Empirically Derived Suitability Maps to Downscale Aggregated Land Use Data

Understanding mechanisms that drive present land use patterns is essential in order to derive appropriate models of land use change. When static analyses of land use drivers are performed, they rarely explicitly deal with spatial autocorrelation. Most studies are undertaken on autocorrelation-free data samples. By doing this, a great deal of information that is present in the dataset is lost. This paper presents a spatially explicit, cross-sectional, logistic analysis of land use drivers in Belgium. It is shown that purely regressive logistic models can only identify trends or global relationships between socio-economic or physico-climatic drivers and the precise location of each land use type. However, when the goal of a study is to obtain the best model of land use distribution, a purely autoregressive (or neighbourhood-based) model is appropriate. Moreover, it is also concluded that a neighbourhood based only on the 8 surrounding cells leads to the best logistic regression models at this scale of observation. This statement is valid for each land use type studied – i.e. built-up, forests, cropland and grassland.

Grain size modulates volcanic ash retention on crop foliage and potential yield loss

Ashfall from volcanic eruptions endangers crop production and food security while jeopardising agricultural livelihoods. As populations in the vicinity of volcanoes continue to grow, strategies to reduce volcanic risks to and impacts on crops are increasingly needed. Current models of crop vulnerability to ash are limited. They also rely solely on ash thickness (or loading) as the hazard intensity metric and fail to reproduce the complex interplay of other volcanic and non-volcanic factors that drive impact. Amongst these, ash retention on crop leaves affects photosynthesis and is ultimately responsible for widespread damage to crops. In this context, we carried out greenhouse experiments to assess how ash grain size, leaf pubescence, and humidity conditions at leaf surfaces influence the retention of ash (defined as the percentage of foliar cover coated with ash) in tomato and chilli pepper plants, two crop types commonly grown in volcanic regions. For a fixed ash mass load (∼570 g m−2), we found that ash retention decreases exponentially with increasing grain size and is enhanced when leaves are pubescent (such as in tomato plants) or when their surfaces are wet. Assuming that leaf area index (LAI) diminishes with ash retention in tomato and chilli pepper plants, we derived a new expression for predicting potential crop yield loss after an ashfall event. We suggest that the measurement of crop LAI in ash-affected areas may serve as an impact metric. Our study demonstrates that quantitative insights into crop vulnerability can be gained rapidly from controlled experiments. We advocate this approach to broaden our understanding of ash–plant interactions and to validate the use of remote sensing methods for assessing crop damage and recovery at various spatial and time scales after an eruption.</p

Deep Learning for scalp High Frequency Oscillations Identification

Since last 2 decades, High Frequency Oscillations (HFOs) are studied as a promising biomarker to localize the epileptogenic zone of patients with refractory focal epilepsy. As HFOs visual detection is time consuming and subjective, automatization of HFO detection is required. Most HFO detectors were developed on invasive electroencephalograms (iEEG) whereas scalp electroencephalograms (EEG) are used in clinical routine. In order HFO detection can benefit to more patients, scalp HFO detectors has to be developed. However, HFOs identification in scalp EEG is more challenging than in iEEG since scalp HFOs are of lower rate, lower amplitude and more likely to be corrupted by several sources of artifacts than iEEG HFOs. The main goal of this study is to explore the ability of deep learning architecture to identify scalp HFOs from the remaining EEG signal. Hence, a binary classification Convolutional Neural Network (CNN) is learned to analyze High Density Electroencephalograms (HD-EEG). EEG signals are first mapped into a 2D time-frequency image, several color definitions are then used as an input for the CNN. Experimental results show that deep learning allows simple end-to-end learning of preprocessing, feature extraction and classification modules while reaching competitive performance

Autoimmune Epilepsy: Some Epilepsy Patients Harbor Autoantibodies to Glutamate Receptors and dsDNA on both Sides of the Blood-brain Barrier, which may Kill Neurons and Decrease in Brain Fluids after Hemispherotomy

Purpose: Elucidating the potential contribution of specific autoantibodies (Ab's) to the etiology and/or pathology of some human epilepsies. Methods: Six epilepsy patients with Rasmussen's encephalitis (RE) and 71 patients with other epilepsies were tested for Ab's to the –B— peptide (amino acids 372-395) of the glutamate/AMPA subtype 3 receptor (GluR3B peptide), double-stranded DNA (dsDNA), and additional autoimmune disease-associated autoantigens, and for the ability of their serum and cerebrospinal-fluid (CSF) to kill neurons. Results: Elevated anti-GluR3B Ab's were found in serum and CSF of most RE patients, and in serum of 17/71 (24%) patients with other epilepsies. In two RE patients, anti-GluR3B Ab's decreased drastically in CSF following functional-hemispherotomy, in association with seizure cessation and neurological improvement. Serum and CSF of two RE patients, and serum of 12/71 (17%) patients with other epilepsies, contained elevated anti-dsDNA Ab's, the hallmark of systemic-lupus-erythematosus. The sera (but not the CSF) of some RE patients contained also clinically elevated levels of –classical— autoimmune Ab's to glutamic-acid-decarboxylase, cardiolipin, β2-glycoprotein-I and nuclear-antigens SS-A and RNP-70. Sera and CSF of some RE patients caused substantial death of hippocampal neurons. Conclusions: Some epilepsy patients harbor Ab's to GluR3 and dsDNA on both sides of the blood-brain barrier, and additional autoimmune Ab's only in serum. Since all these Ab's may be detrimental to the nervous system and/or peripheral organs, we recommend testing for their presence in epilepsy, and silencing their activity in Ab-positive patients

Students’ Willingness to Plant Trees and Pay for Their Maintenance on Campuses in the Democratic Republic of Congo

peer reviewedThere is a growing interest in greening schools, campuses, and workplaces due to the perceived ecosystem services provided by trees. However, students’ willingness to participate in and financially support the greening process is less examined. Using a questionnaire survey based on the contingent valuation method (CVM) and Likert scale, 1278 students from 13 universities were interviewed on their willingness to participate in tree planting and pay for their maintenance to promote green and clean campuses in the Democratic Republic of Congo (DRC). Most of the students interviewed were male (61%) and enrolled as undergraduates (60%). While 65% of the respondents agreed with the idea of planting trees, this agreement was significantly associated with students’ awareness of climate change, the university attended, and the sources of information on the roles of trees in the community. The binary logit results showed that university courses (environment-related) and television broadcasts significantly affected students’ willingness to participate in tree planting. Overall, students (70%) agreed to pay for tree maintenance, and their willingness to pay (WTP) was estimated mainly at less than USD 5 per year. The price to pay was a significant factor in determining students’ willingness to pay for tree maintenance. Findings suggest that students’ willingness to support tree planting and maintenance is a crucial factor for academic authorities and planners to consider in order to successfully implement green infrastructures to improve the campus environment and make educational and work spaces sustainably attractive