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Genetic risk for depressive disorders is poorly understood despite consistent suggestions of a high heritable component. Most genetic studies have focused on risk associated with single variants, a strategy which has so far only yielded small (often non-replicable) risks for depressive disorders. In this paper we argue that more substantial risks are likely to emerge from genetic variants acting in synergy within and across larger neurobiological systems (polygenic risk factors). We show how knowledge of major integrated neurobiological systems provides a robust basis for defining and testing theoretically defensible polygenic risk factors. We do this by describing the architecture of the overall stress response. Maladaptation via impaired stress responsiveness is central to the aetiology of depression and anxiety and provides a framework for a systems biology approach to candidate gene selection. We propose principles for identifying genes and gene networks within the neurosystems involved in the stress response and for defining polygenic risk factors based on the neurobiology of stress-related behaviour. We conclude that knowledge of the neurobiology of the stress response system is likely to play a central role in future efforts to improve genetic prediction of depression and related disorders.

Diabetes is a major metabolic disorder which can affect entire body system adversely. Undiagnosed diabetes can increase the risk of cardiac stroke, diabetic nephropathy and other disorders. All over the world millions of people are affected by this disease. Early detection of diabetes is very important to maintain a healthy life. This disease is a reason of global concern as the cases of diabetes are rising rapidly. Machine learning (ML) is a computational method for automatic learning from experience and improves the performance to make more accurate predictions. In the current research we have utilized machine learning technique in Pima Indian diabetes dataset to develop trends and detect patterns with risk factors using R data manipulation tool. To classify the patients into diabetic and non-diabetic we have developed and analyzed five different predictive models using R data manipulation tool. For this purpose we used supervised machine learning algorithms namely linear kernel support vector machine (SVM-linear), radial basis function (RBF) kernel support vector machine, k-nearest neighbour (k-NN), artificial neural network (ANN) and multifactor dimensionality reduction (MDR).

This article aims to understand the role of intermediaries that manage innovation challenges in the healthcare scenario. More specifically, it explores the role of digital platforms in addressing data challenges and fostering data-driven innovation in the health sector.

For exploring the role of platforms, the authors propose a theoretical model based on the platform’s dynamic capabilities, assuming that, because of their set of capabilities, platforms may trigger innovation practices in actor interactions. To corroborate the theoretical framework, the authors present a detailed in-depth case study analysis of Apheris, an innovative data-driven digital platform operating in the healthcare scenario.

The paper finds that the innovative data-driven digital platform can be used to revolutionize established practices in the health sector (a) accelerating research and innovation; (b) overcoming challenges related to healthcare data. The case study demonstrates how data and intellectual property sharing can be privacy-compliant and enable new capabilities.

The paper attempts to fill the gap between the use of the data-driven digital platform and the critical innovation practices in the healthcare industry.

The purpose of this paper is to offer a Christian perspective on the ethical issues related to natural procreation and artificial reproduction methods.

This paper uses descriptive and comparative methodology between the ethical aspects specific to natural procreation and artificial reproduction.

Religious beliefs play a significant role in shaping the moral perspective when an infertile couple is confronted with the choice between natural procreation and artificial reproduction.

This paper survey a broad bibliography and offers a critical evaluation of the moral aspects specific to different methods of reproductive technologies compared to the natural procreation approach.

Petroleum hydrocarbons are naturally occurring flammable fossil fuels used as conventional energy sources. It has carcinogenic, mutagenic properties and is considered a hazardous pollutant. Soil contaminated with petroleum hydrocarbons adversely affects the properties of soil. This paper aim to remove pollutants from the environment is an urgent need of the hour to maintain the proper functioning of soil ecosystems.

The ability of micro-organisms to degrade petroleum hydrocarbons makes it possible to use these microorganisms to clean the environment from petroleum pollution. For preparing this review, research papers and review articles related to petroleum hydrocarbons degradation by micro-organisms were collected from journals and various search engines.

Various physical and chemical methods are used for remediation of petroleum hydrocarbons contaminants. However, these methods have several disadvantages. This paper will discuss a novel understanding of petroleum hydrocarbons degradation and how micro-organisms help in petroleum-contaminated soil restoration. Bioremediation is recognized as the most environment-friendly technique for remediation. The research studies demonstrated that bacterial consortium have high biodegradation rate of petroleum hydrocarbons ranging from 83% to 89%.

Proper management of petroleum hydrocarbons pollutants from the environment is necessary because of their toxicity effects on human and environmental health.

This paper discussed novel mechanisms adopted by bacteria for biodegradation of petroleum hydrocarbons, aerobic and anaerobic biodegradation pathways, genes and enzymes involved in petroleum hydrocarbons biodegradation.

This study explored health insurance coverage of genetic testing and potential factors associated with precision medicine (PM) reimbursement in Thailand.

The study employed a targeted review method. Thirteen PMs were selected to represent four PM categories: targeted cancer therapy candidate, prediction of adverse drug reactions (ADRs), dose adjustment and cancer risk prediction. Content analysis was performed to compare access to PMs among three health insurance schemes in Thailand. The primary outcome of the study was evaluating PM test reimbursement status. Secondary outcomes included clinical practice guidelines, PMs statement in FDA-approved leaflet and economic evaluation.

Civil Servant Medical Benefits Scheme (CSMBS) provided more generous access to PM than Universal Coverage Scheme (UCS) and Social Security Scheme (SSS). Evidence of economic evaluations likely impacted the reimbursement decisions of SSS and UCS, while the information provided in FDA-approved leaflets seemed to impact the reimbursement decisions of CSMBS. Three health insurance schemes provided adequate access to PM tests for some cancer-targeted therapies, while gaps existed for access to PM tests for serious ADRs prevention, dose adjustment and cancer risk prediction.

This was the first study to explore the situation of access to PMs in Thailand. The evidence alerts public health insurance schemes to reconsider access to PMs. Development of health technology assessment guidelines for PM test reimbursement decisions should be prioritized.

This study focuses on leadership in organizations where big data analytics (BDA) is an essential component of corporate strategy. While leadership researchers have conducted promising studies in the field of digital transformation, the impact of BDA on leadership is still unexplored.

This study is based on semi-structured interviews with 33 organizational leaders and subject-matter experts from various industries. Using a grounded theory approach, a framework is provided for the emergent field of BDA in leadership research.

The authors present a conceptual model comprising foundational competencies and higher order roles that are data analytical skills, data self-efficacy, problem spotter, influencer, knowledge facilitator, visionary and team leader.

This study focuses on BDA competency research emerging as an intersection between leadership research and information systems research. The authors encourage a longitudinal study to validate the findings.

The authors provide a competency framework for organizational leaders. It serves as a guideline for leaders to best support the BDA initiatives of the organization. The competency framework can support recruiting, selection and leader promotion.

This study provides a novel BDA leadership competency framework with a unique combination of competencies and higher order roles.

This paper aims to investigate the factors influencing chief executive officers’ (CEOs') intentions to implement cloud technology in Italian small and medium-sized enterprises (SMEs).

The study proposes a model that integrates the theoretical construct of the technology acceptance model (TAM) with a classification of perceived benefits and risks related to cloud computing. The study employs a structural equation modeling approach to analyze data gathered through a Likert scale-based survey.

The findings indicate that risk perception has a strong negative effect on the intention to introduce cloud technology in firms. This effect is partially offset by the perceived ease of use of the technology.

The study provides a new theoretical framework that integrates the TAM and a classification of perceived risks to provide a clear view of management's cognitive processes during technological change. Moreover, the results show the main factors influencing decisions regarding the implementation of cloud computing in firms in light of the perception of risks. Finally, this study provides interesting findings for cloud service providers (CSPs) about their customers' decision-making processes.

This study investigates how game design, which divides players into static teams, can reinforce group polarisation. The authors study this phenomenon from the perspective of social identity in the context of team-based location-based games, with a focus on game slang.

The authors performed an exploratory data analysis on an original dataset of n = 242,852 messages from five communication channels to find differences in game slang adoption between three teams in the location-based augmented reality game Pokémon GO. A divisive word “jym” (i.e. a Finnish slang derivative of the word “gym”) was discovered, and players' attitudes towards the word were further probed with a survey (n = 185). Finally, selected participants (n = 25) were interviewed in person to discover any underlying reasons for the observed polarised attitudes.

The players' teams were correlated with attitudes towards “jym”. Face-to-face interviews revealed association of the word to a particular player subgroup and it being used with improper grammar as reasons for the observed negative attitudes. Conflict over (virtual) territorial resources reinforced the polarisation.

Game design with static teams and inter-team conflict influences players' social and linguistic identity, which subsequently may result in divisive stratification among otherwise cooperative or friendly player-base.

The presented multi-method study connecting linguistic and social stratification is a novel approach to gaining insight on human social interactions, polarisation and group behaviour in the context of location-based games.

This study aims to analyse the factors that caused Indian tourists to avoid travelling abroad because of the recent outbreak of COVID-19 in 2020. It will also identify the relationship between the perceived risk of travelling and the probability of travel evading in India owing to COVID-19.

This study used an online structured questionnaire to collect data from Indian tourists to study six independent variables linked with their behavioural intentions (travel evading). The health belief model was used to examine tourist behaviour.

The results revealed a positive correlation between the perceived risk associated with COVID-19 and travel avoidance. Familiarity with COVID-19 was positively correlated with travel evading behaviours.

This study will assist stakeholders from around the world to adequately identify and thoroughly plan for logistical problems associated with travel such as travel insurance and pre-travel booking expenses to reduce travel evading behaviour and promote travel.

While a few studies have been conducted related to pandemics (Ebola, MERS-CoV, SARS), there is a paucity of literature that examines the factors which influence tourists’ travel evading behaviour owing to COVID-19. Moreover, most of the previous literature on pandemics is concentrated on American and European countries, whereas studies on the Indian sub-continent are very scarce. This study will fill this gap and will identify the factors which influence tourists in India to evade travel in response to COVID-19.