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A chatbot is a work of artificial intelligence technology that simulates a conversation (or chat) in natural language with a user via messaging applications, internet sites, smartphone apps, or the telephone. Chatbots are used in a range of conversation systems for a variety of purposes, including customer assistance, request processing, and information acquisition. Chatbots have been around for quite some time, but it has only been in the recent past few years that they have seen a significant uptick in popularity among consumers and companies. This change in the perspective of chatbots and conversational interfaces was heavily impacted by the advancements in artificial intelligence and machine learning, as well as by the expanding usage of messaging app technologies. This study offers a comprehensive analysis of the conversational tool known as chatbots, which emerged in the contemporary era. This paper also discusses how this tool is expanding its root in the life of human beings as well as the pros-cons that will be generated by the chatbots.

As data grows exponentially across diverse fields, the ability to effectively leverage big data has become increasingly crucial. In the field of data science, however, minority groups, including African Americans, are significantly underrepresented. With the strategic role of minority-serving institutions to enhance diversity in the data science workforce and apply data science to health disparities, the National Institute for Minority Health Disparities (NIMHD) provided funding in September 2021 to six Research Centers in Minority Institutions (RCMI) to improve their data science capacity and foster collaborations with data scientists. Meharry Medical College (MMC), a historically Black College/University (HBCU), was among the six awardees. This paper summarizes the NIMHD-funded efforts at MMC, which include offering mini-grants to collaborative research groups, surveys to understand the needs of the community to guide project implementation, and data science training to enhance the data analytics skills of the RCMI investigators, staff, medical residents, and graduate students. This study is innovative as it addressed the urgent need to enhance the data science capacity of the RCMI program at MMC, build a diverse data science workforce, and develop collaborations between the RCMI and MMC’s newly established School of Applied Computational Science. This paper presents the progress of this NIMHD-funded project, which clearly shows its positive impact on the local community.

OBJECTIVE To assess the degree to which heavy menstrual bleeding is associated with depression, independent of hormonal contraception. STUDY DESIGN We performed a retrospective cohort study of 1168 female adolescents 9-18 years old presenting to general pediatricians for heavy menstrual bleeding or well visits. Depression was the primary outcome and defined as a diagnosis in the health record. Univariable and multivariable regression models were fit to the data to identify factors associated with depression diagnosis. RESULTS In total, 581 adolescents with heavy menstrual bleeding and 587 without heavy menstrual bleeding were included. Depression diagnoses occurred with greater frequency in youth with heavy menstrual bleeding compared with those without heavy menstrual bleeding (50.9% vs 24.2% P

Since its outbreak in December, 2019, the novel coronavirus has wreaked a havoc across the world. Stringent measures such as lockdown and self-isolation were imposed in various nations to curb the transmission of the life-threatening virus. India had been under a complete lockdown from 25th March, 2020 to 3rd May,2020, compelling its citizens to stay indoors for an unprecedented amount of time. The ‘stay at home orders’ had serious implications on the mental health of the people. In light of the on-going pandemic, the current study aims to understand the lockdown experience of young married adults and its relationship with their psychological well-being. Also, with a spike in the digital habits of young adults during the Covid-19 lockdown, we intended to explore the impact of technology on the psychological well-being of its users. A convenience sample of 140 married, young adults (20 to 40 years old), staying with their partners in different parts of the country, was selected for the study. A single administration of WHO QOL BREF and self-constructed Lockdown Experience Scale was carried out. A separate list of technology usage items was also presented to the participants. Due to lockdown, the questionnaires were administered through Google forms. Results indicate that Lockdown Experience (Quality of Partner Relationship, sharing of household responsibilities, Personal Space) correlated significantly with Psychological Well Being as well as with Technology Use. Implications and suggestions for future research have been given.

As family caregivers of patients undergoing hematopoietic cell transplantation have multifaceted caregiving responsibilities (such as medical, household, financial) of long duration, they also have multiple physical, social, psychological, and informational needs.This study explored the prevalence of electronic health record patient portal use by family caregivers for managing both their own and their hematopoietic cell transplantation care recipient's health, as well as potential factors associated with portal use.An electronic caregiver health survey, first developed via cognitive interviewing methods of hematopoietic cell transplantation caregivers, was distributed nationally (in the United States) by patient advocacy organizations to family caregivers of hematopoietic cell transplantation patients. It was used to assess self-reported caregiver demographics, caregiving characteristics, depression and anxiety with the Patient Health Questionnaire-4, coping with the Brief COPE, and caregiver portal use to manage care recipient's and their own health.We found that 77% of respondents (720/937) accessed electronic health record patient portals for their care recipients, themselves, or both. Multivariate models indicated use of care recipient electronic health record portals by caregivers was more likely with young, White, married, low-income caregivers caring for a parent, residing with the care recipient, and experiencing more caregiver depression. Caregiver use of their own electronic health record portal was more likely with young, White, high-income caregivers caring for a parent and experiencing chronic medical conditions of their own. Partially due to multicollinearity, anxiety and coping did not contribute independently to this model.Findings from the survey could open avenues for future research into caregiver use of technology for informational support or intervention, including wearables and mobile health.RR2-10.2196/4918.

Background As family caregivers of patients undergoing hematopoietic cell transplantation have multifaceted caregiving responsibilities (such as medical, household, financial) of long duration, they also have multiple physical, social, psychological, and informational needs. Objective This study explored the prevalence of electronic health record patient portal use by family caregivers for managing both their own and their hematopoietic cell transplantation care recipient’s health, as well as potential factors associated with portal use. Methods An electronic caregiver health survey, first developed via cognitive interviewing methods of hematopoietic cell transplantation caregivers, was distributed nationally (in the United States) by patient advocacy organizations to family caregivers of hematopoietic cell transplantation patients. It was used to assess self-reported caregiver demographics, caregiving characteristics, depression and anxiety with the Patient Health Questionnaire–4, coping with the Brief COPE, and caregiver portal use to manage care recipient’s and their own health. Results We found that 77% of respondents (720/937) accessed electronic health record patient portals for their care recipients, themselves, or both. Multivariate models indicated use of care recipient electronic health record portals by caregivers was more likely with young, White, married, low-income caregivers caring for a parent, residing with the care recipient, and experiencing more caregiver depression. Caregiver use of their own electronic health record portal was more likely with young, White, high-income caregivers caring for a parent and experiencing chronic medical conditions of their own. Partially due to multicollinearity, anxiety and coping did not contribute independently to this model. Conclusions Findings from the survey could open avenues for future research into caregiver use of technology for informational support or intervention, including wearables and mobile health. International Registered Report Identifier (IRRID) RR2-10.2196/4918

BACKGROUND As family caregivers of patients undergoing hematopoietic cell transplantation have multifaceted caregiving responsibilities (such as medical, household, financial) of long duration, they also have multiple physical, social, psychological, and informational needs. OBJECTIVE This study explored the prevalence of electronic health record patient portal use by family caregivers for managing both their own and their hematopoietic cell transplantation care recipient’s health, as well as potential factors associated with portal use. METHODS An electronic caregiver health survey, first developed via cognitive interviewing methods of hematopoietic cell transplantation caregivers, was distributed nationally (in the United States) by patient advocacy organizations to family caregivers of hematopoietic cell transplantation patients. It was used to assess self-reported caregiver demographics, caregiving characteristics, depression and anxiety with the Patient Health Questionnaire–4, coping with the Brief COPE, and caregiver portal use to manage care recipient’s and their own health. RESULTS We found that 77% of respondents (720/937) accessed electronic health record patient portals for their care recipients, themselves, or both. Multivariate models indicated use of care recipient electronic health record portals by caregivers was more likely with young, White, married, low-income caregivers caring for a parent, residing with the care recipient, and experiencing more caregiver depression. Caregiver use of their own electronic health record portal was more likely with young, White, high-income caregivers caring for a parent and experiencing chronic medical conditions of their own. Partially due to multicollinearity, anxiety and coping did not contribute independently to this model. CONCLUSIONS Findings from the survey could open avenues for future research into caregiver use of technology for informational support or intervention, including wearables and mobile health. INTERNATIONAL REGISTERED REPORT RR2-10.2196/4918

Social media platforms are widely used nowadays by various businesses to promote their products and services through multimedia content. Instagram is one of those platforms, which is used not only by companies to promote their products but also by local governments to promote tourist destinations. Predicting the popularity of the promotional tourist destination images helps marketers to plan strategically. However, given the abundance of images posted on Instagram daily, identifying the factors that determine the popularity of an image is a big challenge, due to informal and noisy visual content, frequent content evolution, a lack of explicit visual elements, and people&rsquo, s informal behavior in liking, commenting on, and viewing the images. We present an approach to identify the factors most responsible for the popularity of tourist destinations-related images on Instagram. Our approach provides a proof of concept for an artificial intelligence (AI)-based real-time content management system, which will help to promote a tourist destination. The experiments on a collection of posts crawled from the official Instagram account of Jeju Island, which is one of the most popular tourist destinations in Korea, show that the recency of the post is the most important predictor of the number of likes and comments it will receive. Moreover, the combination of visual content and context features is an excellent predictor of popularity. The number of likes and comments are found to be complementary to each other for predicting image popularity.

Twitter is a microblogging platform that generates large volumes of data with high velocity. This daily generation of unbounded and continuous data leads to Big Data streams that often require real-time distributed and fully automated processing. Hashtags, hyperlinked words in tweets, are widely used for tweet topic classification, retrieval, and clustering. Hashtags are used widely for analyzing tweet sentiments where emotions can be classified without contexts. However, regardless of the wide usage of hashtags, general tweet topic classification using hashtags is challenging due to its evolving nature, lack of context, slang, abbreviations, and non-standardized expression by users. Most existing approaches, which utilize hashtags for tweet topic classification, focus on extracting hashtag concepts from external lexicon resources to derive semantics. However, due to the rapid evolution and non-standardized expression of hashtags, the majority of these lexicon resources either suffer from the lack of hashtag words in their knowledge bases or use multiple resources at once to derive semantics, which make them unscalable. Along with scalable and automated techniques for tweet topic classification using hashtags, there is also a requirement for real-time analytics approaches to handle huge and dynamic flows of textual streams generated by Twitter. To address these problems, this paper first presents a novel semi-automated technique that derives semantically relevant hashtags using a domain-specific knowledge base of topic concepts and combines them with the existing tweet-based-hashtags to produce Hybrid Hashtags. Further, to deal with the speed and volume of Big Data streams of tweets, we present an online approach that updates the preprocessing and learning model incrementally in a real-time streaming environment using the distributed framework, Apache Storm. Finally, to fully exploit the batch and stream environment performance advantages, we propose a comprehensive framework (Hybrid Hashtag-based Tweet topic classification (HHTC) framework) that combines batch and online mechanisms in the most effective way. Extensive experimental evaluations on a large volume of Twitter data show that the batch and online mechanisms, along with their combination in the proposed framework, are scalable, efficient, and provide effective tweet topic classification using hashtags.

Canine parvovirus-2 antigenic variants (CPV-2a, CPV-2b and CPV-2c) ubiquitously distributed worldwide in canine population causes severe fatal gastroenteritis. Antigenic typing of CPV-2 remains a prime focus of research groups worldwide in understanding the disease epidemiology and virus evolution. The present study was thus envisioned to provide a simple sequencing independent, rapid, robust, specific, user-friendly technique for detecting and typing of presently circulating CPV-2 antigenic variants. ARMS-PCR strategy was employed using specific primers for CPV-2a, CPV-2b and CPV-2c to differentiate these antigenic types. ARMS-PCR was initially optimized with reference positive controls in two steps; where first reaction was used to differentiate CPV-2a from CPV-2b/CPV-2c. The second reaction was carried out with CPV-2c specific primers to confirm the presence of CPV-2c. Initial validation of the ARMS-PCR was carried out with 24 sequenced samples and the results were matched with the sequencing results. ARMS-PCR technique was further used to screen and type 90 suspected clinical samples. Randomly selected 15 suspected clinical samples that were typed with this technique were sequenced. The results of ARMS-PCR and the sequencing matched exactly with each other. The developed technique has a potential to become a sequencing independent method for simultaneous detection and typing of CPV-2 antigenic variants in veterinary disease diagnostic laboratories globally.

Generalized structured component analysis (GSCA) is a theoretically well-founded approach to component-based structural equation modeling (SEM). This approach utilizes the bootstrap method to estimate the confidence intervals of its parameter estimates without recourse to distributional assumptions, such as multivariate normality. It currently provides the bootstrap percentile confidence intervals only. Recently, the potential usefulness of the bias-corrected and accelerated bootstrap (BCa) confidence intervals (CIs) over the percentile method has attracted attention for another component-based SEM approach—partial least squares path modeling. Thus, in this study, we implemented the BCa CI method into GSCA and conducted a rigorous simulation to evaluate the performance of three bootstrap CI methods, including percentile, BCa, and Student's t methods, in terms of coverage and balance. We found that the percentile method produced CIs closer to the desired level of coverage than the other methods, while the BCa method was less prone to imbalance than the other two methods. Study findings and implications are discussed, as well as limitations and directions for future research.

In today's digital era, data are everywhere from Internet of Things to health care or financial applications. This leads to potentially unbounded ever-growing Big data streams and it needs to be utilized effectively. Data normalization is an important preprocessing technique for data analytics. It helps prevent mismodeling and reduce the complexity inherent in the data especially for data integrated from multiple sources and contexts. Normalization of Big Data stream is challenging because of evolving inconsistencies, time and memory constraints, and non-availability of whole data beforehand. This paper proposes a distributed approach to adaptive normalization for Big data stream. Using sliding windows of fixed size, it provides a simple mechanism to adapt the statistics for normalizing changing data in each window. Implemented on Apache Storm, a distributed real-time stream data framework, our approach exploits distributed data processing for efficient normalization. Unlike other existing adaptive approaches that normalize data for a specific use (e.g., classification), ours does not. Moreover, our adaptive mechanism allows flexible controls, via user-specified thresholds, for normalization tradeoffs between time and precision. The paper illustrates our proposed approach along with a few other techniques and experiments on both synthesized and real-world data. The normalized data obtained from our proposed approach, on 160,000 instances of data stream, improves over the baseline by 89% with 0.0041 root-mean-square error compared with the actual data.

Machine learning techniques are widely used nowadays in the healthcare domain for the diagnosis, prognosis, and treatment of diseases. These techniques have applications in the field of hematopoietic cell transplantation (HCT), which is a potentially curative therapy for hematological malignancies. Herein, a systematic review of the application of machine learning (ML) techniques in the HCT setting was conducted. We examined the type of data streams included, specific ML techniques used, and type of clinical outcomes measured. A systematic review of English articles using PubMed, Scopus, Web of Science, and IEEE Xplore databases was performed. Search terms included “hematopoietic cell transplantation (HCT),” “autologous HCT,” “allogeneic HCT,” “machine learning,” and “artificial intelligence.” Only full-text studies reported between January 2015 and July 2020 were included. Data were extracted by two authors using predefined data fields. Following PRISMA guidelines, a total of 242 studies were identified, of which 27 studies met the inclusion criteria. These studies were sub-categorized into three broad topics and the type of ML techniques used included ensemble learning (63%), regression (44%), Bayesian learning (30%), and support vector machine (30%). The majority of studies examined models to predict HCT outcomes (e.g., survival, relapse, graft-versus-host disease). Clinical and genetic data were the most commonly used predictors in the modeling process. Overall, this review provided a systematic review of ML techniques applied in the context of HCT. The evidence is not sufficiently robust to determine the optimal ML technique to use in the HCT setting and/or what minimal data variables are required.

Skin conditions are commonly encountered in primary care; they make up 15% of all consultations. The clinical presentations of blistering conditions are often similar, and therefore, it is important to make an accurate diagnosis. The purpose of this article is to provide an overview of common infective, non-immune and auto-immune causes of blistering skin disease.

The present research examined the impact of personal characteristics on innovative work behaviour of 120 teachers. Using the survey method, participants were asked to give responses on the measures of innovative work behaviour, work engagement, work locus of control, and affect. Pearson’s product moment correlation was calculated to see the relationship between work engagement, work locus of control, affect (positive affect & negative affect), and innovative work behaviour. The effect of confounding variables – grade, age, and experience on innovative work behviour was also observed by calculating a 2x2x3 between subjects ANOVA. The quantitative analysis revealed that positive affect was the strongest predictor of innovative work behaviour followed by internal work locus of control while there was no significant effect of the confounding variables on innovative work behaviour. The study has implications for innovation development and implementation in schools, and developing screening methods for teachers.

The broaden-and-build theory of positive emotions postulated by Barbara L. Fredrickson proposes that emotions like joy, interest, contentment, and love enable an individual to broaden his/her patterns of thinking and acting, which in turn build the personal coping resources, whether intellectual, physical, social, or psychological, by way of creating novel ideas, actions or social relationships. This paper is a review of 15 empirical studies carried out during 1998-2012 that support the contributions of this theory to the creation of a healthy workplace by fostering positive emotions in employees. Positive emotions were found to be pivotal in enhancing employee performance, encouraging innovation and creativity that result in sustainable business practices, helping organizations make good decisions, facilitating work-flow and motivation, developing authentic and charismatic leadership styles, job enrichment, better team performance, and satisfactory customer relations. A link between positive emotions and an upward spiral of personal and organizational resources has also been established where positive self-evaluation, development of resilience, a climate of social support, layout of clearer goals, high quality social-interaction, good health and productivity of workers have been found to promote effective coping.

Interferon (IFN)-gamma is an important Th1 cytokine, which plays a role in immune surveillance and anti-tumor activity. A case-control study involving 54 sporadic breast cancer patients and 144 healthy controls was carried out to explore if the genotype variation of a proposed non-specific enhancer element with a dinucleotide (CA)n repeat in intron 1 has a role in the susceptibility to promote sporadic breast cancer. Genotype analysis carried out by single-strand length polymorphism and confirmed by sequencing showed an increased frequency of (CA)12 allele (P0.001) and decreased frequencies of (CA)15 (P0.01) and (CA)15 (p0.001) alleles in sporadic breast cancer patients as compared to controls. Further, in vitro reporter assays for (CA)12 and (CA)15 alleles suggested these to be associated with decreased and increased expressions, respectively, suggesting the (CA)12/(CA)12 background to act as one of the factors that could lead to low production of IFN-gamma. The study concludes that such genetic background for a proposed non-specific enhancer element with (CA)n repeat within intron 1 of the IFNG gene might put the individuals with this genotype at higher risk to promote the development of sporadic breast cancer due to a resultant compromised immune surveillance.

The DNA mismatch repair (MMR) pathway plays a prominent role in the correction of errors made during DNA replication and genetic recombination and in the repair of small deletions and loops in DNA. Mismatched nucleotides can occur by replication errors, damage to nucleotide precursors, damage to DNA, or during heteroduplex formation between two homologous DNA molecules in the process of genetic recombination. Defects in MMR can precipitate instability in simple sequence repeats (SSRs), also referred to as microsatellite instability (MSI), which appears to be important in certain types of cancers, both spontaneous and hereditary. Variations in the highly polymorphic alleles of specific microsatellite repeats can be identified using PCR with primers derived from the unique flanking sequences. These PCR products are analyzed on denaturing polyacrylamide gels to resolve differences in allele sizes of2 bp. Although (CA)n repeats are the most abundant class among dinucleotide SSRs, trinucleotide and tetranucleotide repeats are also frequent. These polymorphic repeats have the advantage of producing band patterns that are easy to analyze and can be used as an indication of a possible MMR defect in a cell. The presumed association between such allelic variation and an MMR defect should be confirmed by molecular analysis of the structure and/or expression of MMR genes.

Single-strand conformation polymorphism (SSCP) for screening mutations/single-nucleotide polymorphisms (SNPs) is a simple, cost-effective technique, saving an expensive exercise of sequencing each and every polymerase chain reaction product and assisting in choosing only the amplicons of interest with expected mutations. The principle of detection of small changes in DNA sequences is based on changes in single-strand DNA conformations. The changes in electrophoretic mobility that SSCP detects are sequence dependent. The limitations faced in SSCP range from routine polyacrylamide gel electrophoresis (PAGE) problems to the problems of resolving mutant DNA bands. Both these problems can be solved by controlling PAGE conditions and by varying physical and environmental conditions such as pH, temperature, voltage, gel type and percentage, addition of additives or denaturants, and others. Despite much upgrading of the technology for mutation detection, SSCP remains the method of choice to analyze mutations and SNPs in order to understand genomic variations, both spontaneous and induced, and the genetic basis of diseases.

The DNA mismatch repair (MMR) pathway plays a prominent role in the correction of errors made during DNA replication and genetic recombination and in the repair of small deletions and loops in DNA. Mismatched nucleotides can occur by replication error, damage to nucleotide precursors, damage to DNA, or during heteroduplex formation between two homologous DNA molecules in the process of genetic recombination. Defects in MMR can precipitate instability in simple sequence repeats (SSRs), also referred to as microsatellite instability (MSI), which appears to be important in certain types of cancers, both spontaneous and hereditary. Variation in the highly polymorphic alleles of specific microsatellite repeats can be identified using PCR with primers derived from the unique flanking sequences. These PCR products are analyzed on denaturing polyacrylamide gels to resolve differences in allele sizes of more than 2 bp. Although (CA)n repeats are the most abundant class among dinucleotide SSRs, trinucleotide and tetranucleotide repeats are also frequent. These polymorphic repeats have the advantage of producing band patterns that are easy to analyze and can be used as an indication of a possible MMR defect in a cell. The presumed association between such allelic variation and an MMR defect should be confirmed by molecular analysis of the structure and/or expression of MMR genes.

Single-strand conformation polymorphism (SSCP) for screening mutations/single-nucleotide polymorphisms (SNPs) is a simple, cost-effective technique, saving an expensive exercise of sequencing each and every polymerase chain reaction product and assisting in choosing only the amplicons of interest with expected mutations. The principle of detection of small changes in DNA sequences is based on changes in single-strand DNA conformations. The changes in electrophoretic mobility that SSCP detects are sequence dependent. The limitations faced in SSCP range from routine polyacrylamide gel electrophoresis (PAGE) problems to the problems of resolving mutant DNA bands. Both these problems can be solved by controlling PAGE conditions and by varying physical and environmental conditions such as pH, temperature, voltage, gel type and percentage, addition of additives or denaturants, and others. Despite much upgrading of the technology for mutation detection, SSCP remains the method of choice to analyze mutations and SNPs in order to understand genomic variations, both spontaneous and induced, and the genetic basis of diseases.

Liposomes have a variety of applications as model systems to study enclosed biological membranes, as delivery vehicles for a variety of drugs and as micro- and nano-reactors, amongst others. However, preparation of liposomes requires use of expensive raw material (synthetic lipids) from specialized commercial suppliers, and ability to make reproducible preparations remains a specialized art till date. In this work, we prepared liposomes using natural lipids extracted from the bacteria Escherichia coli (E. coli), which are extremely economical compared to the synthetic lipids. We demonstrate robust procedures for convenient and reproducible preparations of 200–300 nm diameter liposomes from bacterial cells. We also show a potential application of these bacterial liposomes in delivery of aqueous molecules to cancer cells. We show not only intracellular uptake, but also biodegradation of the liposomes inside cancer cells. Our economical liposomes promise to serve as excellent model systems for studies on encapsulation of molecules inside soft materials with desired efficiencies. Additionally, they certainly show a strong potential to be tools for research in diverse areas ranging from drug delivery applications to sub-micron reaction engineering for carrying out and understanding the mechanisms of chemical reactions in small enclosed volumes.

Single-strand conformation polymorphism (SSCP) for screening mutations/single-nucleotide polymorphisms (SNPs) is a simple, cost-effective technique, saving an expensive exercise of sequencing each and every PCR reaction product and assisting in choosing only the amplicons of interest with expected mutation. The principle of detection of small changes in DNA sequences is based on the changes in single-strand DNA conformations. The changes in electrophoretic mobility that SSCP detects are sequence-dependent. The limitations faced in SSCP range from the routine polyacrylamide gel electrophoresis (PAGE) problems to the problems of resolving mutant DNA bands. Both these problems could be solved by controlling PAGE conditions and by varying physical and environmental conditions such as pH, temperature, voltage, gel type and percentage, addition of additives or denaturants, and others. Despite much upgrading of the technology for mutation detection, SSCP continues to remain the method of choice to analyze mutations and SNPs in order to understand genomic variations, spontaneous and induced, and the genetic basis of diseases.