the diversity in vessel anchoring and berthing time are analyzed, reflecting the impact of COVID-19. The port congestion caused by COVID-19 is quantified by accounting for the number of visiting ships and their residence time. Finally, a case study is carried out on vessels in the Beibu Gulf, China, operating from 2019 to 2020. The results show that the average anchoring time and berthing time increase by 53% and 26% for cargo ships and by 90% and 63% for oil tankers after the outbreak of COVID-19 compared with that before COVID-19. And the density of ships increases in the port area in 2020. Accordingly, the relevant improvements and countermeasures are proposed to reduce the adverse impact of the epidemic on the port navigation system. The paper has the potential to provide a reference for port management and improving port navigation efficiency in the post-pandemic era.

Two-Year Health Outcomes in Hospitalized COVID-19 Survivors in China.

Importance: Relatively little is known about the persistence of symptoms in patients with COVID-19 for more than 1 year after their acute illness. Objective: To assess the health outcomes among hospitalized COVID-19 survivors over 2 years and to identify factors associated with increased risk of persistent symptoms. Design, Setting, and Participants: This was a longitudinal cohort study of patients who survived COVID-19 at 2 COVID-19-designated hospitals in Wuhan, China, from February 12 to April 10, 2020. All patients were interviewed via telephone at 1 year and 2 years after discharge. The 2-year follow-up study was conducted from March 1 to April 6, 2022. Statistical analysis was conducted from April 20 to May 5, 2022. The severity of disease was defined by World Health Organization guideline for COVID-19. Exposures: COVID-19. Main Outcomes and Measures: The main outcome was symptom changes over 2 years after hospital discharge. All patients completed a symptom questionnaire for evaluation of symptoms, along with a chronic obstructive pulmonary disease assessment test (CAT) at 1-year and 2-year follow-up visits. Results: Of 3988 COVID-19 survivors, a total of 1864 patients (median [IQR] age, 58.5 [49.0-68.0] years; 926 male patients [49.7%]) were available for both 1-year and 2-year follow-up visits. The median (IQR) time from discharge to follow-up at 2 years was 730 (719-743) days. At 2 years after hospital discharge, 370 patients (19.8%) still had symptoms, including 224 (12.0%) with persisting symptoms and 146 (7.8%) with new-onset or worsening of symptoms. The most common symptoms were fatigue, chest tightness, anxiety, dyspnea, and myalgia. Most symptoms resolved over time, but the incidence of dyspnea showed no significant change (1-year vs 2-year, 2.6% [49 patients] vs 2.0% [37 patients]). A total of 116 patients (6.2%) had CAT total scores of at least 10 at 2 years after discharge. Patients who had been admitted to the intensive care unit had higher risks of persistent symptoms (odds ratio, 2.69; 95% CI, 1.02-7.06; P = .04) and CAT scores of 10 or higher (odds ratio, 2.83; 95% CI, 1.21-6.66; P = .02). Conclusions and Relevance: In this cohort study, 2 years after hospital discharge, COVID-19 survivors had a progressive decrease in their symptom burden, but those with severe disease during hospitalization, especially those who required intensive care unit admission, had higher risks of persistent symptoms. These results are related to the original strain of the virus, and their relevance to infections with the Omicron variant is not known.

Latent Transforming Growth Factor-ß Binding Protein-2 Regulates Lung Fibroblast-to-Myofibroblast Differentiation in Pulmonary Fibrosis via NF-κB Signaling.

Despite past extensive studies, the mechanisms underlying pulmonary fibrosis (PF) still remain poorly understood. The aberrantly activated lung myofibroblasts, predominantly emerging through fibroblast-to-myofibroblast differentiation, are considered to be the key cells in PF, resulting in excessive accumulation of extracellular matrix (ECM). Latent transforming growth factor-ß (TGFß) binding protein-2 (LTBP2) has been suggested as playing a critical role in modulating the structural integrity of the ECM. However, its function in PF remains unclear. Here, we demonstrated that lungs originating from different types of patients with PF, including idiopathic PF and rheumatoid arthritis-associated interstitial lung disease, and from mice following bleomycin (BLM)-induced PF were characterized by increased LTBP2 expression in activated lung fibroblasts/myofibroblasts. Moreover, serum LTBP2 was also elevated in patients with COVID-19-related PF. LTBP2 silencing by lentiviral shRNA transfection protected against BLM-induced PF and suppressed fibroblast-to-myofibroblast differentiation in vivo and in vitro. More importantly, LTBP2 overexpression was able to induce differentiation of lung fibroblasts to myofibroblasts in vitro, even in the absence of TGFß1. By further mechanistic analysis, we demonstrated that LTBP2 silencing prevented fibroblast-to-myofibroblast differentiation and subsequent PF by suppressing the phosphorylation and nuclear translocation of NF-κB signaling. LTBP2 overexpression-induced fibroblast-to-myofibroblast differentiation depended on the activation of NF-κB signaling in vitro. Therefore, our data indicate that intervention to silence LTBP2 may represent a promising therapy for PF.

The Impact of Covid-19 on Smartphone Usage.

The outbreak of Covid-19 changed the world as well as human behavior. In this article, we study the impact of Covid-19 on smartphone usage. We gather smartphone usage records from a global data collection platform called Carat, including the usage of mobile users in North America from November 2019 to April 2020. We then conduct the first study on the differences in smartphone usage across the outbreak of Covid-19. We discover that Covid-19 leads to a decrease in users' smartphone engagement and network switches, but an increase in WiFi usage. Also, its outbreak causes new typical diurnal patterns of both memory usage and WiFi usage. Additionally, we investigate the correlations between smartphone usage and daily confirmed cases of Covid-19. The results reveal that memory usage, WiFi usage, and network switches of smartphones have significant correlations, whose absolute values of Pearson coefficients are greater than 0.8. Moreover, smartphone usage behavior has the strongest correlation with the Covid-19 cases occurring after it, which exhibits the potential of inferring outbreak status. By conducting extensive experiments, we demonstrate that for the inference of outbreak stages, both Macro-F1 and Micro-F1 can achieve over 0.8. Our findings explore the values of smartphone usage data for fighting against the epidemic.

Machine learning discovery of distinguishing laboratory features for severity classification of COVID-19 patients

Abstract The exponential spread of COVID-19 worldwide is evident, with devastating outbreaks primarily in the United States, Spain, Italy, the United Kingdom, France, Germany, Turkey and Russia. As of 1 May 2020, a total of 3,308,386 confirmed cases have been reported worldwide, with an accumulative mortality of 233,093. Due to the complexity and uncertainty of the pathology of COVID-19, it is not easy for front-line doctors to categorise severity levels of clinical COVID-19 that are general and severe/critical cases, with consistency. The more than 300 laboratory features, coupled with underlying disease, all combine to complicate proper and rapid patient diagnosis. However, such screening is necessary for early triage, diagnosis, assignment of appropriate level of care facility, and institution of timely intervention. A machine learning analysis was carried out with confirmed COVID-19 patient data from 10 January to 18 February 2020, who were admitted to Tongji Hospital, in Wuhan, China. A softmax neural network-based machine learning model was established to categorise patient severity levels. According to the analysis of 2662 cases using clinical and laboratory data, the present model can be used to reveal the top 30 of more than 300 laboratory features, yielding 86.30% blind test accuracy, 0.8195 F1-score, and 100% consistency using a two-way patient classification of severe/critical to general. For severe/critical cases, F1-score is 0.9081 (i.e. recall is 0.9050, and precision is 0.9113). This model for classification can be accomplished at a mini-second-level computational cost (in contrast to minute-level manual). Based on available COVID-19 patient diagnosis and therapy, an artificial intelligence model paradigm can help doctors quickly classify patients with a high degree of accuracy and 100% consistency to significantly improve diagnostic and classification efficiency. The discovered top 30 laboratory features can be used for greater differentiation to serve as an essential supplement to current guidelines, thus creating a more comprehensive assessment of COVID-19 cases during the early stages of infection. Such early differentiation will help the assignment of the appropriate level of care for individual patients.

Management of Parkinson's disease patients after DBS by remote programming: preliminary application of single center during quarantine of 2019-nCoV.

INTRODUCTION: Deep brain stimulation (DBS) is an effective treatment for patients with Parkinson's disease (PD). On time follow-up and timely programing of symptoms are important measures to maintain the effectiveness of DBS. Due to the highly contagious nature of 2019-nCoV, patients were quarantined. With the help of Internet technologies, we continued to provide motor and non-motor symptom assessment and remote programming services for postsurgical PD-DBS patients during this extraordinary period. METHODS: A retrospective analysis was performed on postsurgical PD-DBS patients who could not come to our hospital for programming due to the impact of the 2019-nCoV. The differences between the pre- and post-programming groups were analyzed. We designed a 5-level Likert rating scale to evaluate the effects and convenience of the remote programming and Internet self-evaluation procedures. RESULTS: Of the 36 patients engaged in the remote programming, 32 patients met the inclusion criteria. Four of the 32 patients set initiated stimulation parameters, and the other 28 patients had significant improvement in UPDRS-III. Nearly all the 28 patients were satisfied with the effect of the remote programming. Most of the patients were willing to use remote programming again. CONCLUSION: Remote programming based on the online evaluation of patient's symptoms can help improve motor symptoms of postsurgical DBS patients with PD during the quarantine period caused by 2019-nCoV.