Covid-19: A wake-up call

Few can doubt the size of the government's challenge or the good faith and expertise of its chief advisers. Yes, writes Ali Mehdi (doi:10.1136/bmj.m1919), in the same way as did the planners of the welfare state during the second world war. After describing how covid-19 disproportionately affects the poorest people in our society, Adam Briggs and Harry Rutter say the pandemic can't become an excuse for worsening population health and widening inequalities (https://bit.ly/3e4PXnS).

Analysis of sunscreens and antibiotics in groundwater during the Covid-19 pandemic in the Riviera Maya, Mexico.

Tourism contributes to groundwater pollution, but quantifying its exact impact is challenging due to the presence of multiple pollution sources. However, the COVID-19 pandemic presented a unique opportunity to conduct a natural experiment and assess the influence of tourism on groundwater pollution. One such tourist destination is the Riviera Maya in Quintana Roo, Mexico (specifically Cancun). Here, water contamination occurs due to the addition of sunscreen and antibiotics during aquatic activities like swimming, as well as from sewage. In this study, water samples were collected during the pandemic and when tourists returned to the region. Samples were taken from sinkholes (cenotes), beaches, and wells then tested using liquid chromatography for antibiotics and active ingredients found in sunscreens. The data revealed that contamination levels from specific sunscreens and antibiotics persisted even when tourists were absent, indicating that local residents significantly contribute to groundwater pollution. However, upon the return of tourists, the diversity of sunscreen and antibiotics found increased, suggesting that tourists bring along various compounds from their home regions. During the initial stages of the pandemic, antibiotic concentrations were highest, primarily due to local residents incorrectly using antibiotics to combat COVID-19. Additionally, the research found that tourist sites had the greatest contribution to groundwater pollution, with sunscreen concentration increasing. Furthermore, installation of a wastewater treatment plant decreased overall groundwater pollution. These findings enhance our understanding of the pollution contributed by tourists in relation to other pollution sources.

Teaching the Simple Network Management Protocol Using the Packet Tracer Anywhere Network Simulator

The new reality of the coronavirus lockdown has prohibited the students' physical presence in laboratories. Administrators, teachers, and students had to think of new alternatives to hold meetings by adopting a virtual format through the development of rapidly available and broadly accessible online resources. Online Open Educational Resources (OERs) can be used in the form of cloud applications to virtualize computers or other physical sciences laboratories, which are necessary for the realization of the objectives of the courses. OERs can efficiently and effectively prepare students to be able to practice their skills. In parallel, OERs offer a degree of flexibility to the teachers, as they allow them to manage information in multiple ways and at the same time accommodate the presentation of knowledge from multiple perspectives. In this article, we propose the use of computer network simulation software as a teaching method in the form of OERs. In this context, we support the teaching of the administrative perspective of a computer network management course utilizing OERs. We explore the effectiveness of the network simulation software Packet Tracer anywhere in online learning of both synchronous and asynchronous education environments. In particular, we examine its suitability and usability in light of group activities at the level of higher education. We investigate its functionality and the teaching benefits that arise through collaborative learning scenarios in a computer lab suitable for the course of network management. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Seasonal and Spatial Variations of the Oxidative Properties of Ambient PM2.5 in the Po Valley, Italy, before and during COVID-19 Lockdown Restrictions.

This study describes the chemical and toxicological characteristics of fine particulate matter (PM2.5) in the Po Valley, one of the largest and most polluted areas in Europe. The investigated samples were collected in the metropolitan area of Milan during the epidemic lockdown and their toxicity was evaluated by the oxidative potential (OP), measured using ascorbic acid (OPAA) and dithiothreitol (OPDTT) acellular assays. The study was also extended to PM2.5 samples collected at different sites in the Po Valley in 2019, to represent the baseline conditions in the area. Univariate correlations were applied to the whole dataset to link the OP responses with the concentrations of the major chemical markers of vehicular and biomass burning emissions. Of the two assays, OPAA was found mainly sensitive towards transition metals released from vehicular traffic, while OPDTT towards the PM carbonaceous components. The impact of the controlling lockdown restrictions on PM2.5 oxidative properties was estimated by comparing the OP values in corresponding time spans in 2020 and 2019. We found that during the full lockdown the OPAA values decreased to 80-86% with respect to the OP data in other urban sites in the area, while the OPDTT values remained nearly constant.

Impact of COVID-19 on Sexual and Gender Minority Communities: Focus Group Discussions.

BACKGROUND: People who identify as sexual and gender minorities (SGM) experienced disproportionate economic and mental health issues related to COVID-19 when compared to the general population. The purpose of this study was to better understand how COVID-19 has impacted the SGM community and ways to address vaccine hesitancy. METHODS: Three focus groups were conducted with 21 members of the SGM community between 5 November and 10 December 2020. A thematic analysis using the reflexive approach was applied to the transcripts of the focus groups. RESULTS: Four themes emerged: (1) Impact of COVID-19 on the Community, (2) Perceptions of Contact Tracing and Testing, (3) Perceptions of a Potential COVID-19 Vaccine, and (4) Decreasing Vaccine Hesitancy. The most relevant subthemes were that social isolation led to anxiety, stress, and fear in the SGM community during COVID-19; resilience and adaptation were positive outcomes of the pandemic; histories of medical racism contributed to hesitancy to get tested; and specific messaging from trusted messengers may be needed to encourage SGM communities to get vaccinated. These findings support other COVID-19 research on the SGM community during the start of the pandemic. CONCLUSIONS: This study provides insight into the impact of the early stages of COVID-19 on the SGM community, highlighting the unique hurdles faced by SGM individuals with regard to contact tracing and vaccine hesitancy.

Fast 5 at RSNA '22: Looking into Radiology's Crystal Ball

In brief TED talk-style presentations on topics ranging from climate change, artificial intelligence (AI) and telemedicine, to the COVID-19 pandemic and emergence of corporate medicine, the radiologists shared their predictions of how these developments could change medical imaging technology and practice during the next five years. Given the expansion of telemedicine, the growth of radiologic consultation, and the emergence of new and improved diagnostic modalities-such as hybrid PET-MRI, photon-counting CT, new nuclear medicine radiotracers, and theranostics-as well as minimally invasive imaging-guided procedures and implementation of AI, Dr Morrison predicted the role of radiologists eventually will go beyond simply supplying and interpreting the images. The Carol D and Henry P Pendergrass Professor, chair of the radiology and radiological science department, and a professor of biomedical engineering at Vanderbilt University Medical Center and School of Medicine in Nashville, Tennessee, addressed the imminent dangers of climate change and radiology's role in both contributing to and helping alleviate those dangers. [...]what was once meant to protect physicians will become a major threat to radiologists over the next several years, says Mark E Schweitzer, MD, vice president of health affairs at Wayne State University in Detroit, Michigan.

Bidirectional remote connections among virtual ICT laboratories ranging over multiple network segments

Since Covid-19 has been prevalent over the world, it has become important for us to observe the following three countermeasures: Actually, these countermeasures have made it impossible to put various kinds of scientific experiments into practice especially in Japanese senior high schools, because scientific experiments necessarily require many students to gather in the inside of one laboratory and to keep academic conversations with each other. In this talk, we introduce a system of constructing virtual ICT laboratories which enables each student participating in a certain ICT experiment to deploy each network application such as a web browser or an e-mailer and to observe the others' ICT settings simultaneously. This system can be realized by the simultaneous combined use of Zoom and Cisco Packet Tracer. Assuming that this system is applied to two network students which are geographically located far from each other, we can classify this system into the following: Finally, we should be careful of the fact that it is one thing to realize Case 1 and quite another to realize Case 2. In other words, While, in the network circumstances following Case 1, two students have only to apply wireless LAN routers for bidirectionally connecting their own PCs to each other, in the network circumstances following Case 2, they have to employ some other network application such as NAT traversal. © 2022 ACM.

Hourly organic tracers-based source apportionment of PM2.5 before and during the Covid-19 lockdown in suburban Shanghai, China: Insights into regional transport influences and response to urban emission reductions.

During the Covid-19 outbreak, strict lockdown measures led to notable reductions in transportation-related emissions and significantly altered atmospheric pollution characteristics in urban and suburban areas. In this work, we compare comprehensive online measurements of PM2.5 major components and organic molecular markers in a suburban location in Shanghai, China before lockdown (Dec. 28, 2019 to Jan. 23, 2020) and during lockdown (Jan. 24 to Feb. 9, 2020). The NOx levels declined sharply by 59% from 44 to 18 ppb during the lockdown, while O3 rose two times higher to 42 ppb. The PM2.5 level dropped from 64 to 49 µg m-3 (-24%). The major components all showed reductions, with the reduction of nitrate most prominent at -58%, followed by organics at -19%, and sulfate at -17%. Positive matrix factorization analysis identifies fourteen source factors, including nine primary sources and five secondary sources. The secondary sources consist of sulfate-rich factor, nitrate-rich factor, and three secondary organic aerosol (SOA) factors, with SOA_I being anthropogenic SOA, SOA_II associated with later generation products of organic oxidation, and SOA_III being biogenic SOA. The combined secondary sources contributed to 69% and 63% (40 and 22 µg m-3) of PM2.5 before and during lockdown, respectively, among which the reductions in the nitrate-rich (-55%) factor was the most prominent. Among primary sources, large reductions (>80%) were observed in contributions from industrial, cooking, and vehicle emissions. Unlike some studies reporting that the restriction during the Covid-19 resulted in enhanced secondary sulfate and SOA formation, we observed decreases in both secondary inorganic and SOA formation despite the overall elevated oxidizing capacity in the suburban site. Our results indicate that the formation change in secondary inorganic and organic compounds in response to substantial reductions in urban primary precursors are different for urban and suburban environments.

RNA in Municipal Wastewater Reveals Magnitudes of COVID-19 Outbreaks across Four Waves Driven by SARS-CoV-2 Variants of Concern

: There are no standardized protocols for quantifying severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in wastewater to date, especially for population normalization. Here, a pipeline was developed, applied, and assessed to quantify SARS-CoV2 and key variants of concern (VOCs) RNA in wastewater at Saskatoon, Canada. Normalization approaches using recovery ratio and extraction efficiency, wastewater parameters, or population indicators were assessed by comparing to daily numbers of new cases. Viral load was positively correlated with daily new cases reported in the sewershed. Wastewater surveillance (WS) had a lead time of approximately 7 days, which indicated surges in the number of new cases. WS revealed the variant alpha and delta driving the third and fourth wave, respectively. The adjustment with the recovery ratio and extraction efficiency improved the correlation between viral load and daily new cases. Normalization of viral concentration to concentrations of the artificial sweetener acesulfame K improved the trend of viral load during the Christmas and New Year holidays when populations were dynamic and variable. Acesulfame K performed better than pepper mild mottle virus, creatinine, and ammonia for population normalization. Hence, quality controls to characterize recovery ratios and extraction efficiencies and population normalization with acesulfame are promising for precise WS programs supporting decision-making in public health.

Tracking SARS-CoV-2 in rivers as a tool for epidemiological surveillance.

The aim of this work was to evaluate if rivers could be used for SARS-CoV-2 surveillance. Five sampling points from three rivers (AR-1 and AR-2 in Arenales River, MR-1 and MR-2 in Mojotoro River, and CR in La Caldera River) from Salta (Argentina), two of them receiving discharges from wastewater plants (WWTP), were monitored from July to December 2020. Fifteen water samples from each point (75 in total) were collected and characterized physico-chemically and microbiologically and SARS-CoV-2 was quantified by RT-qPCR. Also, two targets linked to human contributions, human polyomavirus (HPyV) and RNase P, were quantified and used to normalize SARS-CoV-2 concentration, which was compared to reported COVID-19 cases. Statistical analyses allowed us to verify the correlation between SARS-CoV-2 and the concentration of fecal indicator bacteria (FIB), as well as to find similarities and differences between sampling points. La Caldera River showed the best water quality; FIBs were within acceptable limits for recreational activities. Mojotoro River's water quality was not affected by the northern WWTP of the city. Instead, Arenales River presented the poorest water quality; at AR-2 was negatively affected by the discharges of the southern WWTP, which contributed to significant increase of fecal contamination. SARS-CoV-2 was found in about half of samples in low concentrations in La Caldera and Mojotoro Rivers, while it was high and persistent in Arenales River. No human tracers were detected in CR, only HPyV was found in MR-1, MR-2 and AR-1, and both were quantified in AR-2. The experimental and normalized viral concentrations strongly correlated with reported COVID-19 cases; thus, Arenales River at AR-2 reflected the epidemiological situation of the city. This is the first study showing the dynamic of SARS-CoV-2 concentration in an urban river highly impacted by wastewater and proved that can be used for SARS-CoV-2 surveillance to support health authorities.

A Succession of Cloud, Precipitation, Aerosol, and Air Quality Field Experiments in the Coastal Urban Environment

Within this context, fundamental questions regarding the life cycle of convective clouds, aerosols, and pollutants have brought together a diverse, integrated, and interagency collaboration of scientists to collect and analyze measurements, in the Houston, Texas, area, from the summer of 2021 through the summer of 2022, with subsequent modeling studies to address these important research objectives. The U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Facility and Atmospheric System Research (ASR) Program, the National Science Foundation’s (NSF’s) Physical and Dynamic Meteorology Program, the National Aeronautics and Space Administration’s (NASA’s) Tropospheric Composition Research and Health and Air Quality Applied Sciences Programs and the Texas Commission on Environmental Quality (TCEQ) are collaborating on a joint set of field campaigns to study the interactions of cloud, aerosol, and pollutants within the coastal, urban environment. Measurement platforms to be deployed: (a) Stony Brook University Weather Truck including dual-polarization X-band phased array radar (ESCAPE), (b) NCAR C-130 aircraft (ESCAPE) (photo credit: C. Wolff), (c) Pandora Spectrometer (TAQ) (photo credit: B. Swap), (d) ARM Tethered Balloon System (TRACER), (e) ARM Mobile Facility (TRACER), (f) C-Band ARM Scanning ARM Precipitation Radar (TRACER), (g) Baylor University–University of Houston–Rice University Mobile Air Quality Laboratory (TAQ, TRACER), (h) Johnson Space Flight Center Gulfstream V aircraft (TAQ). Measurement platforms to be deployed: (a) Stony Brook University Weather Truck including dual-polarization X-band phased array radar (ESCAPE), (b) NCAR C-130 aircraft (ESCAPE) (photo credit: C. Wolff), (c) Pandora Spectrometer (TAQ) (photo credit: B. Swap), (d) ARM Tethered Balloon System (TRACER), (e) ARM Mobile Facility (TRACER), (f) C-Band ARM Scanning ARM Precipitation Radar (TRACER), (g) Baylor University–University of Houston–Rice University Mobile Air Quality Laboratory (TAQ, TRACER), (h) Johnson Space Flight Center Gulfstream V aircraft (TAQ). Measurement platforms to be deployed: (a) Stony Brook University Weather Truck including dual-polarization X-band phased array radar (ESCAPE), (b) NCAR C-130 aircraft (ESCAPE) (photo credit: C. Wolff), (c) Pandora Spectrometer (TAQ) (photo credit: B. Swap), (d) ARM Tethered Balloon System (TRACER), (e) ARM Mobile Facility (TRACER), (f) C-Band ARM Scanning ARM Precipitation Radar (TRACER), (g) Baylor University–University of Houston–Rice University Mobile Air Quality Laboratory (TAQ, TRACER), (h) Johnson Space Flight Center Gulfstream V aircraft (TAQ). On the ground, multiple fixed and mobile radar systems (Fig. 1a) will be used to track convective cells and perform multi-Doppler analysis for the derivation of velocities within the convective systems over the course of their life cycle.

Estimation of secondary PM2.5 in China and the United States using a multi-tracer approach

PM2.5, generated via both direct emission and secondary formation, can have varying environmental impacts due to different physical and chemical properties of its components. However, traditional methods to quantify different PM2.5 components are often based on online or offline observations and numerical models, which are generally high economic cost- or labor-intensive. In this study, we develop a new method, named Multi-Tracer Estimation Algorithm (MTEA), to identify the primary and secondary components from routine observation of PM2.5. By comparing with long-term and short-term measurements of aerosol chemical components in China and the United States, it is proven that MTEA can successfully capture the magnitude and variation of the primary PM2.5 (PPM) and secondary PM2.5 (SPM). Applying MTEA to the China National Air Quality Network, we find that (1) SPM accounted for 63.5 % of the PM2.5 in cities in southern China on average during 2014–2018, while the proportion dropped to 57.1 % in the north of China, and at the same time the secondary proportion in regional background regions was ∼ 19 % higher than that in populous regions;(2) the summertime secondary PM2.5 proportion presented a slight but consistent increasing trend (from 58.5 % to 59.2 %) in most populous cities, mainly because of the recent increase in O3 pollution in China;(3) the secondary PM2.5 proportion in Beijing significantly increased by 34 % during the COVID-19 lockdown, which might be the main reason for the observed unexpected PM pollution in this special period;and finally, (4) SPM and O3 showed similar positive correlations in the Beijing-Tianjin-Hebei (BTH) and Yangtze River Delta (YRD) regions, but the correlations between total PM2.5 and O3 in these two regions, as determined from PPM levels, were quite different. In general, MTEA is a promising tool for efficiently estimating PPM and SPM, and has huge potential for future PM mitigation.

Detection of Eight Cannabinoids and One Tracer in Wastewater and River Water by SPE-UPLC–ESI-MS/MS

The consumption of illicit drugs represents a global social and economic problem. Using suitable analytical methods, monitoring, and detection of different illegal drugs residues and their metabolites in wastewater samples can help combat this problem. Our article defines a method to develop, validate, and practically applicate a rapid and robust analytical process for the evaluation of six naturally occurring cannabinoids (CBG, CBD, CBDV, CBN, THC, THCV), two cannabinoids in acidic form (CBDA, THCA-A), and the major cannabis-related human metabolite (THC-COOH). After SPE offline enrichment, we used a UPLC–ESI-MS/MS system, which permitted the determination of several by-products. Studied matrices were samples of different origins: (i) effluent water from a wastewater treatment plant in the Porto urban area;(ii) environmental water from Febros River, the last left-bank tributary of the Douro River. The multi-residue approach was substantiated and successfully employed to analyze the water samples collected in the above locations. The rapid and precise quantification of nine different cannabinoids in different water samples occurred within nine minutes at the ng L−1 level. The appearance of dozens of ng L−1 of some cannabis secondary metabolites, such as CBD, CBDA, CBN, THCA-A, indicates this plant species’ widespread usage among the general population in the considered area.

Experiential learning methodology adopted for effective delivery of Computer Communication Networks course during Covid-19 pandemic

The computer communication course aims at familiarizing the students to the networking standards and technologies. In the recent days several strategies and practices are used to incorporate experiential learning which include presentation, class-room delivery, simulation and course projects. This process plays an important role to enhance the student's understanding. The covid-19 pandemic posed a great challenge for effective course delivery to the students in online mode. We present a new structure with analysis tools and methods for active learning in virtual mode. Cisco packet tracer and Wireshark tools are introduced to improve the student's understanding level. The result shows that the adopted approach helped in enhancement of student learning to a better extent. © 2021 IEEE.

Intrusion detection in computer networks using machine learning algorithms

With the dawn of the COVID-19 age, the communication industry has witnessed a large spike in users as home networks, workplaces and even conferences have gone online. This has led to a rise in the number of victims of cyber network attacks due to lack of ample security measures being taken in most network environments. Hence the introduction of Intrusion Detection Systems (IDSs) is proven to provide an increased security level. Machine Learning (ML) algorithms have been put into extensive use in tasks of intrusion detection. An ML technique that adds to the performance of standard IDS is the Support Vector Machine (SVM) algorithm, owing to their decent generalization nature and the capability to surpass the barriers of dimensionality. The objective of the project is to determine and compare the performance and accuracy of several ML algorithms like k-means clustering, SVM and KNN. The data set used to derive these results is “kddcup99”, which contains 41 features. Data preprocessing is the first step towards achieving this goal, by performing feature extraction, followed by calculating the variance of features. This facilitates the filtering of relevant features from the non-linear dataset. Final objective is to separate the dataset into dissimilar classes based on the attack type faced by the network. © 2021 IEEE.