Resting lateralized activity predicts the cortical response and appraisal of emotions : an fNIRS study

This study explored the effect of lateralized left-right resting brain activity on prefrontal cortical responsiveness to emotional cues and on the explicit appraisal (stimulus evaluation) of emotions based on their valence. Indeed subjective responses to different emotional stimuli should be predicted by brain resting activity and should be lateralized and valence-related (positive vs negative valence). A hemodynamic measure was considered (functional near-infrared spectroscopy). Indeed hemodynamic resting activity and brain response to emotional cues were registered when subjects (N = 19) viewed emotional positive vs negative stimuli (IAPS). Lateralized index response during resting state, LI (lateralized index) during emotional processing and self-assessment manikin rating were considered. Regression analysis showed the significant predictive effect of resting activity (more left or right lateralized) on both brain response and appraisal of emotional cues based on stimuli valence. Moreover, significant effects were found as a function of valence (more right response to negative stimuli; more left response to positive stimuli) during emotion processing. Therefore, resting state may be considered a predictive marker of the successive cortical responsiveness to emotions. The significance of resting condition for emotional behavior was discussed

Boosting advice and knowledge sharing among healthcare professionals

Purpose: This study investigates the dynamics of knowledge sharing in healthcare, exploring some of the factors that are more likely to influence the evolution of idea sharing and advice seeking in healthcare. Design/methodology/approach: We engaged 50 pediatricians representing many subspecialties at a mid-size US children's hospital using a social network survey to map and measure advice seeking and idea sharing networks. Through the application of Stochastic Actor-Oriented Models, we compared the structure of the two networks prior to a leadership program and eight weeks post conclusion. Findings: Our models indicate that healthcare professionals carefully and intentionally choose with whom they share ideas and from whom to seek advice. The process is fluid, non-hierarchical and open to changing partners. Significant transitivity effects indicate that the processes of knowledge sharing can be supported by mediation and brokerage. Originality: Hospital administrators can use this method to assess knowledge-sharing dynamics, design and evaluate professional development initiatives, and promote new organizational structures that break down communication silos. Our work contributes to the literature on knowledge sharing in healthcare by adopting a social network approach, going beyond the dyadic level, and assessing the indirect influence of peers' relationships on individual networks

A leaf area index data set acquired in Sahelian rangelands of Gourma in Mali over the 2005–2017 period

The leaf area index of Sahelian rangelands and related variables such as the vegetation cover fraction, the fraction of absorbed photosynthetically active radiation and the clumping index were measured between 2005 and 2017 in the Gourma region of northern Mali. These variables, known as climate essential variables, were derived from the acquisition and the processing of hemispherical photographs taken along 1&thinsp;km linear sampling transects for five contrasted canopies and one millet field. The same sampling protocol was applied in a seasonally inundated Acacia open forest, along a 0.5&thinsp;km transect, by taking photographs of the understorey and the tree canopy. These observations collected over more than a decade, in a remote and not very accessible region, provide a relevant and unique data set that can be used for a better understanding of the Sahelian vegetation response to the current rainfall changes. The collected data can also be used for satellite product evaluation and land surface model development and validation. This paper aims to present the field work that was carried out during 13 successive rainy seasons, the measured vegetation variables, and the associated open database. Finally, a few examples of data use are shown. DOI of the referenced data set: https://doi.org/10.17178/AMMA-CATCH.CE.Veg_Gh.</p

Observed long-term land cover vs climate impacts on the West African hydrological cycle: lessons for the future ? [P-3330-65]

West Africa has experienced a long lasting, severe drought as from 1970, which seems to be attenuating since 2000. It has induced major changes in living conditions and resources over the region. In the same period, marked changes of land use and land cover have been observed: land clearing for agriculture, driven by high demographic growth rates, and ecosystem evolutions driven by the rainfall deficit. Depending on the region, the combined effects of these climate and environmental changes have induced contrasted impacts on the hydrological cycle. In the Sahel, runoff and river discharges have increased despite the rainfall reduction (“less rain, more water”, the so-called "Sahelian paradox "). Soil crusting and erosion have increased the runoff capacity of the watersheds so that it outperformed the rainfall deficit. Conversely, in the more humid Guinean and Sudanian regions to the South, the opposite (and expected) “less rain, less water” behavior is observed, but the signature of land cover changes can hardly be detected in the hydrological records. These observations over the past 50 years suggest that the hydrological response to climate change can not be analyzed irrespective of other concurrent changes, and primarily ecosystem dynamics and land cover changes. There is no consensus on future rainfall trend over West Africa in IPCC projections, although a higher occurrence of extreme events (rainstorms, dry spells) is expected. An increase in the need for arable land and water resources is expected as well, driven by economic development and demographic growth. Based on past long-term observations on the AMMA-CATCH observatory, we explore in this work various future combinations of climate vs environmental drivers, and we infer the expected resulting trends on water resources, along the west African eco-climatic gradient. (Texte intégral

Control of Transdermal Permeation of Hydrocortisone Acetate from Hydrophilic and Lipophilic Formulations

The purpose of this research was the preparation of four formulations containing hydrocortisone acetate (HCA) for topical application, including two aqueous systems (hydrophilic microemulsion and aqueous gel) and two systems with dominant hydrophobicity (hydrophobic microemulsion and ointment). The formulations were tested for the release and permeation of HCA across an animal membrane. The release of HCA was found comparable for the four systems. The two microemulsions promote permeation across an ex-vivo membrane, examined by means of a Franz cell. Hydrophobic microemulsion guarantees the highest solubility (2,370 μg/ml) and flux (133 μg/cm2.h) of the drug, since it contains almost 40% Transcutol, a permeation enhancer. Gel and ointment provide lower solubility and flux, being the values, related to the ointment, the lowest ones (562 μg/ml and 0.4 μg/cm2.h). Experimental results allow the conclusion that gel and ointment can be suitable when it is desirable to minimize absorption of topically applied HCA as to keep the drug restricted to the diseased area and prevent side effects of the systemic presence of HCA

Extending Data for Urban Health Decision-Making : a Menu of New and Potential Neighborhood-Level Health Determinants Datasets in LMICs

Area-level indicators of the determinants of health are vital to plan and monitor progress toward targets such as the Sustainable Development Goals (SDGs). Tools such as the Urban Health Equity Assessment and Response Tool (Urban HEART) and UN-Habitat Urban Inequities Surveys identify dozens of area-level health determinant indicators that decision-makers can use to track and attempt to address population health burdens and inequalities. However, questions remain as to how such indicators can be measured in a cost-effective way. Area-level health determinants reflect the physical, ecological, and social environments that influence health outcomes at community and societal levels, and include, among others, access to quality health facilities, safe parks, and other urban services, traffic density, level of informality, level of air pollution, degree of social exclusion, and extent of social networks. The identification and disaggregation of indicators is necessarily constrained by which datasets are available. Typically, these include household- and individual-level survey, census, administrative, and health system data. However, continued advancements in earth observation (EO), geographical information system (GIS), and mobile technologies mean that new sources of area-level health determinant indicators derived from satellite imagery, aggregated anonymized mobile phone data, and other sources are also becoming available at granular geographic scale. Not only can these data be used to directly calculate neighborhood- and city-level indicators, they can be combined with survey, census, administrative and health system data to model household- and individual-level outcomes (e.g., population density, household wealth) with tremendous detail and accuracy. WorldPop and the Demographic and Health Surveys (DHS) have already modeled dozens of household survey indicators at country or continental scales at resolutions of 1 × 1 km or even smaller. This paper aims to broaden perceptions about which types of datasets are available for health and development decision-making. For data scientists, we flag area-level indicators at city and sub-city scales identified by health decision-makers in the SDGs, Urban HEART, and other initiatives. For local health decision-makers, we summarize a menu of new datasets that can be feasibly generated from EO, mobile phone, and other spatial data—ideally to be made free and publicly available—and offer lay descriptions of some of the difficulties in generating such data products

Multivariate Prediction of Total Water Storage Changes Over West Africa from Multi-Satellite Data

West African countries have been exposed to changes in rainfall patterns over the last decades, including a significant negative trend. This causes adverse effects on water resources of the region, for instance, reduced freshwater availability. Assessing and predicting large-scale total water storage (TWS) variations are necessary for West Africa, due to its environmental, social, and economical impacts. Hydrological models, however, may perform poorly over West Africa due to data scarcity. This study describes a new statistical, data-driven approach for predicting West African TWS changes from (past) gravity data obtained from the gravity recovery and climate experiment (GRACE), and (concurrent) rainfall data from the tropical rainfall measuring mission (TRMM) and sea surface temperature (SST) data over the Atlantic, Pacific, and Indian Oceans. The proposed method, therefore, capitalizes on the availability of remotely sensed observations for predicting monthly TWS, a quantity which is hard to observe in the field but important for measuring regional energy balance, as well as for agricultural, and water resource management.Major teleconnections within these data sets were identified using independent component analysis and linked via low-degree autoregressive models to build a predictive framework. After a learning phase of 72 months, our approach predicted TWS from rainfall and SST data alone that fitted to the observed GRACE-TWS better than that from a global hydrological model. Our results indicated a fit of 79 % and 67 % for the first-year prediction of the two dominant annual and inter-annual modes of TWS variations. This fit reduces to 62 % and 57 % for the second year of projection. The proposed approach, therefore, represents strong potential to predict the TWS over West Africa up to 2 years. It also has the potential to bridge the present GRACE data gaps of 1 month about each 162days as well as a—hopefully—limited gap between GRACE and the GRACE follow-on mission over West Africa. The method presented could also be used to generate a near real-time GRACE forecast over the regions that exhibit strong teleconnections

Correction to: Water Resources in Africa under Global Change: Monitoring Surface Waters from Space

International audienc

A realist evaluation of the role of communities of practice in changing healthcare practice

<p>Abstract</p> <p>Background</p> <p>Healthcare organisations seeking to manage knowledge and improve organisational performance are increasingly investing in communities of practice (CoPs). Such investments are being made in the absence of empirical evidence demonstrating the impact of CoPs in improving the delivery of healthcare. A realist evaluation is proposed to address this knowledge gap. Underpinned by the principle that outcomes are determined by the context in which an intervention is implemented, a realist evaluation is well suited to understand the role of CoPs in improving healthcare practice. By applying a realist approach, this study will explore the following questions: What outcomes do CoPs achieve in healthcare? Do these outcomes translate into improved practice in healthcare? What are the contexts and mechanisms by which CoPs improve healthcare?</p> <p>Methods</p> <p>The realist evaluation will be conducted by developing, testing, and refining theories on how, why, and when CoPs improve healthcare practice. When collecting data, context will be defined as the setting in which the CoP operates; mechanisms will be the factors and resources that the community offers to influence a change in behaviour or action; and outcomes will be defined as a change in behaviour or work practice that occurs as a result of accessing resources provided by the CoP.</p> <p>Discussion</p> <p>Realist evaluation is being used increasingly to study social interventions where context plays an important role in determining outcomes. This study further enhances the value of realist evaluations by incorporating a social network analysis component to quantify the structural context associated with CoPs. By identifying key mechanisms and contexts that optimise the effectiveness of CoPs, this study will contribute to creating a framework that will guide future establishment and evaluation of CoPs in healthcare.</p