Diverged landscape of restaurant recovery from the COVID-19 pandemic in the United States.

The COVID-19 pandemic has imposed catastrophic impacts on the restaurant industry as a crucial socioeconomic sector that contributes to the global economy. However, the understanding of how the restaurant industry was recovered from COVID-19 remains underexplored. This study constructs a spatially explicit evaluation of the effect of COVID-19 on the restaurant industry in the US, drawing on the attributes of +200,000 restaurants from Yelp and +600 million individual-level restaurant visitations provided by SafeGraph from 1st January 2019 to 31st December 2021. We produce quantitative evidence of lost restaurant visitations and revenue amid the pandemic, the changes in the customers' origins, and the retained visitation law of human mobility-the number of restaurant visitations decreases as the inverse square of their travel distances-though such a distance-decay effect becomes marginal at the later pandemic. Our findings support policy makers to monitor economic relief and design place-based policies for economic recovery.

A global portrait of expressed mental health signals towards COVID-19 in social media space.

Globally, the COVID-19 pandemic has induced a mental health crisis. Social media data offer a unique opportunity to track the mental health signals of a given population and quantify their negativity towards COVID-19. To date, however, we know little about how negative sentiments differ across countries and how these relate to the shifting policy landscape experienced through the pandemic. Using 2.1 billion individual-level geotagged tweets posted between 1 February 2020 and 31 March 2021, we track, monitor and map the shifts in negativity across 217 countries and unpack its relationship with COVID-19 policies. Findings reveal that there are important geographic, demographic, and socioeconomic disparities of negativity across continents, different levels of a nation's income, population density, and the level of COVID-19 infection. Countries with more stringent policies were associated with lower levels of negativity, a relationship that weakened in later phases of the pandemic. This study provides the first global and multilingual evaluation of the public's real-time mental health signals to COVID-19 at a large spatial and temporal scale. We offer an empirical framework to monitor mental health signals globally, helping international authorizations, including the United Nations and World Health Organization, to design smart country-specific mental health initiatives in response to the ongoing pandemic and future public emergencies.

Social media mining under the COVID-19 context: Progress, challenges, and opportunities.

Social media platforms allow users worldwide to create and share information, forging vast sensing networks that allow information on certain topics to be collected, stored, mined, and analyzed in a rapid manner. During the COVID-19 pandemic, extensive social media mining efforts have been undertaken to tackle COVID-19 challenges from various perspectives. This review summarizes the progress of social media data mining studies in the COVID-19 contexts and categorizes them into six major domains, including early warning and detection, human mobility monitoring, communication and information conveying, public attitudes and emotions, infodemic and misinformation, and hatred and violence. We further document essential features of publicly available COVID-19 related social media data archives that will benefit research communities in conducting replicable and reproducible studies. In addition, we discuss seven challenges in social media analytics associated with their potential impacts on derived COVID-19 findings, followed by our visions for the possible paths forward in regard to social media-based COVID-19 investigations. This review serves as a valuable reference that recaps social media mining efforts in COVID-19 related studies and provides future directions along which the information harnessed from social media can be used to address public health emergencies.

Revealing Public Opinion Towards COVID-19 Vaccines With Twitter Data in the United States: Spatiotemporal Perspective.

BACKGROUND: The COVID-19 pandemic has imposed a large, initially uncontrollable, public health crisis both in the United States and across the world, with experts looking to vaccines as the ultimate mechanism of defense. The development and deployment of COVID-19 vaccines have been rapidly advancing via global efforts. Hence, it is crucial for governments, public health officials, and policy makers to understand public attitudes and opinions towards vaccines, such that effective interventions and educational campaigns can be designed to promote vaccine acceptance. OBJECTIVE: The aim of this study was to investigate public opinion and perception on COVID-19 vaccines in the United States. We investigated the spatiotemporal trends of public sentiment and emotion towards COVID-19 vaccines and analyzed how such trends relate to popular topics found on Twitter. METHODS: We collected over 300,000 geotagged tweets in the United States from March 1, 2020 to February 28, 2021. We examined the spatiotemporal patterns of public sentiment and emotion over time at both national and state scales and identified 3 phases along the pandemic timeline with sharp changes in public sentiment and emotion. Using sentiment analysis, emotion analysis (with cloud mapping of keywords), and topic modeling, we further identified 11 key events and major topics as the potential drivers to such changes. RESULTS: An increasing trend in positive sentiment in conjunction with a decrease in negative sentiment were generally observed in most states, reflecting the rising confidence and anticipation of the public towards vaccines. The overall tendency of the 8 types of emotion implies that the public trusts and anticipates the vaccine. This is accompanied by a mixture of fear, sadness, and anger. Critical social or international events or announcements by political leaders and authorities may have potential impacts on public opinion towards vaccines. These factors help identify underlying themes and validate insights from the analysis. CONCLUSIONS: The analyses of near real-time social media big data benefit public health authorities by enabling them to monitor public attitudes and opinions towards vaccine-related information in a geo-aware manner, address the concerns of vaccine skeptics, and promote the confidence that individuals within a certain region or community have towards vaccines.

Racial Segregation, Testing Site Access, and COVID-19 Incidence Rate in Massachusetts, USA.

The U.S. has merely 4% of the world population, but contains 25% of the world's COVID-19 cases. Since the COVID-19 outbreak in the U.S., Massachusetts has been leading other states in the total number of COVID-19 cases. Racial residential segregation is a fundamental cause of racial disparities in health. Moreover, disparities of access to health care have a large impact on COVID-19 cases. Thus, this study estimates racial segregation and disparities in testing site access and employs economic, demographic, and transportation variables at the city/town level in Massachusetts. Spatial regression models are applied to evaluate the relationships between COVID-19 incidence rate and related variables. This is the first study to apply spatial analysis methods across neighborhoods in the U.S. to examine the COVID-19 incidence rate. The findings are: (1) Residential segregations of Hispanic and Non-Hispanic Black/African Americans have a significantly positive association with COVID-19 incidence rate, indicating the higher susceptibility of COVID-19 infections among minority groups. (2) Non-Hispanic Black/African Americans have the shortest drive time to testing sites, followed by Hispanic, Non-Hispanic Asians, and Non-Hispanic Whites. The drive time to testing sites is significantly negatively associated with the COVID-19 incidence rate, implying the importance of the accessibility of testing sites by all populations. (3) Poverty rate and road density are significant explanatory variables. Importantly, overcrowding represented by more than one person per room is a significant variable found to be positively associated with COVID-19 incidence rate, suggesting the effectiveness of social distancing for reducing infection. (4) Different from the findings of previous studies, the elderly population rate is not statistically significantly correlated with the incidence rate because the elderly population in Massachusetts is less distributed in the hotspot regions of COVID-19 infections. The findings in this study provide useful insights for policymakers to propose new strategies to contain the COVID-19 transmissions in Massachusetts.

Building an Open Resources Repository for COVID-19 Research.

The COVID-19 outbreak is a global pandemic declared by the World Health Organization, with rapidly increasing cases in most countries. A wide range of research is urgently needed for understanding the COVID-19 pandemic, such as transmissibility, geographic spreading, risk factors for infections, and economic impacts. Reliable data archive and sharing are essential to jump-start innovative research to combat COVID-19. This research is a collaborative and innovative effort in building such an archive, including the collection of various data resources relevant to COVID-19 research, such as daily cases, social media, population mobility, health facilities, climate, socioeconomic data, research articles, policy and regulation, and global news. Due to the heterogeneity between data sources, our effort also includes processing and integrating different datasets based on GIS (Geographic Information System) base maps to make them relatable and comparable. To keep the data files permanent, we published all open data to the Harvard Dataverse (https://dataverse.harvard.edu/dataverse/2019ncov), an online data management and sharing platform with a permanent Digital Object Identifier number for each dataset. Finally, preliminary studies are conducted based on the shared COVID-19 datasets and revealed different spatial transmission patterns among mainland China, Italy, and the United States.

Network-constrained spatio-temporal clustering analysis of traffic collisions in Jianghan District of Wuhan, China.

The analysis of traffic collisions is essential for urban safety and the sustainable development of the urban environment. Reducing the road traffic injuries and the financial losses caused by collisions is the most important goal of traffic management. In addition, traffic collisions are a major cause of traffic congestion, which is a serious issue that affects everyone in the society. Therefore, traffic collision analysis is essential for all parties, including drivers, pedestrians, and traffic officers, to understand the road risks at a finer spatio-temporal scale. However, traffic collisions in the urban context are dynamic and complex. Thus, it is important to detect how the collision hotspots evolve over time through spatio-temporal clustering analysis. In addition, traffic collisions are not isolated events in space. The characteristics of the traffic collisions and their surrounding locations also present an influence of the clusters. This work tries to explore the spatio-temporal clustering patterns of traffic collisions by combining a set of network-constrained methods. These methods were tested using the traffic collision data in Jianghan District of Wuhan, China. The results demonstrated that these methods offer different perspectives of the spatio-temporal clustering patterns. The weighted network kernel density estimation provides an intuitive way to incorporate attribute information. The network cross K-function shows that there are varying clustering tendencies between traffic collisions and different types of POIs. The proposed network differential Local Moran's I and network local indicators of mobility association provide straightforward and quantitative measures of the hotspot changes. This case study shows that these methods could help researchers, practitioners, and policy-makers to better understand the spatio-temporal clustering patterns of traffic collisions.

Cadmium Induced Apoptosis in MG63 Cells by Increasing ROS, Activation of p38 MAPK and Inhibition of ERK 1/2 Pathways.

BACKGROUND/AIMS: Cadmium (Cd) induces apoptosis in different kinds of cells, including osteoblasts, both in vivo and in vitro. However, little is known about the mechanisms by which Cd induces apoptosis. METHODS: In the present study, we used the human osteosarcoma cell line MG63, which has characteristics similar to human osteoblasts, as an in vitro model to determine the cellular mechanisms by which Cd induces apoptosis. RESULTS: We found that short-term exposure to CdCl2 induced apoptosis in MG63 cells. Furthermore, the incubation of cells with CdCl2 significantly increased the level of phosphorylated p38MAPK and significantly decreased the phosphorylation of ERK1/2 in a concentration-dependent manner. Additionally, the inhibition of the phosphorylation of p38 MAPK by SB202190 protected MG63 cells from Cd-induced apoptosis. The incubation of MG63 cells with the ERK1/2 inhibitor PD98059 significantly increased apoptosis in MG63 cells. CdCl2 also significantly increased the intracellular levels of ROS. N-acetylcysteine (NAC) significantly reduced ROS levels and reversed the effects of CdCl2 on MAPK signaling. CONCLUSION: Our results suggested that Cd induced apoptosis in MG63 cells by increasing ROS, activation of p38 MAPK and inhibition of ERK1/2 pathways.

Relationship between Helicobacter pylori infection and serum interleukin-18 in patients with carotid atherosclerosis.

BACKGROUND: Helicobacter pylori (H. pylori) infection stimulates the production of proinflammatory cytokines associated with the development of atherosclerosis. Levels of circulating interleukin-18 (IL-18) have been positively correlated with carotid intima-media thickness (IMT) and coronary plaque area and have identified IL-18 levels as important predictors of coronary events and cardiovascular mortality. This study aimed to examine the relationship between serum IL-18 and H. pylori-IgG antibody as a sign of H. pylori infection in patients with carotid atherosclerosis. METHODS: The carotid IMT, traditional atherosclerotic risk factors, levels of serum H. pylori-IgG and IL-18 were measured in 573 health checkup examinees. RESULTS: Serum IL-18 and H. pylori-IgG levels were significantly increased in subjects with increased IMT in comparison with those with normal IMT. In subjects with increased IMT, serum H. pylori-IgG was positively correlated with serum IL-18 (r = .402, p = .002), and the association was independent of traditional atherosclerotic risk factors (ß = 0.310, p < .001). CONCLUSIONS: In health checkup examinees with increased IMT, serum IL-18 and H. pylori-IgG were independently correlated and were significantly higher than in subjects with normal IMT.

Aspirin inhibits tumor necrosis factor-alpha-stimulated fractalkine expression in human umbilical vein endothelial cells.

BACKGROUND: Fractalkine is an important chemokine mediating local monocyte accumulation and inflammatory reactions in the vascular wall. Aspirin inhibits inflammatory cytokine expression closely related to atherosclerosis through the way independent of platelet and cyclooxygenase (COX). There has been no report about the effect of aspirin on fractalkine expression. We aimed to determine the fractalkine expression in human umbilical vein endothelial cell (HUVEC) stimulated by tumor necrosis factor (TNF)-alpha and the effect of aspirin intervention. METHODS: Six of 8 HUVEC groups received either different concentrations of aspirin (0.02, 0.2, 1.0, 5.0 mmol/L) or 40 micromol/L pyrrolidinecarbodithioc acid (PDTC) or 0.5 micromol/L NS-398. The other two groups were negative control and positive control (TNF-alpha-stimulated). After being incubated for 24 hours, cells of the 8 groups except the negative control one were stimulated with TNF-alpha (4 ng/ml) for another 24 hours. After that, the cells were collected for RNA isolation and protein extraction. RESULTS: Both mRNA and protein expressions of fractalkine in HUVEC were upregulated by 4 ng/ml TNF-alpha stimulation. Aspirin inhibited fractalkine expression in a dose-dependent manner at mRNA and protein levels. Nuclear factor-kappa B inhibitor, PDTC, effectively decreased the fractalkine expression. Fractalkine expression was not influenced by COX-2 selective inhibitor NS-398. COX-1 protein expression was not changed by either TNF-alpha stimulation or aspirin, PDTC, NS-398 intervention. Both mRNA and protein expression of COX-2 in HUVEC were upregulated by 4 ng/ml TNF-alpha stimulation. Aspirin decreased COX-2 expression in a dose-dependent manner at mRNA and protein levels. CONCLUSIONS: TNF-alpha-stimulated fractalkine expression is suppressed by aspirin in a dose-dependent manner through the nuclear factor-kappa B p65 pathway.