Dopamine modulates dynamic decision-making during foraging

The mesolimbic dopaminergic system exerts a crucial influence on incentive processing. However, the contribution of dopamine in dynamic, ecological situations where reward rates vary, and decisions evolve over time, remains unclear. In such circumstances, current (foreground) reward accrual needs to be compared continuously with potential rewards that could be obtained by travelling elsewhere (background reward rate), in order to determine the opportunity cost of staying versus leaving. We hypothesised that dopamine specifically modulates the influence of background – but not foreground – reward information when making a dynamic comparison of these variables for optimal behaviour. On a novel foraging task based on an ecological account of animal behaviour (marginal value theorem), human participants of either sex decided when to leave locations in situations where foreground rewards depleted at different rates, either in rich or poor environments with high or low background rates. In line with theoretical accounts, people’s decisions to move from current locations were independently modulated by changes in both foreground and background reward rates. Pharmacological manipulation of dopamine D2 receptor activity using the agonist cabergoline significantly affected decisions to move on, specifically modulating the effect of background reward rates. In particular, when on cabergoline, people left patches in poor environments much earlier. These results demonstrate a role of dopamine in signalling the opportunity cost of rewards, not value per se. Using this ecologically derived framework we uncover a specific mechanism by which D2 dopamine receptor activity modulates decision-making when foreground and background reward rates are dynamically compared

Multiorgan MRI findings after hospitalisation with COVID-19 in the UK (C-MORE): a prospective, multicentre, observational cohort study

Introduction: The multiorgan impact of moderate to severe coronavirus infections in the post-acute phase is still poorly understood. We aimed to evaluate the excess burden of multiorgan abnormalities after hospitalisation with COVID-19, evaluate their determinants, and explore associations with patient-related outcome measures. Methods: In a prospective, UK-wide, multicentre MRI follow-up study (C-MORE), adults (aged ≥18 years) discharged from hospital following COVID-19 who were included in Tier 2 of the Post-hospitalisation COVID-19 study (PHOSP-COVID) and contemporary controls with no evidence of previous COVID-19 (SARS-CoV-2 nucleocapsid antibody negative) underwent multiorgan MRI (lungs, heart, brain, liver, and kidneys) with quantitative and qualitative assessment of images and clinical adjudication when relevant. Individuals with end-stage renal failure or contraindications to MRI were excluded. Participants also underwent detailed recording of symptoms, and physiological and biochemical tests. The primary outcome was the excess burden of multiorgan abnormalities (two or more organs) relative to controls, with further adjustments for potential confounders. The C-MORE study is ongoing and is registered with ClinicalTrials.gov, NCT04510025. Findings: Of 2710 participants in Tier 2 of PHOSP-COVID, 531 were recruited across 13 UK-wide C-MORE sites. After exclusions, 259 C-MORE patients (mean age 57 years [SD 12]; 158 [61%] male and 101 [39%] female) who were discharged from hospital with PCR-confirmed or clinically diagnosed COVID-19 between March 1, 2020, and Nov 1, 2021, and 52 non-COVID-19 controls from the community (mean age 49 years [SD 14]; 30 [58%] male and 22 [42%] female) were included in the analysis. Patients were assessed at a median of 5·0 months (IQR 4·2–6·3) after hospital discharge. Compared with non-COVID-19 controls, patients were older, living with more obesity, and had more comorbidities. Multiorgan abnormalities on MRI were more frequent in patients than in controls (157 [61%] of 259 vs 14 [27%] of 52; p<0·0001) and independently associated with COVID-19 status (odds ratio [OR] 2·9 [95% CI 1·5–5·8]; padjusted=0·0023) after adjusting for relevant confounders. Compared with controls, patients were more likely to have MRI evidence of lung abnormalities (p=0·0001; parenchymal abnormalities), brain abnormalities (p<0·0001; more white matter hyperintensities and regional brain volume reduction), and kidney abnormalities (p=0·014; lower medullary T1 and loss of corticomedullary differentiation), whereas cardiac and liver MRI abnormalities were similar between patients and controls. Patients with multiorgan abnormalities were older (difference in mean age 7 years [95% CI 4–10]; mean age of 59·8 years [SD 11·7] with multiorgan abnormalities vs mean age of 52·8 years [11·9] without multiorgan abnormalities; p<0·0001), more likely to have three or more comorbidities (OR 2·47 [1·32–4·82]; padjusted=0·0059), and more likely to have a more severe acute infection (acute CRP >5mg/L, OR 3·55 [1·23–11·88]; padjusted=0·025) than those without multiorgan abnormalities. Presence of lung MRI abnormalities was associated with a two-fold higher risk of chest tightness, and multiorgan MRI abnormalities were associated with severe and very severe persistent physical and mental health impairment (PHOSP-COVID symptom clusters) after hospitalisation. Interpretation: After hospitalisation for COVID-19, people are at risk of multiorgan abnormalities in the medium term. Our findings emphasise the need for proactive multidisciplinary care pathways, with the potential for imaging to guide surveillance frequency and therapeutic stratification

Sentiment Analysis and Google Trends Data for Predicting Car Sales

This article explores the usefulness of sentiment analysis and Google trends data for car sales forecasting. Previous research has demonstrated the use of both techniques for sales forecasting, but current literature is more ambiguous in its results for forecasting the sales of high involvement goods like cars. In this study, about 500,000 social media posts for eleven car models on the Dutch market are analyzed using linear regression models. Furthermore, this study compares these outcomes to the predictive power of Google Trends. The results suggest that social media sentiments have little predictive power towards car sales while Google Trends data and social mention volume show significant results and can be incorporated into an effective prediction model. A prediction model with time lags using decision tree regression is built that can be used by the car industry as an addition to traditional forecasting methods

How DevOps capabilities leverage firm competitive advantage: A systematic review of empirical evidence

DevOps is an agile software delivery approach which combines IT development and operation functions into cross-functional teams and promotes team autonomy and automation of processes. Many companies transform their IT departments according to the DevOps paradigm, hoping to increase their pace of software delivery and achieve a tighter collaboration between business and IT functions. Whereas reasons for adopting DevOps are often described in terms of operational efficiency, the paper at hand aims to investigate whether implementing DevOps can additionally contribute to the strategic advantage of companies. To this end, we have conducted a systematic review of 37 empirical research papers on DevOps capabilities and analyzed the results in the context of the dynamic capabilities theory. Our conceptual model proposes that DevOps teams can contribute to firm competitive advantage by building both business- and technology-related capabilities which enable them to sense market opportunities, make fast and targeted decisions and transform their assets in case of changing circumstances. This research aims to generate a deeper understanding of the impact which software delivery approaches like DevOps can have on firm competitive advantage and provides insight into the sources of dynamic capabilities in modern IT organizations

Rethinking IT governance: Designing a framework for mitigating risk and fostering internal control in a DevOps environment

An increasing amount of companies is transforming their IT departments towards cross-functional teams which are responsible for both development and operation of software and use automation to speed up their delivery process. This novel approach, which is commonly known as “DevOps”, promises many benefits such as increased speed and frequency of deployment. However, companies using DevOps are often struggling with demonstrating control of their software delivery processes to IT auditing parties, due to the decentralized decision-making structures and high degree of automation in DevOps teams. The research at hand presents a framework which aims to provide guidance to organizations in mitigating and governing risks in IT teams and departments that make use of the DevOps paradigm. We have adopted a design science research approach, building on a literature review and semi-structured interviews with seventeen employees from nine Dutch companies that are in different stages of their DevOps transition. The results suggest that two main factors which influence how departments design their DevOps environment are risk appetite and the DevOps maturity. We furthermore find that companies in practice often use a mixture of traditional, manual IT controls and the automated controls suggested in literature. Based on these insights, a situational control framework is designed which suggests suitable risk mitigation practices

Fungal social influencers: secondary metabolites as a platform for shaping the plant-associated community

Fungal secondary metabolites (FSMs) are capable of manipulating plant community dynamics by inhibiting or facilitating the establishment of co-habitating organisms. Although production of FSMs is not crucial for survival of the producer, their absence can indirectly impair growth and/or niche competition of these fungi on the plant. The presence of FSMs with no obvious consequence on the fitness of the producer leaves questions regarding ecological impact. This review investigates how fungi employ FSMs as a platform to mediate fungal-fungal, fungal-bacterial and fungal-animal interactions associated with the plant community. We discuss how the biological function of FSMs may indirectly benefit the producer by altering the dynamics of surrounding organisms. We introduce several instances where FSMs influence antagonistic- or alliance-driven interactions. Part of our aim is to decipher the meaning of the FSM 'language' as it is widely noted to impact the surrounding community. Here, we highlight the contribution of FSMs to plant-associated interaction networks that affect the host either broadly or in ways that may have previously been unclear