Neural regulation of pancreatic islet cell mass and function.

Intracellular glucose signalling pathways control the secretion of glucagon and insulin by pancreatic islet α- and β-cells, respectively. However, glucose also indirectly controls the secretion of these hormones through regulation of the autonomic nervous system that richly innervates this endocrine organ. Both parasympathetic and sympathetic nervous systems also impact endocrine pancreas postnatal development and plasticity in adult animals. Defects in these autonomic regulations impair β-cell mass expansion during the weaning period and β-cell mass adaptation in adult life. Both branches of the autonomic nervous system also regulate glucagon secretion. In type 2 diabetes, impaired glucose-dependent autonomic activity causes the loss of cephalic and first phases of insulin secretion, and impaired suppression of glucagon secretion in the postabsorptive phase; in diabetic patients treated with insulin, it causes a progressive failure of hypoglycaemia to trigger the secretion of glucagon and other counterregulatory hormones. Therefore, identification of the glucose-sensing cells that control the autonomic innervation of the endocrine pancreatic and insulin and glucagon secretion is an important goal of research. This is required for a better understanding of the physiological control of glucose homeostasis and its deregulation in diabetes. This review will discuss recent advances in this field of investigation.

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.

CHARACTERIZATION OF NrsZ, A NOVEL SMALL REGULATORY NON-CODING RNA INVOLVED IN THE ADAPTATION OF PSEUDOMONAS AERUGINOSA PAO1

The opportunistic ubiquitous pathogen Pseudomonas aeruginosa strain PAOl is a versatile Gram-negative bacterium that has the extraordinary capacity to colonize a wide diversity of ecological niches and to cause severe and persistent infections in humans. To ensure an optimal coordination of the genes involved in nutrient utilization, this bacterium uses the NtrB/C and/or the CbrA/B two-component systems, to sense nutrients availability and to regulate in consequence the expression of genes involved in their uptake and catabolism. NtrB/C is specialized in nitrogen utilization, while the CbrA/B system is involved in both carbon and nitrogen utilization and both systems activate their target genes expression in concert with the alternative sigma factor RpoN. Moreover, the NtrB/C and CbrA/B two- component systems regulate the secondary metabolism of the bacterium, such as the production of virulence factors. In addition to the fine-tuning transcriptional regulation, P. aeruginosa can rapidly modulate its metabolism using small non-coding regulatory RNAs (sRNAs), which regulate gene expression at the post-transcriptional level by diverse and sophisticated mechanisms and contribute to the fast physiological adaptability of this bacterium. In our search for novel RpoN-dependent sRNAs modulating the nutritional adaptation of P. aeruginosa PAOl, we discovered NrsZ (Nitrogen regulated sRNA), a novel RpoN-dependent sRNA that is induced under nitrogen starvation by the NtrB/C two-component system. NrsZ has a unique architecture, formed of three similar stem-loop structures (SL I, II and II) separated by variant spacer sequences. Moreover, this sRNA is processed in short individual stem-loop molecules, by internal cleavage involving the endoribonuclease RNAse E. Concerning NrsZ functions in P. aeruginosa PAOl, this sRNA was shown to trigger the swarming motility and the rhamnolipid biosurfactants production. This regulation is due to the NrsZ-mediated activation of rhlA expression, a gene encoding for an enzyme essential for swarming motility and rhamnolipids production. Interestingly, the SL I structure of NrsZ ensures its regulatory function on rhlA expression, suggesting that the similar SLs are the functional units of this modular sRNA. However, the regulatory mechanism of action of NrsZ on rhlA expression activation remains unclear and is currently being investigated. Additionally, the NrsZ regulatory network was investigated by a transcriptome analysis, suggesting that numerous genes involved in both primary and secondary metabolism are regulated by this sRNA. To emphasize the importance of NrsZ, we investigated its conservation in other Pseudomonas species and demonstrated that NrsZ is conserved and expressed under nitrogen limitation in Pseudomonas protegens Pf-5, Pseudomonas putida KT2442, Pseudomonas entomophila L48 and Pseudomonas syringae pv. tomato DC3000, strains having different ecological features, suggesting an important role of NrsZ in the adaptation of Pseudomonads to nitrogen starvation. Interestingly the architecture of the different NrsZ homologs is similarly composed by SL structures and variant spacer sequences. However, the number of SL repetitions is not identical, and one to six SLs were predicted on the different NrsZ homologs. Moreover, NrsZ is processed in short molecules in all the strains, similarly to what was previously observed in P. aeruginosa PAOl, and the heterologous expression of the NrsZ homologs restored rhlA expression, swarming motility and rhamnolipids production in the P. aeruginosa NrsZ mutant. In many aspects, NrsZ is an atypical sRNA in the bacterial panorama. To our knowledge, NrsZ is the first described sRNA induced by the NtrB/C. Moreover, its unique modular architecture and its processing in similar short SL molecules suggest that NrsZ belongs to a novel family of bacterial sRNAs. -- L'agent pathogène opportuniste et ubiquitaire Pseudomonas aeruginosa souche PAOl est une bactérie Gram négative versatile ayant l'extraordinaire capacité de coloniser différentes niches écologiques et de causer des infections sévères et persistantes chez l'être humain. Afin d'assurer une coordination optimale des gènes impliqués dans l'utilisation de différents nutriments, cette bactérie se sert de systèmes à deux composants tel que NtrB/C et CbrA/B afin de détecter la disponibilité des ressources nutritives, puis de réguler en conséquence l'expression des gènes impliqués dans leur importation et leur catabolisme. Le système NtrB/C régule l'utilisation des sources d'azote alors que le système CbrA/B est impliqué à la fois dans l'utilisation des sources de carbone et d'azote. Ces deux systèmes activent l'expression de leurs gènes-cibles de concert avec le facteur sigma alternatif RpoN. En outre, NtrB/C et CbrA/B régulent aussi le métabolisme secondaire, contrôlant notamment la production d'importants facteurs de virulence. En plus de toutes ces régulations génétiques fines ayant lieu au niveau transcriptionnel, P. aeruginosa est aussi capable de moduler son métabolisme en se servant de petits ARNs régulateurs non-codants (ARNncs), qui régulent l'expression génétique à un niveau post- transcriptionnel par divers mécanismes sophistiqués et contribuent à rendre particulièrement rapide l'adaptation physiologique de cette bactérie. Au cours de nos recherches sur de nouveaux ARNncs dépendant du facteur sigma RpoN et impliqués dans l'adaptation nutritionnelle de P. aeruginosa PAOl, nous avons découvert NrsZ (Nitrogen regulated sRNA), un ARNnc induit par la cascade NtrB/C-RpoN en condition de carence en azote. NrsZ a une architecture unique, composée de trois structures en tige- boucle (TB I, II et III) hautement similaires et séparées par des « espaceurs » ayant des séquences variables. De plus, cet ARNnc est clivé en petits fragments correspondant au trois molécules en tige-boucle, par un processus de clivage interne impliquant l'endoribonucléase RNase E. Concernant les fonctions de NrsZ chez P. aeruginosa PAOl, cet ARNnc est capable d'induire la motilité de type « swarming » et la production de biosurfactants, nommés rhamnolipides. Cette régulation est due à l'activation par NrsZ de l'expression de rhlA, un gène essentiel pour la motilité de type swarming et pour la production de rhamnolipides. Étonnamment, la structure TB I est capable d'assurer à elle seule la fonction régulatrice de NrsZ sur l'expression de rhlA, suggérant que ces molécules TBs sont les unités fonctionnelles de cet ARNnc modulaire. Cependant, le mécanisme moléculaire par lequel NrsZ active l'expression de rhlA demeure à ce jour incertain et est actuellement à l'étude. En plus, le réseau de régulations médiées par NrsZ a été étudié par une analyse de transcriptome qui a indiqué que de nombreux gènes impliqués dans le métabolisme primaire ou secondaire seraient régulés par NrsZ. Pour accentuer l'importance de NrsZ, nous avons étudié sa conservation dans d'autres espèces de Pseudomonas. Ainsi, nous avons démontré que NrsZ est conservé et exprimé en situation de carence d'azote par les souches Pseudomonas protegens Pf-5, Pseudomonas putida KT2442, Pseudomonas entomophila L48, Pseudomonas syringae pv. tomato DC3000, quatre espèces ayant des caractéristiques écologiques très différentes, suggérant que NrsZ joue un rôle important dans l'adaptation du genre Pseudomonas envers la carence en azote. Chez toutes les souches étudiées, les différents homologues de NrsZ présentent une architecture similaire faite de TBs conservées et d'espaceurs. Cependant, le nombre de TBs n'est pas identique et peut varier de une à six copies selon la souche. Les différentes versions de NrsZ sont clivées en petites molécules dans ces quatre souches, comme il a été observé chez P. aeruginosa PAOl. De plus, l'expression hétérologue des différentes variantes de NrsZ est capable de restaurer l'expression de rhlA, la motilité swarming et la production de rhamnolipides dans une souche de P. aeruginosa dont nrsZ a été inactivé. Par bien des aspects, NrsZ est un ARNnc atypique dans le monde bactérien. À notre connaissance, NrsZ est le premier ARNnc décrit comme étant régulé par le système NtrB/C. De plus, son unique architecture modulaire et son clivage en petites molécules similaires suggèrent que NrsZ appartient à une nouvelle famille d'ARNncs bactériens.

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

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.

Design of Optical Systems With Extended Depth of Field: an Educational Approach to Wavefront Coding Techniques

A practical activity designed to introduce wavefront coding techniques as a method to extend the depth of field in optical systems is presented. The activity is suitable for advanced undergraduate students since it combines different topics in optical engineering such as optical system design, aberration theory, Fourier optics, and digital image processing. This paper provides the theoretical background and technical information for performing the experiment. The proposed activity requires students able to develop a wide range of skills since they are expected to deal with optical components, including spatial light modulators, and develop scripts to perform some calculations.

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.

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

Podocalyxin is a novel polysialylated neural adhesion protein with multiple roles in neural development and synapse formation

Neural development and plasticity are regulated by neural adhesion proteins, including the polysialylated form of NCAM (PSA-NCAM). Podocalyxin (PC) is a renal PSA-containing protein that has been reported to function as an anti-adhesin in kidney podocytes. Here we show that PC is widely expressed in neurons during neural development. Neural PC interacts with the ERM protein family, and with NHERF1/2 and RhoA/G. Experiments in vitro and phenotypic analyses of podxl-deficient mice indicate that PC is involved in neurite growth, branching and axonal fasciculation, and that PC loss-of-function reduces the number of synapses in the CNS and in the neuromuscular system. We also show that whereas some of the brain PC functions require PSA, others depend on PC per se. Our results show that PC, the second highly sialylated neural adhesion protein, plays multiple roles in neural development.