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.

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.

Neural networks as mechanisms to regulate division of labor.

Abstract In social insects, workers perform a multitude of tasks, such as foraging, nest construction, and brood rearing, without central control of how work is allocated among individuals. It has been suggested that workers choose a task by responding to stimuli gathered from the environment. Response-threshold models assume that individuals in a colony vary in the stimulus intensity (response threshold) at which they begin to perform the corresponding task. Here we highlight the limitations of these models with respect to colony performance in task allocation. First, we show with analysis and quantitative simulations that the deterministic response-threshold model constrains the workers' behavioral flexibility under some stimulus conditions. Next, we show that the probabilistic response-threshold model fails to explain precise colony responses to varying stimuli. Both of these limitations would be detrimental to colony performance when dynamic and precise task allocation is needed. To address these problems, we propose extensions of the response-threshold model by adding variables that weigh stimuli. We test the extended response-threshold model in a foraging scenario and show in simulations that it results in an efficient task allocation. Finally, we show that response-threshold models can be formulated as artificial neural networks, which consequently provide a comprehensive framework for modeling task allocation in social insects.

Neural differentiation and therapeutic potential of adipose tissue derived stem cells.

Neural tissue has historically been regarded as having poor regenerative capacity but recent advances in the growing fields of tissue engineering and regenerative medicine have opened new hopes for the treatment of nerve injuries and neurodegenerative disorders. Adipose tissue has been shown to contain a large quantity of adult stem cells (ASC). These cells can be easily harvested with low associated morbidity and because of their potential to differentiate into multiple cell types, their use has been suggested for a wide variety of therapeutic applications. In this review we examine the evidence indicating that ASC can stimulate nerve regeneration by both undergoing neural differentiation and through the release of a range of growth factors. We also discuss some of the issues that need to be addressed before ASC can be developed as an effective cellular therapy for the treatment of neural tissue disorders.

Free microvascular iliac crest flap for extensive talar necrosis--case report with a 16-year long-term follow up.

Atraumatic osteonecrosis of the talus can be extremely painful and lead to significant functional impairment. Although clinical, radiographic, and demographic characteristics of atraumatic osteonecrosis of the talus have been well documented, the diagnosis is frequently missed or delayed; the most common causes are use of corticosteroids and the presence of immune disorders. Operative treatment of large osteochondral lesions of the talus is difficult because the blood supply is poor in the talar dome. Microvascular reconstruction of the talar dome with iliac crest autografts is a complex but functionally excellent therapeutic option. We present a 48-year-old man, who developed an extensive atraumatic avascular necrosis of the talar dome without collapse. Except for insulin dependent diabetes mellitus no further comorbidities were known. A microvascular iliac crest bone flap was inserted into the talus. A follow-up 16 years postoperatively showed a clinically as well as radiographically stable reconstruction of the talar dome and an excellent mobility of the ankle joint. The AOFAS hindfoot scale had improved from initially 33 points to 100 on the last follow-up. Free microvascular bony reconstruction of the talar dome should not only be considered in younger patients but also for middle aged active patients, since our follow-up shows an excellent long term result. Early reconstruction can prevent collapse of the talar bone.

Etude de la variation interlaboratoire du test de dépistage prénatal des défauts de fermeture du tube neural [Interlaboratory variation in the prenatal screening test for neural tube closing defects].

Early detection of neural-tude defects is possible by determining Alpha-fetoprotein (AFP) in maternal serum. 16'685 pregnant women were observed. Three methods for the determination of the "normal" range are compared. The first one, already used in similar studies, makes use of a constant multiple of the median. The other two ones make use of robust estimates of location and scale. Their comparison shows the interest of the robust methods to reduce the interlaboratory variability.

A circuit-based gatekeeper for adult neural stem cell proliferation: Parvalbumin-expressing interneurons of the dentate gyrus control the activation and proliferation of quiescent adult neural stem cells.

Newborn neurons are generated in the adult hippocampus from a pool of self-renewing stem cells located in the subgranular zone (SGZ) of the dentate gyrus. Their activation, proliferation, and maturation depend on a host of environmental and cellular factors but, until recently, the contribution of local neuronal circuitry to this process was relatively unknown. In their recent publication, Song and colleagues have uncovered a novel circuit-based mechanism by which release of the neurotransmitter, γ-aminobutyric acid (GABA), from parvalbumin-expressing (PV) interneurons, can hold radial glia-like (RGL) stem cells of the adult SGZ in a quiescent state. This tonic GABAergic signal, dependent upon the activation of γ(2) subunit-containing GABA(A) receptors of RGL stem cells, can thus prevent their proliferation and subsequent maturation or return them to quiescence if previously activated. PV interneurons are thus capable of suppressing neurogenesis during periods of high network activity and facilitating neurogenesis when network activity is low.

Coagulation Factor Xa Activates Thrombin and Signals Ischemic Neuronal Death Via Par-1 and JNK in Ischemic Neural Tissue

Background: The coagulation factor thrombin mediates ischemic neuronal deathand, at a low concentration, induces tolerance to ischemia.We investigated its modeof activation in ischemic neural tissue using an in vitro approach to distinguish therole of circulating coagulation factors from endogenous cerebral mechanisms. Wealso studied the signalling pathway downstream of thrombin in ischemia and afterthrombin preconditioning.Methods: Rat organotypic hippocampal slice cultures to 30 minute oxygen (5%)and glucose (1 mmol/L) deprivation (OGD).Results: Selective factor Xa (FXa) inhibition by fondaparinux during and afterOGD significantly reduced neuronal death in the CA1 after 48 hours. Thrombinactivity was increased in the medium 24 hours after OGD and this increasewas prevented by fondaparinux suggesting that FXa catalyzes the conversion ofprothrombin to thrombin in neural tissue after ischemia in vitro. Treatment withSCH79797, a selective antagonist of the thrombin receptor protease activatedreceptor-1 (PAR-1), significantly decreased neuronal cell death indicating thatthrombin signals ischemic damage via PAR-1. The JNK pathway plays an importantrole in cerebral ischemia and we observed activation of the JNK substrate,c-Jun in our model. Both the FXa inhibitor, fondaparinux and the PAR-1 antagonistSCH79797, decreased the level of phospho-c-Jun Ser73. After thrombin preconditioningc-Jun was activated by phosphorylation in the nuclei of neurons of the CA1.Treatment with a synthetic thrombin receptor agonist resulted in the same c-Junactivation profile and protection against subsequent OGD indicating that thrombinalso signals via PAR-1 and c-Jun in cell protection.Conclusion: These results indicate that FXa activates thrombin in cerebral ischemia,leading via PAR-1 to the activation of the JNK pathway resulting in neuronal death.Thrombin induced tolerance also involves PAR-1 and JNK, revealing commonfeatures in cell death and survival signalling.