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Journal articles on the topic 'AI in recruitment'
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Wilfred, Dennis. "AI in Recruitment." NHRD Network Journal 11, no. 2 (April 2018): 15–18. http://dx.doi.org/10.1177/0974173920180204.
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Liu, Jia, Shih-Hsuan Chang, Yu-Ci Xu, Guo-An Wu, and Shih-Feng Chang. "Using AI to Enhance Recruitment Effect." Journal of Physics: Conference Series 1827, no. 1 (March 1, 2021): 012150. http://dx.doi.org/10.1088/1742-6596/1827/1/012150.
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Upadhyay, Ashwani Kumar, and Komal Khandelwal. "Applying artificial intelligence: implications for recruitment." Strategic HR Review 17, no. 5 (October 8, 2018): 255–58. http://dx.doi.org/10.1108/shr-07-2018-0051.
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Purpose This paper aims to review the applications of artificial intelligence (AI) in the hiring process and its practical implications. This paper highlights the strategic shift in recruitment industry caused due to the adoption of AI in the recruitment process. Design/methodology/approach This paper is prepared by independent academicians who have synthesized their views by a review of the latest reports, articles, research papers and other relevant literature. Findings This paper describes the impact of developments in the field of AI on the hiring process and the recruitment industry. The application of AI for managing the recruitment process is leading to efficiency as well as qualitative gains for both clients and candidates. Practical implications This paper offers strategic insights into automation of the recruitment process and presents practical ideas for implementation of AI in the recruitment industry. It also discusses the strategic implications of the usage of AI in the recruitment industry. Originality/value This article describes the role of technological advancements in AI and its application for creating value for the recruitment industry as well as the clients. It saves the valuable reading time of practitioners and researchers by highlighting the AI applications in the recruitment industry in a concise and simple format.
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Raveendra, P. V., Y. M. Satish, and Padmalini Singh. "Changing Landscape of Recruitment Industry: A Study on the Impact of Artificial Intelligence on Eliminating Hiring Bias from Recruitment and Selection Process." Journal of Computational and Theoretical Nanoscience 17, no. 9 (July 1, 2020): 4404–7. http://dx.doi.org/10.1166/jctn.2020.9086.
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An emerging trend of implementing Artificial Intelligence (AI) technologies can be seen in such domains that were solely dominated by humans. Today, AI is utilized extensively in HR department to assist and accelerate recruitment and selection process (Martin, F.R., 2019. Employers Are Now Using Artificial Intelligence To Stop Bias In Hiring. Retrieved September 22, 2019, from analyticsindiamag. com: https://analyticsindiamag.com/employersare-using-ai-stop-bias-hiring/.). This paper attempts to present the impact of AI on recruitment and selection process, incorporation of AI in eliminating unconscious biases during hiring. The study addresses the rising questions such as how AI has changed the landscape of recruitment industry, role of AI in recruitment and selection process, whether AI can help in eliminating the unconscious bias during recruitment and selection process. In order to uncover the understanding and figure out the potential solutions that AI brings to the HR process, an extensive review of literature has been carried out. It is concluded by analyzing the past contributions that AI offers potential solution to recruitment managers in optimizing the recruitment and selection process and is able to negate human biases prevalent during hiring. The future waits for augmented intelligence technologies offering better results taking over repetitive administrative jobs completely.
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Kazim, Emre, Adriano Soares Koshiyama, Airlie Hilliard, and Roseline Polle. "Systematizing Audit in Algorithmic Recruitment." Journal of Intelligence 9, no. 3 (September 17, 2021): 46. http://dx.doi.org/10.3390/jintelligence9030046.
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Business psychologists study and assess relevant individual differences, such as intelligence and personality, in the context of work. Such studies have informed the development of artificial intelligence systems (AI) designed to measure individual differences. This has been capitalized on by companies who have developed AI-driven recruitment solutions that include aggregation of appropriate candidates (Hiretual), interviewing through a chatbot (Paradox), video interview assessment (MyInterview), and CV-analysis (Textio), as well as estimation of psychometric characteristics through image-(Traitify) and game-based assessments (HireVue) and video interviews (Cammio). However, driven by concern that such high-impact technology must be used responsibly due to the potential for unfair hiring to result from the algorithms used by these tools, there is an active effort towards proving mechanisms of governance for such automation. In this article, we apply a systematic algorithm audit framework in the context of the ethically critical industry of algorithmic recruitment systems, exploring how audit assessments on AI-driven systems can be used to assure that such systems are being responsibly deployed in a fair and well-governed manner. We outline sources of risk for the use of algorithmic hiring tools, suggest the most appropriate opportunities for audits to take place, recommend ways to measure bias in algorithms, and discuss the transparency of algorithms.
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Kot, Sebastian, Hafezali Iqbal Hussain, Svitlana Bilan, Muhammad Haseeb, and Leonardus W. W. Mihardjo. "THE ROLE OF ARTIFICIAL INTELLIGENCE RECRUITMENT AND QUALITY TO EXPLAIN THE PHENOMENON OF EMPLOYER REPUTATION." Journal of Business Economics and Management 22, no. 4 (May 14, 2021): 867–83. http://dx.doi.org/10.3846/jbem.2021.14606.
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The prime contribution of current research entails the explanation of role of artificial intelligence based human resource management function to determine the employer reputation among pharmaceutical industry of Indonesia. The study intends to examine the empirically investigation the role and impact of artificial intelligence-based recruitment and artificial intelligence-based quality to determine the employer reputation with mediating role of artificial intelligence adoption. The study contributes to the body of knowledge and claims to be novel in explaining the AI based HR function to explain the phenomenon of employer reputation. The study examined the empirical investigation between AI based recruitment and AI based quality to influence the AI adoption that further predicts the phenomenon of employer reputation. The study was conducted on pharmaceutical industry of Indonesia and convenience sampling was used for data collected and Smart-PLS was utilized for data analysis. The study found that AI based recruitment and quality significantly influences the AI adoption and further it influences the employer reputation. The mediation role of artificial intelligence adoption is significant where it is found that artificial intelligence mediates the relationship between artificial intelligence recruitment and employer reputation, with similar significant mediation role between artificial intelligence quality and employer reputation.
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Wang, Xuhui, Md Jamirul Haque, Wenjing Li, Asad Hassan Butt, Hassan Ahmad, and Hamid Ali Shaikh. "AI-Enabled E-Recruitment Services Make Job Searching, Application Submission, and Employee Selection More Interactive." Information Resources Management Journal 34, no. 4 (October 2021): 48–68. http://dx.doi.org/10.4018/irmj.2021100103.
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Personnel recruitment and selection is changing rapidly with the adoption of artificial intelligence (AI) tools. This chapter looks at how job applicants perceive AI in recruitment. The results show that AI tools encourage a larger number of quality application submissions and for two reasons. First, AI entrains a perception of a novel approach to job searching. Second, AI is perceived to be able to interactively tailor the application experience to what the individual applicant expects and has to offer. These perceptions increase the likelihood the user will submit a job application and so improves the size and quality of the pool from which to recruit personnel.
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Albert, Edward Tristram. "AI in talent acquisition: a review of AI-applications used in recruitment and selection." Strategic HR Review 18, no. 5 (October 14, 2019): 215–21. http://dx.doi.org/10.1108/shr-04-2019-0024.
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Purpose The purpose of this study is to explore the current use of artificial intelligence (AI) in the recruitment and selection of candidates. More specifically, this research investigates the level, rate and potential adoption areas for AI-tools across the hiring process. Design/methodology/approach To fulfill that purpose, a two-step approach was adopted. First, the literature was extensively reviewed to identify potential AI-application areas supporting the recruitment and selection (R&S) process. Second, primary research was carried out in the form of semi-structured thematic interviews with different types of R&S specialists including HR managers, consultants and academics to evaluate how much of the AI-applications areas identified in the literature review are being used in practice. Findings This study presents a multitude of findings. First, it identifies 11 areas across the R&S Process where AI-applications can be applied. However, practitioners currently seem to rely mostly on three: chatbots, screening software and task automation tools. Second, most companies adopting these AI-tools tend to be larger, tech-focussed and/or innovative firms. Finally, despite the exponential rate of AI-adoption, companies have yet to reach an inflection point as they currently show reluctance to invest in that technology for R&S. Research limitations/implications Due to the qualitative and exploratory nature behind the research, this study displays a significant amount of subjectivity, and therefore, lacks generalisability. Despite this limitation, this study opens the door to many opportunities for academic research, both qualitative and quantitative. Originality/value This paper addresses the huge research gap surrounding AI in R&S, pertaining specifically to the scarcity and poor quality of the current academic literature. Furthermore, this research provides a comprehensive overview of the state of AI in R&S, which will be helpful for academics and practitioners looking to rapidly gain a holistic understanding of AI in R&S.
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Willis, Kate, and Chris Woods. "Managing invasive Styela clava populations: inhibiting larval recruitment with medetomidine." Aquatic Invasions 6, no. 4 (December 2011): 511–14. http://dx.doi.org/10.3391/ai.2011.6.4.16.
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FraiJ, JihaD, and Várallyai László. "literature Review: Artificial Intelligence Impact on the Recruitment ProcessA LITERATURE REVIEW: ARTIFICIAL INTELLIGENCE IMPACT ON THE RECRUITMENT PROCESS." International Journal of Engineering and Management Sciences 6, no. 1 (May 13, 2021): 108–19. http://dx.doi.org/10.21791/ijems.2021.1.10.
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This paper aim is to review the implementation of artificial intelligence (AI) in the Human Resources Management (HRM) recruitment processes. A systematic review was adopted in which academic papers, magazine articles as well as high rated websites with related fields were checked. The findings of this study should contribute to the general understanding of the impact of AI on the HRM recruitment process. It was impossible to track and cover all topics related to the subject. However, the research methodology used seems to be reasonable and acceptable as it covers a good number of articles which are related to the core subject area. The results and findings were almost clear that using AI is advantages in the area of recruitment as technology can serve best in this area. Moreover, time, efforts, and boring daily tasks are transformed to be computerized which makes a good space for humans to focus on more important subjects related to boosting performance and development. Acquiring automation and cognitive insights as well as cognitive engagement in the recruitment process would make it possible for systems to work similarly to the human brain in terms of data analysis and the ability to build an effective systematic engagement to process the data in an unbiased, efficient and fast way.
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Miasato, Alessandra, and Fabiana Reis Silva. "Artificial Intelligence as an Instrument of Discrimination in Workforce Recruitment." Acta Universitatis Sapientiae Legal Studies 8, no. 2 (January 19, 2020): 191–212. http://dx.doi.org/10.47745/ausleg.2019.8.2.04.
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The purpose of this article is to reflect on the use of artificial intelligence in the process of hiring and on how biased algorithms can pose a great risk of discrimination to particular groups if artificial intelligence is not used properly with an emphasis on labour relations. Based on current research, we present the wide range of uses how AI technology can be deployed in the search for employees who satisfy the needs of employers on the labour market. The various manifestations of bias in AI implementations utilized in the field of human resources as well as their causes are presented. We conclude that in order to avoid discrimination due to either wilful programmer behaviour or implicit in the data used to train AI agents, the observance of legal and ethical norms, as outlined in tentative projects underway worldwide, is necessary.
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Lechelt, Joseph, and Przemyslaw Bajer. "Elucidating the mechanism underlying the productivity-recruitment hypothesis in the invasive common carp." Aquatic Invasions 11, no. 4 (2016): 469–82. http://dx.doi.org/10.3391/ai.2016.11.4.11.
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Morris, James. "Impact of the invasive colonial tunicate Didemnum vexillum on the recruitment of the bay scallop (Argopecten irradians irradians) and implications for recruitment of the sea scallop (Placopecten magellanicus) on Georges Bank." Aquatic Invasions 4, no. 1 (2009): 207–11. http://dx.doi.org/10.3391/ai.2009.4.1.21.
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van Esch, Patrick, J. Stewart Black, and Joseph Ferolie. "Marketing AI recruitment: The next phase in job application and selection." Computers in Human Behavior 90 (January 2019): 215–22. http://dx.doi.org/10.1016/j.chb.2018.09.009.
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Kumar, Ravindra. "Biases in Artificial Intelligence Applications Affecting Human Life: A Review." International Journal of Recent Technology and Engineering 10, no. 1 (May 30, 2021): 54–55. http://dx.doi.org/10.35940/ijrte.a5719.0510121.
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The introduction of Artificial Intelligence has improved operations in almost every sector, industry, and part of human life. The use of AI has been vital in the department of justice, recruitment by organizations, facial recognition by police, and school admissions. The aim of introducing AI algorithms in various fields was to reduce human bias in decision-making. Despite the progress, there are ethical concerns that the AI algorithms also exhibit biases. The main reason behind the claim is because human developers are in charge of training data used by the algorithms. There are areas where the issue of biases affects human life directly and can do damages to a person, physically or emotionally. Some examples are college admissions, recruitment, administration of justice at the courts, public benefits systems, police, public safety, and healthcare. There are high chances that the development process introduced biases in artificial intelligence algorithms, knowingly or unknowingly, during any area mentioned above. The paper provides background knowledge on AI bias and possible solutions to solve the problem
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Milardi, Marco, Mattia Lanzoni, Mikko Kiljunen, Jyrki Torniainen, and Giuseppe Castaldelli. "Natural recruitment contributes to high densities of grass carp Ctenopharyngodon idella (Valenciennes, 1844) in Western Europe." Aquatic Invasions 10, no. 4 (2015): 439–48. http://dx.doi.org/10.3391/ai.2015.10.4.07.
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Ramsay, Aaron. "Recruitment patterns and population development of the invasive ascidian Ciona intestinalis in Prince Edward Island, Canada." Aquatic Invasions 4, no. 1 (2009): 169–76. http://dx.doi.org/10.3391/ai.2009.4.1.17.
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Rąb-Kettler, Karolina, and Bada Lehnervp. "Recruitment in the Times of Machine Learning." Management Systems in Production Engineering 27, no. 2 (June 1, 2019): 105–9. http://dx.doi.org/10.1515/mspe-2019-0018.
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Abstract How do socio-economic change and technological revolution change the way we manage people. How does the development of AI (Artificial Intelligence) affect the process of talent acquisition? The author will present the concepts of technological unemployment, creative class, millennials (generation Y), humanistic management, sustainable development, CSR and new managerial models in light of current social changes. Humanistic management as a broader concept, and humanistic talent attraction as its direct implication, will be presented as an answer to the current technological development. The author presents a narrower topic of human resources management but sees potential in the topic to develop a discussion on future of work in a broader sense.
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Sinclair, Ka‘imi, Cara Carty, Kelly Gonzales, Cassandra Nikolaus, Lucas Gillespie, and Dedra Buchwald. "Strong Men, Strong Communities: Design of a Randomized Controlled Trial of a Diabetes Prevention Intervention for American Indian and Alaska Native Men." American Journal of Men's Health 14, no. 4 (July 2020): 155798832094545. http://dx.doi.org/10.1177/1557988320945457.
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Type 2 diabetes is a serious global epidemic that disproportionately affects disadvantaged populations. American Indians and Alaska Natives (AIs/ANs) have the highest rates of diabetes in the nation with a prevalence of 14.7% in 2018, more than twice that of non-Hispanic Whites. AI/AN men have the highest prevalence of diagnosed type 2 diabetes (14.5%) compared to non-Hispanic Black (11.4%), non-Hispanic Asian (10.0%), and non-Hispanic White (8.6%) men. Several landmark clinical trials have shown that lifestyle interventions can effectively prevent or delay the onset of diabetes among those at risk, including in AIs/ANs. Despite positive outcomes for AIs/ANs in these studies, very few were men. To date, there have been no concerted efforts to recruit and retain AI/AN men in interventions that promote weight loss and healthy lifestyles to prevent diabetes, and they remain underrepresented in these types of studies. This article describes the design and methods of the first randomized controlled trial of a diabetes prevention program with a study sample comprised entirely of AI/AN men. Research to date has demonstrated suboptimal patterns of recruitment and retention of AI/AN men, resulting in their virtual absence in health and intervention research. Effective methods to recruit and retain AI/AN men, and potential benefit gained from participation in diabetes prevention research, are unknown for this population who experience a high prevalence of type 2 diabetes. The study design presented in this article offers promising insights to help remedy these important shortcomings in the science of recruitment and retention of AI/AN men in research.
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McNaught, Douglas, and Wendy Norden. "Generalized regional spatial patterns of larval recruitment of invasive ascidians, mussels, and other organisms along the coast of Maine." Aquatic Invasions 6, no. 4 (December 2011): 519–23. http://dx.doi.org/10.3391/ai.2011.6.4.18.
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Iwasaki, Keiji, and Hanako Yamamoto. "Recruitment and population structure of the non-indigenous brackish-water mytilid Xenostrobus securis (Lamark, 1819) in the Kino River, Japan." Aquatic Invasions 9, no. 4 (November 2014): 479–87. http://dx.doi.org/10.3391/ai.2014.9.4.06.
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Montes, Agar, Elsa Vázquez, Laura Peteiro, and Celia Olabarria. "Dynamics and processes influencing recruitment of the invasive mussel Xenostrobus securis and the coexisting indigenous Mytilus galloprovincialis in north-western Spain." Aquatic Invasions 16, no. 3 (2021): 391–414. http://dx.doi.org/10.3391/ai.2021.16.3.02.
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Arcidiacono, Davide. "@@@Lavoratori Cercasi: un'analisi della domanda di lavoro on line ai tempi dell'e-recruitment." SOCIOLOGIA DEL LAVORO, no. 149 (February 2018): 134–55. http://dx.doi.org/10.3280/sl2018-149009.
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Edison, Bianca R., Melissa A. Christino, and Katherine H. Rizzone. "Athletic Identity in Youth Athletes: A Systematic Review of the Literature." International Journal of Environmental Research and Public Health 18, no. 14 (July 8, 2021): 7331. http://dx.doi.org/10.3390/ijerph18147331.
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Athletic identity (AI), the degree of personal connection to sport, is well-described in adult research; however, this social trait has been less studied in younger age groups. This systematic review describes epidemiological characteristics of AI in youth athletes. PubMed, Embase and PsycInfo were searched to identify AI studies involving quantitative athlete identity outcomes and cohorts 22 years and younger. The search strategy was developed for each database using the Boolean method. PRISMA guidelines and the Appraisal Tool for Cross-Sectional Studies (AXIS) were utilized. Ten out of ninety-one studies met inclusion criteria. AI scores differed by race/ethnicity. Two studies found increased AI during adolescence compared to later in ones’ training. Mental health-focused studies revealed higher AI levels protect against burnout, but in injured athletes, increased depression risk. Transitioning to a higher level of play during adolescence can correlate with stronger senses of AI. Further research should explore the concept of athletic identity saliency as one moves through an athletic career or training program and how thoughts of perceived success, professional progression, recruitment prospect or injury affect levels of athletic identity.
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Altvater, Luciana, Maria Haddad, and Ricardo Coutinho. "Temporal patterns of recruitment and substrate use by the nonindigenous octocoral Stragulum bicolor van Ofwegen and Haddad, 2011 (Alcyonacea) in the Southern Brazilian Coast." Aquatic Invasions 14, no. 2 (2019): 206–20. http://dx.doi.org/10.3391/ai.2019.14.2.04.
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Wahl, S. M., J. B. Allen, G. L. Costa, H. L. Wong, and J. R. Dasch. "Reversal of acute and chronic synovial inflammation by anti-transforming growth factor beta." Journal of Experimental Medicine 177, no. 1 (January 1, 1993): 225–30. http://dx.doi.org/10.1084/jem.177.1.225.
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Transforming growth factor beta (TGF-beta) induces leukocyte recruitment and activation, events central to an inflammatory response. In this study, we demonstrate that antagonism of TGF-beta with a neutralizing antibody not only blocks inflammatory cell accumulation, but also tissue pathology in an experimental model of chronic erosive polyarthritis. Intraarticular injection of monoclonal antibody 1D11.16, which inhibits both TGF-beta 1 and TGF-beta 2 bioactivity, into animals receiving an arthropathic dose of bacterial cell walls significantly inhibits arthritis. Inhibition was observed with a single injection of 50 micrograms antibody, and a 1-mg injection blocked acute inflammation > 75% compared with the contralateral joints injected with an irrelevant isotype control antibody (MOPC21) as quantitated by an articular index (AI = 0.93 +/- 0.23 for 1D11.16, and AI = 4.0 +/- 0 on day 4; p < 0.001). Moreover, suppression of the acute arthritis achieved with a single injection of antibody was sustained into the chronic, destructive phase of the disease (on day 18, AI = 0.93 +/- 0.07 vs. AI = 2.6 +/- 0.5; p < 0.01). The decreased inflammatory index associated with anti-TGF-beta treatment was consistent with histopathologic and radiologic evidence of a therapeutic response. These data implicate TGF-beta as a profound agonist not only in the early events responsible for synovial inflammation, but also in the chronicity of streptococcal cell wall fragment-induced inflammation culminating in destructive pathology. Interrupting the cycle of leukocyte recruitment and activation with TGF-beta antagonists may provide a mechanism for resolution of chronic destructive lesions.
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Valentine, Page. "Larval recruitment of the invasive colonial ascidian Didemnum vexillum, seasonal water temperatures in New England coastal and offshore waters, and implications for spread of the species." Aquatic Invasions 4, no. 1 (2009): 153–68. http://dx.doi.org/10.3391/ai.2009.4.1.16.
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L. Dickerson, Daniel, Kamilla L. Venner, and Bonnie Duran. "Clinical trials and American Indians/Alaska Natives with substance use disorders: identifying potential strategies for a new cultural-based intervention." Journal of Public Mental Health 13, no. 4 (December 9, 2014): 175–78. http://dx.doi.org/10.1108/jpmh-01-2014-0003.
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Purpose – The purpose of this paper is to address a significant public mental health disparity affecting American Indians/Alaska Natives (AI/ANs): the shortage of clinical trials research analyzing the benefits of AI/AN traditional-based treatments, e.g. drumming. Design/methodology/approach – A total of four focus groups were conducted among outpatient and inpatient AI/AN substance abuse patients and providers serving AI/ANs. The purpose of these focus groups was to obtain insights relating to the recent challenges of conducting a clinical trial within the outpatient treatment setting seeking to analyze the benefits of a new substance abuse treatment intervention utilizing drumming for AI/ANs [Drum-assisted Recovery Therapy for Native Americans (DARTNA)] and to obtain recommendations to successfully conduct a similar study within an inpatient treatment setting. Findings – The most prevalent barriers to conducting a clinical trial within an outpatient setting were transportation and child care issues. Recommendations were obtained with regard to optimizing recruitment and retention for a future study within an inpatient setting. Originality/value – This research offers the field rare information that helps toward identifying strategies to successfully conduct clinical trials investigating the benefits of culturally-appropriate treatments for AI/ANs with substance use disorders.
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Altemeyer, Boris. "Making the business case for AI in HR: two case studies." Strategic HR Review 18, no. 2 (April 8, 2019): 66–70. http://dx.doi.org/10.1108/shr-12-2018-0101.
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Purpose This paper aims to analyse two large-scale business case studies for the benefits of using AI, computer science and machine learning to assess, recruit and retain staff. Design/methodology/approach The authors interrogate two large-scale case studies, including metrics on the success of AI in relation to user experience, compatibility, psychometric benchmarking. Findings The authors conclude that AI removes bias from assessment, recruitment and training processes and can save businesses significant time and resources as well as improve the cultural fit and diversity of their recruits. There is a balance to be struck, though, as the systems used are only as good as the science supporting the selection – and humans must ultimately retain the role of decision makers. Originality/value These business case studies are examined in detail for the first time in this paper.
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Futia, Giuseppe, and Antonio Vetrò. "On the Integration of Knowledge Graphs into Deep Learning Models for a More Comprehensible AI—Three Challenges for Future Research." Information 11, no. 2 (February 22, 2020): 122. http://dx.doi.org/10.3390/info11020122.
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Deep learning models contributed to reaching unprecedented results in prediction and classification tasks of Artificial Intelligence (AI) systems. However, alongside this notable progress, they do not provide human-understandable insights on how a specific result was achieved. In contexts where the impact of AI on human life is relevant (e.g., recruitment tools, medical diagnoses, etc.), explainability is not only a desirable property, but it is -or, in some cases, it will be soon-a legal requirement. Most of the available approaches to implement eXplainable Artificial Intelligence (XAI) focus on technical solutions usable only by experts able to manipulate the recursive mathematical functions in deep learning algorithms. A complementary approach is represented by symbolic AI, where symbols are elements of a lingua franca between humans and deep learning. In this context, Knowledge Graphs (KGs) and their underlying semantic technologies are the modern implementation of symbolic AI—while being less flexible and robust to noise compared to deep learning models, KGs are natively developed to be explainable. In this paper, we review the main XAI approaches existing in the literature, underlying their strengths and limitations, and we propose neural-symbolic integration as a cornerstone to design an AI which is closer to non-insiders comprehension. Within such a general direction, we identify three specific challenges for future research—knowledge matching, cross-disciplinary explanations and interactive explanations.
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Richart, Adele L., Medini Reddy, Mina Khalaji, Alaina L. Natoli, Sarah E. Heywood, Andrew L. Siebel, Graeme L. Lancaster, et al. "Apo AI Nanoparticles Delivered Post Myocardial Infarction Moderate Inflammation." Circulation Research 127, no. 11 (November 6, 2020): 1422–36. http://dx.doi.org/10.1161/circresaha.120.316848.
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Rationale: Decades of research have examined immune-modulatory strategies to protect the heart after an acute myocardial infarction and prevent progression to heart failure but have failed to translate to clinical benefit. Objective: To determine anti-inflammatory actions of n-apo AI (Apo AI nanoparticles) that contribute to cardiac tissue recovery after myocardial infarction. Methods and Results: Using a preclinical mouse model of myocardial infarction, we demonstrate that a single intravenous bolus of n-apo AI (CSL111, 80 mg/kg) delivered immediately after reperfusion reduced the systemic and cardiac inflammatory response. N-apo AI treatment lowered the number of circulating leukocytes by 30±7% and their recruitment into the ischemic heart by 25±10% (all P <5.0×10 −2 ). This was associated with a reduction in plasma levels of the clinical biomarker of cardiac injury, cardiac troponin-I, by 52±17% ( P =1.01×10 −2 ). N-apo AI reduced the cardiac expression of chemokines that attract neutrophils and monocytes by 60% to 80% and lowered surface expression of integrin CD11b on monocytes by 20±5% (all P <5.0×10 −2 ). Fluorescently labeled n-apo AI entered the infarct and peri-infarct regions and colocalized with cardiomyocytes undergoing apoptosis and with leukocytes. We further demonstrate that n-apo AI binds to neutrophils and monocytes, with preferential binding to the proinflammatory monocyte subtype and partially via SR-BI (scavenger receptor BI). In patients with type 2 diabetes, we also observed that intravenous infusion of the same n-apo AI (CSL111, 80 mg/kg) similarly reduced the level of circulating leukocytes by 12±5% (all P <5.0×10 −2 ). Conclusions: A single intravenous bolus of n-apo AI delivered immediately post–myocardial infarction reduced the systemic and cardiac inflammatory response through direct actions on both the ischemic myocardium and leukocytes. These data highlight the anti-inflammatory effects of n-apo AI and provide preclinical support for investigation of its use for management of acute coronary syndromes in the setting of primary percutaneous coronary interventions.
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Kamakura, Tetzushi, and Shinsaku Matsumoto. "An exploratory study for the substitutability between human and AI in the classification of recruitment information." Proceedings of the Annual Convention of the Japanese Psychological Association 81 (September 20, 2017): 1A—096–1A—096. http://dx.doi.org/10.4992/pacjpa.81.0_1a-096.
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Martselyak, Oleg Volodymyrovych, Vladyslav Volodymyrovych Karelin, Ihor Mykhailovych Koropatnik, and Rostislav Andriyovych Kalyuzhnyi. "Object and subject of staffing of the national police of Ukraine at the regional level." Revista Amazonia Investiga 9, no. 26 (February 21, 2020): 174–80. http://dx.doi.org/10.34069/ai/2020.26.02.19.
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The purpose of the article is to investigate the specifics of the object and subject of staffing of the National Police of Ukraine at the regional level based on scientific points of view and regulatory legal acts. Writing the article, the following methods were used: dialectical, logical-semantic, and logical-legal methods. The relevance of the article is in the need to study the essence of the object and subject of staffing of the National Police at the regional level. The subject of the study is the legislation of Ukraine regarding the requirements for a police officer as an object of the police staffing, including at the regional level. The features of object and subject of the police staffing at the regional level are identified in the paper, as well as the restrictions that are currently placed upon the police officer on recruitment. The guarantees, determined by the law and which the police officer can use during the performance of official duties, are analyzed. The legislative requirements for candidates for senior posts are examined. The powers of the head of the territorial police authority are administrative and regulatory in nature, as evidenced by their rights and obligations. The authors concluded that the service in the National Police at the regional level is specific, requires professionalism and compliance with the law, which underlines the urgent need to strengthen staffing in this area. In addition, the author’s opinion of the concepts of the object and subject of staffing of the National Police of Ukraine at the regional level is provided due to the results of the study.
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Nease, Donald E., Matthew J. Simpson, Linda Zittleman, Jodi Summers Holtrop, Tristen L. Hall, Mary Fisher, Maret Felzien, and John M. Westfall. "Making the Random the Usual: Appreciative Inquiry/Boot Camp Translation—Developing Community-Oriented Evidence That Matters." Journal of Primary Care & Community Health 11 (January 2020): 215013272090417. http://dx.doi.org/10.1177/2150132720904176.
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Background: The evidence underlying clinical guidelines arising from typical scientific inquiry may not always match the needs and concerns of local communities. Our High Plains Research Network Community Advisory Council (HPRN CAC) identified a need for evidence regarding how to assist members of their community suffering from mental health issues to recognize their need for help and then obtain access to mental health care. The lack of evidence led our academic team to pursue linking Appreciative Inquiry with Boot Camp Translation (AI/BCT). This article describes the development and testing of this linked method. Method: We worked with the HPRN CAC and other communities affiliated with the State Networks of Colorado Ambulatory Practices and Partners (SNOCAP) practice-based research networks to identify 5 topics for testing of AI/BCT. For each topic, we developed AI interview recruitment strategies and guides with our community partners, conducted interviews, and analyzed the interview data. Resulting themes for each topic were then utilized by 5 groups with the BCT method to develop community relevant messages and materials to communicate the evidence generated in each AI set of interviews. At each stage for each topic, notes on adaptations, barriers, and successes were recorded by the project team. Results: Each topic successfully led to generation of community specific evidence, messages, and materials for dissemination using the AI/BCT method. Beyond this, 5 important lessons emerged regarding the AI/BCT method: Researchers must (1) first ensure whether the topic is a good fit for AI, (2) maintain a focus on “what works” throughout all stages, (3) recruit one or more experienced qualitative analysts, (4) ensure adequate time and resources for the extensive AI/BCT process, and (5) present AI findings to BCT participants in the context of existing evidence and the local community and allow time for community partners to ask questions and request additional data analyses to be done. Conclusions: AI/BCT represents an effective way of responding to a community’s need for evidence around a specific topic where standard evidence and/or guidelines do not exist. AI/BCT is a method for turning the “random” successes of individuals into “usual” practice at a community level.
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Claw, Katrina G., Julie A. Beans, Seung-Been Lee, Jaedon P. Avey, Patricia A. Stapleton, Steven E. Scherer, Ahmed El-Boraie, et al. "Pharmacogenomics of Nicotine Metabolism: Novel CYP2A6 and CYP2B6 Genetic Variation Patterns in Alaska Native and American Indian Populations." Nicotine & Tobacco Research 22, no. 6 (June 25, 2019): 910–18. http://dx.doi.org/10.1093/ntr/ntz105.
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Abstract Introduction Alaska Native and American Indian (AN/AI) populations have higher tobacco use prevalence than other ethnic/racial groups. Pharmacogenetic testing to tailor tobacco cessation treatment may improve cessation rates. This study characterized polymorphic variations among AN/AI people in genes associated with metabolism of nicotine and drugs used for tobacco cessation. Methods Recruitment of AN/AI individuals represented six subgroups, five geographic subgroups throughout Alaska and a subgroup comprised of AIs from the lower 48 states living in Alaska. We sequenced the CYP2A6 and CYP2B6 genes to identify known and novel gain, reduced, and loss-of-function alleles, including structural variation (eg, gene deletions, duplications, and hybridizations). Results Variant allele frequencies differed substantially between AN/AI subgroups. The gene deletion CYP2A6*4 and reduced function CYP2A6*9 alleles were found at high frequency in Northern/Western subgroups and in Lower 48/Interior subgroups, respectively. The reduced function CYP2B6*6 allele was observed in all subgroups and a novel, predicted reduced function CYP2B6 variant was found at relatively high frequency in the Southeastern subgroup. Conclusions Diverse CYP2A6 and CYP2B6 variation among the subgroups highlight the need for comprehensive pharmacogenetic testing to guide tobacco cessation therapy for AN/AI populations. Implications Nicotine metabolism is largely determined by CYP2A6 genotype, and variation in CYP2A6 activity has altered the treatment success in other populations. These findings suggest pharmacogenetic-guided smoking cessation drug treatment could provide benefit to this unique population seeking tobacco cessation therapy.
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Niehueser, Wilfried, and George Boak. "Introducing artificial intelligence into a human resources function." Industrial and Commercial Training 52, no. 2 (June 1, 2020): 121–30. http://dx.doi.org/10.1108/ict-10-2019-0097.
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Purpose The purpose of this paper is to examine the attitudes of employees in a company dedicated to strategic recruitment towards the introduction of artificial intelligence (AI) into their work processes and to consider the implications for training and development. Design/methodology/approach Semi-structured interviews were carried out with seven employees who were using the new technology. Survey data was gathered from 109 employees who had not, at the time of the research, used the new technology. Findings The introduction of AI considerably improved the speed and efficiency of the work processes. The research found that those employees who had used the new technology were positive about its effects, indicating that it was easy to use, robust and highly productive. A proportion of employees who had not, at the time of the research, used the new system, were less sure that it would improve their ability to do their job. Implications for introducing such a system and for employee training are discussed. Research limitations/implications This is a relatively small sample in one organisation; further research should be undertaken to assess whether these findings apply more widely. Practical implications If these attitudes are found elsewhere, there are a number of simple, practical suggestions for how to introduce AI into similar work processes. Originality/value The use of AI is a topic attracting increasing interest and speculation, but there is as yet little empirical research on factors affecting its introduction and use.
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Ni, Li, Chun-Song Yang, Daniel Gioeli, Henry Frierson, David O. Toft, and Bryce M. Paschal. "FKBP51 Promotes Assembly of the Hsp90 Chaperone Complex and Regulates Androgen Receptor Signaling in Prostate Cancer Cells." Molecular and Cellular Biology 30, no. 5 (January 4, 2010): 1243–53. http://dx.doi.org/10.1128/mcb.01891-08.
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ABSTRACT Prostate cancer progression to the androgen-independent (AI) state involves acquisition of pathways that allow tumor growth under low-androgen conditions. We hypothesized that expression of molecular chaperones that modulate androgen binding to AR might be altered in prostate cancer and contribute to progression to the AI state. Here, we report that the Hsp90 cochaperone FKBP51 is upregulated in LAPC-4 AI tumors grown in castrated mice and describe a molecular mechanism by which FKBP51 regulates AR activity. Using recombinant proteins, we show that FKBP51 stimulates recruitment of the cochaperone p23 to the ATP-bound form of Hsp90, forming an FKBP51-Hsp90-p23 superchaperone complex. In cells, FKBP51 expression promotes superchaperone complex association with AR and increases the number of AR molecules that undergo androgen binding. FKBP51 stimulates androgen-dependent transcription and cell growth, and FKBP51 is part of a positive feedback loop that is regulated by AR and androgen. Finally, depleting FKBP51 levels by short hairpin RNA reduces the transcript levels of genes regulated by AR and androgen. Because the superchaperone complex plays a critical role in determining the ligand-binding competence and transcription function of AR, it provides an attractive target for inhibiting AR activity in prostate cancer cells.
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White, Scott N., and Shanthanu Krishna Kumar. "Potential Role of Sequential Glufosinate and Foramsulfuron Applications for Management of Fescues (Festucaspp.) in Wild Blueberry." Weed Technology 31, no. 1 (January 2017): 100–110. http://dx.doi.org/10.1614/wt-d-16-00086.1.
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Sheep and hair fescue are perennial, tuft forming grasses that spread by seed and form dense sods in wild blueberry fields. These sods compete with the crop for resources and hinder harvest. Field and greenhouse studies were conducted in 2015 to evaluate 1) the effect of sequential glufosinate and foramsulfuron applications on suppression of fescues in the greenhouse and field, and 2) efficacy of glufosinate and foramsulfuron on fescue seedlings when applied at 2, 4, 6, and 8 wk after seedling emergence in the greenhouse. Glufosinate applications at 750 and 1,005 g ai ha−1followed by foramsulfuron application at 35 g ai ha−1reduced fescue leaf number and biomass relative to foramsulfuron application alone in the greenhouse. In the field study, fescue flowering tuft density, tuft inflorescence height, seed production, and seed viability were reduced by foramsulfuron alone, but there was a trend towards lower seed production and tuft height when fescues were treated with glufosinate at 1,005 g ha−1followed by foramsulfuron. Foramsulfuron caused low seedling mortality at all application timings evaluated, but glufosinate caused >90% mortality in seedlings when applied at 2, 4, 6, or 8 wk after seedling emergence. Our results suggest that sequential applications of these herbicides are less effective under field conditions relative to results obtained in the greenhouse, though burndown glufosinate applications may have a role in reducing fescue seedling recruitment. Additional research should be conducted to determine the effect of early spring and autumn glufosinate applications on fescue seedling recruitment and suppression of established fescue tufts with subsequent foramsulfuron applications.
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Shinners, Lucy, Christina Aggar, Sandra Grace, and Stuart Smith. "Exploring healthcare professionals’ perceptions of artificial intelligence: Validating a questionnaire using the e-Delphi method." DIGITAL HEALTH 7 (January 2021): 205520762110034. http://dx.doi.org/10.1177/20552076211003433.
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Objective The aim of this study was to draw upon the collective knowledge of experts in the fields of health and technology to develop a questionnaire that measured healthcare professionals’ perceptions of Artificial Intelligence (AI). Methods The panel for this study were carefully selected participants who demonstrated an interest and/or involvement in AI from the fields of health or information technology. Recruitment was accomplished via email which invited the panel member to participate and included study and consent information. Data were collected from three rounds in the form of an online survey, an online group meeting and email communication. A 75% median threshold was used to define consensus. Results Between January and March 2019, five healthcare professionals and three IT experts participated in three rounds of study to reach consensus on the structure and content of the questionnaire. In Round 1 panel members identified issues about general understanding of AI and achieved consensus on nine draft questionnaire items. In Round 2 the panel achieved consensus on demographic questions and comprehensive group discussion resulted in the development of two further questionnaire items for inclusion. In a final e-Delphi round, a draft of the final questionnaire was distributed via email to the panel members for comment. No further amendments were put forward and 100% consensus was achieved. Conclusion A modified e-Delphi method was used to validate and develop a questionnaire to explore healthcare professionals’ perceptions of AI. The e-Delphi method was successful in achieving consensus from an interdisciplinary panel of experts from health and IT. Further research is recommended to test the reliability of this questionnaire.
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Gikopoulos, John. "Alongside, not against: balancing man with machine in the HR function." Strategic HR Review 18, no. 2 (April 8, 2019): 56–61. http://dx.doi.org/10.1108/shr-12-2018-0103.
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Purpose In a world of mass technological advancement in our daily lives and in business, the HR function is facing an uphill battle. How can HR professionals ensure they are digitally transforming at the right pace for their business without losing the all-important human touch? Design/methodology/approach This paper outlines the thinking behind integrating artificial intelligence (AI) and automation technologies into HR, and it explores in depth each of the key ways in which we are beginning to see these technologies change HR as we know it. From operations to recruitment and interviewing, to on-boarding employees and maintaining performance, the opportunities are numerous – and they are right on the horizon. Findings AI and automation are already being integrated into HR in many organisations around the world. However, we can in the near future expect to see technology not only automating back-office functions, but increasingly taking on the more “human” elements of HR roles. There is a fine balance between man and machine, and while these technologies will increase efficiency, decrease bias and improve the value of HR in businesses, the human touch will always be the key to success. Originality/value This paper assesses not only how technology is impacting HR but also the interplay between man and machine, and it offers insights into how HR professionals can balance the need for digital transformation with the core human element of human resources. As such, it ties the human and the technology together inextricably, concluding that AI and humans work better together.
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Evseeva, Svetlana, Oksana Evseeva, Andrei Burmistrov, and Maria Siniavina. "Application of artificial intelligence in human resource management in the agricultural sector." E3S Web of Conferences 258 (2021): 01010. http://dx.doi.org/10.1051/e3sconf/202125801010.
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Digitalization of all spheres of life has become one of the main directions of development in many countries of the world. Artificial intelligence technology has a wide range of applications in various fields. The study aims to identify artificial intelligence usage in human resource management by analysing functions. Qualitative methods are used in the study. As the result, artificial intelligence tools were analysed by matching them on human resource management functions: recruitment, onboarding, assessment, training and development, talent management, salary system, culture and engagement management, management and leadership, misconduct and compliance. Artificial intelligence technologies provide many opportunities for modern companies. Study in detail companies using AI in HR functions and possible changes in the HR manager profession and role of HR in business are future research directions.
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DiTomaso, Joseph M., Jennifer J. Drewitz, and Guy B. Kyser. "Jubatagrass (Cortaderia jubata) Control Using Chemical and Mechanical Methods." Invasive Plant Science and Management 1, no. 1 (January 2008): 82–90. http://dx.doi.org/10.1614/ipsm-07-028.
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AbstractJubatagrass is one of the most invasive plants along the California and southern Oregon coast. It establishes dense populations that can severely impact native plant diversity and conifer seedling recruitment following forestry operations. This goal of this study was to evaluate the effectiveness of both manual removal and several herbicide control options and application techniques. In addition, a cost analysis was also conducted for the most successful herbicide control methods. Results demonstrate that mechanical removal through digging is effective, although labor intensive. Among the herbicides tested, glyphosate applied as a high-volume (spray-to-wet) application (0.6% ae) in early summer, low-volume application (2.4% ae) in early summer or fall, and ropewick technique in early summer or fall (> 9.9% ae) all provided ≥ 88% jubatagrass control, but the low-volume treatments were the most cost effective. Although the graminicide sethoxydim at the highest rate (0.36% ai) did not give effective control, fluazifop applied in the fall in a low-volume treatment (0.98% ai) gave 87% control of jubatagrass. Imazapyr gave some level of control but does not appear to provide an economical option for jubatagrass management. Results of this study demonstrate that in addition to the more conventional methods of mechanical removal and spray-to-wet glyphosate (0.6% ae), control of jubatagrass can also be equally or more effective with low-volume and ropewick applications of glyphosate.
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Bowen, TJ, Laura Stephens, Mark Vance, Yancui Huang, Deborah Fridman, and Chadi Nabhan. "Novel artificial intelligence (AI)-based technology to improve oncology clinical trial fulfillment." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): 2052. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.2052.
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2052 Background: Less than 5% of US adult cancer pts are enrolled on clinical trials. Challenges in clinical trial fulfillment limit available treatment options, slow enrollment and ultimately delay new therapies from reaching market. Pt screening requires multiple clinical team members to find pts that meet strict inclusion/exclusion criteria. We evaluated the impact of new technology, Deep Lens VIPER, in identifying more qualified pts for clinical studies, and reduction of staff burden. Methods: We implemented Deep Lens VIPER at Hoag Hospital (Newport Beach, California), accessing the electronic medical records and pathology systems (EMR/LIS) to effectively identify pts who are candidates for 20 ongoing recruiting clinical studies. VIPER was fed pt data from 5,706 surgical pathology pts over a 4-month period (October 1, 2019 - January 31, 2020). Proprietary AI identification and matching technology was configured to align cancer pts with those 20 clinical studies, each with unique study criteria. Following an initial machine-assisted triage step, a research coordinator was alerted when pts who met protocol criteria were ready for final approval steps. Results were analyzed and a qualitative assessment of usability was also performed. Results: VIPER was able to triage all 5,706 surgical pathology cases (100%), identifying 1,045 pts (18.3%) with malignant neoplasms that would qualify for further analysis for clinical trials enrollment. Further triage based on inclusion and exclusion criteria led to the identification of 150 previously unidentified pts for 16 of the 20 studies. The 16 different studies for which potential pts were identified, included 11 tumor types, 12 biomarkers and 3 basket studies. Working with the VIPER system, 1 novice care team member performed initial identification of all 150 previously unidentified pts. The VIPER system increased monthly candidate pt catchment for 16 of the 20 studies under investigation, which is approximately 600 patients annually added for final triage for studies being conducted. Conclusions: We demonstrate the use of an AI-based platform to identify pts for clinical trial enrollment who would be missed using traditional recruiting methods. One staff member effectively triaged participants from 20 different studies with unique inclusion/exclusion criteria. These studies were previously managed by 6 different care team members with limited time for recruitment. Scaling this platform to additional institutions and more studies is ongoing to validate these findings.
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Irwin, Melinda, Brenda Cartmel, Elizabeth Ercolano, Martha Fiellin, Marianna Rothbard, Scott Capozza, Yang Zhou, et al. "Aromatase inhibitors, arthralgias, and exercise in breast cancer survivors." Journal of Clinical Oncology 30, no. 15_suppl (May 20, 2012): TPS669. http://dx.doi.org/10.1200/jco.2012.30.15_suppl.tps669.
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TPS669 Background: A substantial number of breast cancer survivors who are taking aromatase inhibitors (AIs) complain of side effects including arthralgias. Given the efficacy of AIs, it is essential that we identify approaches to mitigate arthralgias. We are conducting an NCI-funded trial is to examine the impact of a year-long exercise intervention in 180 breast cancer survivors taking an AI and experiencing at least mild arthralgias. Outcomes include severity of arthralgias, endocrine-related quality of life, % body fat, bone mass, and serum markers of inflammation. The purpose of this abstract is to present preliminary recruitment and adherence results. Methods: Women are being recruited via the Connecticut Tumor Registry and Rapid Case Ascertainment Shared Resource of Yale Cancer Center into the Hormones and Physical Exercise (HOPE) Study. Participants are randomly assigned to exercise (n = 90) or usual care (n = 90). The exercise intervention includes supervised resistance and unsupervised aerobic exercise sessions over 12 months. Data are collected at baseline, 3-, 6-, 9 and 12-months. Results: Study recruitment began in April 2010 and will continue through December 2012. As of January 1, 2012, we have received 1605 names of potentially eligible women. Of these 1605, 777 women are waiting to be screened (e.g., letter mailed to patient or on hold because recently diagnosed), 244 women could not be screened (e.g., unable to contact by phone), and 584 women have completed a telephone screening. Of the 584 women screened, 24% were ineligible because of discontinuing AIs because of arthralgias or chose not to take AIs primarily because of this potential side effect; another 36% were ineligible for various reasons; 24% were not interested; 11% were randomized (n = 65 women); and 5% are undergoing baseline visits (n = 29). On average, women are 62 yrs old and have been taking an AI for 1.8 yrs. Twenty-three women have completed the trial, with high exercise adherence rates (attendance to supervised sessions = 82% ). Conclusions: A growing number of women are choosing to not take AIs or are discontinuing AIs because of side effects. Our study may prove beneficial for the growing number of women diagnosed with hormone receptor-positive breast cancer each year.
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Birkenbihl, Colin, Mohammad Asif Emon, Henri Vrooman, Sarah Westwood, Simon Lovestone, Martin Hofmann-Apitius, and Holger Fröhlich. "Differences in cohort study data affect external validation of artificial intelligence models for predictive diagnostics of dementia - lessons for translation into clinical practice." EPMA Journal 11, no. 3 (June 22, 2020): 367–76. http://dx.doi.org/10.1007/s13167-020-00216-z.
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Abstract Artificial intelligence (AI) approaches pose a great opportunity for individualized, pre-symptomatic disease diagnosis which plays a key role in the context of personalized, predictive, and finally preventive medicine (PPPM). However, to translate PPPM into clinical practice, it is of utmost importance that AI-based models are carefully validated. The validation process comprises several steps, one of which is testing the model on patient-level data from an independent clinical cohort study. However, recruitment criteria can bias statistical analysis of cohort study data and impede model application beyond the training data. To evaluate whether and how data from independent clinical cohort studies differ from each other, this study systematically compares the datasets collected from two major dementia cohorts, namely, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and AddNeuroMed. The presented comparison was conducted on individual feature level and revealed significant differences among both cohorts. Such systematic deviations can potentially hamper the generalizability of results which were based on a single cohort dataset. Despite identified differences, validation of a previously published, ADNI trained model for prediction of personalized dementia risk scores on 244 AddNeuroMed subjects was successful: External validation resulted in a high prediction performance of above 80% area under receiver operator characteristic curve up to 6 years before dementia diagnosis. Propensity score matching identified a subset of patients from AddNeuroMed, which showed significantly smaller demographic differences to ADNI. For these patients, an even higher prediction performance was achieved, which demonstrates the influence systematic differences between cohorts can have on validation results. In conclusion, this study exposes challenges in external validation of AI models on cohort study data and is one of the rare cases in the neurology field in which such external validation was performed. The presented model represents a proof of concept that reliable models for personalized predictive diagnostics are feasible, which, in turn, could lead to adequate disease prevention and hereby enable the PPPM paradigm in the dementia field.
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Pulikkan, John Anto, Mahesh Hegde, Houda Belaghzal, Anuradha Illendula, Jun Yu, Hafiz Ahmed, Kelsey O'Hagen, et al. "CBFβ-SMMHC Inhibition Disrupts Enhancer Chromatin Dynamics and Represses MYC Transcriptional Program in Inv(16) Leukemia." Blood 130, Suppl_1 (December 7, 2017): 784. http://dx.doi.org/10.1182/blood.v130.suppl_1.784.784.
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Abstract Chromatin complexes regulate gene expression in normal and malignant hematopoiesis. The significance of transcription factor deregulation on the control of epigenetic dynamics in leukemia is poorly understood. The leukemia fusion CBFβ-SMMHC is expressed in inv(16) acute myeloid leukemia (AML), and deregulates the activity of the transcription factor RUNX1. This fusion protein blocks myeloid differentiation, expands pre-leukemic myeloid progenitor cells, and drives AML development. The CBFβ-SMMHC inhibitor, AI-10-49, specifically disrupts its binding to RUNX1 resulting in an acute RUNX1 release, inducing apoptosis in inv(16) AML cells and delaying leukemia latency in mice. However, the mechanism by which AI-10-49 induces apoptosis is unknown. In this study, we utilize pharmacologic, genomic and genetic approaches to demonstrate a RUNX-dependent mechanism by which CBFβ-SMMHC maintains leukemia survival, and that this mechanism can be pharmacologically targeted in the treatment of inv(16) AML. Gene Set Enrichment Analysis of RNA-seq data from inv(16) AML cells treated with AI-10-49 identified the deregulation of a MYC signature, including cell cycle, ribosome biogenesis and metabolism, and the reduction of MYC transcript levels (10-fold). MYC shRNA knockdown induced apoptosis of inv(16) AML cells, and MYC expression rescued AI-10-49 induced apoptosis. Furthermore, mouse leukemia cells transduced with Myc shRNAs showed significant delay in leukemic latency upon transplantation, validating the requirement of MYC in inv(16) AML maintenance in vivo. Similarly, pharmacologic inhibition of MYC activity, using a combined treatment with AI-10-49 and the BET-family bromodomain inhibitor JQ1, revealed a strong synergy in inv(16) AML cells and significant delay in leukemia latency in mice. Analysis of chromatin immunoprecipitation followed by deep-sequencing (ChIP-seq) revealed that AI-10-49 treatment increased RUNX1 occupancy at three MYC distal enhancer elements (including two new enhancers: +0.18 Mb and +0.5 Mb, and the E3 enhancer of the BDME superenhancer at +1.7 Mb) downstream from MYC TSS but not at the MYC TSS or the T-cell leukemia associated N-ME enhancer. The open-chromatin and active nature of these regions was further validated by Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq) and H3K27Ac ChIP-seq. Since the MYC locus is occupied by a number of tumor-specific enhancers, we analyzed the enhancer interactions in a 4 Mb region (-1Mb to +3 Mb of MYC TSS) by chromosome conformation capture carbon copy (5C) in inv(16) AML cells. The 5C analysis revealed that the three enhancers are physically connected with each other and with the MYC promoter. Again, the T-cell ALL associated N-ME enhancer was not active in these cells. Furthermore, the six-hour AI-10-49 treatment had not altered these interactions, indicating that MYC repression was probably not due to changes in open chromatin but to alterations in regulatory factors. Therefore, we analyzed the occupancy of chromatin complex components at the three enhancers, N-ME and MYC TSS regions, utilizing ChIP-quantitative PCR. We demonstrate that AI-10-49 treatment results in the repression of the SWI/SNF complex component BRG1 and recruitment of polycomb-repressive complex 1 (PRC1) component RING1B at the three MYC enhancers. This switch was associated with a reduction of active enhancer mark H3K4me1 and an increase of the repressive mark H3K27me3 at these sites. Finally, deletion of a small region in each of these enhancers surrounding the RUNX1 binding site (41 bp, 67 bp and 295 bp) by genome editing (CRISPR/Cas9) reduced MYC transcript levels by 50% and the viability of inv(16) AML cells in vitro, indicating that each one of these enhancers plays a critical role in regulating MYC levels and sustaining the survival of inv(16) AML cells. Our results demonstrate that CBFβ-SMMHC regulates MYC levels by repression of RUNX1 activity at three distal MYC enhancers in inv(16) AML cells. AI-10-49 treatment induces an acute release of RUNX1, increases RUNX1 occupancy at the distal enhancers, and disrupts enhancer chromatin dynamics which in turn induces apoptosis by repressing MYC. Furthermore, this study suggests that combined treatment of inv(16) AML with AI-10-49 and BET-family inhibitors may represent a promising targeted therapy. Disclosures No relevant conflicts of interest to declare.
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Dalvi, Dipali Shrikant. "Employer Brand Equity and Its Impact on the Application Intent of the Prospective Employees." International Journal of Business Intelligence Research 12, no. 1 (January 2021): 1–25. http://dx.doi.org/10.4018/ijbir.20210101.oa1.
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Skilled human capital marks organisational success. Attracting, recruiting, and retaining these employees is vital. Companies are applying branding principles to recruitment function. This is termed as employer branding (EB). Identifying a right fit to suit organizational needs is essential. Organizational communication plays a critical role to relate to job seekers of generation-i; they rely on information imparted via various media and decode their perception. In this study, it is evident that for job seekers, corporate websites are important sources of information while deciding their employer. The reviews on job hunting sites aid perception. Use of AI to relate to prospective employees adds competitive edge. This study was conducted to ascertain dimensions of EB and study the application intent of potential employees. It included students from engineering and management and the employees from the job market. The findings allowed comparison of application intent of three groups. Qualification and experience moderates' application intent of prospective employees was evident.
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Dobrovsky, Aline, Uwe M. Borghoff, and Marko Hofmann. "Applying and Augmenting Deep Reinforcement Learning in Serious Games through Interaction." Periodica Polytechnica Electrical Engineering and Computer Science 61, no. 2 (May 5, 2017): 198. http://dx.doi.org/10.3311/ppee.10313.
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Serious games belong to the most important future e-learning trends and are frequently used in recruitment and training. Their development, however, is still a demanding and tedious process, especially when regarding reasonable non-player character behaviour. Serious games can generally profit from diverse, adaptive behaviour to increase learning effectiveness. Deep reinforcement learning has already shown considerable results in automatically generating successful AI behaviour, but its past applications were mainly focused on optimization and short-horizon games. To expand the underlying ideas to serious games, we introduce a new approach of augmenting the application of deep reinforcement learning methods by interactively making use of domain experts’ knowledge to guide the learning process. Thereby, we aim to establish a synergistic combination of experts and emergent cognitive systems to create adaptive and more human behaviour. We call this approach interactive deep reinforcement learning and point out important aspects regarding realization within a novel framework.
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Leonov, V., YEkatyerina Kashtanova, and A. Lobacheva. "Ethics of Artificial Intelligence: Problems and Initiatives in the Social Environment." Management of the Personnel and Intellectual Resources in Russia 10, no. 2 (May 26, 2021): 5–12. http://dx.doi.org/10.12737/2305-7807-2021-10-2-5-12.
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Technologies based on artificial intelligence (AI) have achieved significant results, including facial recognition, medical diagnostics, self-driving cars, insurance management and exchange assets, property, human resources, search and recruitment. Artificial intelligence promises huge benefits for economic growth, social development, and improving the well-being and people security. Of course, artificial intelligence and robotics are among the most discussed issues and technological trends around the world today. In the light of their widespread use and implementation in all spheres of human life, often the expected opportunities, achievements and scientific breakthroughs overshadow the reasonableness and expediency of using artificial intelligence technologies in a particular field from a legal and ethical point of view. Companies, in the pursuit of profit and leading positions in the market, are often irresponsible about the legal and ethical issues of interaction with artificial intelligence technologies. Nevertheless, the ethical aspects of the use of artificial intelligence technologies are gaining high importance these days. The emergence of high-tech systems and software that can function more and more independently of humans and can replace the performance of tasks by humans requires special attention. These systems raise a number of important and tough moral questions. The article discusses the main directions of the artificial intelligence technologies spread and the ethical consequences and moral issues that arise in this regard, both at the state and organizational levels. The main trends characteristic of the labor market that arise in the process of workplaces robotization and the intelligent robots introduction into the production process are studied. The authors convincingly prove the priority of ethics and human safety issues in the design and implementation of AI systems. During the discussion of the ethical problems of the artificial intelligence introduction in organizations, the emphasis is placed on the use of these technologies not from the point of view of automation and improving the efficiency of performing direct management functions, but from the point of view of the organization of personnel work. Based on this, the article concludes with recommendations for the development of ethical principles adapted to the design and use of AI systems.
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Kathad, Umesh, Yuvanesh Vedaraju, Aditya Kulkarni, Gregory Tobin, and Panna Sharma. "Development and clinical validation of Lantern Pharma’s AI engine: Response algorithm for drug positioning and rescue (RADR)." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): 3114. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.3114.
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3114 Background: The Response Algorithm for Drug positioning and Rescue (RADR) technology is Lantern Pharma's proprietary Artificial Intelligence (Al)-based machine learning approach for biomarker identification and patient stratification. RADR is a combination of three automated modules working sequentially to generate drug- and tumor type-specific gene signatures predictive of response. Methods: RADR integrates genomics, drug sensitivity and systems biology inputs with supervised machine learning strategies and generates gene expression-based responder/ non-responder profiles for specific tumor indications with high accuracy, in addition to identification of new correlations of genetic biomarkers with drug activity. Pre-treatment patient gene expression profiles along with corresponding treatment outcomes were used as algorithm inputs. Model training was typically performed using an initial set of genes derived from cancer cell line data when available, and further applied to patient data for model tuning, cross-validation and final gene signature development. Model testing and performance computation were carried out on patient records held out as blinded datasets. Response prediction accuracy and sensitivity were among the model performance metrics calculated. Results: On average, RADR achieved a response prediction accuracy of 80% during clinical validation. We present retrospective analyses performed as part of RADR validation using more than 10 independent datasets of patients from selected cancer types treated with approved drugs including chemotherapy, targeted therapy and immunotherapy agents. For an instance, the application of the RADR program to a Paclitaxel trial in breast cancer patients could have potentially reduced the number of patients in the treatment arm from 92 unselected patients to 24 biomarker-selected patients to produce the same number of responders. Also, we cite published evidence correlating genes from RADR derived biomarkers with increased Paclitaxel sensitivity in breast cancer. Conclusions: The value of RADR platform architecture is derived from its validation through the analysis of about ~17 million oncology-specific clinical data points, and ~1000 patient records. By implementing unique biological, statistical and machine learning workflows, Lantern Pharma's RADR technology is capable of deriving robust biomarker panels for pre-selecting true responders for recruitment into clinical trials which may improve the success rate of oncology drug approvals.