Highlights d BD-368-2 blocks all three ACE2 binding sites regardless of RBD spatial conformations d BD-368-2 treats severely infected hamsters at low dosages and various dose windows d New cocktail design based on BD-368-2 neutralizes escaping SARS-CoV-2 mutants

Network anomaly detection is a vibrant research area. Researchers have approached this problem using various techniques such as artificial intelligence, machine learning, and state machine modeling. In this paper, we first review these anomaly detection methods and then describe in detail a statistical signal processing technique based on abrupt change detection. We show that this signal processing technique is effective at detecting several network anomalies. Case studies from real network data that demonstrate the power of the signal processing approach to network anomaly detection are presented. The application of signal processing techniques to this area is still in its infancy, and we believe that it has great potential to enhance the field, and thereby improve the reliability of IP networks.

SignificanceCurcumin is an ancient drug derived from turmeric and has been found to exhibit potent anticancer properties albeit through controversial mechanisms of action. Using a biochemical model, mouse cancer model, and cellular models, we show that curcumin is a highly potent and selective inhibitor of dual-specificity tyrosine-regulated kinase 2 (DYRK2), a positive regulator of the 26S proteasome. Curcumin perturbs 26S proteasome activity via DYRK2 inhibition in various cancer cells and in the mouse cancer model leading to impairment of cell proliferation and reduction of cancer burden in mice. This novel mechanism of action of curcumin opens up new avenues for potential preventative or therapeutic strategies in proteasome-addicted cancers like triple-negative breast cancer and multiple myeloma.

In conclusion, our study suggests that the neuroprotective effects of acupuncture in VD are mediated through reducing expression of TXNIP-associated oxidative stress and inflammation.

This paper describes the nationwide prevalence of childhood overweight/obesity in Chinese urban population. Data sets of boys and girls aged 7-18 yrs were collected from the series of Chinese national surveillance on students' constitution and health between 1985 and 2000 were divided into five socioeconomic groups, while WGOC BMI-reference was used as definitions of overweight and obesity. In 2000, the prevalence of childhood obesity/overweight in the coastal big cities, followed by that of the coastal middle/small cities, had reached the average level of the developed countries. The prevalence of obesity was low in most of the inland cities at an early stage of epidemic overweight. The epidemic manifested a gradient distribution in groups, which was closely related to socioeconomic status of the populations. A dramatic and steady increasing trend was witnessed among all sex-age subgroups in these urban groups, and the increments in obesity/overweight are exceptionally high in recent 5 years, and the prospect of epidemic obesity in China is in no way optimistic. Preventive program should be focused on the improvement of the balance between caloric intake and energy expenditure, and interventions aimed at changing children's life styles.

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A real-time understanding of the distribution and duration of power outages after a major disaster is a precursor to minimizing their harmful consequences. Here, we develop an approach for using daily satellite nighttime lights data to create spatially disaggregated power outage estimates, tracking electricity restoration efforts after disasters strike. In contrast to existing utility data, these estimates are independent, open, and publicly-available, consistently measured across regions that may be serviced by several different power companies, and inclusive of distributed power supply (off-grid systems). We apply the methodology in Puerto Rico following Hurricane Maria, which caused the longest blackout in US history. Within all of the island’s settlements, we track outages and recovery times, and link these measures to census-based demographic characteristics of residents. Our results show an 80% decrease in lights, in total, immediately after Hurricane Maria. During the recovery, a disproportionate share of long-duration power failures (> 120 days) occurred in rural municipalities (41% of rural municipalities vs. 29% of urban municipalities), and in the northern and eastern districts. Unexpectedly, we also identify large disparities in electricity recovery between neighborhoods within the same urban area, based primarily on the density of housing. For many urban areas, poor residents, the most vulnerable to increased mortality and morbidity risks from power losses, shouldered the longest outages because they lived in less dense, detached housing where electricity restoration lagged. The approach developed in this study demonstrates the potential of satellite-based estimates of power recovery to improve the real-time monitoring of disaster impacts, globally, at a spatial resolution that is actionable for the disaster response community.

The increasing role of communication networks in today's society results in a demand for higher levels of network availability and reliability. At the same time, fault management is becoming more dificult due to the dynamic nature and heterogeneity of networks. We propose an intelligent monitoring system using adaptive statistical techniques. The system continually learns the normal behavior of the network and detects deviations from the norm. Within the monitoring system, the measurements are segmented, and features extracted from the segments are used to describe the normal behavior of the measurement variables. This information is combined in the structure of a Bayesian network. The proposed system is thereby able to detect unknown or unseen faults. Experimental results on real network data demonstrate that the proposed system can detect abnormal behavior before a fault actually occurs.