Mine and me: exploring the neural basis of object ownership.

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When "It" becomes "Mine": attentional biases triggered by object ownership.

Abstract Previous research has demonstrated that higher-order cognitive processes associated with the allocation of selective attention are engaged when highly familiar self-relevant items are encountered, such as one's name, face, personal possessions and the like. The goal of our study was to determine whether these effects on attentional processing are triggered on-line at the moment self-relevance is established. In a pair of experiments, we recorded ERPs as participants viewed common objects (e.g., apple, socks, and ketchup) in the context of an “ownership” paradigm, where the presentation of each object was followed by a cue indicating whether the object nominally belonged either to the participant (a “self” cue) or the experimenter (an “other” cue). In Experiment 1, we found that “self” ownership cues were associated with increased attentional processing, as measured via the P300 component. In Experiment 2, we replicated this effect while demonstrating that at a visual–perceptual level, spatial attention became more narrowly focused on objects owned by self, as measured via the lateral occipital P1 ERP component. Taken together, our findings indicate that self-relevant attention effects are triggered by the act of taking ownership of objects associated with both perceptual and postperceptual processing in cortex.</jats:p

Crop yield response to climate change varies with cropping intensity

Projections of the response of crop yield to climate change at different spatial scales are known to vary. However, understanding of the causes of systematic differences across scale is limited. Here, we hypothesize that heterogeneous cropping intensity is one source of scale dependency. Analysis of observed global data and regional crop modelling demonstrate that areas of high vs. low cropping intensity can have systematically different yields, in both observations and simulations. Analysis of global crop data suggests that heterogeneity in cropping intensity is a likely source of scale dependency for a number of crops across the globe. Further crop modelling and a meta-analysis of projected tropical maize yields are used to assess the implications for climate change assessments. The results show that scale dependency is a potential source of systematic bias. We conclude that spatially comprehensive assessments of climate impacts based on yield alone, without accounting for cropping intensity, are prone to systematic overestimation of climate impacts. The findings therefore suggest a need for greater attention to crop suitability and land use change when assessing the impacts of climate change

Prediction models for diagnosis and prognosis of covid-19: : systematic review and critical appraisal

Readers’ note This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity. Funding: LW, BVC, LH, and MDV acknowledge specific funding for this work from Internal Funds KU Leuven, KOOR, and the COVID-19 Fund. LW is a postdoctoral fellow of Research Foundation-Flanders (FWO) and receives support from ZonMw (grant 10430012010001). BVC received support from FWO (grant G0B4716N) and Internal Funds KU Leuven (grant C24/15/037). TPAD acknowledges financial support from the Netherlands Organisation for Health Research and Development (grant 91617050). VMTdJ was supported by the European Union Horizon 2020 Research and Innovation Programme under ReCoDID grant agreement 825746. KGMM and JAAD acknowledge financial support from Cochrane Collaboration (SMF 2018). KIES is funded by the National Institute for Health Research (NIHR) School for Primary Care Research. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. GSC was supported by the NIHR Biomedical Research Centre, Oxford, and Cancer Research UK (programme grant C49297/A27294). JM was supported by the Cancer Research UK (programme grant C49297/A27294). PD was supported by the NIHR Biomedical Research Centre, Oxford. MOH is supported by the National Heart, Lung, and Blood Institute of the United States National Institutes of Health (grant R00 HL141678). ICCvDH and BCTvB received funding from Euregio Meuse-Rhine (grant Covid Data Platform (coDaP) interref EMR187). The funders played no role in study design, data collection, data analysis, data interpretation, or reporting.Peer reviewedPublisher PD

The utility of screening for perinatal depression in the second trimester among Chinese: a three-wave prospective longitudinal study

This paper aims to study the pattern of perinatal depressive symptomatology and determine the predictive power of second trimester perinatal depressive symptoms for future perinatal periods. A population-based sample of 2,178 women completed the Edinburgh Postnatal Depression Scale (EPDS) in the second and third trimesters and at 6 weeks postpartum. Repeated measures ANOVAs were used to determine the EPDS scores across three stages. The predictive power of the second trimester EPDS score in identifying women with an elevated EPDS score in the third trimester and at 6 weeks postpartum were determined. The predictive power of the second trimester EPDS score was further assessed using stepwise logistic regression and receiver operator characteristic curves. EPDS scores differed significantly across three stages. The rates were 9.9%, 7.8%, and 8.7% for an EPDS score of >14 in the second and third trimesters and at 6 weeks postpartum, respectively. Using a cut-off of 14/15, the second trimester EPDS score accurately classified 89.6% of women in the third trimester and 87.2% of those at 6 weeks postpartum with or without perinatal depressive symptomatology. Women with a second trimester EPDS score >14 were 11.78 times more likely in the third trimester and 7.15 times more likely at 6 weeks postpartum to exhibit perinatal depressive symptomatology after adjustment of sociodemographic variables. The area under the curve for perinatal depressive symptomatology was 0.85 in the third trimester and 0.77 at 6 weeks postpartum. To identify women at high risk for postpartum depression, healthcare professionals could consider screening all pregnant women in the second trimester so that secondary preventive intervention may be implemented

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Macroscopic Modeling of Polymer-Electrolyte Membranes

In this chapter, the various approaches for the macroscopic modeling of transport phenomena in polymer-electrolyte membranes are discussed. This includes general background and modeling methodologies, as well as exploration of the governing equations and some membrane-related topic of interest