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Andriamananjara, A.

Publications and source records attributed to Andriamananjara, A..

2 recordsLinked to original sources

Farm yard manure enhances phosphate fertilizer use efficiency in different upland rice genotypes through mitigating aluminium toxicity

Upland rice production on weathered soils is often constrained by phosphorus (P) deficiency and soil acidity. Farmyard manure application (FYM) can sharply enhance yields and agronomic P fertilizer (TSP) efficiency. We tested the hypothesis that rice genotypes differ in the extent of using organic P and offering distinct benefits under TSP-FYM combinations. Multiple field trials were conducted in the uplands of Madagascar, with factorial combinations of six genotypes, FYM, and TSP applications, with blanket N&K additions. Rice grain yields reached 6 t ha-1 after three years of TSP+FYM application, were lower when FYM or TSP were used separately, while crops failed under zero P input. Genotypic differences were inferior to the large treatment effects. Application of FYM increased soil pH and CaCl2-extractable P while decreasing CaCl2-extractable aluminium. An additional liming trial indicated that beneficial effects of FYM over TSP relate to liming effects. Genotypic ranking of yields and agronomic efficiency was inconsistent, without superior genotypes under FYM versus TSP. However, Chomrong Dhan and FOFIFA 172 showed superior yields under TSP+FYM. The FYM application lowers aluminium toxicity which overrules potential effects of organic P supply. Aluminium tolerance should be included when developing rice genotypes for low P tolerance in weathered soils.

plant biology↗

Overlaps in Brain Dynamical Functional Connectivity between Schizophrenia and Autism Spectrum Disorder

Schizophrenia and autism share some genotipic and phenotypic aspects as connectome miswiring and common cognitive deficits. Currently, there are no medical tests available for either disorders, and diagnostics for both of them include direct reports of relatives and clinical evaluation by a psychiatrist. Despite several medical imaging biomarkers have been proposed in the past, novel effective biomarkers or improvements of the existing ones is still need. This work proposes a dynamic functional connectome analysis combined with machine learning techniques to complement the present diagnostic procedure. We used the moving window technique to locate a set of dynamic functional connectivity states, and then use them as features to classify subjects as autism/schizophrenia or control. Moreover, by using dynamic functional connectivity measures we investigate the question whether those two disorders overlap, namely whether schizophrenia is part of the autism spectrum and which brain region could be involved in both disorders. The results reveal that both static and dynamic functional connectivity can be used to classify subjects with schizophrenia or autism. Lastly, some brain regions show similar functional flexibility in both autism and schizophrenia cohorts giving further possible proofs of their overlaps.

neuroscience↗