Impacts of COVID-19 lockdowns and stimulus payments on low-income population's spending in the United States.

The COVID-19 pandemic has profoundly impacted the economy and human lives worldwide, particularly the vulnerable low-income population. We employ a large panel data of 5.6 million daily transactions from 2.6 million debit cards owned by the low-income population in the U.S. to quantify the joint impacts of the state lockdowns and stimulus payments on this population's spending along the inter-temporal, geo-spatial, and cross-categorical dimensions. Leveraging the difference-in-differences analyses at the per card and zip code levels, we uncover three key findings. (1) Inter-temporally, the state lockdowns diminished the daily average spending relative to the same period in 2019 by $3.9 per card and $2,214 per zip code, whereas the stimulus payments elevated the daily average spending by $15.7 per card and $3,307 per zip code. (2) Spatial heterogeneity prevailed: Democratic zip codes displayed much more volatile dynamics, with an initial decline three times that of Republican zip codes, followed by a higher rebound and a net gain after the stimulus payments; also, Southwest exhibited the highest initial decline whereas Southeast had the largest net gain after the stimulus payments. (3) Across 26 categories, the stimulus payments promoted spending in those categories that enhanced public health and charitable donations, reduced food insecurity and digital divide, while having also stimulated non-essential and even undesirable categories, such as liquor and cigar. In addition, spatial association analysis was employed to identify spatial dependency and local hot spots of spending changes at the county level. Overall, these analyses reveal the imperative need for more geo- and category-targeted stimulus programs, as well as more effective and strategic policy communications, to protect and promote the well-being of the low-income population during public health and economic crises.

Multiscale dynamic human mobility flow dataset in the U.S. during the COVID-19 epidemic.

Understanding dynamic human mobility changes and spatial interaction patterns at different geographic scales is crucial for assessing the impacts of non-pharmaceutical interventions (such as stay-at-home orders) during the COVID-19 pandemic. In this data descriptor, we introduce a regularly-updated multiscale dynamic human mobility flow dataset across the United States, with data starting from March 1st, 2020. By analysing millions of anonymous mobile phone users' visits to various places provided by SafeGraph, the daily and weekly dynamic origin-to-destination (O-D) population flows are computed, aggregated, and inferred at three geographic scales: census tract, county, and state. There is high correlation between our mobility flow dataset and openly available data sources, which shows the reliability of the produced data. Such a high spatiotemporal resolution human mobility flow dataset at different geographic scales over time may help monitor epidemic spreading dynamics, inform public health policy, and deepen our understanding of human behaviour changes under the unprecedented public health crisis. This up-to-date O-D flow open data can support many other social sensing and transportation applications.

Association of Mobile Phone Location Data Indications of Travel and Stay-at-Home Mandates With COVID-19 Infection Rates in the US.

Importance: A stay-at-home social distancing mandate is a key nonpharmacological measure to reduce the transmission rate of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), but a high rate of adherence is needed. Objective: To examine the association between the rate of human mobility changes and the rate of confirmed cases of SARS-CoV-2 infection. Design, Setting, and Participants: This cross-sectional study used daily travel distance and home dwell time derived from millions of anonymous mobile phone location data from March 11 to April 10, 2020, provided by the Descartes Labs and SafeGraph to quantify the degree to which social distancing mandates were followed in the 50 US states and District of Columbia and the association of mobility changes with rates of coronavirus disease 2019 (COVID-19) cases. Exposure: State-level stay-at-home orders during the COVID-19 pandemic. Main Outcomes and Measures: The main outcome was the association of state-specific rates of COVID-19 confirmed cases with the change rates of median travel distance and median home dwell time of anonymous mobile phone users. The increase rates are measured by the exponent in curve fitting of the COVID-19 cumulative confirmed cases, while the mobility change (increase or decrease) rates were measured by the slope coefficient in curve fitting of median travel distance and median home dwell time for each state. Results: Data from more than 45 million anonymous mobile phone devices were analyzed. The correlation between the COVID-19 increase rate and travel distance decrease rate was -0.586 (95% CI, -0.742 to -0.370) and the correlation between COVID-19 increase rate and home dwell time increase rate was 0.526 (95% CI, 0.293 to 0.700). Increases in state-specific doubling time of total cases ranged from 1.0 to 6.9 days (median [interquartile range], 2.7 [2.3-3.3] days) before stay-at-home orders were enacted to 3.7 to 30.3 days (median [interquartile range], 6.0 [4.8-7.1] days) after stay-at-home social distancing orders were put in place, consistent with pandemic modeling results. Conclusions and Relevance: These findings suggest that stay-at-home social distancing mandates, when they were followed by measurable mobility changes, were associated with reduction in COVID-19 spread. These results come at a particularly critical period when US states are beginning to relax social distancing policies and reopen their economies. These findings support the efficacy of social distancing and could help inform future implementation of social distancing policies should they need to be reinstated during later periods of COVID-19 reemergence.

Estimation of Regional Economic Development Indicator from Transportation Network Analytics.

With the booming economy in China, many researches have pointed out that the improvement of regional transportation infrastructure among other factors had an important effect on economic growth. Utilizing a large-scale dataset which includes 3.5 billion entry and exit records of vehicles along highways generated from toll collection systems, we attempt to establish the relevance of mid-distance land transport patterns to regional economic status through transportation network analyses. We apply standard measurements of complex networks to analyze the highway transportation networks. A set of traffic flow features are computed and correlated to the regional economic development indicator. The multi-linear regression models explain about 89% to 96% of the variation of cities' GDP across three provinces in China. We then fit gravity models using annual traffic volumes of cars, buses, and freight trucks between pairs of cities for each province separately as well as for the whole dataset. We find the temporal changes of distance-decay effects on spatial interactions between cities in transportation networks, which link to the economic development patterns of each province. We conclude that transportation big data reveal the status of regional economic development and contain valuable information of human mobility, production linkages, and logistics for regional management and planning. Our research offers insights into the investigation of regional economic development status using highway transportation big data.

A Novel Position Compensation Scheme for Cable-Pulley Mechanisms Used in Laparoscopic Surgical Robots.

The tendon driven mechanism using a cable and pulley to transmit power is adopted by many surgical robots. However, backlash hysteresis objectively exists in cable-pulley mechanisms, and this nonlinear problem is a great challenge in precise position control during the surgical procedure. Previous studies mainly focused on the transmission characteristics of the cable-driven system and constructed transmission models under particular assumptions to solve nonlinear problems. However, these approaches are limited because the modeling process is complex and the transmission models lack general applicability. This paper presents a novel position compensation control scheme to reduce the impact of backlash hysteresis on the positioning accuracy of surgical robots' end-effectors. In this paper, a position compensation scheme using a support vector machine based on feedforward control is presented to reduce the position tracking error. To validate the proposed approach, experimental validations are conducted on our cable-pulley system and comparative experiments are carried out. The results show remarkable improvements in the performance of reducing the positioning error for the use of the proposed scheme.

The GABAB receptor inhibits activation of hepatic stellate cells.

BACKGROUND/AIMS: Angiotensin II (Ang II) plays an important role in the activation of hepatic stellate cells (HSCs). In this study it was found that expression of the GABA(B) receptor was elevated in HSCs treated with Ang II. We attempted to elucidate the mechanism of the GABA(B) receptor in HSCs activation. METHODS: First, the target gene (GABA(B) receptor) was screened by gene chip in HSCs treated with Ang II. Second, the biological function of the GABA(B) receptor was analyzed by MTT, cell-cycle assay, real-time PCR, and western blot. The methods of MTT and cell-cycle assay were used to evaluate the effect of the GABA(B) receptor on proliferation and DNA synthesis of HSCs. Expression of ECM, TGF-beta1, and alpha-SMA was analyzed by real-time PCR and western blot. RESULTS: The GABA(B) receptor's specific agonist CGP35348 inhibited the activation of HSCs, which could be partially reversed by the GABA(B) receptor's antagonist. CONCLUSIONS: Our in-vitro results demonstrated that the GABA(B) receptor could inhibit HSCs activation.

[Expression, purification and polyclonal antibody preparation for a novel gene BC097361].

OBJECTIVE: To express and purify of the BC097361 recombinant protein, and to prepare the BC097361 specific rabbit polyclonal antibody. METHODS: BC097361 cDNA was ligated into the prokaryotic expressive vector pET-32a (+), and the resulting plasmid was transformed into E.coli BL21 (DE3). The protein expression was induced with IPTG and the protein was analyzed with SDS-PAGE and western blotting. The expressed product was purified using Ni+ affinity column chromatography.Then the purified pET-32a (+) -BC097361 fusion protein was used to immunize New Zealand rabbits to gain polyclonal antibody. The specificity and potency of polyclonal antibody were evaluated by Western blot and ELISA. RESULTS: The BC097361 fusion protein was highly expressed.The protein was mainly in the inclusion body. ELISA indicated the titer of polyclonal antibody more than 1:320000. The high specificity was confirmed with Western blot. CONCLUSIONS: The recombinant BC097361 fusion protein and the BC097361 specific polyclonal antibody will be valuable tools for the investigation on the biological function of BC097361.

[Specific T-cell responses to Mycobacterium tuberculosis protein ESAT-6 in Chinese HIV positive individuals].

OBJECTIVE: To detect TB specific T cell responses by using the recombinant ESAT-6 protein as stimulus in Chinese HIV infected patients. METHODS: ELISPOT-IFN-gamma assay by using the recombinant ESAT-6 protein as stimulus to detect specific T cell responses in HIV+ patients with or without clinical manifestation of TB diseases. RESULTS: Recombinant ESAT-6 protein specific T cell responses show significant high frequencies in both of TB patients with or without HIV infection than that in the healthy control and HIV+ group without clinical TB diseases. CONCLUSION: The ELISPOT-IFN-gamma assay by using recombinant ESAT-6 protein as stimulus could be used in diagnoses of TB infection in Chinese HIV infected patients.