Valeur de référence pourl'humus des terres assolées - Etablissement d’une valeur de référence applicable à la teneur en matière organique des sols minéraux agricole

La présente brochure s’adresseaux organes exécutifs de protection des sols et aux professionnels avertis. Elle présente les possibilités d’établir une valeur de référence applicable à la teneur en matière organique des sols minéraux agricoles. Elle intègre la réglementation suisse ainsi que les acquiset les avis d'un atelier réunissant des acteurs de l'agriculture, de la protection de l'environnement et de l’exécution cantonale, qui s'est tenu le 16novembre 201

Fungal diversity within organic and conventional farming systems in Central Highlands of Kenya

Open Access Article; Published online: 30 June 2020Fungal diversity in agro-ecosystems is influenced by various factors related to soil and crop management practices. However, due to the complexity in fungal cultivation, only a limited number has been extensively studied. In this study, amplicon sequencing of the Internal Transcribed Spacer (ITS) region was used to explore their diversity and composition within long-term farming system comparison trials at Chuka and Thika in Kenya. Sequences were grouped into operational taxonomic units (OTUs) at 97% similarity and taxonomy assigned via BLASTn against UNITE ITS database and a curated database derived from GreenGenes, RDPII and NCBI. Statistical analyses were done using Vegan package in R. A total of 1,002,188 high quality sequences were obtained and assigned to 1,128 OTUs; they were further classified into eight phyla including Ascomycota, Basidiomycota, Chytridiomycota, Glomeromycota, Calcarisporiellomycota, Kickxellomycota, Mortierellomycota and unassigned fungal phyla. Ascomycota was abundant in conventional systems at Chuka site while Basidiomycota and Chytridiomycota were dominant in conventional systems in both sites. Kickxellomycota and Calcarisporiellomycota phyla were present in all organic systems in both sites. Conventional farming systems showed a higher species abundance and diversity compared to organic farming systems due to integration of organic and inorganic inputs

Resting-state alterations in behavioral variant frontotemporal dementia are related to the distribution of monoamine and GABA neurotransmitter systems

Aside to clinical changes, behavioral variant frontotemporal dementia (bvFTD) is characterized by progressive structural and functional alterations in frontal and temporal regions. We examined if there is a selective vulnerability of specific neurotransmitter systems in bvFTD by evaluating the link between disease-related functional alterations and the spatial distribution of specific neurotransmitter systems and their underlying gene expression levels.Maps of fractional amplitude of low frequency fluctuations (fALFF) were derived as a measure of local activity from resting-state functional magnetic resonance imaging for 52 bvFTD patients (mean age = 61.5 ± 10.0 years; 14 female) and 22 healthy controls (HC) (mean age = 63.6 ± 11.9 years; 13 female). We tested if alterations of fALFF in patients co-localize with the non-pathological distribution of specific neurotransmitter systems and their coding mRNA gene expression. Further, we evaluated if the strength of co-localization is associated with the observed clinical symptoms.Patients displayed significantly reduced fALFF in fronto-temporal and fronto-parietal regions. These alterations co-localized with the distribution of serotonin (5-HT1b, 5-HT2a), dopamine (D2), and γ-aminobutyric acid (GABAa) receptors, the norepinephrine transporter (NET), and their encoding mRNA gene expression. The strength of co-localization with D2 and NET was associated with cognitive symptoms and disease severity of bvFTD.Local brain functional activity reductions in bvFTD followed the distribution of specific neurotransmitter systems indicating a selective vulnerability. These findings provide novel insight into the disease mechanisms underlying functional alterations. Our data-driven method opens the road to generate new hypotheses for pharmacological interventions in neurodegenerative diseases even beyond bvFTD

Many-body approach to proton emission and the role of spectroscopic factors

The process of proton emission from nuclei is studied by utilizing the two-potential approach of Gurvitz and Kalbermann in the context of the full many-body problem. A time-dependent approach is used for calculating the decay width. Starting from an initial many-body quasi-stationary state, we employ the Feshbach projection operator approach and reduce the formalism to an effective one-body problem. We show that the decay width can be expressed in terms of a one-body matrix element multiplied by a normalization factor. We demonstrate that the traditional interpretation of this normalization as the square root of a spectroscopic factor is only valid for one particular choice of projection operator. This causes no problem for the calculation of the decay width in a consistent microscopic approach, but it leads to ambiguities in the interpretation of experimental results. In particular, spectroscopic factors extracted from a comparison of the measured decay width with a calculated single-particle width may be affected.Comment: 17 pages, Revte

Singularity-free model of electric charge in physical vacuum: Non-zero spatial extent and mass generation

We propose a model of a spinless electrical charge as a self-consistent field configuration of the electromagnetic (EM) field interacting with a physical vacuum effectively described by the logarithmic quantum Bose liquid. We show that, in contrast to the EM field propagating in a trivial vacuum, a regular solution does exist, and both its mass and spatial extent emerge naturally from dynamics. It is demonstrated that the charge and energy density distribution acquire Gaussian-like form. The solution in the logarithmic model is stable and energetically favourable, unlike that obtained in a model with a quartic (Higgs-like) potential.Comment: 10 pages, 9 figures, final/published versio

Nominal or Real? The Impact of Regional Price Levels on Satisfaction with Life

According to economic theory, real income, i.e., nominal income adjusted for purchasing power, should be the relevant source of life satisfaction. Previous work, however, has only studied the impact of inflation adjusted nominal income and not taken into account regional differences in purchasing power. Therefore, we use a novel data set to study how regional price levels affect satisfaction with life. The data set comprises about 7 million data points that are used to construct a price level for each of the 428 administrative districts in Germany. We estimate pooled OLS and ordered probit models that include a comprehensive set of individual level, time-varying and time-invariant control variables as well as control variables that capture district heterogeneity other than the price level. Our results show that higher price levels significantly reduce life satisfaction. Furthermore, we find that a higher price level tends to induce a larger loss in life satisfaction than a corresponding decrease in nominal income. A formal test of neutrality of money, however, does not reject neutrality of money. Our results provide an argument in favor of regional indexation of government transfer payments such as social welfare benefits

Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging

IntroductionDementia syndromes can be difficult to diagnose. We aimed at building a classifier for multiple dementia syndromes using magnetic resonance imaging (MRI).MethodsAtlas-based volumetry was performed on T1-weighted MRI data of 426 patients and 51 controls from the multi-centric German Research Consortium of Frontotemporal Lobar Degeneration including patients with behavioral variant frontotemporal dementia, Alzheimer’s disease, the three subtypes of primary progressive aphasia, i.e., semantic, logopenic and nonfluent-agrammatic variant, and the atypical parkinsonian syndromes progressive supranuclear palsy and corticobasal syndrome. Support vector machine classification was used to classify each patient group against controls (binary classification) and all seven diagnostic groups against each other in a multi-syndrome classifier (multiclass classification).ResultsThe binary classification models reached high prediction accuracies between 71 and 95% with a chance level of 50%. Feature importance reflected disease-specific atrophy patterns. The multi-syndrome model reached accuracies of more than three times higher than chance level but was far from 100%. Multi-syndrome model performance was not homogenous across dementia syndromes, with better performance in syndromes characterized by regionally specific atrophy patterns. Whereas diseases generally could be classified vs controls more correctly with increasing severity and duration, differentiation between diseases was optimal in disease-specific windows of severity and duration.DiscussionResults suggest that automated methods applied to MR imaging data can support physicians in diagnosis of dementia syndromes. It is particularly relevant for orphan diseases beside frequent syndromes such as Alzheimer’s disease

The DOK long-term experiment - lessons learned from 40 years of interdisciplinary research

The world’s growing population calls for sustainable food production within the limits of planetary boundaries. With respect to nitrogen and phosphorus cycling, the loss of biodiversity, land use change and the emission of greenhouse gases, four of these boundaries have been crossed already. Although fragmented knowledge of effects of different cropping systems on these focal planetary boundaries exists, there is a lack of comprehensive data from comparative cropping system experiments over the long run. Four decades back, farmers and researchers co-designed a system comparison experiment, located in Therwil (Basel-Land) Switzerland, comprising a seven-year ley crop rotation. Two conventional (with and without manure), and two organic systems (biodynamic and bioorganic) are compared. This experiment has served as a platform for national and international interdisciplinary research teams in the field of agronomy, soil quality, biodiversity, plant nutrition, food quality, sustainability assessment and modelling. Results of the 40years old DOK experiment show that organic systems, receiving distinctly less external inputs (chemical N, P, K and pesticides), maintained a higher biodiversity and produced lower greenhouse gas emissions. Yield averages over 40 years were 20% lower in organic systems across all crops. A nitrogen balance, including biological nitrogen fixation and stock changes of soil nitrogen, revealed a surplus for all manured systems, whereas the conventional system with sole mineral fertiliser was well balanced. Soil nitrogen stocks only increased slightly in the biodynamic system receiving composted manure. The biodynamic soil showed also increased soil organic carbon stocks, while the conventional soil receiving only mineral fertilizer acted as source for atmospheric CO2. A climate impact analyses encountering nitrous oxide, methane and soil organic matter changes resulted in lower CO2eq emissions in organic compared to the conventional systems, both area and yield scaled. Biodiversity and especially biomass of invertebrate fauna and plant seeds was enhanced in the organically managed systems. Our results demonstrate that organic cropping systems can contribute to a more sustainable production with respect to key planetary boundaries. To further improve system performance, yield gaps between organic and conventional systems need to be reduced by adapted cultivars, more effective organic plant protection and by closing urban and rural nutrient cycles

Comparative analysis of machine learning algorithms for multi-syndrome classification of neurodegenerative syndromes

Importance: The entry of artificial intelligence into medicine is pending. Several methods have been used for the predictions of structured neuroimaging data, yet nobody compared them in this context. Objective: Multi-class prediction is key for building computational aid systems for differential diagnosis. We compared support vector machine, random forest, gradient boosting, and deep feed-forward neural networks for the classification of different neurodegenerative syndromes based on structural magnetic resonance imaging. Design, setting, and participants: Atlas-based volumetry was performed on multi-centric T1-weighted MRI data from 940 subjects, i.e., 124 healthy controls and 816 patients with ten different neurodegenerative diseases, leading to a multi-diagnostic multi-class classification task with eleven different classes. Interventions: N.A. Main outcomes and measures: Cohen's kappa, accuracy, and F1-score to assess model performance. Results: Overall, the neural network produced both the best performance measures and the most robust results. The smaller classes however were better classified by either the ensemble learning methods or the support vector machine, while performance measures for small classes were comparatively low, as expected. Diseases with regionally specific and pronounced atrophy patterns were generally better classified than diseases with widespread and rather weak atrophy. Conclusions and relevance: Our study furthermore underlines the necessity of larger data sets but also calls for a careful consideration of different machine learning methods that can handle the type of data and the classification task best