“Nobody knows, or seems to know how rheumatology and breastfeeding works”: Women's experiences of breastfeeding whilst managing a long-term limiting condition – A qualitative visual methods study

Background Only around 1% of babies in the UK are breastfed exclusively until six months of age as recommended by the World Health Organisation. One in ten women who have recently given birth in the UK have a long-term illness and they are at increased risk of stopping breastfeeding early. We considered women with autoimmune rheumatic diseases as an exemplar group of long term illnesses, to explore the barriers and enablers to breastfeeding Aim To understand the experiences of infant feeding among women with autoimmune rheumatic diseases and to identify potential barriers and enablers. Design Qualitative visual timeline-facilitated interviews. Participants and setting 128 women with autoimmune rheumatic diseases who were considering pregnancy, pregnant, or had young children took part in an online survey as part of the STAR Family Study. Of these, 13 women who had children were purposefully sampled to be interviewed. Interviews took place in person or on the telephone. Timeline-facilitated interviews were used to focus on lived experiences and topics important to the women, including early parenting. We conducted a focused thematic analysis of women's lived experiences of infant feeding. Results Three main themes were identified in relation to breastfeeding: lack of information about medication safety, lack of support in decision-making and maintaining breastfeeding, and maternal guilt. Conclusions Women with autoimmune rheumatic diseases found it difficult to access the information they needed about medications to make informed decisions about breastfeeding. They often also felt pressurised into breastfeeding and experienced feelings of guilt if they were unable, or did not wish to breastfeed. Tailored interventions are required that adopt a non-judgmental and person-centred approach to support decision-making in regard to infant feeding, providing women with information that can best enable them to make infant feeding choices

A Community-Based Marketing Campaign at Farmers Markets to Encourage Fruit and Vegetable Purchases in Rural Counties with High Rates of Obesity, Kentucky, 2015-2016

Availability of farmers markets may increase fruit and vegetable consumption among rural residents of the United States. We conducted a community-based marketing campaign, Plate it Up Kentucky Proud (PIUKP), in 6 rural communities over 2 years to determine the association between exposure to the campaign and fruit and vegetable purchases, adjusted for Supplemental Nutrition Assistance Program recipient status. Logistic regression was used to examine the odds of the PIUKP campaign influencing purchases. Awareness of the PIUKP marketing campaign was significantly associated with a willingness to prepare fruits and vegetables at home. Using marketing strategies at farmers markets may be an effective way to improve fruit and vegetable purchases in rural communities

Citizen and Community Science Approaches to Understanding Changes in Coastal Habitats Using Anecdata.org

Coastal ecosystems are facing increasing threats from human activities and environmental changes. Climate change, in particular, presents challenges for policymaking as it is causing significant changes to the oceans and coastlines, with social, economic, and environmental impacts on coastal communities. However, there is often a lack of data at the appropriate scales to address these concerns. Online tools that support the collection of citizen science and community science data can provide stakeholders and policymakers with a wealth of information and data on ocean-related topics, such as water quality, marine biodiversity, and ocean health. Citizen science platforms, like Anecdata.org, can facilitate data collection, raise public awareness, and encourage the engagement of stakeholders in ocean policymaking. Anecdata.org is a citizen science platform developed in Maine that supports data collection and project management for coastal conservation efforts worldwide. This community-driven platform promotes effective, open, and democratized science, hosting numerous active projects with users who are helping to address critical coastal issues. Here, we review Maine coastal projects on the Anecdata platform, examine the environmental trends highlighted in these projects, and discuss how citizen science data can play a role in coastal decision-making. These case studies will demonstrate the utility of citizen and community science approaches to monitoring Maine coastal ecosystems, understanding and predicting the impacts of climate change, and informing policymaking for coastal conservation. Taken together, these projects provide diverse, critically important data as well as a meaningful way for individuals and communities to engage in protecting and preserving Maine’s iconic coastal resources

Coupled versus uncoupled hindcast simulations of the Madden-Julian oscillation in the Year of Tropical Convection

This study investigates the impact of a full interactive ocean on daily initialised 15 day hindcasts of the Madden-Julian oscillation (MJO), measured against a Met Office Unified Model (MetUM) atmosphere control simulation (AGCM) during a 3 month period of the Year of Tropical Convection (YOTC). Results indicated that the coupled configuration (CGCM) extends MJO predictability over that of the AGCM, by up to 3-5 days. Propagation is improved in the CGCM, which we partly attribute to a more realistic phase relationship between sea surface temperature (SST) and convection. In addition, the CGCM demonstrates skill in representing downwelling oceanic Kelvin and Rossby waves which warm SSTs along their trajectory, with the potential to feed back on the atmosphere. These results imply that an ocean model capable of simulating internal ocean waves may be required to capture the full effect of air-sea coupling for the MJO

Pelagic Sargassum events in Jamaica : Provenance, morphotype abundance, and influence of sample processing on biochemical composition of the biomass

Pelagic Sargassum species have been known for centuries in the Sargasso Sea of the North Atlantic Ocean. In 2011, a new area concentrating high biomass of these brown algae started developing in the Tropical Atlantic Ocean. Since then, massive and recurrent Sargassum influxes have been reported in the Caribbean and off the coast of Western Africa. These Sargassum events have a major negative impact on coastal ecosystems and nearshore marine life, and affect socio-economic sectors, including public health, coastal living, tourism, fisheries, andmaritime transport. Despite recent advances in the forecasting of Sargassum events, and elucidation of the seaweed composition, many knowledge gaps remain, including morphotype abundance during Sargassum events, drift of the seaweeds in the months prior to stranding, and influence of sample processing methods on biomass biochemical composition. Using seaweeds harvested on the coasts of Jamaica in summer of 2020,we observed that S. fluitans III was themost abundantmorphotype at different times and sampling locations. No clear difference in the geographical origin, or provenance, of the Sargassummats was observed. Themajority of Sargassumbacktracked fromboth north and south of Jamaica experienced ambient temperatures of around 27 °C and salinity in the range of 34–36 psu before stranding.We also showed that cheap (sun) compared to expensive (freeze) drying techniques influence the biochemical composition of biomass. Sun-drying increased the proportion of phenolic compounds, but had a deleterious impact on fucoxanthin content and on the quantities of monosaccharides, except for mannitol. Effects on the content of fucose containing sulfated polysaccharides depended on the method used for their extraction, and limited variation was observed in ash, protein, and fatty acid content within most of the sample locations investigated. These observations are important for the storage and transport of the biomass in the context of its valorisation

Universal clinical Parkinson’s disease axes identify a major influence of neuroinflammation

: Background: There is large individual variation in both clinical presentation and progression between Parkinson’s disease patients. Generation of deeply and longitudinally phenotyped patient cohorts has enormous potential to identify disease subtypes for prognosis and therapeutic targeting. Methods: Replicating across three large Parkinson’s cohorts (Oxford Discovery cohort (n = 842)/Tracking UK Parkinson’s study (n = 1807) and Parkinson’s Progression Markers Initiative (n = 472)) with clinical observational measures collected longitudinally over 5–10 years, we developed a Bayesian multiple phenotypes mixed model incorporating genetic relationships between individuals able to explain many diverse clinical measurements as a smaller number of continuous underlying factors (“phenotypic axes”). Results: When applied to disease severity at diagnosis, the most influential of three phenotypic axes “Axis 1” was characterised by severe non-tremor motor phenotype, anxiety and depression at diagnosis, accompanied by faster progression in cognitive function measures. Axis 1 was associated with increased genetic risk of Alzheimer’s disease and reduced CSF Aβ1-42 levels. As observed previously for Alzheimer’s disease genetic risk, and in contrast to Parkinson’s disease genetic risk, the loci influencing Axis 1 were associated with microglia-expressed genes implicating neuroinflammation. When applied to measures of disease progression for each individual, integration of Alzheimer’s disease genetic loci haplotypes improved the accuracy of progression modelling, while integrating Parkinson’s disease genetics did not. Conclusions: We identify universal axes of Parkinson’s disease phenotypic variation which reveal that Parkinson’s patients with high concomitant genetic risk for Alzheimer’s disease are more likely to present with severe motor and non-motor features at baseline and progress more rapidly to early dementia

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy

Measuring Global Credibility with Application to Local Sequence Alignment

Computational biology is replete with high-dimensional (high-D) discrete prediction and inference problems, including sequence alignment, RNA structure prediction, phylogenetic inference, motif finding, prediction of pathways, and model selection problems in statistical genetics. Even though prediction and inference in these settings are uncertain, little attention has been focused on the development of global measures of uncertainty. Regardless of the procedure employed to produce a prediction, when a procedure delivers a single answer, that answer is a point estimate selected from the solution ensemble, the set of all possible solutions. For high-D discrete space, these ensembles are immense, and thus there is considerable uncertainty. We recommend the use of Bayesian credibility limits to describe this uncertainty, where a (1−α)%, 0≤α≤1, credibility limit is the minimum Hamming distance radius of a hyper-sphere containing (1−α)% of the posterior distribution. Because sequence alignment is arguably the most extensively used procedure in computational biology, we employ it here to make these general concepts more concrete. The maximum similarity estimator (i.e., the alignment that maximizes the likelihood) and the centroid estimator (i.e., the alignment that minimizes the mean Hamming distance from the posterior weighted ensemble of alignments) are used to demonstrate the application of Bayesian credibility limits to alignment estimators. Application of Bayesian credibility limits to the alignment of 20 human/rodent orthologous sequence pairs and 125 orthologous sequence pairs from six Shewanella species shows that credibility limits of the alignments of promoter sequences of these species vary widely, and that centroid alignments dependably have tighter credibility limits than traditional maximum similarity alignments

Improving the use of crop models for risk assessment and climate change adaptation

Crop models are used for an increasingly broad range of applications, with a commensurate proliferation of methods. Careful framing of research questions and development of targeted and appropriate methods are therefore increasingly important. In conjunction with the other authors in this special issue, we have developed a set of criteria for use of crop models in assessments of impacts, adaptation and risk. Our analysis drew on the other papers in this special issue, and on our experience in the UK Climate Change Risk Assessment 2017 and the MACSUR, AgMIP and ISIMIP projects. The criteria were used to assess how improvements could be made to the framing of climate change risks, and to outline the good practice and new developments that are needed to improve risk assessment. Key areas of good practice include: i. the development, running and documentation of crop models, with attention given to issues of spatial scale and complexity; ii. the methods used to form crop-climate ensembles, which can be based on model skill and/or spread; iii. the methods used to assess adaptation, which need broadening to account for technological development and to reflect the full range options available. The analysis highlights the limitations of focussing only on projections of future impacts and adaptation options using pre-determined time slices. Whilst this long-standing approach may remain an essential component of risk assessments, we identify three further key components: 1. Working with stakeholders to identify the timing of risks. What are the key vulnerabilities of food systems and what does crop-climate modelling tell us about when those systems are at risk? 2. Use of multiple methods that critically assess the use of climate model output and avoid any presumption that analyses should begin and end with gridded output. 3. Increasing transparency and inter-comparability in risk assessments. Whilst studies frequently produce ranges that quantify uncertainty, the assumptions underlying these ranges are not always clear. We suggest that the contingency of results upon assumptions is made explicit via a common uncertainty reporting format; and/or that studies are assessed against a set of criteria, such as those presented in this paper

Global wheat production with 1.5 and 2.0°C above pre‐industrial warming

Efforts to limit global warming to below 2°C in relation to the pre‐industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre‐industrial period) on global wheat production and local yield variability. A multi‐crop and multi‐climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by −2.3% to 7.0% under the 1.5°C scenario and −2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980–2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter‐annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer—India, which supplies more than 14% of global wheat. The projected global impact of warming <2°C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade