Characterizing the Entities in Harmful Memes: Who is the Hero, the Villain, the Victim?

Memes can sway people's opinions over social media as they combine visual and textual information in an easy-to-consume manner. Since memes instantly turn viral, it becomes crucial to infer their intent and potentially associated harmfulness to take timely measures as needed. A common problem associated with meme comprehension lies in detecting the entities referenced and characterizing the role of each of these entities. Here, we aim to understand whether the meme glorifies, vilifies, or victimizes each entity it refers to. To this end, we address the task of role identification of entities in harmful memes, i.e., detecting who is the 'hero', the 'villain', and the 'victim' in the meme, if any. We utilize HVVMemes - a memes dataset on US Politics and Covid-19 memes, released recently as part of the CONSTRAINT@ACL-2022 shared-task. It contains memes, entities referenced, and their associated roles: hero, villain, victim, and other. We further design VECTOR (Visual-semantic role dEteCToR), a robust multi-modal framework for the task, which integrates entity-based contextual information in the multi-modal representation and compare it to several standard unimodal (text-only or image-only) or multi-modal (image+text) models. Our experimental results show that our proposed model achieves an improvement of 4% over the best baseline and 1% over the best competing stand-alone submission from the shared-task. Besides divulging an extensive experimental setup with comparative analyses, we finally highlight the challenges encountered in addressing the complex task of semantic role labeling within memes. © 2023 Association for Computational Linguistics.

PREDICTION OF MORTALITY USING CLUSTERS BASED ON CLINICAL AND LAB PARAMETERS: DATA FROM 2072 PATIENTS FROM INSPIRE COHORT

BackgroundLupus is a heterogenous diseases which results in significant premature mortality. Most studies have evaluated risk factors for lupus mortality using regression models which considers the phenotype in isolation. Identifying clusters of patients on the other hand may help overcome the limitations of such analyses.ObjectivesThe objectives of this study were to describe the causes of mortality and to analyze survival across clusters based on clinical phenotype and autoantibodies in patients of the Indian SLE Inception cohort for Research (INSPIRE)MethodsOut of all patients, enrolled in the INSPIRE database till March 3st 2022, those who had <10% missing variables in the clustering variables were included in the study. The cause of mortality and duration between the recruitment into the cohort and mortality was calculated. Agglomerative unsupervised hierarchical cluster analysis was performed using 25 variables that define SLE phenotype in clinical practice. The number of clusters were fixed using the elbow and silhouette methods. Survival rates were examined using Cox proportional hazards models: unadjusted, adjusted for age at disease onset, socio-economic status, steroid pulse, CYC, MMF usage and cluster of the patients.ResultsIndian patients with lupus have significant early mortality and the majority of deaths occurs outside the hospital setting.Out of 2211 patients in the cohort, 2072 were included into the analysis. The median (IQR) age of the patients was 26 (20-33) years and 91.7% were females. There were 288 (13.1%) patients with juvenile onset lupus. The median (range) duration of follow up of the patients was 37 (6-42) months. There were 170 deaths, with only 77 deaths occurring in a health care setting. Death within 6 months of enrollment occured in in 80 (47.1%) patients. Majority (n=87) succumbed to disease activity, 23 to infections, 24 to coexisting disease activity and infection and 21 to other causes. Pneumonia was the leading cause of death (n=24). Pneumococcal infection led to death in 11 patients and SARS-COV2 infection in 7 patients. The hierarchical clustering resulted in 4 clusters and the characteristics of these clusters are represented in a heatmap (Figure-1A,B). The mean (95% confidence interval [95% CI] survival was 39.17 (38.45-39.90), 39.52 (38.71-40.34), 37.73 (36.77-38.70) and 35.80 (34.10-37.49) months (p<0.001) in clusters 1, 2, 3 and 4, respectively with an HR (95% CI) of 2.34 (1.56, 3.49) for cluster 4 with cluster 1 as reference(Figure 1C). The adjusted model showed an HR (95%CI) for cluster 4 of 2.22 (1.48, 3.22) with an HR(95%CI) of 1.78 (1.29, 2.45) for low socioeconomic status as opposed to a high socioeconomic status (Table 1).ConclusionIndian patients with lupus have significant early mortality and the majority of deaths occurs outside the hospital setting. Disease activity as determined by the traditional activity measures may not be sufficient to understand the true magnitude of organ involvement resulting in mortality. Clinically relevant clusters can help clinicians identify those at high risk for mortality with greater accuracy.Table 1.Univariate and multivariate Cox regression models predicting mortalityUnivariateMultivariateVariablesHazard ratio (95% Confidence interval)P valueHazard ratio (95% Confidence interval)P valueCluster1Reference-Reference-20.87 (0.57, 1.34)0.5320.89 (0.57, 1.38)0.59831.22 (0.81, 1.84)0.3371.15 (0.76, 1.73)0.51342.34 (1.56, 3.49)<0.0012.22(1.48, 3.22)<0.001Socioeconomic statusLower1.78 (1.29, 2.45)<0.001Pulse steroidYes1.6 (0.99, 2.58)0.051MMFYes0.71 (0.48, 1.05)0.083CYCYes1.42 (0.99, 2.02)0.052Proliferative LNYes0.99 (0.62, 1.56)0.952Date of birth age0.99 (0.98, 1.01)0.657CYC- cyclophosphamide, MMF- Mycophenolate mofetilFigure 1.A. Agglomerative clustering dendrogram depicting the formation of four clusters. B.Heatmap depicting distribution of variables used in clustering C. Kaplan-Meier curve showing the survival function across the 4 clusters[Figure omitted. See PDF]REFERENCES:NIL.Acknowledgements:NIL.Disclosure of InterestsNone eclared.

Empowering the Fact-checkers! Automatic Identification of Claim Spans on Twitter

The widespread diffusion of medical and political claims in the wake of COVID-19 has led to a voluminous rise in misinformation and fake news. The current vogue is to employ manual fact-checkers to efficiently classify and verify such data to combat this avalanche of claim-ridden misinformation. However, the rate of information dissemination is such that it vastly outpaces the fact-checkers' strength. Therefore, to aid manual fact-checkers in eliminating the superfluous content, it becomes imperative to automatically identify and extract the snippets of claim-worthy (mis)information present in a post. In this work, we introduce the novel task of Claim Span Identification (CSI). We propose CURT, a large-scale Twitter corpus with token-level claim spans on more than 7.5k tweets. Furthermore, along with the standard token classification baselines, we benchmark our dataset with DABERTa, an adapter-based variation of RoBERTa. The experimental results attest that DABERTa outperforms the baseline systems across several evaluation metrics, improving by about 1.5 points. We also report detailed error analysis to validate the model's performance along with the ablation studies. Lastly, we release our comprehensive span annotation guidelines for public use. © 2022 Association for Computational Linguistics.

Mental Health, Self-Esteem, Life Satisfaction among Ethiopian Population during Covid-19 Pandemic: A Survey Based Study

Background: Many people are at risk of developing mental health problems due to the current pandemic. However, little has been explored about the magnitude of the risk to psychological factors to gender and their location and designation in the context of the current pandemic. Hence, the purpose of this study was to investigate the psychological impact on the Ethiopian population. Method(s): An online survey using google form with 310 Ethiopian respondents was conducted. The adopted questionnaire covers the participant's sociodemographic information, and three different questionnaires (mental health inventory, self-esteem, and life satisfaction) used to collect data. The data were not distributed normally. The Mann-Whitney U-test was applied to find differences between different categories of mental health, self-esteem, and life satisfaction. Result(s): The results indicate that urban males have higher mental health and self-esteem compared to females, and little difference in mental health appeared between students, academics, government employees, private employees, and business people. Females belonging to the rural area have higher life satisfaction than males. A significant difference in self-esteem and life satisfaction was found between participants belonging to different designations. Conclusion(s): The results of all these psychological factors provide a comprehensive picture of Ethiopianpeoples during the current pandemic. In such stressful situations, the concerned government, hospitals, educational institutions, organizations and individuals need to consider psychological intervention and take necessary action. In addition to educate and prepare individuals for the various mental health issues that they may face during the pandemic period.Copyright © 2020 Ubiquity Press. All rights reserved.

Emergence of a New Zoonotic COVID-19 that Creates a Pandemic: Update on Current Pharmacotherapeutics and Future Prospective of Plasma Therapy

The current decade started on an unexpected note, with almost the entire world grappling with a newly arisen pandemic. A novel coronavirus, tracing its first human host to a Chinese province, has spread to all geographical areas with human populations. The virus, named SARS-CoV-2, infects the lower respiratory tract, much like other coronaviruses, that caused the 2002 epidemic, to which it is eponymous. The severity of infection is seen in individuals with comorbidities like diabetes, cardiovascular disorders, chronic respiratory problems, hypertension, cancer, etc. This virus represents another incidence of zoonosis to humans and has infected over eighteen million people since December 2019, of its first human transmission. All the currently employed therapies are either aimed at alleviating the severity of the symptoms or being administered on a trial basis. This review attempts to summarize brief aetiology of the virus, epidemiology of the outbreak, clinical symptoms of the disease with a postulated mechanism of pathogenesis and several existing and approved drugs and therapeutics along with plasma therapy, which are being clinically reviewed for their activity, as well as safety, against the disease;none of which are approved yet. A few promising vaccine candidates, as per in vivo studies, are also underway, but their evaluation might take a year at least. Meanwhile, experts have come up with the concept of "social distancing" to stem the viral spread, as the medical research fraternity of the world strives hard to find a safe, successful and effective cure for it.Copyright © 2021 Bentham Science Publishers.

Has COVID-19 Pandemic affected the management of patient's with Colorectal cancer?

Introduction: Patients on The Cancer pathway should be investigated on The 2 weeks wait pathway, but COVID-19 pandemic had universal impact on The Healthcare systems. one of The main worries was The impact on Cancer patients due to delayed diagnosis and management. Our study looks at The timeframe of investigations for Colorectal Cancer during The second wave of The pandemic compared to pre COVID time. Method(s): Retrospective study looking at The waiting time to investigate patients with +ve qFIT test during The second wave of pandemic (from November 2020 till March 2021). Result(s): During this period 150 patients had +ve qFIT test, The main presenting symptom was Change in bowel habits. 90 patients were investigated with colonoscopy, only 16 (17%) patients had The colonoscopy done within 2 weeks from The qFIT result. 23 patients had colonoscopy 2-3 weeks from The result. 30 patients (33%) had The colonoscopy between 3-4 weeks, and 21 patients had to wait between 1-6 months to have The colonoscopy. Out of The 150 patients, 60 patients were investigated primarily with CT scan or CT colon. Conclusion(s): During The COVID-19 pandemic, majority of patients in our trust were investigated within one month of +ve qFIT test but yet there was some delay in carrying out The investigations compared to The normal pathway and more patients had CT scans as primary investigations before being referred for colonoscopy.

Comparison between the outcome of patient's with qFIT>120 and patients with qFIT<120.Can qFIT<120 be considered a negative test?

Introduction: When FIT is used for symptomatic patients presenting to primary care a positive result is considered >=10 mcgHb/gStool. When qFIT is used for The asymptomatic screening population (i.e NBCSp) a positive result is >=120mcgHb/gStool. During COVID-19 Pandemic The 120 cut of f was used in some of The hospitals to triage patients who require further investigations for either colonoscopy or CT scan. Method(s): Retrospective cohort study done in General district hospital comparing The pathology identified in patients with Qfit results>120 and patients with result <120. Result(s): In The period between July 2020 and November 2021, 448 patients had +ve qFIT result (>=10 micrograms).In The first group, 340 patients had qFIT result <120. 191 patients had colonoscopy with 8 confirmed Colorectal cancer, and 137 patients had CT/CT colon with only 1 patient was found to have Colorectal cancer. Overall 2.6% of The patients had cancer. While in The second group, 108 patients had qFIT>120. 69 patients had colonoscopy with 9 confirmed cancer. The rest of The patients had CT/CT colon with 2 patients showing features of malignancy. Overall 10.1% of The patients had cancer. Conclusion(s): The incidence of Colorectal Cancer in patients with qFIT result >120 is much higher than The other group, but The incidence of Colorectal Cancer in patients with qFIT<120 is still significant and The patients shouldn't be discharged without investigations.

Risk- Taking Among Older Youth at the Outset of the COVID- 19 Pandemic in the USA

A robust literature suggests that older adolescents, aged 18 to 21 years, routinely engage in unsafe behaviors that range from smoking, driving recklessly, and drinking to sexual risk-taking. The coronavirus disease 2019 (COVID-19) outbreak sparked renewed interest in risk-taking behavior among young people because as asymptomatic carriers of COVID-19, young people may have contributed to early community spread and increased mortality rates among older and vulnerable populations in Wuhan, China. The central question addressed in this chapter is whether self-perceived health status, knowledge of COVID-19, anxiety over COVID-19, and trust in the media were associated with youth risk-taking at the start of the COVID-19 pandemic. The study results offer three meaningful insights into risk-taking among older youth at the beginning of the COVID-19 pandemic. First, being male is associated with higher risk-taking. Second, experiencing little fear over a very young family member, a healthy adult family member, an elderly family member, or oneself contracting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is associated with increased COVID-19 risk-taking. Third, increased knowledge of COVID-19 marginally reduces risk-taking among male youth. The chapter suggests that gender and race are more than just markers for classifying and comparing health behaviors and outcomes;they may interact with other social factors to structure adherence or nonadherence to preventive health behaviors among older youth. © 2022 selection and editorial matter, Nazneen Khan;individual chapters, the contributors

Investigating the impact on learning outcomes through the use of EdTech during COVID-19: Evidence from an RCT in the Punjab province of Pakistan

The objective of the current study is to test the impact of low-tech solutions during COVID-19 school closures on maths, Urdu, and English scores for students in the underdeveloped district of Bahawalnagar, which is situated in the southern part of Punjab in Pakistan. The target population for this study is Grade 8 students attending private schools. Simple random sampling technique is adopted for the selection of participants in the study. We have tested the impact of three interventions, namely: Teaching at the right level (TaRL), Fortnightly assessments (FAS), and Digital teacher training sessions (DTS). Our findings show a significant and positive ‘Intention to Treat’ (ITT) impact on Urdu and English scores of the students in the TaRL treatment group. The students increased their English and Urdu scores by 0.56 SD. However, we found no significant impact of the intervention on maths scores in the TaRL treatment group. Fortnightly assessments and digital teacher training sessions were also found to contribute to higher English scores of the students. However, we found no ITT impact on the maths and Urdu scores for these treatment groups. The Local Average Treatment Effect (LATE) analysis revealed positive and significant improvement in Urdu and English scores of the students in the TaRL treatment group. Key stakeholders whom we interviewed suggested that redesigning the curriculum and incorporating TaRL within this approach could facilitate enhancement in learning outcomes in students in deprived areas. Our findings are important to help inform policymakers on the importance of designing and implementing cost-effective, low-tech solutions to help reduce learning gaps. Copyright © 2022 Adil, Nazir and Akhtar.

Has COVID-19 pandemic affected the management of patient's with colorectal cancer?

Introduction: Patients on the cancer pathway should be investigated on the 2 weeks wait pathway, but COVID-19 pandemic had universal impact on the Healthcare systems. One of the main worries was the impact on cancer patients due to delayed diagnosis and management. Our study looks at the timeframe of investigations for colorectal cancer during the second wave of the pandemic compared to pre covid time. Method(s): Retrospective study looking at the waiting time to investigate patients with +ve qFIT test during the second wave of pandemic (from November 2020 till March 2021). Result(s): During this period 150 patients had +ve qFIT test, the main presenting symptom was Change in bowel habits. 90 patients were investigated with colonoscopy, only 16 (17%) patients had the colonoscopy done within 2 weeks from the qFIT result. 23 patients had colonoscopy 2-3 weeks from the result. 30 patients (33%) had the colonoscopy between 3-4 weeks, and 21 patients had to wait between 1-6 months to have the colonoscopy. Out of the 150 patients, 60 patients were investigated primarily with CT scan or CT colon. Conclusion(s): During the COVID-19 pandemic, majority of patients in our trust were investigated within one month of +ve qFIT test but yet there was some delay in carrying out the investigations compared to the normal pathway and more patients had CT scans as primary investigations before being referred for colonoscopy.

Comparison between the outcome of patient's with qFIT > 120 and patients with qFIT < 120. Can qFIT < 120 be considered a negative test?

Introduction: When FIT is used for symptomatic patients presenting to primary care a positive result is considered >=10 mcgHb/gStool. When qFIT is used for the asymptomatic screening population (i.e NBCSP) a positive result is >=120 mcgHb/gStool. During COVID-19 Pandemic the 120 cut off was used in some of the hospitals to triage patients who require further investigations for either colonoscopy or CT scan. Method(s): Retrospective cohort study done in general district hospital comparing the pathology identified in patients with Qfit results> 120 and patients with result <120. Result(s): In the period between July 2020 and November 2021, 448 patients had +ve qFIT result (>=10 micrograms).In the first group, 340 patients had qFIT result <120. 191 patients had colonoscopy with 8 confirmed colorectal cancer, and 137 patients had CT/CT colon with only 1 patient was found to have colorectal cancer. Overall 2.6 % of the patients had cancer. While in the second group, 108 patients had qFIT>120. 69 patients had colonoscopy with 9 confirmed cancer. the rest of the patients had CT/CT colon with 2 patients showing features of malignancy. Overall 10.1% of the patients had cancer. Conclusion(s): The incidence of colorectal cancer in patients with qFIT result >120 is much higher than the other group, but the incidence of colorectal cancer in patients with qFIT < 120 is still significant and the patients shouldn't be discharged without investigations.

Development of Potent SARS-CoV 3CL Protease Inhibitors: In silico Analogues Designing of Darunavir, Molecular Modelling and Molecular Dynamic Simulation

COVID pandemic initiated in early 2019 and the origin from where it initiated was Wuhan city of China. It changed the whole world. A huge population died due to COVID-19 in spite of taking precautions. New treatments and vaccines are introduced for the treatment and prevention. Among successful treatments, antivirals were found effective against COVID-19. But there is a need to find derivatives, which could be more effective for the treatment of COVID-19. The current research is focused on computational studies on one of the antiviral, darunavir. A computational strategy, molecular docking and molecular dynamic simulation techniques is presented to discover the potent analogues of darunavir for inhibiting protease 3CLpro of SARS-CoV2. The newly discovered X-ray structure (PDB ID: 6LU7) was selected for docking study and generated analogues were docked. The docking results showed that the compounds were bound in the active site of receptor with good binding affinity. It was concluded that compounds D8 and D15 were have good binding affinity value of -9.85 and -8.95 kcal/mol, respectively and these compounds were selected for molecular dynamic simulation (MDS) study to check their stability in pocket of receptor. © 2022 Chemical Publishing Co.. All rights reserved.

Document Retrieval and Claim Verification to Mitigate COVID-19 Misinformation

During the COVID-19 pandemic, the spread of misinformation on online social media has grown exponentially. Unverified bogus claims on these platforms regularly mislead people, leading them to believe in half-baked truths. The current vogue is to employ manual fact-checkers to verify claims to combat this avalanche of misinformation. However, establishing such claims' veracity is becoming increasingly challenging, partly due to the plethora of information available, which is difficult to process manually. Thus, it becomes imperative to verify claims automatically without human interventions. To cope up with this issue, we propose an automated claim verification solution encompassing two steps - document retrieval and veracity prediction. For the retrieval module, we employ a hybrid search-based system with BM25 as a base retriever and experiment with recent state-of-the-art transformer-based models for re-ranking. Furthermore, we use a BART-based textual entailment architecture to authenticate the retrieved documents in the later step. We report experimental findings, demonstrating that our retrieval module outperforms the best baseline system by 10.32 NDCG@100 points. We escort a demonstration to assess the efficacy and impact of our suggested solution. As a byproduct of this study, we present an open-source, easily deployable, and user-friendly Python API that the community can adopt.

Findings of the CONSTRAINT 2022 Shared Task on Detecting the Hero, the Villain, and the Victim in Memes

We present the findings of the shared task at the CONSTRAINT 2022 workshop on "Hero, Villain, and Victim: Dissecting Harmful Memes for Semantic Role Labeling of Entities." The task aims to delve deeper into meme comprehension by deciphering the connotations behind the entities present in a meme. In more nuanced terms, the shared task focuses on determining the victimizing, glorifying, and vilifying intentions embedded in meme entities to explicate their connotations. To this end, we curate HVVMemes, a novel meme dataset of about 7,000 memes spanning the domains of COVID-19 and US Politics, each containing entities and their associated roles: hero, villain, victim, or other. The shared task attracted 105 registered participants, but eventually only nine of them made official submissions. The most successful systems used ensembles combining textual and multimodal models, with the best system achieving an F1-score of 58.67.

COVIDX: Computer-aided diagnosis of COVID-19 and its severity prediction with raw digital chest X-ray scans

Background: Coronavirus disease (COVID-19) is a contagious infection caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) and it has infected and killed millions of people across the globe. Objective: In the absence or inadequate provision of therapeutic treatments of COVID-19 and the limited convenience of diagnostic techniques, there is a necessity for some alternate spontaneous screening systems that can easily be used by the physicians to rapidly recognize and isolate the infected patients to circumvent onward surge. A chest X-ray (CXR) image can effortlessly be used as a substitute modality to diagnose the COVID-19. Method: In this study, we present an automatic COVID-19 diagnostic and severity prediction system (COVIDX) that uses deep feature maps of CXR images along with classical machine learning algorithms to identify COVID-19 and forecast its severity. The proposed system uses a three-phase classification approach (healthy vs unhealthy, COVID-19 vs pneumonia, and COVID-19 severity) using different conventional supervised classification algorithms. Results: We evaluated COVIDX through 10-fold cross-validation, by using an external validation dataset, and also in a real setting by involving an experienced radiologist. In all the adopted evaluation settings, COVIDX showed strong generalization power and outperforms all the prevailing state-of-the-art methods designed for this purpose. Conclusions: Our proposed method (COVIDX), with vivid performance in COVID-19 diagnosis and its severity prediction, can be used as an aiding tool for clinical physicians and radiologists in the diagnosis and follow-up studies of COVID-19 infected patients. © The Author(s) 2022. Published by Higher Education Press.

Baseline assessment of heavy metal pollution during COVID-19 near river mouth of Kerian River, Malaysia

River water quality is a serious concern among scientist and government agencies due to increasing anthropogenic activities and uncontrolled industrial discharge to rivers. The present study was conducted near the river mouth of the Kerian River to assess heavy metal pollution during COVID-19 pandemic-lockdown conditions and post-COVID-19 pandemic-unlock conditions. Twelve samples of shallow, middle, and bottom depths were collected at four locations along a 9.6 km reach. A concentration of eight heavy metals including Cadmium, Chromium, Copper, Iron, Manganese, Nickel, Lead, and Zinc were extracted through atomic absorption spectrometry. Total suspended solid was measured during laboratory experimentation. The results showed that, during the pandemic, concentrations of Nickel, Zinc, and Iron were high at shallow, middle, and bottom depths, respectively. Decreasing orders of heavy metal concentration are variable at different depths due to either their high sinking tendency with other existing components of water matrix or the anthropogenic source. However, almost all values of heavy metals are under the permissible limit of National Water Quality Standards of Malaysia and Food and Drug Administration. A possible reason for the lack of heavy metal pollution may be the restriction of anthropogenic activities during the COVID-19 pandemic. Additionally, no significant differences were observed in total suspended solid.

Home Quarantine Among COVID Positive Health Professionals: A Cross-Sectional Study From Hyderabad Pakistan

Objective: To determine the preference for home quarantine and its reasons among health professionals during the COVID-19 pandemic. Study Design: Cross-sectional study. Place and Duration: Department of Obstetrics/Gynecology, Liaquat University of Medical and Health Sciences Jamshoro, and other affiliated hospitals with LUMHS, from Apr to Sep 2021. Methodology: Eighty home-quarantined health professionals (HPs) at our institute following the exposure to confirm COVID-19 cases were included. A pre-designed questionnaire was used to analyze its reliability using Cronbach's alpha value in SPSS after completing the home quarantine period and analyzed. Results: We enrolled 80 health professionals. 43 (53.8%) were PG Trainees, followed by consultant (20%), doctors (20.0%), nurses (6.2%), and technicians (6.9%). The mean quarantined period was 15.72 ± 6.15 days (7-20 days). Doctors, PG trainees, and nurses perceived a greater level of irritability than the other health professionals. We found an association between irritability and advanced age, confinement, and being single. Generally, PG Trainee revealed higher physical activity (p= <0.001). Physical activity was significantly lower among respondents who were unmarried (p= 0.004). Conclusion: Increased satisfaction and a more homelike environment, less aggravation of symptoms, and a mild level of depression and anxiety among health care professionals were associated with home quarantine measures during the COVID-19 pandemic. © 2022, Army Medical College. All rights reserved.

Analysing biosensor clinical pathogen information using mayfly optimized convolute neural network approach

Viral and bacterial infection diseases are the most common things caused by microbes. Infection diseases are serious issues because of the growth of COVID‐19. Because of the current living situation, clinical pathogens are difficult to identify. Therefore, biosensors have been widely utilized to sense the biomolecules relevant to viruses and bacteria. The biosensors observe the nanoparticles from the pathogens and help improve the infection analysis. The sensor information is processed using machine learning techniques because it consists of several learning patterns. However, the existing methods have multi‐objective optimization problems while analysing the changes in the nanoparticles. This work utilizes a mayfly optimized convoluted neural network (MOCNN) to overcome this research issue. The grid uses the fully convolution layer that processes the extracted biosensor features to determine the infections. The network performance is optimized by applying the exploitation and exploration properties of nuptial dance that help to escape from the local optima solutions. The effective utilization of the optimized training patterns improves the convergence speed and convergence rate compared to traditional methods. From the results, MOCNN ensures 98.97% accuracy, 0.388 error rate, and 0.322833 convergence rate on various iterations with different learning rates. [ FROM AUTHOR] Copyright of Expert Systems is the property of Wiley-Blackwell 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.)

Evaluation of the Clinical Parameters of Patients with Influenza and Covid-19 using Blood Test Data

Objective: The characteristics of the novel corona virus ailment 2019 (COVID-19) vs influenza were not described, such as blood test data. As a result, we compared the diagnostic features of COVID-19 and flu, along with blood test data. Materials and Methods: A cross-sectional study was conducted at Nishtar Hospital, Multan. We enrolled individuals diagnosed with COVID-19 between January 1, 2020, and December 31, 2020, and had they undergo blood tests. In comparing, we enlisted an equal percentage of participants who'd been identified with flu that had blood tests. Results: During the course of the study, 228 people were identified of COVID-19 (men:women ratio, 123 [54.0 percent]:105 [46.0 percent];age, 54.68 18.98 years). We also enlisted the help of 228 flu clients (male:female, 129 [56.6 percent ]:99 [43.4 percent ];age, 69.6 21.25 years). Clients with COVID-19 had a vastly greater age range of 15 to 70 years (vs. 71 years), respiratory problems, as well as ennui than someone with flu. Nevertheless, discomfort, a body temperature greater than 38.1oC, as well as a white blood cell count greater than 9000/lL were far more prevalent in flu patient populations. Conclusions: Our findings are helpful in distinguishing COVID-19 from flu, so they'll be remarkably helpful for future practice as we understand to interoperate with COVID-19.

Speaker and time-aware joint contextual learning for dialogue-act classification in counselling conversations

The onset of the COVID-19 pandemic has brought the mental health of people under risk. Social counselling has gained remarkable significance in this environment. Unlike general goal-oriented dialogues, a conversation between a patient and a therapist is considerably implicit, though the objective of the conversation is quite apparent. In such a case, understanding the intent of the patient is imperative in providing effective counselling in therapy sessions, and the same applies to a dialogue system as well. In this work, we take forward a small but an important step in the development of an automated dialogue system for mental-health counselling. We develop a novel dataset, named HOPE, to provide a platform for the dialogue-act classification in counselling conversations. We identify the requirement of such conversation and propose twelve domain-specific dialogue-act (DAC) labels. We collect ∼ 12.9K utterances from publicly-available counselling session videos on YouTube, extract their transcripts, clean, and annotate them with DAC labels. Further, we propose SPARTA, a transformer-based architecture with a novel speaker- and time-aware contextual learning for the dialogue-act classification. Our evaluation shows convincing performance over several baselines, achieving state-of-the-art on HOPE. We also supplement our experiments with extensive empirical and qualitative analyses of SPARTA. © 2022 ACM.