Teaching civics/history and english to adult lawful permanent residents with limited english proficiency at local libraries: Challenges and practices during the COVID-19 pandemic

This chapter introduces a citizenship instruction program offered by the Teaching English to Speakers of Other Languages (TESOL) program at Oklahoma City University (OCU) in collaboration with two local libraries in the Oklahoma City metro area with a grant funded by the Institute of Museum and Library Services and the Oklahoma Department of Libraries. It specifically discusses the challenges that teachers and learners have encountered during the COVID-19 pandemic and the approaches utilized to address these challenges. Implications of this experience for instruction of civics/history and civics-based English, as well as the professionalization of ESL teachers, post-COVID-19 are discussed. © Springer Nature Switzerland AG 2021. All rights reserved.

Semi-Supervised Modified-UNet for Lung Infection Image Segmentation

Automatic lung infection segmentation in computed tomography (CT) scans can offer great assistance in radiological diagnosis by improving accuracy and reducing time required for diagnosis. The biggest challenges for deep learning (DL) models in segmenting infection region are the high variances in infection characteristics, fuzzy boundaries between infected and normal tissues, and the troubles in getting large number of annotated data for training. To resolve such issues, we propose a Modified U-Net (Mod-UNet) model with minor architectural changes and significant modifications in the training process of vanilla 2D UNet. As part of these modifications, we updated the loss function, optimization function, and regularization methods, added a learning rate scheduler and applied advanced data augmentation techniques. Segmentation results on two Covid-19 Lung CT segmentation datasets show that the performance of Mod-UNet is considerably better than the baseline U-Net. Furthermore, to mitigate the issue of lack of annotated data, the Mod-UNet is used in a semi-supervised framework (Semi-Mod-UNet) which works on a random sampling approach to progressively enlarge the training dataset from a large pool of unannotated CT slices. Exhaustive experiments on the two Covid-19 CT segmentation datasets and on a real lung CT volume show that the Mod-UNet and Semi-Mod-UNet significantly outperform other state-of-theart approaches in automated lung infection segmentation. IEEE

X-rays imaging analysis for early diagnosis of the thoracic disorders using Capsule Neural Network with Transfer learning

Over the past few years, millions of people around the world have developed thoracic ailments. MRI, CT scan, reverse transcription, and other methods are among those used to detect thoracic disorders. These procedures demand medical knowledge and are exceedingly pricy and delicate. An alternate and more widely used method to diagnose diseases of the chest is X-ray imaging. The goal of this study was to increase detection precision in order to develop a computationally assisted diagnostic tool. Different diseases can be identified by combining radiological imaging with various artificial intelligence application approaches. In this study, transfer learning (TL) and capsule neural network techniques are used to propose a method for the automatic detection of various thoracic illnesses utilizing digitized chest X-ray pictures of suspected patients. Four public databases were combined to build a dataset for this purpose. Three pre trained convolutional neural networks (CNNs) were utilized in TL with augmentation as a preprocessing technique to train and evaluate the model. Pneumonia, COVID19, normal, and TB (Tb) were the four class classifiers used to train the network to categorize. © 2023 IEEE.

The Predictive Value of Cystatin C for AKI in Patients with COVID-19.

Objective. To evaluate a potential of cystatin C blood concentration to predict acute kidney injury (AKI) in patients with severe and extremely severe pneumonia associated with a COVID-19. Materials and methods. An observational prospective study of 117 patients with severe and extremely severe pneumonia associated with a COVID-19 in an ICU setting was conducted in 2020-2022 (site: multifunctional Medical Center, 1586 Military Clinical Hospital of the Ministry of Defense of Russia, Moscow Region, Russia). Routine laboratory tests and instrumental examinations were performed according to generally accepted protocols. Cystatin C concentrations in blood (s-CysC) and urine (u-CysC) were measured by immunoturbidimetric method. Results. AKI was diagnosed in 21 (17.9%) patients, kidney dysfunction without AKI was found in 22 (18.8%) patients with severe and extremely severe pneumonia associated with COVID-19. s-CysC and u-CysC levels in the group of patients with AKI were statistically significantly higher compared to the levels in the group of patients without AKI. The levels of s-CysC obtained within Day 1 - T (-1), and Day 2 - T (-2) prior to AKI onset turned out to be the independent factors for AKI development in patients with severe and extremely severe pneumonia associated with COVID-19: OR 5.37, Wald chi-square 5.534 (CI: 1.324;21.788);P=0.019 and OR 3.225, Wald chi-square 4.121 (CI: 1.041;9.989);P=0.042, respectively. s-CysC T (-2) value is informative, and s- CysC T (-1) is a highly informative predictor of AKI development in severe and extremely severe pneumonia associated with COVID-19: ROC AUC 0.853 (95% CI, 0.74-0.966), P_0.001) with 90% sensitivity and 73% specificity at a cut-off of 1.67 mg/L, and ROC AUC 0.905 (95% CI, 0.837-0.973), P_0.001) with 90% sensitivity and 73% specificity at a cut-off of 1.69 mg/l, respectively. Serum CysC levels started increasing 3 days prior to AKI onset, outpacing the increase of SCr levels. The u-CysC levels were not predictive of AKI development. Impaired renal function probability was increasing with patients' age (P_0.0001). Conclusions. Serum CysC seems to be a statistically significant predictor of AKI. s-CysC levels started increasing 3 days prior to AKI onset, surpassing the increase of SCr levels in patients with severe and extremely severe pneumonia associated with COVID-19. Urine CysC did not achieve statistical significance as a predictor for AKI, although u-CysC concentrations were significantly higher on days 3, 2, 1 prior to AKI onset and on the day of AKI onset in the group of patients with AKI.Copyright © 2023, V.A. Negovsky Research Institute of General Reanimatology. All rights reserved.

An Applied Research Impact Case: U-Trap Refill Design for Preventing Transmission of COVID-19 and Other Infectious Diseases

COVID-19 has sparked a rise in creative inventions to help people adjust to the new normal. To reduce the risk of coronavirus spreading from entering the indoor areas through drainage pipes, a team from the Engineering Discipline of the Hong Kong Institute of Vocational Education has developed the U-trap Refill Automator to assist residents in monitoring the water level in common U-shaped trap drainage pipes while refilling water automatically when the water contained in the U-trap is insufficient. The practice of research-led teaching and research-informed teaching for the benefits of students is substantial in the project. The team practices research-led teaching by sharing the relevant research findings with colleagues and encouraging critique. On the other hand, the research-informed teaching is actualized by the relevant professional development, constructive feedback and guided design. The project is an interplay between research and teaching in that building synergies between research and teaching should be a central element of excellence. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Multi-Attentional U-Net for Medical Image Segmentation

Deep learning (DL) approaches for image segmentation have been gaining state-of-the-art performance in recent years. Particularly, in deep learning, U-Net model has been successfully used in the field of image segmentation. However, traditional U-Net methods extract features, aggregate remote information, and reconstruct images by stacking convolution, pooling, and up sampling blocks. The traditional approach is very inefficient due of the stacked local operators. In this paper, we propose the multi-attentional U-Net that is equipped with non-local blocks based self-attention, channel-attention, and spatial-attention for image segmentation. These blocks can be inserted into U-Net to flexibly aggregate information on the plane and spatial scales. We perform and evaluate the multi-attentional U-Net model on three benchmark data sets, which are COVID-19 segmentation, skin cancer segmentation, thyroid nodules segmentation. Results show that our proposed models achieve better performances with faster computation and fewer parameters. The multi-attention U-Net can improve the medical image segmentation results. © 2022 IEEE.

Population-Based Model of the Fraction of Incidental COVID-19 Hospitalizations during the Omicron BA.1 Wave in the United States

1. Background: Some reports have suggested that as many as one-half of all hospital inpatients identified as COVID-19-positive during the Omicron BA.1 variant-driven wave were incidental cases admitted primarily for reasons other than their viral infections. To date, however, there are no prospective longitudinal studies of a representative panel of hospitals based on pre-established criteria for determining whether a patient was, in fact, admitted as a result of the disease. 2. Materials and Methods: To fill this gap, we developed a formula to estimate the fraction of incidental COVID-19 hospitalizations that relies on measurable, population-based parameters. We applied our approach to a longitudinal panel of 164 counties throughout the United States, covering a 4-week interval ending in the first week of January 2022. 3. Results: Within this panel, we estimated that COVID-19 incidence was rising exponentially at a rate of 9.34% per day (95% CI, 8.93–9.87). Assuming that only one-quarter of all Omicron BA.1 infections had been reported by public authorities, we further estimated the aggregate prevalence of active SARS-CoV-2 infection during the first week of January to be 3.45%. During the same week, among 250 high-COVID-volume hospitals within our 164-county panel, an estimated one in four inpatients was COVID-positive. Based upon these estimates, we computed that 10.6% of such COVID-19-positive hospitalized patients were incidental infections. Across individual counties, the median fraction of incidental COVID-19 hospitalizations was 9.5%, with an interquartile range of 6.7 to 12.7%. 4. Conclusion: Incidental COVID-19 infections appear to have been a nontrivial fraction of all COVID-19-positive hospitalized patients during the Omicron BA.1 wave. In the aggregate, however, the burden of patients admitted for complications of their viral infections was far greater. [ FROM AUTHOR] Copyright of COVID is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Border securocracy: Global expansion of the U.S. Department of homeland security bureaucratic apparatus before, during and beyond covid-19

The U.S. border security apparatus is moving around the globe as climate change, the coronavirus pandemic and corporatization create political and economic chaos. Global north governments seek to keep out migrants and refugees from the global south while corporations in the global north want protection to maintain their wealth. U.S. government bureaucratic agencies such as Custom and Border Protection's Border Patrol Tactical Unit are sent abroad to expand U.S. influence in an empire of borders to train receptive government security and border forces and to regulate, detain and prevent migrants and refugees well beyond the U.S. border. Governments are waging war against the people, creating a "securocracy" comprised of profit seeking military arms corporations and allied government agents to quell resistance and border crossers. Examined are the effects and impacts of the U.S. Department of Homeland Security on global border securocracy beginning with an analysis of the Mexico-U.S. border, moving to international borders in Latin America and beyond. The theoretical concept of border securocracy is expanded from the securocracy literature in the context of the north versus south globalization conflict. [ FROM AUTHOR] Copyright of Administrative Theory & Praxis (Taylor & Francis Ltd) is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Safe at Last? Late Effects of a Mass Immunization Campaign on Households' Economic Insecurity

We study the effects of receiving immunization from COVID-19 on households' economic insecurity. To provide causal estimates we use a fuzzy regression discontinuity design which takes advantage of the UK's immunization plan. The plan was primarily based on age, granting differential eligibility to proximate cohorts. Our estimated local average treatment effect indicates that the share of households who declared being economically insecure dropped by 41 percentage points among those who received the vaccine due to the eligibility criteria. Using a difference-in-discontinuity design we next document that immunization was more salient for women as well as for large households and those with children. Our results suggest that the mass immunization campaign against COVID-19 had relevant short-run economic effects, well beyond its expected impact on people's health.

Should population density be used to rank social vulnerability in disaster preparedness planning?

The CDC Social Vulnerability Index (SVI) was developed to help public health officials and policymakers to identify geospatial variations in social vulnerability for each community to better respond to hazardous events, including disease outbreaks. However, the SVI does not include information on population density, which is a significant omission when considering the usefulness of the index in allocating scarce resources such as medical supplies and personnel, bedding, food, and water to locations they are most needed. Using county-level data from the initial U.S. COVID-19 outbreak, we provide empirical evidence that the existing SVI underestimates (overestimates) county-level infection rates in densely (sparsely) populated counties if population density is not accounted for. Population density remains significant even after allowing for spatial spillover effects. Going forward, the inclusion of population density to construct SVI can improve its usefulness in aiding policymakers in allocating scarce resources for future disasters, especially those with spatial dependence. © 2022 Elsevier B.V.

(Re)framing the emerging mobility regime at the U.S.-Mexico borderlands: Covid-19, temporality, and racial capitalism

In this paper, we examine transborder commuters' experiences (i.e. individuals who commute between U.S. and Mexican border cities frequently) during the Covid-19 pandemic, with keen attention to the links between racial capitalism and temporality. We address two interrelated issues: first, we unpack how the United States framed the pandemic through the metaphor of war and the production of the categories of 'essential work(er)' and 'essential travel' to ensure racial capitalism's surplus labor and continuation. These categories function like a double-edged sword, tying racialized populations to racial capitalism's temporality to exploit them while excluding privileged others. We argue that Covid-19's temporality conflicts with racial capitalism's temporality. While the former relies on the deceleration of everyday life, the latter depends on constant acceleration driven by profit-seeking. Using queer and feminist theoretical lenses, we then demonstrate how U.S. Covid-19 border restrictions at land ports of entry exacerbated transborder commuters' cross-border travels and privileged some based on legal status. As a result, they used public Facebook groups to navigate and comprehend new commuting conditions, disidentifying with the United States' official pandemic framing and producing their own. This shared experience catalyzed 'digital transborder kinships' or temporally-bound socialities rooted in relational care, advocacy, and knowledge production. [ FROM AUTHOR] Copyright of Mobilities is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

A Systematic Literature Review of Deep Learning Algorithms for Segmentation of the COVID-19 Infection

Coronavirus has infected more than 753 million people, ranging in severity from one person to another, where more than six million infected people died worldwide. Computer-aided diagnostic (CAD) with artificial intelligence (AI) showed outstanding performance in effectively diagnosing this virus in real-time. Computed tomography is a complementary diagnostic tool to clarify the damage of COVID-19 in the lungs even before symptoms appear in patients. This paper conducts a systematic literature review of deep learning methods for classifying the segmentation of COVID-19 infection in the lungs. We used the methodology of systematic reviews and meta-analyses (PRISMA) flow method. This research aims to systematically analyze the supervised deep learning methods, open resource datasets, data augmentation methods, and loss functions used for various segment shapes of COVID-19 infection from computerized tomography (CT) chest images. We have selected 56 primary studies relevant to the topic of the paper. We have compared different aspects of the algorithms used to segment infected areas in the CT images. Limitations to deep learning in the segmentation of infected areas still need to be developed to predict smaller regions of infection at the beginning of their appearance.

Food insufficiency and Twitter emotions during a pandemic.

The COVID-19 pandemic initially caused worldwide concerns about food insecurity. Tweets analyzed in real-time may help food assistance providers target food supplies to where they are most urgently needed. In this exploratory study, we use natural language processing to extract sentiments and emotions expressed in food security-related tweets early in the pandemic in U.S. states. The emotion joy dominated in these tweets nationally, but only anger, disgust, and fear were also statistically correlated with contemporaneous food insufficiency rates reported in the Household Pulse Survey; more nuanced and statistically stronger correlations are detected within states, including a negative correlation with joy.

Assessing the Effect of the U.S. Vaccination Program on the Coronavirus Positivity Rate With a Multivariate Framework.

The factors influencing the incidence of COVID-19, including the impact of the vaccination programs, have been studied in the literature. Most studies focus on one or two factors, without considering their interactions, which is not enough to assess a vaccination program in a statistically robust manner. We examine the impact of the U.S. vaccination program on the SARS-CoV-2 positivity rate while simultaneously considering a large number of factors involved in the spread of the virus and the feedbacks among them. We consider the effects of the following sets of factors: socioeconomic factors, public policy factors, environmental factors, and non-observable factors. A time series Error Correction Model (ECM) was used to estimate the impact of the vaccination program at the national level on the positivity rate. Additionally, state-level ECMs with panel data were combined with machine learning techniques to assess the impact of the program and identify relevant factors to build the best-fitting models. We find that the vaccination program reduced the virus positivity rate. However, the program was partially undermined by a feedback loop in which increased vaccination led to increased mobility. Although some external factors reduced the positivity rate, the emergence of new variants increased the positivity rate. The positivity rate was associated with several forces acting simultaneously in opposite directions such as the number of vaccine doses administered and mobility. The existence of complex interactions, between the factors studied, implies that there is a need to combine different public policies to strengthen the impact of the vaccination program.

CRV-NET: Robust Intensity Recognition of Coronavirus in Lung Computerized Tomography Scan Images.

The early diagnosis of infectious diseases is demanded by digital healthcare systems. Currently, the detection of the new coronavirus disease (COVID-19) is a major clinical requirement. For COVID-19 detection, deep learning models are used in various studies, but the robustness is still compromised. In recent years, deep learning models have increased in popularity in almost every area, particularly in medical image processing and analysis. The visualization of the human body's internal structure is critical in medical analysis; many imaging techniques are in use to perform this job. A computerized tomography (CT) scan is one of them, and it has been generally used for the non-invasive observation of the human body. The development of an automatic segmentation method for lung CT scans showing COVID-19 can save experts time and can reduce human error. In this article, the CRV-NET is proposed for the robust detection of COVID-19 in lung CT scan images. A public dataset (SARS-CoV-2 CT Scan dataset), is used for the experimental work and customized according to the scenario of the proposed model. The proposed modified deep-learning-based U-Net model is trained on a custom dataset with 221 training images and their ground truth, which was labeled by an expert. The proposed model is tested on 100 test images, and the results show that the model segments COVID-19 with a satisfactory level of accuracy. Moreover, the comparison of the proposed CRV-NET with different state-of-the-art convolutional neural network models (CNNs), including the U-Net Model, shows better results in terms of accuracy (96.67%) and robustness (low epoch value in detection and the smallest training data size).

The Use of Qualitative Methods to Guide the Development of the Border Resilience Scale in a Participatory Research Study.

U.S.-Mexico border residents experience pervasive social and ecological stressors that contribute to a high burden of chronic disease. However, the border region is primarily composed of high-density Mexican-origin neighborhoods, a characteristic that is most commonly health-promoting. Understanding factors that contribute to border stress and resilience is essential to informing the effective design of community-level health promotion strategies. La Vida en La Frontera is a mixed-methods, participatory study designed to understand factors that may contribute to border resilience in San Luis, Arizona. The study's initial qualitative phase included interviews with 30 Mexican-origin adults exploring community perceptions of the border environment, cross-border ties, and health-related concepts. Border residents described the border as a Mexican enclave characterized by individuals with a common language and shared cultural values and perspectives. Positive characteristics related to living in proximity to Mexico included close extended family relationships, access to Mexican food and products, and access to more affordable health care and other services. Based on these findings, we co-designed the 9-item Border Resilience Scale that measures agreement with the psychosocial benefits of these border attributes. Pilot data with 60 residents suggest there are positive sociocultural attributes associated with living in border communities. Further research should test if they mitigate environmental stressors and contribute to a health-promoting environment for residents.

Recurrent bilateral adrenal infarction with myelodysplastic/myeloproliferative neoplasm-unclassifiable (MDS/MPN-U): a case report.

BACKGROUND: Bilateral adrenal infarction is rare and only a small number of cases have been reported so far. Adrenal infarction is usually caused by thrombophilia or a hypercoagulable state, such as antiphospholipid antibody syndrome, pregnancy, and coronavirus disease 2019. However, adrenal infarction with myelodysplastic/myeloproliferative neoplasm (MDS/MPN) has not been reported. CASE PRESENTATION: An 81-year-old man with a sudden severe bilateral backache presented to our hospital. Contrast-enhanced computed tomography (CT) led to the diagnosis of bilateral adrenal infarction. Previously reported causes of adrenal infarction were all excluded and a diagnosis of MDS/MPN-unclassifiable (MDS/MPN-U) was reached, which was considered to be attributed to adrenal infarction. He developed a relapse of bilateral adrenal infarction, and aspirin administration was initiated. Partial primary adrenal insufficiency was suspected as the serum adrenocorticotropic hormone level was persistently high after the second bilateral adrenal infarction. CONCLUSION: This is the first case of bilateral adrenal infarction with MDS/MPN-U encountered. MDS/MPN has the clinical characteristics of MPN. It is reasonable to assume that MDS/MPN-U may have influenced bilateral adrenal infarction development, considering the absence of thrombosis history and a current comorbid hypercoagulable disease. This is also the first case of recurrent bilateral adrenal infarction. It is important to carefully investigate the underlying cause of adrenal infarction once adrenal infarction is diagnosed, as well as to assess adrenocortical function.

COVID-19 and housing prices: evidence from U.S. county-level data.

This paper investigates the effects of coronavirus disease 2019 (COVID-19) on housing prices at the U.S. county level. The effects of COVID-19 cases on housing prices are formally investigated by using a two-way fixed effects panel regression, where county-specific factors, time-specific factors, and mobility measures of individuals are controlled for. The benchmark results show evidence for negative and significant effects of COVID-19 cases on housing prices, robust to the consideration of several permutation tests, where the negative effects are more evident in counties with higher poverty rates. Exclusion tests further suggest that U.S. counties in the state of California or the month of May 2020 are more responsible for the empirical results, although the results based on other counties and months are still in line with the benchmark results.

The Changes in the Travel Characteristics Due to Corona Virus Pandemic: A Case Study in Al-Qadisiyah Governorate, Iraq

Many measures have been imposed by governments around the world to limit the spread of COVID-19. Iraqi authorities adopted a continuous cycles of reinforcement and relaxation in the measures, thus travel behaviors are significantly affected as a result of these measures. This study focuses on the impact of the COVID-19 pandemic on travel pattern in Al-Qadisiyah governorate, Iraq, during three different phases, one phase before pandemic and two phases during the pandemic to study the impact of the variation in imposed measures. Data were collected through a qualitative interviews and online questionnaire surveys that included questions on primary purpose of trip, mode choice of transport, frequency of trips before and during pandemic (including two phases). Descriptive analysis and inferential statistical analysis (especially nonparametric tests) were conducted to analyze the collected data. Results show a significant shifting from work/studying trips to shopping and others trips purposes during the first phase, where distance education and teleworking were imposed. In the second phase, the recommendation for work and study from home were lifted thus the primary trips for work or study purposes have resumed somewhat like their previous pace but with less frequent than its usual in pre-pandemic. A significant reduction in public transport and car-sharing usage was observed during first phase-COVID-19 compared to pre-COVID-19 and second phase-COVID-19. People gave the pandemicrelated factors a higher priority than general factors when selecting a transport mode during the pandemic. However, they put less priority for such factors during second phase of COVID-19 as compared to first phase, due to relaxation in the imposed measures. Changes in travel behavior during a pandemic have an impact on travel demand and favorable transport modes, thus understanding this behavior can help in transportation planning, and decision-making. © 2023 WITPress. All rights reserved.

Charting well-being over adulthood into pandemic times: a longitudinal perspective

The purpose of this study is to investigate how well-being changes over the adult life course from early adulthood in 1998 through to the COVID-19 pandemic in 2021. We identify diverse well-being trajectories over time in a cohort of British Columbians and explore the extent to which changes in well-being associated with the pandemic varied for individuals in these different trajectory groups. Specifically, we ask: what was the effect of the pandemic on the well-being of individuals with different prior well-being trajectories over adulthood and how were these effects related to personal, educational and employment factors? To address this question, we model well-being trajectories over a large span of adulthood from the age of 28 to 51 years old. We find a diversity of distinct patterns in well-being changes over adulthood. The majority experience high well-being over time, while almost one in five experiences either chronically low or drastically decreased well-being in mid-adulthood, which coincides with the pandemic. Notably, those who have completed post-secondary education are less likely to report low well-being trajectories. Those with the lowest well-being over time also report the largest negative effects of the pandemic, which illustrates the compounding effects of the pandemic on existing inequalities.