I'm out of breath from laughing! I think? A dataset of COVID-19 Humor and its toxic variants

Humor is a cognitive construct that predominantly evokes the feeling of mirth. During the COVID-19 pandemic, the situations that arouse out of the pandemic were so incongruous to the world we knew that even factual statements often had a humorous reaction. In this paper, we present a dataset of 2510 samples hand-annotated with labels such as humor style, type, theme, target and stereotypes formed or exploited while creating the humor in addition to 909 memes. Our dataset comprises Reddit posts, comments, Onion news headlines, real news headlines, and tweets. We evaluate the task of humor detection and maladaptive humor detection on state-of-the-art models namely RoBERTa and GPT-3. The finetuned models trained on our dataset show significant gains over zero-shot models including GPT-3 when detecting humor. Even though GPT-3 is good at generating meaningful explanations, we observed that it fails to detect maladaptive humor due to the absence of overt targets and profanities. We believe that the presented dataset will be helpful in designing computational methods for topical humor processing as it provides a unique sample set to study the theory of incongruity in a post-pandemic world. The data is available to research community at https://github.com/smritae01/Covid19-Humor. © 2023 ACM.

Design of a cost-effective material handling system

The COVID-19 pandemic has revolutionized the world and set forth a new normal. The sharp decline of availability of human resources has adversely affected industries relying on laborious work. Hence, these industries are experiencing a halt in their manufacturing processes, leading to a loss of time and business opportunities. In consultation with one such industry looking to automate their material handling system, we present a survey of possible automation solutions that could reduce human intervention to increase production efficiency based on certain key parameters. Automating the movement of machined parts from one station to another within a manufacturing plant is essential since manual part handling often leads to part mix-up, part damage and low traceability of components. © 2023 Author(s).

The effects of COVID-19 pandemic on the UK construction industry and the process of future-proofing business

Purpose: COVID-19 was officially declared as a worldwide pandemic by the World Health Organisation on 11th March 2020, before the UK was put into lockdown on the 23rd March 2020. Organisations had to reconsider their policies and procedures to allow their businesses to continue. This paper aims to focus on the effects of COVID-19 that the UK construction sector has had to undertake to enable businesses while employees had to adhere to COVID-19 lockdown rules. In addition, how the sector can positively continue once normality has returned within the industry. In doing so, this paper understands the historical issues within the construction sector and has had an effect during COVID-19. Design/methodology/approach: A qualitative research methodology approach was taken to help obtain live information. In total, 19 semi-structured interviews from 15 organisations related to the construction sector were conducted to collect data. This information was evaluated using thematic analysis to arrive at the results, inferences and recommendations to the sector. Findings: This research has revealed that companies have had to adopt a three-stage process to overcome a new dimensional challenge of COVID-19. These include: 1. Making quick decisions during the first stage of the pandemic. 2. Producing new policies and procedures to restart businesses enabling staff to return to the workplace safely. 3. Implementing methods to future-proof organisations against any potential pandemics. To help organisations future-proof their business five C's are recommended. Originality/value: This paper provides a rich insight into the understanding and awareness of the effects of COVID-19 and the changes that the construction sector has had to undertake to adhere to the lockdown rules while remaining productive. This research contributes towards informing policymakers on some of the lessons learned during the management of the COVID-19 pandemic from a construction sector perspective. © 2022, Emerald Publishing Limited.

Managing COVID-19-related knowledge: A smart cities perspective

Effective management of coronavirus disease 2019 (COVID-19) and the urgent need to improve epidemic prevention require rapid response and immediate solutions, deploying appropriate knowledge management procedures and facilitating effective decision-making and managerial efforts. The increased adoption of smart cities (SC) technologies offers various technologies that can support knowledge capturing, acquisition, sharing, and transferring. However, knowledge management practitioners and decision-makers face various challenges to manage huge data generated from the various SC platforms. Managing COVID-19-related knowledge necessitates filtering, cleaning, keeping, and sharing only useful data. Therefore, the aim of this paper is to investigate managing knowledge related to COVID-19 from a SC perspective. The methodological approach for this study is a systematic literature review. The findings indicate that SC technologies, through the advanced deployment of information communications technology (ICT) applications, have a crucial role in knowledge capturing and sharing. Smart cities strategies enable knowledge extraction through facilitating data collection and analysis over various disparate databases, as well as facilitating quick and accurate handling and analysis of huge and unpredicted amount of data. Managing knowledge related to COVID-19 pandemic has the potential to improve the planning, treatment and controlling the pandemic, enhance decision-making, and enable disaster management. However, the managing of a huge amount of complex, unstructured data and information remains a big challenge for COVID-19 knowledge management (KM) initiatives. The paper proposes a conceptual model and illustrates the various components and links between SC strategies, KM and COVID-19, and how this can inform, facilitate, and enhance decision-making to take steps for the path of recovery. © 2022 The Authors. Knowledge and Process Management published by John Wiley & Sons Ltd.

A prospective study to evaluate the efficacy and adverse effects of alpha-blockers in the management of benign prostatic hyperplasia

Conclusion: The obtained results showed that both silodosin and tamsulosin produced significant improvement in IPSS and quality of Life in BPH patients. BPH is a nonemergency medical condition, during COVID pandemic situation outpatient visit to hospitals were postponed or cancelled, even if they struggle with troublesome urinary symptoms and unavoidable impact on quality of life. [...]the need for medical management for symptomatic relief of BPH increased. Exclusion Criteria The following criteria were excluded from the study: * Prostate carcinoma. * Concomitant HIV, HBV or HCV infection. * Renal failure. * Liver disease. * Recent cataract surgery. * Psychiatry disorders.

Occurrence, transformation, bioaccumulation, risk and analysis of pharmaceutical and personal care products from wastewater: a review.

Almost all aspects of society from food security to disease control and prevention have benefited from pharmaceutical and personal care products, yet these products are a major source of contamination that ends up in wastewater and ecosystems. This issue has been sharply accentuated during the coronavirus disease pandemic 2019 (COVID-19) due to the higher use of disinfectants and other products. Here we review pharmaceutical and personal care products with focus on their occurrence in the environment, detection, risk, and removal. Supplementary Information: The online version contains supplementary material available at 10.1007/s10311-022-01498-7.

Impact of Workforce Challenges and Funds Flow on Cancer Clinical Trial Development and Conduct.

The conduct of clinical cancer research has faced considerable challenges in recent years, and the situation has only been exacerbated by the global pandemic. The growing complexity of clinical trials and rising administrative burdens had been causing greater expense and difficulty in recruiting and retaining an appropriately trained workforce even before the well-publicized increase in turnover caused by the pandemic. Longstanding issues such as restrictive inclusion criteria and complicated trial designs have negatively affected already low clinical trial accrual rates, limited sites capable of opening studies and enrolling patients, and worsened disparities in trial participation. Opposing these elements are efforts by ASCO and other organizations to increase affordability, access, and equity in clinical trial enrollment. To provide diverse perspectives on how these challenges are affecting cancer research as we emerge from the pandemic, we asked a panel of experienced clinical research leaders from both academic and community cancer centers to answer questions they felt most pressing about the business of conducting clinical research today and where they felt the field was moving in the near future.

Microbial strategies for degradation of microplastics generated from COVID-19 healthcare waste.

COVID-19 pandemic has led to the generation of massive plastic wastes, comprising of onetime useable gloves, masks, tissues, and other personal protective equipment (PPE). Recommendations for the employ of single-use disposable masks made up of various polymeric materials like polyethylene, polyurethane, polyacrylonitrile, and polypropylene, polystyrene, can have significant aftermath on environmental, human as well as animal health. Improper disposal and handling of healthcare wastes and lack of proper management practices are creating serious health hazards and an extra challenge for the local authorities designated for management of solid waste. Most of the COVID-19 medical wastes generated are now being treated by incineration which generates microplastic particles (MPs), dioxin, furans, and various toxic metals, such as cadmium and lead. Moreover, natural degradation and mechanical abrasion of these wastes can lead to the generation of MPs which cause a serious health risk to living beings. It is a major threat to aquatic lives and gets into foods subsequently jeopardizing global food safety. Moreover, the presence of plastic is also considered a threat owing to the increased carbon emission and poses a profound danger to the global food chain. Degradation of MPs by axenic and mixed culture microorganisms, such as bacteria, fungi, microalgae etc. can be considered an eco-sustainable technique for the mitigation of the microplastic menace. This review primarily deals with the increase in microplastic pollution due to increased use of PPE along with different disinfection methods using chemicals, steam, microwave, autoclave, and incineration which are presently being employed for the treatment of COVID-19 pandemic-related wastes. The biological treatment of the MPs by diverse groups of fungi and bacteria can be an alternative option for the mitigation of microplastic wastes generated from COVID-19 healthcare waste.

Comprehensive Performance Analysis based on Classical Machine Learning and Deep Learning Methods for Predicting the COVID-19 Infections

The COVID-19 (coronavirus disease) has been declared a pandemic throughout the world by the WHO (World Health Organization). The number of active COVID-19 cases is increasing day by day and clinical laboratory findings consume more time while interpreting the COVID-19 infected result. There are limited treatment facilities and proper guidelines for reducing infection rates. To overcome these limitations, the requirement of clinical decision support systems embedded with prediction algorithms is raised. In our study, we have architected the clinical prediction system using classical machine learning, deep learning algorithms, and experimental laboratory data. Our model estimated which patients were likely infected with COVID-19 disease. The prediction performances of our models are evaluated based on the accuracy score. The experimental dataset has been provided by Hospital Israelita Albert Einstein at Sao Paulo, Brazil, which included the records of 600 patients from 18 laboratory findings with 10% COVID-19 disease infected patients. Our model has been validated with a train-test split approach, 10-fold cross-validation, and AUC-ROC curve score. The experimental results show that the infected patients with COVID-19 disease are identified at an accuracy of 91.88% through the deep learning method (Convolutional Neural Network (CNN)) and 89.79 % through classical machine learning (Logistic Regression) respectively. This high accuracy is evidence that our prediction model could be readily used for predicting the COVID-19 infections and assisting the health experts in better diagnosis and clinical studies. © 2022 Instituto Politecnico Nacional. All rights reserved.

Microbial strategies for degradation of microplastics generated from COVID-19 healthcare waste

COVID-19 pandemic has led to the generation of massive plastic wastes, comprising of onetime useable gloves, masks, tissues, and other personal protective equipment (PPE). Recommendations for the employ of single-use disposable masks made up of various polymeric materials like polyethylene, polyurethane, polyacrylonitrile, and polypropylene, polystyrene, can have significant aftermath on environmental, human as well as animal health. Improper disposal and handling of healthcare wastes and lack of proper management practices are creating serious health hazards and an extra challenge for the local authorities designated for management of solid waste. Most of the COVID-19 medical wastes generated are now being treated by incineration which generates microplastic particles (MPs), dioxin, furans, and various toxic metals, such as cadmium and lead. Moreover, natural degradation and mechanical abrasion of these wastes can lead to the generation of MPs which cause a serious health risk to living beings. It is a major threat to aquatic lives and gets into foods subsequently jeopardizing global food safety. Moreover, the presence of plastic is also considered a threat owing to the increased carbon emission and poses a profound danger to the global food chain. Degradation of MPs by axenic and mixed culture microorganisms, such as bacteria, fungi, microalgae etc. Can be considered an eco-sustainable technique for the mitigation of the microplastic menace. This review primarily deals with the increase in microplastic pollution due to increased use of PPE along with different disinfection methods using chemicals, steam, microwave, autoclave, and incineration which are presently being employed for the treatment of COVID-19 pandemic-related wastes. The biological treatment of the MPs by diverse groups of fungi and bacteria can be an alternative option for the mitigation of microplastic wastes generated from COVID-19 healthcare waste. Graphical Image 1

Effective DDoS Attack Detection using Deep Generative Radial Neural Network in the Cloud Environment

Recently, internet services have increased rapidly due to the Covid-19 epidemic. As a result, cloud computing applications, which serve end-users as subscriptions, are rising. Cloud computing provides various possibilities like cost savings, time and access to online resources via the internet for end-users. But as the number of cloud users increases, so does the potential for attacks. The availability and efficiency of cloud computing resources may be affected by a Distributed Denial of Service (DDoS) attack that could disrupt services' availability and processing power. DDoS attacks pose a serious threat to the integrity and confidentiality of computer networks and systems that remain important assets in the world today. Since there is no effective way to detect DDoS attacks, it is a reliable weapon for cyber attackers. However, the existing methods have limitations, such as relatively low accuracy detection and high false rate performance. To tackle these issues, this paper proposes a Deep Generative Radial Neural Network (DGRNN) with a sigmoid activation function and Mutual Information Gain based Feature Selection (MIGFS) techniques for detecting DDoS attacks for the cloud environment. Specifically, the proposed first pre-processing step uses data preparation using the (Network Security Lab) NSL-KDD dataset. The MIGFS algorithm detects the most efficient relevant features for DDoS attacks from the pre-processed dataset. The features are calculated by trust evaluation for detecting the attack based on relative features. After that, the proposed DGRNN algorithm is utilized for classification to detect DDoS attacks. The sigmoid activation function is to find accurate results for prediction in the cloud environment. So thus, the proposed experiment provides effective classification accuracy, performance, and time complexity. © 2022 IEEE.

Salivary Cytokines in Children with and Without Severe Disease Due to sars-cov-2 Infection

Background: Most children exposed to SARS-CoV-2 virus present with mild symptoms, but some may experience severe illnesses such as Multisystem inflammatory syndrome (MISC) or respiratory failure. Currently there are no established biomarkers to predict progression to severe disease. Although specific serum cytokines have been found to be higher in adults with severe COVID-19, their role as predictors of severe disease in children remains unclear. Further, the role of salivary cytokines in COVID-19 associated inflammation is unknown. Our objective was to compare cytokine levels in saliva of children with and without severe disease due to SARS-CoV-2 infection. Methods: This prospective observational study, conducted at two tertiary children's hospitals, was supported by a grant from the National Institute of Health RADx Program. Children ≤ 18 years of age with symptoms due to SARS-CoV-2 infection (positive PCR test, serology or immunological link) were enrolled after informed consent. Severe cases were defined as the occurrence of any of the following within 30 days of testing: diagnosis of MISC or Kawasaki disease, requirement for >2L oxygen, inotropes, mechanical ventilation or ECMO, or death. A saliva sample was obtained through passive drool using MicroSAL kits (Oasis Diagnostics) and a viral transport medium (VTM-C19, Biomed). Abundance levels of six cytokines (TNFR1, IL13, IL-15, CCL7, CXCL10 and CXCL9) were measured in triplicate using microfluidic immunoassays (Ella, Protein Simple). Mean concentrations for each sample were determined against a standard curve and corrected for dilution. Levels of the six cytokines were compared between those with severe or nonsevere SARS-CoV-2 symptoms using a non-parametric t-test. The relationship between salivary levels of individual cytokines was assessed among children with severe and non-severe SARS-CoV2 using a Pearson correlation analysis Results: A total of 150 children were enrolled from 03/29/2021 to 05/30/2021 (mean age of 7.1 years ± 5.7 years, 54.6% females). Of the total, 38 (25.3%) children met criteria for severe SARS-CoV-2 infection. CXCL10 displayed significantly (fold change>2, p < 0.05) elevated levels in the saliva of children with severe SARS-CoV-2 (Figure 1). The relationship between levels of CXCL9 (MIG) and CXCL10 showed greater levels association (R2 = 0.93) in children with severe SARS-CoV-2 than in peers with non-severe SARS-CoV-2 (R2 = 0.65;Figure 2). Conclusion: In this preliminary analysis of salivary cytokines among children with SARS-CoV-2 infection, we found CXCL10 displayed differential expression with severe symptoms. These findings may provide critical information about the pathophysiology of severe SARS-CoV-2. Confirmation in further studies is necessary. Saliva concentrations of CXCL10 in children with severe SARSCoV-2 symptoms. The whisker box plots display salivary concentrations of CXCL10 in children with severe (green) and non-severe (red) SARS-CoV-2 infection as measured with next generation enzyme linked immunosorbent assay. Levels of CXCL10 (p < 0.01;fold change = 3.04) were elevated in children with severe SARS-CoV-2 symptoms on Wilcoxon testing. .

Microrna Profiles in Saliva of Children with and Without Severe Disease Due to Severe Acute Respiratory Syndrome Corona Virus 2 Infection

Background: The majority of children with exposure to SARSCoV-2 virus have mild disease. However severe diseases such as Multisystem inflammatory syndrome (MISC) and pneumonia do occur in children. Currently, there are no established biomarkers that can predict progression to severe disease in children exposed to the virus. MicroRNAs (miRNAs) are non-coding RNAs that can be found in saliva and are thought to play a role in the regulation of inflammation following an infection. Our objective was to compare the miRNA profile in saliva of children with or without severe disease due to SARS-CoV-2 infection. Methods: This prospective observational study was supported by the National Institutes of Health (NIH) RADx Program. Children ≤ 18 years of age presenting to two tertiary care children's hospitals with symptoms of SARS-CoV-2 infection (confirmed by PCR test, serology or epidemiological link) were enrolled between 03/29/2021 and 04/30/2021. Severe infection was defined as any of the following within 30 days of testing: MISC or Kawasaki disease diagnosis, requirement for oxygen > 2L, inotropes, mechanical ventilation or ECMO, or the occurrence of death. Informed consent and a saliva swab were obtained at the time of SARS-CoV-2 diagnosis (DNA Genotek, Ottowa Canada), and RNA was extracted (Qiagen, Germantown, MD). Small RNA species (<50 base pairs) were interrogated via shotgun sequencing (HiSeq 2500, Illumina, San Diego, CA) and miRNAs were quantified through alignment to the human genome (GRCh38). RNA features with sparse counts (<10 in 90% of samples) were filtered, and the data was quantile normalized and mean-center scaled. Salivary miRNA levels were compared between those with severe and non-severe SARS-CoV-2 infection using Wilcoxon tests with Benjamini Hochberg multiple testing corrections. In addition, a logistic regression analysis was used to identify miRNA pairs that could best discriminate severe cases based on a Monte Carlo 100-fold cross-validated area under receiver operating characteristic curve (AUROC). Results: Samples from 33 children were analyzed. Median age was 3 (3, 10) years and 54.5% were males. Of the total, 29 were RT PCR positive, 4 had a positive serology and 6 children had severe infection. Seven miRNAs displayed significant differences (Fold change >2, FDR adjusted p < 0.1) among children with severe SARS-CoV-2 infection (Table). All seven miRNAs were up-regulated in severe SARS-CoV-2 cases. A logistic regression using a single ratio of miR-296-5p/miR-378j yielded 1.0 AUROC for differentiating children with severe infection (Figure). Conclusion: In this interim analysis of salivary miRNA in childhood SARS-CoV-2 infection, we found a differential expression of 7 salivary miRNAs in children with severe infection. Ongoing work will seek to validate these findings and explore the role of miRNA in predicting severe SARS-CoV-2 infection in children. Receiver operating characteristic curve and box plot displaying the complete differentiation of severe and non- severe SARSCoV-2 cases using a ratio of miR-296-5p and miR-378j levels in saliva.

Social Determinants of Health in Children with Severe and Non-severe sars-cov-2 Infection

Background: The COVID-19 pandemic has been shown to have a compounding effect on families across various social and healthcare needs. However, the impact of social determinants of health (SDOH) on COVID-19 disease severity in children is unknown. Our objectives were to describe the SDOH in children with SARS-CoV-2 infection and determine their association with severity of the infection Methods: This prospective observational study was supported by the National Institutes of Health RADx program and conducted in the emergency department (ED) of two large children's hospitals. Children ≤ 18 years of age with symptoms due to SARS-CoV-2 infection (positive RT PCR test, serology or epidemiological link) were enrolled between 03/29/2021 and 05/30/2021. Data collected from electronic medical records included demographics, clinical features, treatment, disposition, and outcomes. Severe cases were defined as the following within 30 days of test positivity: diagnosis of Multisystem inflammatory syndrome in children or Kawasaki disease, requirement for oxygen > 2L, inotropes, mechanical ventilation, extracorporeal membrane oxygenation (ECMO), or death. Following informed consent, caregivers were surveyed via an electronic device on previously validated PhenX questions. Aligned with the Healthy People 2020 SDOH framework, caregivers reported on economic stability, education, social and community context, health and health care, and neighborhood and built environment. Stata was used to analyze descriptive statistics, and unadjusted comparisons between groups were assessed using two sample t-tests for continuous variables and Fisher's exact test for categorical variables due to small sizes. Results: A total of 107 children (mean age 6.9 (±5.9) years, 44.9% males), with SARS-CoV-2 infection were enrolled, and 85 caregivers (79.4%) completed the survey (71.4% Black). In this sample, 97% of children were RT PCR positive, 3% had an epidemiological link, and 23 (27.1%) were categorized as severe. Almost half of caregivers (47.6%) reported employment or income loss due to COVID-19. The three most common SDOH needs identified were that of childcare (22.0%), housing instability (22.0%), and food insecurity (21.7%). Children with severe COVID-19 were significantly more likely to have a caregiver who was single, including never married, separated/divorced, and widowed (82.6% vs. 52.5%;Table 1). Although not statistically significant, children with severe COVID-19 tended to have higher levels of social needs including housing instability, poor caregiver mental health, and lower levels of social support compared to children with nonsevere infection (Table 2). Conclusion: Our preliminary data on SDOH suggest that among children with SARS-CoV-2 infection, housing instability, food insecurity and childcare needs are particularly prevalent. Children with severe SARS-CoV-2 infection were more likely to have single caregivers. Family structure may influence severe COVID-19 in children and programming and supports for single parent households should be considered. Larger studies in the ED setting will help confirm these findings and to direct resources to address these social needs.

Occurrence, transformation, bioaccumulation, risk and analysis of pharmaceutical and personal care products from wastewater: a review

Almost all aspects of society from food security to disease control and prevention have benefited from pharmaceutical and personal care products, yet these products are a major source of contamination that ends up in wastewater and ecosystems. This issue has been sharply accentuated during the coronavirus disease pandemic 2019 (COVID-19) due to the higher use of disinfectants and other products. Here we review pharmaceutical and personal care products with focus on their occurrence in the environment, detection, risk, and removal. Supplementary Information The online version contains supplementary material available at 10.1007/s10311-022-01498-7.

Guillain-Barre Syndrome As An Emerging Side Effect of SARS-CoV-2 Vaccination: A Literature Review

Objective: Our review aims to study the significance of the association between the manifestations of Guillain-Barre syndrome (GBS) and COVID-19 immunization, as well as provide medical practitioners with relevant clinical information through a detailed summary of the current cases of GBS related to the COVID-19 vaccines. Additionally, we will shed light on the impact of associated demographic risk factors such as age, gender, and comorbid conditions in the development of GBS post-vaccination. Background: Guillain-Barre syndrome (GBS) is a rare and potentially fatal post-infectious, immune-mediated neuropathy characterized by rapidly progressive weakness and ascending paralysis. As an adverse reaction to the COVID-19 vaccines, GBS is becoming an arising catastrophe increasingly reported as a complication of the COVID-19 vaccines. Design/Methods: A literature search was conducted across four databases: PubMed, PubMed Central, Medline (through PubMed), and Google Scholar using predefined keywords. These keywords included “Guillain Barre Syndrome, ” “COVID-19 vaccination”, “COVID-19”. The search criteria were set to filter cases of GBS in post-COVID-19 vaccination, reported between March 2020 to October 2021. Results: A total of eighteen articles were selected from peer-reviewed journals which documented twenty-eight patients (ages ranged between 20-82 years old) that had developed GBS after receiving COVID-19 vaccinations;fifteen males and thirteen females. GBS side effects were reported with five COVID-19 vaccines including Pfizer, Moderna, Janssen, AstraZeneca (now called Vaxzevria), and a vector-based vaccine. In addition, the average duration between COVID-19 vaccine administration and GBS symptoms onset was noted to be 12.46 days. Conclusions: Although it is too early to draw conclusions concerning GBS following COVID-19 vaccination, we recommend monitoring for cases suggestive of GBS following vaccination and implementing post-vaccination surveillance to ensure adequate data gathering of this outcome, as well as to determine its cause. Additionally, we encourage even further large-scale research into this area.