Morning brain: Real-world neural evidence that high school class times matter

Researchers, parents and educators consistently observe a stark mismatch between biologically preferred and socially imposed sleep–wake hours in adolescents, fueling debate about high school start times. We contribute neural evidence to this debate with electroencephalogram data collected from high school students during their regular morning, mid-morning and afternoon classes. Overall, student alpha power was lower when class content was taught via videos than through lectures. Students’ resting state alpha brain activity decreased as the day progressed, consistent with adolescents being least attentive early in the morning. During the lessons, students showed consistently worse performance and higher alpha power for early morning classes than for mid-morning classes, while afternoon quiz scores and alpha levels varied. Together, our findings demonstrate that both class activity and class time are reflected in adolescents’ brain states in a real-world setting, and corroborate educational research suggesting that mid-morning may be the best time to learn

Prospective association of social circumstance, socioeconomic, lifestyle and mental health factors with subsequent hospitalisation over 6–7 year follow up in people living with HIV

Background: Predictors of hospitalisation in people with HIV (PLHIV) in the contemporary treatment era are not well understood. / Methods: This ASTRA sub-study used clinic data linkage and record review to determine occurrence of hospitalisations among 798 PLHIV from baseline questionnaire (February to December 2011) until 1 June 2018. Associations of baseline social circumstance, socioeconomic, lifestyle, mental health, demographic and clinical factors with repeated all-cause hospitalisation from longitudinal data were investigated using Prentice-Williams-Peterson models. Associations were also assessed in 461 individuals on antiretroviral therapy (ART) with viral load ≤50 copies/ml and CD4 count ≥500 cells/ µl. / Findings: Rate of hospitalisation was 5.8/100 person-years (95% CI: 5.1–6.5). Adjusted for age, demographic group and time with diagnosed HIV, the following social circumstance, socioeconomic, lifestyle and mental health factors predicted hospitalisation: no stable partner (adjusted hazard ratio (aHR)=1.59; 95% CI=1.16–2.20 vs living with partner); having children (aHR=1.50; 1.08–2.10); non-employment (aHR=1.56; 1.07–2.27 for unemployment; aHR=2.39; 1.70–3.37 for sick/disabled vs employed); rented housing (aHR=1.72; 1.26–2.37 vs homeowner); not enough money for basic needs (aHR=1.82; 1.19–2.78 vs enough); current smoking (aHR=1.39; 1.02–1.91 vs never); recent injection-drug use (aHR=2.11; 1.30–3.43); anxiety symptoms (aHRs=1.39; 1.01–1.91, 2.06; 1.43–2.95 for mild and moderate vs none/minimal); depressive symptoms (aHRs=1.67; 1.17–2.38, 1.91; 1.30–2.78 for moderate and severe vs none/minimal); treated/untreated depression (aHRs=1.65; 1.03–2.64 for treated depression only, 1.87; 1.39–2.52 for depressive symptoms only; 1.53; 1.05–2.24; for treated depression and depressive symptoms, versus neither). Associations were broadly similar in those with controlled HIV and high CD4. / Interpretation: Social circumstance, socioeconomic disadvantage, adverse lifestyle factors and poorer mental health are strong predictors of hospitalisation in PLHIV, highlighting the need for targeted interventions and care. / Funding: British HIV Association (BHIVA) Research Award (2017); SMR funded by a PhD fellowship from the Royal Free Charity

Chronogram:an R package for data curation and analysis of infection and vaccination cohort studies

Motivation: Observational cohort studies that track vaccine and infection responses offer real-world data to inform pandemic policy. Translating biological hypotheses, such as whether different patterns of accumulated antigenic exposures confer differing antibody responses, into analysis code can be onerous, particularly when source data is dis-aggregated. Results: The R package chronogram introduces the class chronogram, where metadata is seamlessly aggregated with sparse infection episode, clinical and laboratory data. Each experimental modality is added sequentially, allowing the incorporation of new data, such as specialized time-consuming research assays, or their downstream analyses. Source data can be any rectangular data format, including database tables (such as structured query language databases). This supports annotations that aggregate data types/sources, for example, combining symptoms, molecular testing, and sequencing of one or more infectious episodes in a pathogen-agnostic manner. Chronogram arranges observational data to allow the translation of biological hypotheses into their corresponding code via a shared vocabulary. Availability and implementation: Chronogram is implemented R and available under an MIT licence at: https://www.github.com/FrancisCrickInstitute/chronogram; a user manual is available at: https://franciscrickinstitute.github.io/chronogram

Using microalgae in the circular economy to valorise anaerobic digestate::Challenges and Opportunities

Managing organic waste streams is a major challenge for the agricultural industry. Anaerobic digestion (AD) of organicwastes is a preferred option in the waste management hierarchy, as this processcangenerate renewableenergy, reduce emissions from wastestorage, andproduce fertiliser material.However, Nitrate Vulnerable Zone legislation and seasonal restrictions can limit the use of digestate on agricultural land. In this paper we demonstrate the potential of cultivating microalgae on digestate as a feedstock, either directlyafter dilution, or indirectlyfromeffluent remaining after biofertiliser extraction. Resultant microalgal biomass can then be used to produce livestock feed, biofuel or for higher value bio-products. The approach could mitigate for possible regional excesses, and substitute conventional high-impactproducts with bio-resources, enhancing sustainability withinacircular economy. Recycling nutrients from digestate with algal technology is at an early stage. We present and discuss challenges and opportunities associated with developing this new technology

Chronogram:an R package for data curation and analysis of infection and vaccination cohort studies

Motivation: Observational cohort studies that track vaccine and infection responses offer real-world data to inform pandemic policy. Translating biological hypotheses, such as whether different patterns of accumulated antigenic exposures confer differing antibody responses, into analysis code can be onerous, particularly when source data is dis-aggregated. Results: The R package chronogram introduces the class chronogram, where metadata is seamlessly aggregated with sparse infection episode, clinical and laboratory data. Each experimental modality is added sequentially, allowing the incorporation of new data, such as specialized time-consuming research assays, or their downstream analyses. Source data can be any rectangular data format, including database tables (such as structured query language databases). This supports annotations that aggregate data types/sources, for example, combining symptoms, molecular testing, and sequencing of one or more infectious episodes in a pathogen-agnostic manner. Chronogram arranges observational data to allow the translation of biological hypotheses into their corresponding code via a shared vocabulary. Availability and implementation: Chronogram is implemented R and available under an MIT licence at: https://www.github.com/FrancisCrickInstitute/chronogram; a user manual is available at: https://franciscrickinstitute.github.io/chronogram

The foot in forensic human identification - a review

The identification of human remains is a process which can be attempted irrespective of the stage of decomposition in which the remains are found or the anatomical regions recovered. In recent years, the discovery of fragmented human remains has garnered significant attention from the national and international media, particularly the recovery of multiple lower limbs and feet from coastlines in North America. While cases such as these stimulate public curiosity, they present unique challenges to forensic practitioners in relation to the identification of the individual from whom the body part originated. There is a paucity of literature pertaining to the foot in forensic human identification and in particular, in relation to the assessment of the parameters represented by the biological profile. This article presents a review of the literature relating to the role of the foot in forensic human identification and highlights the areas in which greater research is required. © 2013

Lighting the way: Compelling open questions in photosynthesis research.

Photosynthesis - the conversion of energy from sunlight into chemical energy - is essential for life on Earth. Yet there is much we do not understand about photosynthetic energy conversion on a fundamental level: how it evolved and the extent of its diversity, its dynamics, and all the components and connections involved in its regulation. In this commentary, researchers working on fundamental aspects of photosynthesis including the light-dependent reactions, photorespiration, and C4 photosynthetic metabolism pose and discuss what they view as the most compelling open questions in their areas of research

One virus, many lives: a qualitative study of lived experiences and quality of life of adults from diverse backgrounds living in the UK during the COVID-19 pandemic

Objectives The coronavirus disease 2019 (COVID-2019) pandemic has had far-reaching consequences for people's lives. In the UK, more than 23 million have been infected and nearly 185 000 have lost their lives. Previous research has looked at differential outcomes of COVID-19, based on socio-demographic factors such as age, sex, ethnicity and deprivation. We conducted a qualitative study with a diverse sample of adults living in the UK, to understand their lived experiences and quality of life (QoL) during the pandemic. Methods Participants were recruited with the help of civil society partners and community organisations. Semi-structured interviews were conducted between May and July 2021. Interviews were recorded with permission and transcribed. Transcripts were analysed following an inductive analytical approach as outlined in the Framework Method. Results 18 participants (≥16 years) representing different ethnicities, sexes, migration and employment statuses and educational qualifications took part. Five key themes and 14 subthemes were identified and presented using the QoL framework. The five key themes describe how COVID-19 affected the following aspects of QoL: (1) financial and economic, (2) physical health, (3) social, (4) mental health and (5) personal fulfilment and affective well-being. The narratives illustrated inequities in the impact of COVID-19 for individuals with intersecting social, economic, and health disparities. Conclusion Our findings demonstrate the multidimensional and differential impact of the pandemic on different population groups, with most of the negative economic impacts being borne by people in low-paid and insecure jobs. Similarly, adverse social, physical and mental health impacts particularly affected people already experiencing displacement, violence, physical and mental illnesses or even those living alone. These findings indicate that COVID-19 impacts have been influenced by intersecting health and socioeconomic inequalities, which pre-existed. These inequities should be taken into consideration while designing pandemic recovery and rebuilding packages

Bayesian Life Test Planning for the Log-Location-Scale Family of Distributions

This paper describes Bayesian methods for life test planning with censored data from a log-location-scale distribution, when prior information of the distribution parameters is available. We use a Bayesian criterion based on the estimation precision of a distribution quantile. A large sample normal approximation gives a simplified, easy-tointerpret, yet valid approach to this planning problem, where in general no closed form solutions are available. To illustrate this approach, we present numerical investigations using the Weibull distribution with Type II censoring. We also assess the effects of prior distribution choice. A simulation approach of the same Bayesian problem is also presented as a tool for visualization and validation. The validation results generally are consistent with those from the large sample approximation approach