Research and innovation identified to decarbonise the maritime sector

The maritime sector requires technically, environmentally, socially, and economically informed pathways to decarbonise and eliminate all emissions harmful to the environment and health. This is extremely challenging and complex, and a wide range of technologies and solutions are currently being explored. However, it is important to assess the state-of-the-art and identify further research and innovation required to accelerate decarbonisation. The UK National Clean Maritime Research Hub have identified key priority areas to drive this process, with particular focus on marine fuels, power and propulsion, vessel efficiency, port operations and infrastructure, digitalisation, finance, regulation, and policy.This article was delivered by the UK National Clean Maritime Research Hub established on the 1st September 2023 supported by the UK Department for Transport (DfT) as part of the UK Shipping Office for Reducing Emissions (UK SHORE) Programme and Engineering and Physical Sciences Research Council (EPSRC) [grant number EP/Y024605/1]

Magnetically sensitive nanodiamond-doped tellurite glass fibers

Traditional optical fibers are insensitive to magnetic fields, however many applications would benefit from fiber-based magnetometry devices. In this work, we demonstrate a magnetically sensitive optical fiber by doping nanodiamonds containing nitrogen vacancy centers into tellurite glass fibers. The fabrication process provides a robust and isolated sensing platform as the magnetic sensors are fixed in the tellurite glass matrix. Using optically detected magnetic resonance from the doped nanodiamonds, we demonstrate detection of local magnetic fields via side excitation and longitudinal collection. This is a first step towards intrinsically magneto-sensitive fiber devices with future applications in medical magneto-endoscopy and remote mineral exploration sensing

Genomic diversifications of five Gossypium allopolyploid species and their impact on cotton improvement

Polyploidy is an evolutionary innovation for many animals and all flowering plants, but its impact on selection and domestication remains elusive. Here we analyze genome evolution and diversification for all five allopolyploid cotton species, including economically important Upland and Pima cottons. Although these polyploid genomes are conserved in gene content and synteny, they have diversified by subgenomic transposon exchanges that equilibrate genome size, evolutionary rate heterogeneities and positive selection between homoeologs within and among lineages. These differential evolutionary trajectories are accompanied by gene-family diversification and homoeolog expression divergence among polyploid lineages. Selection and domestication drive parallel gene expression similarities in fibers of two cultivated cottons, involving coexpression networks and N6-methyladenosine RNA modifications. Furthermore, polyploidy induces recombination suppression, which correlates with altered epigenetic landscapes and can be overcome by wild introgression. These genomic insights will empower efforts to manipulate genetic recombination and modify epigenetic landscapes and target genes for crop improvement

Multidisciplinary research priorities for the COVID-19 pandemic - Authors' reply.

No abstract available

Atmospheric Evolution

Earth's atmosphere has evolved as volatile species cycle between the atmosphere, ocean, biomass and the solid Earth. The geochemical, biological and astrophysical processes that control atmospheric evolution are reviewed from an "Earth Systems" perspective, with a view not only to understanding the history of Earth, but also to generalizing to other solar system planets and exoplanets.Comment: 34 pages, 3 figures, 2 tables. Accepted as a chapter in "Encyclopaedia of Geochemistry", Editor Bill White, Springer-Nature, 201

Treatable traits in the European U-BIOPRED adult asthma cohorts

No abstract availabl

A Model for Interprofessional Health Disparities Education: Student-Led Curriculum on Chronic Hepatitis B Infection

Although health disparities are commonly addressed in preclinical didactic curricula, direct patient care activities with affected communities are more limited. To address this problem, health professional students designed a preclinical service-learning curriculum on hepatitis B viral (HBV) infection, a major health disparity affecting the Asian/Pacific Islander (API) population, integrating lectures, skills training, and direct patient care at student-run clinics. An urban health professions campus. Medical and other health professional students at University of California, San Francisco, organized a preclinical didactic and experiential elective, and established two monthly clinics offering HBV screening, vaccination, and education to the community. Between 2004 and 2009, 477 students enrolled in the student-led HBV curriculum. Since the clinics’ inception in 2007, 804 patients have been screened for chronic HBV; 87% were API immigrants, 63% had limited English proficiency, and 46% were uninsured. Serologically, 10% were found to be chronic HBV carriers, 44% were susceptible to HBV, and 46% were immune. Our student-led didactic and experiential elective can serve as an interprofessional curricular model for learning about specific health disparities while providing important services to the local community

White matter diffusion estimates in obsessive-compulsive disorder across 1,653 individuals: Machine learning findings from the ENIGMA OCD Working Group

White matter pathways, typically studied with diffusion tensor imaging (DTI), have been implicated in the neurobiology of obsessive-compulsive disorder (OCD). However, due to limited sample sizes and the predominance of single-site studies, the generalizability of OCD classification based on diffusion white matter estimates remains unclear. Here, we tested classification accuracy using the largest OCD DTI dataset to date, involving 1,336 adult participants (690 OCD patients and 646 healthy controls) and 317 pediatric participants (175 OCD patients and 142 healthy controls) from 18 international sites within the ENIGMA OCD Working Group. We used an automatic machine learning pipeline (with feature engineering and selection, and model optimization) and examined the cross-site generalizability of the OCD classification models using leave-one-site-out cross-validation. Our models showed low-to-moderate accuracy in classifying (1) “OCD vs. healthy controls'' (Adults, receiver operator characteristic-area under the curve = 57.19 ± 3.47 in the replication set; Children, 59.8 ± 7.39), (2) “unmedicated OCD vs. healthy controls” (Adults, 62.67 ± 3.84; Children, 48.51 ± 10.14), and (3) “medicated OCD vs. unmedicated OCD” (Adults, 76.72 ± 3.97; Children, 72.45 ± 8.87). There was significant site variability in model performance (cross-validated ROC AUC ranges 51.6–79.1 in adults; 35.9–63.2 in children). Machine learning interpretation showed that diffusivity measures of the corpus callosum, internal capsule, and posterior thalamic radiation contributed to the classification of OCD from HC. The classification performance appeared greater than the model trained on grey matter morphometry in the prior ENIGMA OCD study (our study includes subsamples from the morphometry study). Taken together, this study points to the meaningful multivariate patterns of white matter features relevant to the neurobiology of OCD, but with low-to-moderate classification accuracy. The OCD classification performance may be constrained by site variability and medication effects on the white matter integrity, indicating room for improvement for future research