Corporate Tax Integrity and the Market Reactions to Covid-19: Evidence from China

We compare the market reactions to the COVID-19 crisis of Chinese listed firms with high versus low tax integrity. We show negative market reactions to the crisis across all firms, which is consistent with investors expecting COVID-19 to negatively impact firms' future prospects. Using tax-paying credit rating as a proxy for tax integrity, we find that the negative reaction to the COVID-19 crisis is significantly less for firms with high tax integrity, consistent with investors expecting tax integrity to benefit firms during the crisis. In contrast, we find no difference in the negative market reactions to the 2003 SARS outbreak for the same set of firms. As there was no tax credit rating disclosure in 2003, this serves as a control for the treatment effect. Overall, our results suggest that investors expect corporate tax integrity to mitigate the negative effect of exogenous crises such as COVID-19 on firms.

School resources, self-control and problem behaviors in Chinese adolescents: a longitudinal study in the post-pandemic era.

The outbreak of COVID-19 has brought many challenges to youth development. During this specific period, adolescents have suffered from numerous behavioral problems, which will lead to more maladaptive consequences. It is necessary to explore several protective factors to prevent or reduce the occurrence of problem behaviors in adolescence. The current study combined school resources and self-control to evaluate the multiple protective effects on adolescents' problematic behaviors in a two-wave longitudinal study. A sample of 789 Chinese adolescents (Mage = 14.00 years, SD = 2.05, 418 boys) were recruited via the random cluster sampling method to participate in the survey. The results confirmed the assumptions about the multiple protective effects of school resources and self-control on adolescents' problem behaviors. Specifically, school resources could negatively predict IGD and victimization, and self-control mediated these associations. Moreover, one problematic behavior could also mediate the associations between self-control and another problematic behavior. This is the first study to focus on the multiple protective effects of positive factors on adolescents' problem behaviors during the post-pandemic period, which has made several contributions to the literature and practice.

E-scooter related injuries: Using natural language processing to rapidly search 36 million medical notes.

BACKGROUND: Shareable e-scooters have become popular, but injuries to riders and bystanders have not been well characterized. The goal of this study was to describe e-scooter injuries and estimate the rate of injury per e-scooter trip. METHODS AND FINDINGS: Retrospective review of patients presenting to 180 clinics and 2 hospitals in greater Los Angeles between January 1, 2014 and May 14, 2020. Injuries were identified using a natural language processing (NLP) algorithm not previously used to identify injuries, tallied, and described along with required healthcare resources. We combine these tallies with municipal data on scooter use to report a monthly utilization-corrected rate of e-scooter injuries. We searched 36 million clinical notes. Our NLP algorithm correctly classified 92% of notes in the testing set compared with the gold standard of investigator review. In total, we identified 1,354 people injured by e-scooters; 30% were seen in more than one clinical setting (e.g., emergency department and a follow-up outpatient visit), 29% required advanced imaging, 6% required inpatient admission, and 2 died. We estimate 115 injuries per million e-scooter trips were treated in our health system. CONCLUSIONS: Our observed e-scooter injury rate is likely an underestimate, but is similar to that previously reported for motorcycles. However, the comparative severity of injuries is unknown. Our methodology may prove useful to study other clinical conditions not identifiable by existing diagnostic systems.

Development of off-the-shelf hematopoietic stem cell-engineered invariant natural killer T cells for COVID-19 therapeutic intervention.

BACKGROUND: New COVID-19 treatments are desperately needed as case numbers continue to rise and emergent strains threaten vaccine efficacy. Cell therapy has revolutionized cancer treatment and holds much promise in combatting infectious disease, including COVID-19. Invariant natural killer T (iNKT) cells are a rare subset of T cells with potent antiviral and immunoregulatory functions and an excellent safety profile. Current iNKT cell strategies are hindered by the extremely low presence of iNKT cells, and we have developed a platform to overcome this critical limitation. METHODS: We produced allogeneic HSC-engineered iNKT (AlloHSC-iNKT) cells through TCR engineering of human cord blood CD34+ hematopoietic stem cells (HSCs) and differentiation of these HSCs into iNKT cells in an Ex Vivo HSC-Derived iNKT Cell Culture. We then established in vitro SARS-CoV-2 infection assays to assess AlloHSC-iNKT cell antiviral and anti-hyperinflammation functions. Lastly, using in vitro and in vivo preclinical models, we evaluated AlloHSC-iNKT cell safety and immunogenicity for off-the-shelf application. RESULTS: We reliably generated AlloHSC-iNKT cells at high-yield and of high-purity; these resulting cells closely resembled endogenous human iNKT cells in phenotypes and functionalities. In cell culture, AlloHSC-iNKT cells directly killed SARS-CoV-2 infected cells and also selectively eliminated SARS-CoV-2 infection-stimulated inflammatory monocytes. In an in vitro mixed lymphocyte reaction (MLR) assay and an NSG mouse xenograft model, AlloHSC-iNKT cells were resistant to T cell-mediated alloreaction and did not cause GvHD. CONCLUSIONS: Here, we report a method to robustly produce therapeutic levels of AlloHSC-iNKT cells. Preclinical studies showed that these AlloHSC-iNKT cells closely resembled endogenous human iNKT cells, could reduce SARS-CoV-2 virus infection load and mitigate virus infection-induced hyperinflammation, and meanwhile were free of GvHD-risk and resistant to T cell-mediated allorejection. These results support the development of AlloHSC-iNKT cells as a promising off-the-shelf cell product for treating COVID-19; such a cell product has the potential to target the new emerging SARS-CoV-2 variants as well as the future new emerging viruses.

Retrospective Detection of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in Symptomatic Patients Prior to Widespread Diagnostic Testing in Southern California.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused one of the worst pandemics in recent history. Few reports have revealed that SARS-CoV-2 was spreading in the United States as early as the end of January. In this study, we aimed to determine if SARS-CoV-2 had been circulating in the Los Angeles (LA) area at a time when access to diagnostic testing for coronavirus disease 2019 (COVID-19) was severely limited. METHODS: We used a pooling strategy to look for SARS-CoV-2 in remnant respiratory samples submitted for regular respiratory pathogen testing from symptomatic patients from November 2019 to early March 2020. We then performed sequencing on the positive samples. RESULTS: We detected SARS-CoV-2 in 7 specimens from 6 patients, dating back to mid-January. The earliest positive patient, with a sample collected on January 13, 2020 had no relevant travel history but did have a sibling with similar symptoms. Sequencing of these SARS-CoV-2 genomes revealed that the virus was introduced into the LA area from both domestic and international sources as early as January. CONCLUSIONS: We present strong evidence of community spread of SARS-CoV-2 in the LA area well before widespread diagnostic testing was being performed in early 2020. These genomic data demonstrate that SARS-CoV-2 was being introduced into Los Angeles County from both international and domestic sources in January 2020.

F3S: Free Flow Fever Screening (preprint)

Identification of people with elevated body temperature can reduce or dramatically slow down the spread of infectious diseases like COVID-19. We present a novel fever-screening system, F3S, that uses edge machine learning techniques to accurately measure core body temperatures of multiple individuals in a free-flow setting. F3S performs real-time sensor fusion of visual camera with thermal camera data streams to detect elevated body temperature, and it has several unique features: (a) visual and thermal streams represent very different modalities, and we dynamically associate semantically-equivalent regions across visual and thermal frames by using a new, dynamic alignment technique that analyzes content and context in real-time, (b) we track people through occlusions, identify the eye (inner canthus), forehead, face and head regions where possible, and provide an accurate temperature reading by using a prioritized refinement algorithm, and (c) we robustly detect elevated body temperature even in the presence of personal protective equipment like masks, or sunglasses or hats, all of which can be affected by hot weather and lead to spurious temperature readings. F3S has been deployed at over a dozen large commercial establishments, providing contact-less, free-flow, real-time fever screening for thousands of employees and customers in indoors and outdoor settings.

Efficient facemask decontamination via forced ozone convection.

The COVID-19 crisis has taken a significant toll on human life and the global economy since its start in early 2020. Healthcare professionals have been particularly vulnerable because of the unprecedented shortage of Facepiece Respirators (FPRs), which act as fundamental tools to protect the medical staff treating the coronavirus patients. In addition, many FPRs are designed to be disposable single-use devices, creating an issue related to the generation of large quantities of non-biodegradable waste. In this contribution, we describe a plasma-based decontamination technique designed to circumvent the shortages of FPRs and alleviate the environmental problems posed by waste generation. The system utilizes a Dielectric Barrier Discharge (DBD) to generate ozone and feed it through the fibers of the FPRs. The flow-through configuration is different than canonical ozone-based sterilization methods, in which the equipment is placed in a sealed ozone-containing enclosure without any flow through the mask polymer fibers. We demonstrate the rapid decontamination of surgical masks using Escherichia coli (E. coli) and Vesicular Stomatitis Virus (VSV) as model pathogens, with the flow-through configuration providing a drastic reduction in sterilization time compared to the canonical approach. We also demonstrate that there is no deterioration in mask structure or filtration efficiency resulting from sterilization. Finally, we show that this decontamination approach can be implemented using readily available tools, such as a plastic box, a glass tube, few 3D printed components, and the high-voltage power supply from a plasma globe toy. The prototype assembled for this study is portable and affordable, with effectiveness comparable to that of larger and more expensive equipment.

Limited role for meteorological factors on the variability in COVID-19 incidence: A retrospective study of 102 Chinese cities.

While many studies have focused on identifying the association between meteorological factors and the activity of COVID-19, we argue that the contribution of meteorological factors to a reduction of the risk of COVID-19 was minimal when the effects of control measures were taken into account. In this study, we assessed how much variability in COVID-19 activity is attributable to city-level socio-demographic characteristics, meteorological factors, and the control measures imposed. We obtained the daily incidence of COVID-19, city-level characteristics, and meteorological data from a total of 102 cities situated in 27 provinces/municipalities outside Hubei province in China from 1 January 2020 to 8 March 2020, which largely covers almost the first wave of the epidemic. Generalized linear mixed effect models were employed to examine the variance in the incidence of COVID-19 explained by different combinations of variables. According to the results, including the control measure effects in a model substantially raised the explained variance to 45%, which increased by >40% compared to the null model that did not include any covariates. On top of that, including temperature and relative humidity in the model could only result in < 1% increase in the explained variance even though the meteorological factors showed a statistically significant association with the incidence rate of COVID-19. In conclusion, we showed that very limited variability of the COVID-19 incidence was attributable to meteorological factors. Instead, the control measures could explain a larger proportion of variance.

Excess Patient Visits for Cough and Pulmonary Disease at a Large US Health System in the Months Prior to the COVID-19 Pandemic: Time-Series Analysis.

BACKGROUND: Accurately assessing the regional activity of diseases such as COVID-19 is important in guiding public health interventions. Leveraging electronic health records (EHRs) to monitor outpatient clinical encounters may lead to the identification of emerging outbreaks. OBJECTIVE: The aim of this study is to investigate whether excess visits where the word "cough" was present in the EHR reason for visit, and hospitalizations with acute respiratory failure were more frequent from December 2019 to February 2020 compared with the preceding 5 years. METHODS: A retrospective observational cohort was identified from a large US health system with 3 hospitals, over 180 clinics, and 2.5 million patient encounters annually. Data from patient encounters from July 1, 2014, to February 29, 2020, were included. Seasonal autoregressive integrated moving average (SARIMA) time-series models were used to evaluate if the observed winter 2019/2020 rates were higher than the forecast 95% prediction intervals. The estimated excess number of visits and hospitalizations in winter 2019/2020 were calculated compared to previous seasons. RESULTS: The percentage of patients presenting with an EHR reason for visit containing the word "cough" to clinics exceeded the 95% prediction interval the week of December 22, 2019, and was consistently above the 95% prediction interval all 10 weeks through the end of February 2020. Similar trends were noted for emergency department visits and hospitalizations starting December 22, 2019, where observed data exceeded the 95% prediction interval in 6 and 7 of the 10 weeks, respectively. The estimated excess over the 3-month 2019/2020 winter season, obtained by either subtracting the maximum or subtracting the average of the five previous seasons from the current season, was 1.6 or 2.0 excess visits for cough per 1000 outpatient visits, 11.0 or 19.2 excess visits for cough per 1000 emergency department visits, and 21.4 or 39.1 excess visits per 1000 hospitalizations with acute respiratory failure, respectively. The total numbers of excess cases above the 95% predicted forecast interval were 168 cases in the outpatient clinics, 56 cases for the emergency department, and 18 hospitalized with acute respiratory failure. CONCLUSIONS: A significantly higher number of patients with respiratory complaints and diseases starting in late December 2019 and continuing through February 2020 suggests community spread of SARS-CoV-2 prior to established clinical awareness and testing capabilities. This provides a case example of how health system analytics combined with EHR data can provide powerful and agile tools for identifying when future trends in patient populations are outside of the expected ranges.