This paper studies the effect of the economic impact payment (EIP) on individual contributions to COVID-19 mitigation efforts in the USA, where the mitigation efforts are measured by the reduction of ...daily human mobility. I empirically estimate the effect of the EIP in April 2020 and use cellphone GPS data of 45 million smartphone devices as a proxy for human mobility across 216,069 Census Block Groups. The results show that when receiving the EIP, households significantly increased “Median Home Dwell Time” by an average of 3–5% (about 26–45 min). The paper highlights this unintended effect of the EIP, namely, that in addition to providing economic assistance, the EIP also helped increase individual contributions to mitigation efforts that slowed COVID-19 virus transmission in early 2020.
Resveratrol, a natural product, has demonstrated anti-tumor effects in various kinds of tumor types, including colon, breast, and pancreatic cancers. Most research has focused on the inhibitory ...effects of resveratrol on tumor cells themselves rather than resveratrol’s effects on tumor immunology. In this study, we found that resveratrol inhibited the growth of lung adenocarcinoma in a subcutaneous tumor model by using the β-cyclodextrin-resveratrol inclusion complex. After resveratrol treatment, the proportion of M2-like tumor-associated macrophages (TAMs) was reduced and tumor-infiltrating CD8T cells showed significantly increased activation. The results of co-culture and antibody neutralization experiments suggested that macrophage-derived IL-18 may be a key cytokine in the resveratrol anti-tumor effect of CD8T cell activation. The results of this study demonstrate a novel view of the mechanisms of resveratrol tumor suppression. This natural product could reprogram TAMs and CD8T effector cells for tumor treatment.
COVID-19 mortality rates increase rapidly with age, are higher among men than women, and vary across racial/ethnic groups, but this is also true for other natural causes of death. Prior research on ...COVID-19 mortality rates and racial/ethnic disparities in those rates has not considered to what extent disparities reflect COVID-19-specific factors, versus preexisting health differences. This study examines both questions. We study the COVID-19-related increase in mortality risk and racial/ethnic disparities in COVID-19 mortality, and how both vary with age, gender, and time period. We use a novel measure validated in prior work, the COVID Excess Mortality Percentage (CEMP), defined as the COVID-19 mortality rate (Covid-MR), divided by the non-COVID natural mortality rate during the same time period (non-Covid NMR), converted to a percentage. The CEMP denominator uses Non-COVID NMR to adjust COVID-19 mortality risk for underlying population health. The CEMP measure generates insights which differ from those using two common measures-the COVID-MR and the all-cause excess mortality rate. By studying both CEMP and COVID-MRMR, we can separate the effects of background health from Covid-specific factors affecting COVID-19 mortality. We study how CEMP and COVID-MR vary by age, gender, race/ethnicity, and time period, using data on all adult decedents from natural causes in Indiana and Wisconsin over April 2020-June 2022 and Illinois over April 2020-December 2021. CEMP levels for racial and ethnic minority groups can be very high relative to White levels, especially for Hispanics in 2020 and the first-half of 2021. For example, during 2020, CEMP for Hispanics aged 18-59 was 68.9% versus 7.2% for non-Hispanic Whites; a ratio of 9.57:1. CEMP disparities are substantial but less extreme for other demographic groups. Disparities were generally lower after age 60 and declined over our sample period. Differences in socio-economic status and education explain only a small part of these disparities.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The rapid development of Internet of Things (IoT) technology, together with mobile network technology, has created a never-before-seen world of interconnection, evoking research on how to make it ...vaster, faster, and safer. To support the ongoing fight against the malicious misuse of networks, in this paper we propose a novel algorithm called AMDES (unmanned aerial system multifractal analysis intrusion detection system) for spoofing attack detection. This novel algorithm is based on both wavelet leader multifractal analysis (WLM) and machine learning (ML) principles. In earlier research on unmanned aerial systems (UAS), intrusion detection systems (IDS) based on multifractal (MF) spectral analysis have been used to provide accurate MF spectrum estimations of network traffic. Such an estimation is then used to detect and characterize flooding anomalies that can be observed in an unmanned aerial vehicle (UAV) network. However, the previous contributions have lacked the consideration of other types of network intrusions commonly observed in UAS networks, such as the man in the middle attack (MITM). In this work, this promising methodology has been accommodated to detect a spoofing attack within a UAS. This methodology highlights a robust approach in terms of false positive performance in detecting intrusions in a UAS location reporting system.
COVID-19 vaccines have saved millions of lives; however, understanding the long-term effectiveness of these vaccines is imperative to developing recommendations for booster doses and other ...precautions. Comparisons of mortality rates between more and less vaccinated groups may be misleading due to selection bias, as these groups may differ in underlying health status. We studied all adult deaths during the period of 1 April 2021-30 June 2022 in Milwaukee County, Wisconsin, linked to vaccination records, and we used mortality from other natural causes to proxy for underlying health. We report relative COVID-19 mortality risk (RMR) for those vaccinated with two and three doses versus the unvaccinated, using a novel outcome measure that controls for selection effects. This measure, COVID Excess Mortality Percentage (CEMP), uses the non-COVID natural mortality rate (Non-COVID-NMR) as a measure of population risk of COVID mortality without vaccination. We validate this measure during the pre-vaccine period (Pearson correlation coefficient = 0.97) and demonstrate that selection effects are large, with non-COVID-NMRs for two-dose vaccinees often less than half those for the unvaccinated, and non-COVID NMRs often still lower for three-dose (booster) recipients. Progressive waning of two-dose effectiveness is observed, with an RMR of 10.6% for two-dose vaccinees aged 60+ versus the unvaccinated during April-June 2021, rising steadily to 36.2% during the Omicron period (January-June, 2022). A booster dose reduced RMR to 9.5% and 10.8% for ages 60+ during the two periods when boosters were available (October-December, 2021; January-June, 2022). Boosters thus provide important additional protection against mortality.
Prior research generally finds that the Pfizer-BioNTech (BNT162b2) and Moderna (mRNA1273) COVID-19 vaccines provide similar protection against mortality, sometimes with a Moderna advantage due to ...slower waning. However, most comparisons do not address selection effects for those who are vaccinated and with which vaccine. We report evidence on large selection effects, and use a novel method to control for these effects. Instead of directly studying COVID-19 mortality, we study the COVID-19 excess mortality percentage (CEMP), defined as the COVID-19 deaths divided by non-COVID-19 natural deaths for the same population, converted to a percentage. The CEMP measure uses non-COVID-19 natural deaths to proxy for population health and control for selection effects. We report the relative mortality risk (RMR) for each vaccine relative to the unvaccinated population and to the other vaccine, using linked mortality and vaccination records for all adults in Milwaukee County, Wisconsin, from 1 April 2021 through 30 June 2022. For two-dose vaccinees aged 60+, RMRs for Pfizer vaccinees were consistently over twice those for Moderna, and averaged 248% of Moderna (95% CI = 175%,353%). In the Omicron period, Pfizer RMR was 57% versus 23% for Moderna. Both vaccines demonstrated waning of two-dose effectiveness over time, especially for ages 60+. For booster recipients, the Pfizer-Moderna gap is much smaller and statistically insignificant. A possible explanation for the Moderna advantage for older persons is the higher Moderna dose of 100 μg, versus 30 μg for Pfizer. Younger persons (aged 18-59) were well-protected against death by two doses of either vaccine, and highly protected by three doses (no deaths among over 100,000 vaccinees). These results support the importance of a booster dose for ages 60+, especially for Pfizer recipients. They suggest, but do not prove, that a larger vaccine dose may be appropriate for older persons than for younger persons.
The decline in human mobility and socioeconomic activities during the COVID-19 pandemic has been accompanied by reports of significant improvements in air quality. We evaluate whether there was a ...uniform improvement in air quality across neighborhoods, with a special attention on differences by race. We focus on the COVID-19 lockdown in New York State, an early epicenter of the pandemic in the United States. Using a triple difference-in-differences model, we find that, despite the seasonal decline in particulate matter pollution starting late March (concurrent with the lockdown period), the lockdown narrowed the disparity in air quality between census tracts with high and low shares of non-white population in rural New York, whereas the racial gap in air quality remained unchanged in urban New York.
The use of a swarm of low-cost, mission-specific drones to form a Flying Ad-hoc Network (FANET) has literally become a ’hotspot’ in the drone community. A number of studies have been conducted on how ...to achieve a FANET, but few have considered the security perspectives of this subject. FANET’s unique features have made it difficult to strengthen its defense against ever-changing security threats. Today, more and more FANET applications are implemented into civil airspace, but the development of FANET security has remained unsatisfactory. In this paper, we try to address this issue by proposing a new Intrusion Detection System (IDS), an hybrid method based on both spectral traffic analysis and a robust controller / observer for anomaly estimation inside UAV networks. The proposed hybrid method considers, as a preliminary step, a statistical signature of the traffic exchanged in the network. By examining the resulted signatures, the differences are used to select the accurate model for accurate estimation of that abnormal traffic. The proposed IDS design has been successfully applied to some relevant practical problems such as ad hoc networks for aerial vehicles, and the effectiveness is illustrated by using real traffic traces including Distributed Denial of Service (DDoS) attacks. Our first results show promising perspectives for Intrusion Detection System (IDS) in UAV communication networks. Indeed, different types of anomaly have been considered and they are all accurately detected by the intrusion detection process we propose in this paper. Finally, both simulation-based validation and real-time real-world based implementation of our IDS are described in this article.
Abstract
This paper proposes a YOLO (You Only Look Once) sparse training and model pruning technique for recognizing house numbers in street view images. YOLO is a popular object detection algorithm ...that has achieved state-of-the-art performance in various computer vision tasks. However, its large model size and computational complexity limit its deployment on resource-constrained devices such as smartphones and embedded systems. To address this issue, we use a sparse training technique that trains YOLO with L1 norm regularization to encourage the network to learn sparse representations. This results in a significant reduction in the number of parameters and computation without sacrificing accuracy. Furthermore, we apply a model pruning technique to the sparse-trained model to reduce the model size and computation. We evaluate our proposed method on the SVHN (Street View House Numbers) dataset. We show that it performs comparably to the original YOLO model while reducing the model size by 5% and the computation time by 7%. Overall, our proposed YOLO sparse training and model pruning technique provides an effective solution for deploying YOLO-based object detection models on resource-constrained devices.
Highly toxic reactive oxygen species (ROS) induced apoptosis and ferroptosis have been considered as significant cell death pathways for cancer therapy. However, insufficient amount of intracellular ...ROS extremely restricts the therapeutic effect. Toward this, we report a rationally designed nanocomposite (mUCC) with enhanced ROS generation ability, inducing the combination of apoptosis and ferroptosis through synergistic photodynamic therapy (PDT) and chemodynamic therapy (CDT). Under 808 nm near-infrared (NIR) light irradiation, photocatalytic reaction is triggered starting from the separation of electron-hole pairs on the surface of heterojunction (CeO
2
/CuO), realizing improved ROS production. Simultaneously, mUCC served as Fenton-like agent exhibits considerable ability to generate highly toxic ·OH under tumor microenvironment (TME). The boosted accumulation of ROS disrupts the redox balance within tumor cells and results in the integration of apoptosis and ferroptosis. In addition, mUCC shows satisfactory tumor targeting property benefiting from the cancer cell membrane functionalization under the guidance of magnetic resonance imaging (MRI) and NIR fluorescence imaging. The intelligent mUCC with good biocompatibility and excellent antitumor response achieves efficient tumor elimination under synergistic PDT and CDT. This work offers an elective approach for further development of ROS-based therapeutic nanoplatform in cancer therapy.
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