Messinian salinity crisis impact on the groundwater quality in Kert aquifer NE Morocco: Hydrochemical and statistical approaches.

Groundwater's studies at middle Kert aquifer in northeast of Morocco are very important due to the semi-arid character and its geological history. The region is recognized by messinian salinity crisis already 5.6 Ma. Water chemistry is mainly dominated by dissolution of evaporate rocks (Halite and Gypsum) related to outcropping and basement limits developed in Messinian age. Freshwater with total dissolved solids 740 mg/l (average value) in Tafersite district is chemically distinct from saline water with total dissolved solids of 9803 mg/l in the south zone. In wadis, water is S04-Cl-Ca type; they are influenced by the surrounding highlands located at the south of the plain. The investigation reveals that weathering of evaporated rocks is the processes responsible for high Na+, Ca2+, Mg2+, Cl andSO42- concentrations. Also, hydro chemical data displays that freshwater observed in the northwest part reflect the influence of freshwaters coming from metamorphic massive of Temsamane. The factorial analysis reveal three sources of salinization, the principal one is described above, whilst the dissolution of carbonates and human influence represented by NO3- played only a secondary role

Multi-view Brain Network Prediction from a Source View Using Sample Selection via CCA-Based Multi-kernel Connectomic Manifold Learning

Several challenges emerged from the dataclysm of neuroimaging datasets spanning both healthy and disordered brain spectrum. In particular, samples with missing data views (e.g., functional imaging modality) constitute a hurdle to conventional big data learning techniques which ideally would be trained using a maximum number of samples across all views. Existing works on predicting target data views from a source data view mainly used brain images such as predicting PET image from MRI image. However, to the best of our knowledge, predicting a set of target brain networks from a source network remains unexplored. To ll this gap, a multi-kernel manifold learning (MKML) framework is proposed to learn how to predict multi-view brain networks from a source network to impute missing views in a connectomic dataset. Prior to performing multiple kernel learning of multi-view data, it is typically assumed that the source and target data come from the same distribution. However, multi-view connectomic data can be drawn from different distributions. In order to build robust predictors for predicting target multi-view networks from a source network view, it is necessary to take into account the shift between the source and target domains. Hence, we first estimate a mapping function that transforms the source and the target domains into a shared space where their correlation is maximized using canonical correlation analysis (CCA). Next, we nest the projected training and testing source samples into a connectomic manifold using multiple kernel learning, where we identify the most similar training samples to the testing source network. Given a testing subject, we introduce a cross-domain trust score to assess the reliability of each selected training sample for the target prediction task. Our model outperformed both conventional MKML technique and the proposed CCA-based MKMLtechnique without enhancement by trust scores

Joint Correlational and Discriminative Ensemble Classifier Learning for Dementia Stratification Using Shallow Brain Multiplexes

The demented brain wiring undergoes several changes with dementia progression. However, in early dementia stages, particularly early mild cognitive impairment (eMCI), these remain challenging to spot. Hence, developing accurate diagnostic techniques for eMCI identification is critical for early intervention to prevent the onset of Alzheimer’s Disease (AD). There is a large body of machine-learning based research developed for classifying different brain states (e.g., AD vs MCI). These works can be fundamentally grouped into two categories. The first uses correlational methods, such as canonical correlation analysis (CCA) and its variants, with the aim to identify most correlated features for diagnosis. The second includes discriminative methods, such as feature selection methods and linear discriminative analysis (LDA) and its variants to identify brain features that distinguish between two brain states. However, existing methods examine these correlational and discriminative brain data independently, which overlooks the complementary information provided by both techniques, which could prove to be useful in the classification of patients with dementia. On the other hand, how early dementia affects cortical brain connections in morphology remains largely unexplored. To address these limitations, we propose a joint correlational and discriminative ensemble learning framework for eMCI diagnosis that leverages a novel brain network representation, derived from the cortex. Specifically, we devise ‘the shallow convolutional brain multiplex’ (SCBM), which not only measures the similarity in morphology between pairs of brain regions, but also encodes the relationship between two morphological brain networks. Then, we represent each individual brain using a set of SCBMs, which are used to train joint ensemble CCA-SVM and LDA-based classifier. Our framework outperformed several state-of-the-art methods by 3-7% including independent correlational and discriminative methods.</p

Évaluation de la qualité des eaux souterraines pour l’utilisation dans l’eau potable et l’agriculture : plaine de Tadla, Maroc

La plaine de Tadla fait partie du bassin de l’oued Oum Erbia situé au centre du Maroc. Ses ressources en eau souterraine sont développées pour l’approvisionnement en eau potable, industrielle et agricole. Afind'évaluer la qualité des eaux souterraines dans la zone d’ d'étude, 25 échantillons d'eau souterraines ont été prélevés et différents paramètres ont été analysés sur le plan physico-chimique et bactériologique:température, conductivité électrique, pH, TDS, Na+, K+, Ca2+, Mg2+, Cl-, HCO-3, SO2-4, NO-3, NH+4, FeT, streptocoques fécaux, coliformes fécaux et coliformes totaux. L'indice chimique tel que le coefficient d’absorption du sodium (SAR) et l'indice de perméabilité (IP) ont également été déterminés. Les résultats obtenus montrent que les eaux souterraines du bassin sont généralement dure à très à dure. Les concentrations sont classées comme suit : Na+ &gt; Ca2+ &gt; Mg2+ &gt; K+ et Cl- &gt; HCO3- &gt; SO42- &gt; NO-3. Les faciès chimiques trouvés sont le bicarbonaté calcique et le Chloruré sodique avec une prédominance de ce dernier. La qualité des eaux souterraines est liée à la lithologie du secteur. Les valeurs de l'indice de saturation (calculés par le programme PHREEQC) montre que presque tous les échantillons d'eau sont saturés à sous-saturés en carbonate et sous saturés en sulfate. Le rapport d’adsorption du sodium (SAR)nous a permis de qualifier les eaux souterraines destinées à l’irrigation. L’analyse hydrochimique a montré la mauvaise qualité des eaux se traduisant par des valeurs importantes en chlorures, en nitrites et nitrates.Ainsi que la contamination de tous les puits par les germes de la  contamination fécale. Il ressort de cette analyse que les eaux souterraines sont chimiquement non appropriées à la consommation humaine et auxusages agricoles.Mots-clés : plaine de Tadla, hydrochimie, qualité des eaux souterraines, hydrogéologie, type d’eau

Reducing the clique and chromatic number via edge contractions and vertex deletions

We consider the following problem: can a certain graph parameter of some given graph G be reduced by at least d, for some integer d, via at most k graph operations from some specified set S, for some given integer k? As graph parameters we take the chromatic number and the clique number. We let the set S consist of either an edge contraction or a vertex deletion. As all these problems are NP-complete for general graphs even if d is fixed, we restrict the input graph G to some special graph class. We continue a line of research that considers these problems for subclasses of perfect graphs, but our main results are full classifications, from a computational complexity point of view, for graph classes characterized by forbidding a single induced connected subgraph H

Histiocytose langerhansienne multiviscerale avec atteinte auriculaire bilaterale a propos d’une observation

L’histiocytose langerhansienne multi-viscérale est une prolifération clonale des cellules de Langerhans, touchant plusieurs organes. Cette entité se voit surtout chez l’enfant. Dans ce travail, nous rappelons les aspects cliniques avec la fréquence d’atteinte oto-rhino-laryngologique, ainsi que les moyens de diagnostic et le traitement de cette affection rare. Nous présentons le cas d’un enfant âgé de 2 ans qui a été hospitalisé pour une pneumopathie interstitielle, associée à une otorrhée bilatérale. L’examen a montré un comblement des 2 conduits auditifs externes et des lésions cutanées squameuses. La biopsie a conclu à une histiocytose langerhansienne. Malgré la chimiothérapie, l’enfant est décédé après 11 mois.Mots-clés : Histiocytose langerhansienne, atteinte auriculaire

Epigenetic reprogramming of cancer cells under embryonic microenvironment

The idea of epigenetic reprogramming of cancer cells by an embryonic microenvironment possesses potential interest from the prospect of both basic science and potential therapeutic strategies. Chick embryo extract (CEE) has been used for the successful expansion of many specific stem cells and has demonstrated the ability to facilitate DNA demethylation. The current study was conducted to compare the status of DNA methylation in highly metastatic and less metastatic osteosarcoma cells and to investigate whether CEE may affect the epigenetic regulation of tumor suppressor genes and thus change the metastatic phenotypes of highly metastatic osteosarcoma cells

A problem encapsulated – role of CT

Sclerosing encapsulating peritonitis (SEP) is a rare but serious complication of abdominal surgery, recurrent peritonitis, and continuous ambulatory peritoneal dialysis with a high morbidity and mortality. The etiology of this condition is largely unknown. Diagnosis is usually established at laparotomy in patients with recurrent attacks of non-strangulating, small bowel obstruction. We report a case of a patient who presented with intestinal obstruction and who showed typical CT findings of SEP which was diagnosed pre-operatively on a CT scan and confirmed at surgery. The interest of this case lies in its rarity and difficult pre-operative diagnosis

Effect of heat treatment on the migration behaviour of Sr and Ag CO-implanted in glassy carbon

The effect of annealing on the diffusion of silver, silver and strontium co-implanted in glassy carbon was investigated. Glassy carbon samples were implanted with 360 keV Ag ions at room temperature. The RBS profile showed that Fickian diffusion of Ag in glassy carbon is only observed at temperatures ranging from 500 °C–600 °C. At higher annealing temperatures, there was a significant loss of Ag and no Ag was retained in glassy carbon at 700 °C. Glassy carbon samples were also co-implanted with Ag and Sr. The diffusion behaviour of Ag when co-implanted with Sr was similar to that of the singly implanted Ag sample. However, the introduction of Sr into the glassy carbon matrix assisted in the retainment of the Ag ions. The co-implantation of Ag and Sr resulted in a change in the diffusion behaviour of Sr in glassy carbon. The implantation of Ag with Sr prevented the movement of Sr deeper into the bulk of the glassy carbon. The non-movement of Sr into the bulk of the glassy carbon was attributed to the increase of radiation damage near the surface of the glassy carbon making diffusion of Sr towards the surface of glassy carbon an easier choice.The National Research Foundation, South Africa and the TWAS-DFG Co-operation Programme.http://www.journals.elsevier.com/vacuumhj2021Physic