Associations between dimensions of behaviour, personality traits, and mental-health during the COVID-19 pandemic in the United Kingdom.

The COVID-19 pandemic (including lockdown) is likely to have had profound but diverse implications for mental health and well-being, yet little is known about individual experiences of the pandemic (positive and negative) and how this relates to mental health and well-being, as well as other important contextual variables. Here, we analyse data sampled in a large-scale manner from 379,875 people in the United Kingdom (UK) during 2020 to identify population variables associated with mood and mental health during the COVID-19 pandemic, and to investigate self-perceived pandemic impact in relation to those variables. We report that while there are relatively small population-level differences in mood assessment scores pre- to peak-UK lockdown, the size of the differences is larger for people from specific groups, e.g. older adults and people with lower incomes. Multiple dimensions underlie peoples' perceptions, both positive and negative, of the pandemic's impact on daily life. These dimensions explain variance in mental health and can be statistically predicted from age, demographics, home and work circumstances, pre-existing conditions, maladaptive technology use and personality traits (e.g., compulsivity). We conclude that a holistic view, incorporating the broad range of relevant population factors, can better characterise people whose mental health is most at risk during the COVID-19 pandemic

Mapping the sociodemographic distribution and self-reported justifications for non-compliance with COVID-19 guidelines in the United Kingdom

Which population factors have predisposed people to disregard government safety guidelines during the COVID-19 pandemic and what justifications do they give for this non-compliance? To address these questions, we analyse fixed-choice and free-text responses to survey questions about compliance and government handling of the pandemic, collected from tens of thousands of members of the UK public at three 6-monthly timepoints. We report that sceptical opinions about the government and mainstream-media narrative, especially as pertaining to justification for guidelines, significantly predict non-compliance. However, free text topic modelling shows that such opinions are diverse, spanning from scepticism about government competence and self-interest to full-blown conspiracy theories, and covary in prevalence with sociodemographic variables. These results indicate that attempts to counter non-compliance through argument should account for this diversity in peoples’ underlying opinions, and inform conversations aimed at bridging the gap between the general public and bodies of authority accordingly

An automated machine learning approach to predict brain age from cortical anatomical measures

The use of machine learning (ML) algorithms has significantly increased in neuroscience. However, from the vast extent of possible ML algorithms, which one is the optimal model to predict the target variable? What are the hyperparameters for such a model? Given the plethora of possible answers to these questions, in the last years, automated ML (autoML) has been gaining attention. Here, we apply an autoML library called Tree-based Pipeline Optimisation Tool (TPOT) which uses a tree-based representation of ML pipelines and conducts a genetic programming-based approach to find the model and its hyperparameters that more closely predicts the subject's true age. To explore autoML and evaluate its efficacy within neuroimaging data sets, we chose a problem that has been the focus of previous extensive study: brain age prediction. Without any prior knowledge, TPOT was able to scan through the model space and create pipelines that outperformed the state-of-the-art accuracy for Freesurfer-based models using only thickness and volume information for anatomical structure. In particular, we compared the performance of TPOT (mean absolute error [MAE]: 4.612 ± .124 years) and a relevance vector regression (MAE 5.474 ± .140 years). TPOT also suggested interesting combinations of models that do not match the current most used models for brain prediction but generalise well to unseen data. AutoML showed promising results as a data-driven approach to find optimal models for neuroimaging applications

Storage oil hydrolysis during early seedling growth

Storage oil breakdown plays an important role in the life cycle of many plants by providing the carbon skeletons that support seedling growth immediately following germination. This metabolic process is initiated by lipases (EC: 3.1.1.3), which catalyze the hydrolysis of triacylglycerols (TAGs) to release free fatty acids and glycerol. A number of lipases have been purified to near homogeneity from seed tissues and analysed for their in vitro activities. Furthermore, several genes encoding lipases have been cloned and characterised from plants. However, only recently has data been presented to establish the molecular identity of a lipase that has been shown to be required for TAG breakdown in seeds. In this review we briefly outline the processes of TAG synthesis and breakdown. We then discuss some of the biochemical literature on seed lipases and describe the cloning and characterisation of a lipase called SUGAR-DEPENDENT1, which is required for TAG breakdown in Arabidopsis thaliana seeds

The effects of COVID-19 on cognitive performance in a community-based cohort: a COVID symptom study biobank prospective cohort study

BACKGROUND: Cognitive impairment has been reported after many types of infection, including SARS-CoV-2. Whether deficits following SARS-CoV-2 improve over time is unclear. Studies to date have focused on hospitalised individuals with up to a year follow-up. The presence, magnitude, persistence and correlations of effects in community-based cases remain relatively unexplored. METHODS: Cognitive performance (working memory, attention, reasoning, motor control) was assessed in a prospective cohort study of participants from the United Kingdom COVID Symptom Study Biobank between July 12, 2021 and August 27, 2021 (Round 1), and between April 28, 2022 and June 21, 2022 (Round 2). Participants, recruited from the COVID Symptom Study smartphone app, comprised individuals with and without SARS-CoV-2 infection and varying symptom duration. Effects of COVID-19 exposures on cognitive accuracy and reaction time scores were estimated using multivariable ordinary least squares linear regression models weighted for inverse probability of participation, adjusting for potential confounders and mediators. The role of ongoing symptoms after COVID-19 infection was examined stratifying for self-perceived recovery. Longitudinal analysis assessed change in cognitive performance between rounds. FINDINGS: 3335 individuals completed Round 1, of whom 1768 also completed Round 2. At Round 1, individuals with previous positive SARS-CoV-2 tests had lower cognitive accuracy (N = 1737, β = −0.14 standard deviations, SDs, 95% confidence intervals, CI: −0.21, −0.07) than negative controls. Deficits were largest for positive individuals with ≥12 weeks of symptoms (N = 495, β = −0.22 SDs, 95% CI: −0.35, −0.09). Effects were comparable to hospital presentation during illness (N = 281, β = −0.31 SDs, 95% CI: −0.44, −0.18), and 10 years age difference (60–70 years vs. 50–60 years, β = −0.21 SDs, 95% CI: −0.30, −0.13) in the whole study population. Stratification by self-reported recovery revealed that deficits were only detectable in SARS-CoV-2 positive individuals who did not feel recovered from COVID-19, whereas individuals who reported full recovery showed no deficits. Longitudinal analysis showed no evidence of cognitive change over time, suggesting that cognitive deficits for affected individuals persisted at almost 2 years since initial infection. INTERPRETATION: Cognitive deficits following SARS-CoV-2 infection were detectable nearly two years post infection, and largest for individuals with longer symptom durations, ongoing symptoms, and/or more severe infection. However, no such deficits were detected in individuals who reported full recovery from COVID-19. Further work is needed to monitor and develop understanding of recovery mechanisms for those with ongoing symptoms. FUNDING: Chronic Disease Research Foundation, Wellcome Trust, National Institute for Health and Care Research, Medical Research Council, British Heart Foundation, Alzheimer's Society, European Union, COVID-19 Driver Relief Fund, French National Research Agency