Coagulation factor Xa activates thrombin in ischemic neural tissue.

Thrombin is involved in mediating neuronal death in cerebral ischemia. We investigated its so far unknown mode of activation in ischemic neural tissue. We used an in vitro approach to distinguish the role of circulating coagulation factors from endogenous cerebral mechanisms. We modeled ischemic stroke by subjecting rat organotypic hippocampal slice cultures to 30-min oxygen (5%) and glucose (1 mmol/L) deprivation (OGD). Perinuclear activated factor X (FXa) immunoreactivity was observed in CA1 neurons after OGD. Selective FXa inhibition by fondaparinux during and after OGD significantly reduced neuronal death in the CA1 after 48 h. Thrombin enzyme activity was increased in the medium 24 h after OGD and this increase was prevented by fondaparinux suggesting that FXa catalyzes the conversion of prothrombin to thrombin in neural tissue after ischemia in vitro. Treatment with SCH79797, a selective antagonist of the thrombin receptor protease-activated receptor-1 (PAR-1), significantly decreased neuronal cell death indicating that thrombin signals ischemic damage via PAR-1. The c-Jun N-terminal kinase (JNK) pathway plays an important role in excitotoxicity and cerebral ischemia and we observed activation of the JNK substrate, c-Jun in our model. Both the FXa inhibitor, fondaparinux and the PAR-1 antagonist SCH79797, decreased the level of phospho-c-Jun Ser73. These results indicate that FXa activates thrombin in cerebral ischemia, which leads via PAR-1 to the activation of the JNK pathway resulting in neuronal death.

Synaptic Plasticity at Intrathalamic Connections via CaV3.3 T-type Ca2+ Channels and GluN2B-Containing NMDA Receptors.

The T-type Ca(2+) channels encoded by the Ca(V)3 genes are well established electrogenic drivers for burst discharge. Here, using Ca(V)3.3(-/-) mice we found that Ca(V)3.3 channels trigger synaptic plasticity in reticular thalamic neurons. Burst discharge via Ca(V)3.3 channels induced long-term potentiation at thalamoreticular inputs when coactivated with GluN2B-containing NMDA receptors, which are the dominant subtype at these synapses. Notably, oscillatory burst discharge of reticular neurons is typical for sleep-related rhythms, suggesting that sleep contributes to strengthening intrathalamic circuits.

Wild-type ALK and activating ALK-R1275Q and ALK-F1174L mutations upregulate Myc and initiate tumor formation in murine neural crest progenitor cells.

The anaplastic lymphoma kinase (ALK) gene is overexpressed, mutated or amplified in most neuroblastoma (NB), a pediatric neural crest-derived embryonal tumor. The two most frequent mutations, ALK-F1174L and ALK-R1275Q, contribute to NB tumorigenesis in mouse models, and cooperate with MYCN in the oncogenic process. However, the precise role of activating ALK mutations or ALK-wt overexpression in NB tumor initiation needs further clarification. Human ALK-wt, ALK-F1174L, or ALK-R1275Q were stably expressed in murine neural crest progenitor cells (NCPC), MONC-1 or JoMa1, immortalized with v-Myc or Tamoxifen-inducible Myc-ERT, respectively. While orthotopic implantations of MONC- 1 parental cells in nude mice generated various tumor types, such as NB, osteo/ chondrosarcoma, and undifferentiated tumors, due to v-Myc oncogenic activity, MONC-1-ALK-F1174L cells only produced undifferentiated tumors. Furthermore, our data represent the first demonstration of ALK-wt transforming capacity, as ALK-wt expression in JoMa1 cells, likewise ALK-F1174L, or ALK-R1275Q, in absence of exogenous Myc-ERT activity, was sufficient to induce the formation of aggressive and undifferentiated neural crest cell-derived tumors, but not to drive NB development. Interestingly, JoMa1-ALK tumors and their derived cell lines upregulated Myc endogenous expression, resulting from ALK activation, and both ALK and Myc activities were necessary to confer tumorigenic properties on tumor-derived JoMa1 cells in vitro.

Autocrine secretion of IGF2 regulates adult ß cell functional plasticity

In the pathogenesis of type 2 diabetes, hyperglycemia appears when ß cell mass and insulin secretory capacity are no longer sufficient to compensate for insulin resistance. The reduction in ß cell mass results from increased apoptosis. Therefore, finding strategies to preserve ß cell mass and function may be useful for the treatment or prevention of diabetes. Glucagon-like peptide-1 (GLP-1) protects ß cells against apoptosis, increases their glucose competence, and induces their proliferation. Previous studies in the lab of Prof. Bernard Thorens showed that the GLP-1 anti- apoptotic effect was mediated by robust up-regulation of IGF-1R expression, and this was paralleled with an increase in Akt phosphorylation. This effect was dependent not only on increased IGF-1R expression but also on the autocrine secretion of insulin-like growth factor 2 (IGF2). They also demonstrated that GLP-1 up-regulated IGF-1R expression by a protein a kinase A-dependent translational control mechanism. The main aim of this PhD work has been to further investigate the role of the IGF2/IGF-1 Receptor autocrine loop in ß cell function and to determine the physiological role of IGF2 in ß cell plasticity and its regulation by nutrients. This PhD thesis is divided in 3 chapters. The first chapter describes the role of IGF2/IGF-1R autocrine loop in ß cell glucose competence and proliferation. Here using MIN6 cells and primary mouse islets as an experimental model we demonstrated that the glucose competence of these cells was dependent on the level of IGF-1R expression and on IGF2 secretion. Furthermore, we showed that GLP-1-induced primary ß cell proliferation was significantly reduced by Igf-lr gene inactivation and by IGF2 immunoneutralization or knockdown. In the second chapter we examined the role of this IGF2/IGF-1R autocrine loop on the ß cell functional plasticity during ageing, pregnancy, and in response to acute induction of insulin resistance using mice with ß cell-specific inactivation of ig/2. Here we showed a gender-dependent role of ß cell IGF2 in ageing and high fat diet-induced metabolic stress; we demonstrated that the autocrine secretion of IGF2 is essential for ß cell mass adaptation during pregnancy. Further we also showed that this autocrine loop plays an important role in ß cell expansion in response to acute induction of insulin resistance. The aim of the third chapter was to investigate whether we can modulate the expression and secretion of IGF2 by nutrients in order to increase the activity of autocrine loop. Here we showed that glutamine induces IGF2 biosynthesis and its fast secretion through the regulated pathway, a mechanism enhanced in the presence of glucose. Furthermore, we demonstrated that glutamine-mediated Akt phosphorylation is dependent on IGF2 secretion, indicating that glutamine controls the activity of the IGF2/IGF1R autocrine loop through IGF2 up-regulation. In summary, this PhD work highlights that autocrine secretion of IGF2 is required for compensatory ß cell adaptation to ageing, pregnancy, and insulin resistance. Moreover IGF2/IGF1R autocrine loop is regulated by two feeding-related cues, GLP-1 to increase IGF-1R expression and glutamine to control IGF2 biosynthesis and secretion. -- Dans le diabète de type 2, lorsque la sécrétion d'insuline des cellules Beta du pancréas n'est plus suffisante pour compenser la résistance à l'insuline, une hyperglycémie est observée. Cette baisse de sécrétion d'insuline est Causée par la diminution de la masse de cellules Beta suite à l'augmentation du phénomène de mort cellulaire ou « apoptose ». En diabétologie, une des stratégies médicales concerne la préservation des cellules Beta du pancréas. Une des protéines intervenant dans cette fonction est GLP-1 (Glucagon-like peptide-1). GLP-1 est capable de protéger les cellules Beta contre la mort cellulaire et d'induire leur prolifération. Des études précédemment menées dans le laboratoire du Professeur Bernard Thorens ont montrées que l'activité « anti-apoptotique » de GLP-1 est le résultat l'une augmentation de l'expression du gène IGF-1R sous la dépendance de la sécrétion autocrine d'IGF2 (Insulin-Like Growth Factor). Le but de mon travail de thèse aura été d'étudier le mécanisme de la régulation de GLP-1 par IGF2 et plus précisément de déterminer le rôle physiologique d'IGF2 dans la plasticité des cellules ß ainsi que sa régulation par les nutriments. Ce manuscrit est ainsi divisé en trois chapitres : Le premier chapitre décrit la fonction d'IGF2/IGF- R1 dans la réponse des cellules Beta au glucose ainsi que dans leur capacité à proliférer. Dans ce chapitre nous avons montré l'importance du niveau d'expression d'IGFR-1 et de la sécrétion d'IGF2 dans la régulation du métabolisme du glucose. Dans un deuxième chapitre, nous étudions la boucle de régulation IGF2/IGF-R1 sur la plasticité des cellules Beta lors du vieillissement, de la grossesse ainsi que dans un modèle de souris résistantes à l'insuline. Cette étude met en évidence un dimorphisme sexuel dans le rôle d'IGF2 lors du vieillissement et lors d'un stress métabolique. Nous montrons également l'importance d'IGF2 pour l'adaptation des cellules Beta tout au long de la grossesse ou lors du phénomène de résistance à l'insuline. Dans un troisième chapitre, nous mettons en évidence la possibilité de moduler l'expression et la sécrétion d'IGF2 par les nutriments. En conclusion, ce travail de thèse aura permis de mettre en évidence l'importance d'IGF2 dans la plasticité des cellules ß, une plasticité indispensable lors du vieillissement, de la grossesse ou encore dans le cas d'une résistance à l'insuline.

Modelling non-industrial private forest landowners' strategic decision making by using logistic regression and neural networks: case of predicting the choice of forest taxation basis

Summary

Structural brain plasticity in Parkinson's disease induced by balance training.

We investigated morphometric brain changes in patients with Parkinson's disease (PD) that are associated with balance training. A total of 20 patients and 16 healthy matched controls learned a balance task over a period of 6 weeks. Balance testing and structural magnetic resonance imaging were performed before and after 2, 4, and 6 training weeks. Balance performance was re-evaluated after ∼20 months. Balance training resulted in performance improvements in both groups. Voxel-based morphometry revealed learning-dependent gray matter changes in the left hippocampus in healthy controls. In PD patients, performance improvements were correlated with gray matter changes in the right anterior precuneus, left inferior parietal cortex, left ventral premotor cortex, bilateral anterior cingulate cortex, and left middle temporal gyrus. Furthermore, a TIME × GROUP interaction analysis revealed time-dependent gray matter changes in the right cerebellum. Our results highlight training-induced balance improvements in PD patients that may be associated with specific patterns of structural brain plasticity. In summary, we provide novel evidence for the capacity of the human brain to undergo learning-related structural plasticity even in a pathophysiological disease state such as in PD.

Soil infiltration based on bp neural network and grey relational analysis

Soil infiltration is a key link of the natural water cycle process. Studies on soil permeability are conducive for water resources assessment and estimation, runoff regulation and management, soil erosion modeling, nonpoint and point source pollution of farmland, among other aspects. The unequal influence of rainfall duration, rainfall intensity, antecedent soil moisture, vegetation cover, vegetation type, and slope gradient on soil cumulative infiltration was studied under simulated rainfall and different underlying surfaces. We established a six factor-model of soil cumulative infiltration by the improved back propagation (BP)-based artificial neural network algorithm with a momentum term and self-adjusting learning rate. Compared to the multiple nonlinear regression method, the stability and accuracy of the improved BP algorithm was better. Based on the improved BP model, the sensitive index of these six factors on soil cumulative infiltration was investigated. Secondly, the grey relational analysis method was used to individually study grey correlations among these six factors and soil cumulative infiltration. The results of the two methods were very similar. Rainfall duration was the most influential factor, followed by vegetation cover, vegetation type, rainfall intensity and antecedent soil moisture. The effect of slope gradient on soil cumulative infiltration was not significant.

Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs) was greater than of the classic Maximum Likelihood Classifier (MLC). Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 %) was superior to the MLC map (57.94 %). The main errors when using the two classifiers were caused by: a) the geological heterogeneity of the area coupled with problems related to the geological map; b) the depth of lithic contact and/or rock exposure, and c) problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.

Plasticity of protein expression during culture of fetal skin cells.

In order to gain insight into the biology of fetal skin during culture, cellular proteins were studied during four culture passages (P00, P01, P04 as well as P10) using high-resolution two-dimensional (2-D) gel electrophoresis and mass spectrometry (MS). Bioinformatic analyses were focused on a region of each gel corresponding to pI between 4 and 8 and M(r) from 8000 to 35 000. In this area, 373 +/- 42 spots were detected (N = 18). Twenty-six spots presented an integrated intensity that increased in the higher passages, whereas five spots showed a progressively lower intensity in subsequent passaging. MS analysis was performed on spots that were unambiguously identified on preparative 2-D gels. Among the 26 spots showing an increased size between P00 and P10, 9 were identified, and corresponded to 3 proteins: (i) peptidyl-prolyl cis-trans isomerase A (P05092; cyclophilin A or cyclosporin A-binding protein), (ii) triosephosphate isomerase (P00938), and (iii) enoyl-CoA hydratase (P30084). Among these nine identified spots, three were absent at P00, but were present at P10. They corresponded to isoforms of peptidyl-prolyl cis-trans isomerase and triosephosphate isomerase, respectively. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analyses of the acidic isoforms of triosephosphate isomerase showed modifications of cysteine residues to cysteic acid. All these isoforms were clearly present in the skin cells of a 4-year-old child, as well as in skin cells from a 80-year-old man, at P00. These observations probably reflect either an oxidative stress related to cell culture, or, alternatively, maturation, differentiation and the aging of the cells.

Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar¹

Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI) derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13). Excluding the variable profile curvature (set 12), overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.

Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and pH at different moisture content levels using visible and near-infrared spectroscopy

Visible and near infrared (vis-NIR) spectroscopy is widely used to detect soil properties. The objective of this study is to evaluate the combined effect of moisture content (MC) and the modeling algorithm on prediction of soil organic carbon (SOC) and pH. Partial least squares (PLS) and the Artificial neural network (ANN) for modeling of SOC and pH at different MC levels were compared in terms of efficiency in prediction of regression. A total of 270 soil samples were used. Before spectral measurement, dry soil samples were weighed to determine the amount of water to be added by weight to achieve the specified gravimetric MC levels of 5, 10, 15, 20, and 25 %. A fiber-optic vis-NIR spectrophotometer (350-2500 nm) was used to measure spectra of soil samples in the diffuse reflectance mode. Spectra preprocessing and PLS regression were carried using Unscrambler® software. Statistica® software was used for ANN modeling. The best prediction result for SOC was obtained using the ANN (RMSEP = 0.82 % and RPD = 4.23) for soil samples with 25 % MC. The best prediction results for pH were obtained with PLS for dry soil samples (RMSEP = 0.65 % and RPD = 1.68) and soil samples with 10 % MC (RMSEP = 0.61 % and RPD = 1.71). Whereas the ANN showed better performance for SOC prediction at all MC levels, PLS showed better predictive accuracy of pH at all MC levels except for 25 % MC. Therefore, based on the data set used in the current study, the ANN is recommended for the analyses of SOC at all MC levels, whereas PLS is recommended for the analysis of pH at MC levels below 20 %.

Hierarchical neural network with high storage capacity

A recent method used to optimize biased neural networks with low levels of activity is applied to a hierarchical model. As a consequence, the performance of the system is strongly enhanced. The steps to achieve optimization are analyzed in detail.

Noise and periodic modulations in neural excitable medium

We have analyzed the interplay between noise and periodic modulations in a mean field model of a neural excitable medium. For this purpose, we have considered two types of modulations, namely, variations of the resistance and oscillations of the threshold. In both cases, stochastic resonance is present, irrespective of whether the system is monostable or bistable.

Synaptic plasticity and the Warburg effect.

Functional brain imaging studies show that in certain brain regions glucose utilization exceeds oxygen consumption, indicating the predominance of aerobic glycolysis. In this issue, Goyal et al. (2014) report that this metabolic profile is associated with an enrichment in the expression of genes involved in synaptic plasticity and remodeling processes.

Prevalence of Neural Tube Defects (NTD) in Younger Mothers in Europe 2000-2008: Analysis of the EUROCAT Database - Report to Bayer Schiering Pharma

M.C. Addor is included in the Eurocat Working Group