Abnormal resting state corticolimbic blood flow in depressed unmedicated patients with major depression: A 15O-H2O PET study

We investigated the differences in the resting state corticolimbic blood flow between 20 unmedicated depressed patients and 21 healthy comparisons. Resting state cerebral blood flow (CBF) was measured with H215O PET. Anatomical MRI scans were performed on an Elscint 1.9 T Prestige system for PET-MRI coregistration. Significant changes in cerebral blood flow indicating neural activity were detected using an ROI-free image subtraction strategy. In addition, the resting blood flow in patients was correlated with the severity of depression as measured by HAM-D scores. Depressed patients showed decreases in blood flow in right anterior cingulate (Brodmann areas 24 and 32) and increased blood flow in left and right posterior cingulate (Brodmann areas 23, 29, 30), left parahippocampal gyrus (Brodmann area 36), and right caudate compared with healthy volunteers. The severity of depression was inversely correlated with the left middle and inferior frontal gyri (Brodmann areas 9 and 47) and right medial frontal gyrus (Brodmann area 10) and right anterior cingulate (Brodmann areas 24, 32) blood flow, and directly correlated with the right thalamus blood flow. These findings support previous reports of abnormalities in the resting state blood flow in the limbic-frontal structures in depressed patients compared to healthy volunteers. Hum Brain Mapp, 2012. (C) 2011 Wiley Periodicals, Inc.

Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing

Background: In the analysis of effects by cell treatment such as drug dosing, identifying changes on gene network structures between normal and treated cells is a key task. A possible way for identifying the changes is to compare structures of networks estimated from data on normal and treated cells separately. However, this approach usually fails to estimate accurate gene networks due to the limited length of time series data and measurement noise. Thus, approaches that identify changes on regulations by using time series data on both conditions in an efficient manner are demanded. Methods: We propose a new statistical approach that is based on the state space representation of the vector autoregressive model and estimates gene networks on two different conditions in order to identify changes on regulations between the conditions. In the mathematical model of our approach, hidden binary variables are newly introduced to indicate the presence of regulations on each condition. The use of the hidden binary variables enables an efficient data usage; data on both conditions are used for commonly existing regulations, while for condition specific regulations corresponding data are only applied. Also, the similarity of networks on two conditions is automatically considered from the design of the potential function for the hidden binary variables. For the estimation of the hidden binary variables, we derive a new variational annealing method that searches the configuration of the binary variables maximizing the marginal likelihood. Results: For the performance evaluation, we use time series data from two topologically similar synthetic networks, and confirm that our proposed approach estimates commonly existing regulations as well as changes on regulations with higher coverage and precision than other existing approaches in almost all the experimental settings. For a real data application, our proposed approach is applied to time series data from normal Human lung cells and Human lung cells treated by stimulating EGF-receptors and dosing an anticancer drug termed Gefitinib. In the treated lung cells, a cancer cell condition is simulated by the stimulation of EGF-receptors, but the effect would be counteracted due to the selective inhibition of EGF-receptors by Gefitinib. However, gene expression profiles are actually different between the conditions, and the genes related to the identified changes are considered as possible off-targets of Gefitinib. Conclusions: From the synthetically generated time series data, our proposed approach can identify changes on regulations more accurately than existing methods. By applying the proposed approach to the time series data on normal and treated Human lung cells, candidates of off-target genes of Gefitinib are found. According to the published clinical information, one of the genes can be related to a factor of interstitial pneumonia, which is known as a side effect of Gefitinib.

STATE INSTITUTIONS, POWER, AND SOCIAL NETWORKS IN BRAZILIAN URBAN POLICIES

A large historiographic tradition has studied the Brazilian state, yet we know relatively little about its internal dynamics and particularities. The role of informal, personal, and unintentional ties has remained underexplored in most policy network studies, mainly because of the pluralist origin of that tradition. It is possible to use network analysis to expand this knowledge by developing mesolevel analysis of those processes. This article proposes an analytical framework for studying networks inside policy communities. This framework considers the stable and resilient patterns that characterize state institutions, especially in contexts of low institutionalization, particularly those found in Latin America and Brazil. The article builds on research on urban policies in Brazil to suggest that networks made of institutional and personal ties structure state organizations internally and insert them,into broader political scenarios. These networks, which I call state fabric, frame politics, influence public policies, and introduce more stability and predictability than the majority of the literature usually considers. They also form a specific power resource-positional power, associated with the positions that political actors occupy-that influences politics inside and around the state.

Particle Competition and Cooperation in Networks for Semi-Supervised Learning

Semi-supervised learning is one of the important topics in machine learning, concerning with pattern classification where only a small subset of data is labeled. In this paper, a new network-based (or graph-based) semi-supervised classification model is proposed. It employs a combined random-greedy walk of particles, with competition and cooperation mechanisms, to propagate class labels to the whole network. Due to the competition mechanism, the proposed model has a local label spreading fashion, i.e., each particle only visits a portion of nodes potentially belonging to it, while it is not allowed to visit those nodes definitely occupied by particles of other classes. In this way, a "divide-and-conquer" effect is naturally embedded in the model. As a result, the proposed model can achieve a good classification rate while exhibiting low computational complexity order in comparison to other network-based semi-supervised algorithms. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method.

Architectures, stability and optimization for clock distribution networks

Synchronous telecommunication networks, distributed control systems and integrated circuits have its accuracy of operation dependent on the existence of a reliable time basis signal extracted from the line data stream and acquirable to each node. In this sense, the existence of a sub-network (inside the main network) dedicated to the distribution of the clock signals is crucially important. There are different solutions for the architecture of the time distribution sub-network and choosing one of them depends on cost, precision, reliability and operational security. In this work we expose: (i) the possible time distribution networks and their usual topologies and arrangements. (ii) How parameters of the network nodes can affect the reachability and stability of the synchronous state of a network. (iii) Optimizations methods for synchronous networks which can provide low cost architectures with operational precision, reliability and security. (C) 2011 Elsevier B. V. All rights reserved.

Scientific research output evaluation of professors of Sao Paulo State University, Marilia/SP

In this work, an analysis of scientific bibliographic productivity was made using the Faculdade de Filosofia e Ciencias, Universidade Estadual Paulista (FFC-UNESP) as example. It is composed by nine departments which offer altogether nine undergraduate courses: 1) Archival, 2) Library, 3) Speech Therapy, 4) Pedagogy, 5) International Relations, 6) Physiotherapy, 7) Occupational Therapy, 8) Philosophy, 9) Social Sciences and six graduate programs leading to M. S. and Ph.D. degrees. Moreover, when analyzing the different courses of FFC-UNESP, they represent typical academic organization in Brazil and Latin America and could be taken as a model for analyzing other Brazilian research institutions. Using data retrieved from the Lattes Plataform database (Curriculum Lattes) we have quantitatively the scientific productivity percentage of professors at UNESP. We observed that bibliometric evaluations using the Curriculum Lattes (CL) showed that the professors published papers in journal are not indexed by ISI and SCOPUS. This analysis was made using: 1) the total number of papers (indexed in Curriculum Lattes database), 2) the number of papers indexed by Thomson ISI Web of Science database and SCOPUS database, and 3) the Hirsch (h-index) by ISI and SCOPUS. Bibliometric evaluations of departments showed a better performance of Political Science and Economics Department when compared to others departments, in relation total number of papers (indexed in Curriculum Lattes database). We also analyzed the academic advisory (Master's Thesis and Ph. D. Thesis) by nine departments of FFC/UNESP. The Administration and School Supervision Department presented a higher academic advisory (concluded and current) when compared to the others departments.

Semi-supervised learning guided by the modularity measure in complex networks

Semi-supervised learning techniques have gained increasing attention in the machine learning community, as a result of two main factors: (1) the available data is exponentially increasing; (2) the task of data labeling is cumbersome and expensive, involving human experts in the process. In this paper, we propose a network-based semi-supervised learning method inspired by the modularity greedy algorithm, which was originally applied for unsupervised learning. Changes have been made in the process of modularity maximization in a way to adapt the model to propagate labels throughout the network. Furthermore, a network reduction technique is introduced, as well as an extensive analysis of its impact on the network. Computer simulations are performed for artificial and real-world databases, providing a numerical quantitative basis for the performance of the proposed method.

Stochastic competitive learning in complex networks

Competitive learning is an important machine learning approach which is widely employed in artificial neural networks. In this paper, we present a rigorous definition of a new type of competitive learning scheme realized on large-scale networks. The model consists of several particles walking within the network and competing with each other to occupy as many nodes as possible, while attempting to reject intruder particles. The particle's walking rule is composed of a stochastic combination of random and preferential movements. The model has been applied to solve community detection and data clustering problems. Computer simulations reveal that the proposed technique presents high precision of community and cluster detections, as well as low computational complexity. Moreover, we have developed an efficient method for estimating the most likely number of clusters by using an evaluator index that monitors the information generated by the competition process itself. We hope this paper will provide an alternative way to the study of competitive learning.

Texture descriptor based on partially self-avoiding deterministic walker on networks

Texture image analysis is an important field of investigation that has attracted the attention from computer vision community in the last decades. In this paper, a novel approach for texture image analysis is proposed by using a combination of graph theory and partially self-avoiding deterministic walks. From the image, we build a regular graph where each vertex represents a pixel and it is connected to neighboring pixels (pixels whose spatial distance is less than a given radius). Transformations on the regular graph are applied to emphasize different image features. To characterize the transformed graphs, partially self-avoiding deterministic walks are performed to compose the feature vector. Experimental results on three databases indicate that the proposed method significantly improves correct classification rate compared to the state-of-the-art, e.g. from 89.37% (original tourist walk) to 94.32% on the Brodatz database, from 84.86% (Gabor filter) to 85.07% on the Vistex database and from 92.60% (original tourist walk) to 98.00% on the plant leaves database. In view of these results, it is expected that this method could provide good results in other applications such as texture synthesis and texture segmentation. (C) 2012 Elsevier Ltd. All rights reserved.

Hierarchical multi-label classification using local neural networks

Hierarchical multi-label classification is a complex classification task where the classes involved in the problem are hierarchically structured and each example may simultaneously belong to more than one class in each hierarchical level. In this paper, we extend our previous works, where we investigated a new local-based classification method that incrementally trains a multi-layer perceptron for each level of the classification hierarchy. Predictions made by a neural network in a given level are used as inputs to the neural network responsible for the prediction in the next level. We compare the proposed method with one state-of-the-art decision-tree induction method and two decision-tree induction methods, using several hierarchical multi-label classification datasets. We perform a thorough experimental analysis, showing that our method obtains competitive results to a robust global method regarding both precision and recall evaluation measures.

ScPdZn and ScPtZn with YAlGe type structure: group-subgroup relation and 45Sc solid state NMR spectroscopy

The intermetallic compounds ScPdZn and ScPtZn were prepared from the elements by high-frequency melting in sealed tantalum ampoules. Both structures were refined from single crystal X-ray diffractometer data: YAlGe type, Cmcm, a = 429.53(8), b = 907.7(1), c = 527.86(1) pm, wR2 = 0.0375, 231 F2 values, for ScPdZn and a = 425.3(1), b = 918.4(2), c = 523.3(1) pm, wR2 = 0.0399, 213 F2 values for ScPtZn with 14 variables per refinement. The structures are orthorhombically distorted variants of the AlB2 type. The scandium and palladium (platinum atoms) build up ordered networks Sc3Pd3 and Sc3Pt3 (boron networks) which are slightly shifted with respect to each other. These networks are penetrated by chains of zinc atoms (262 pm in ScPtZn) which correspond to the aluminum positions, i.e. Zn(ScPd) and Zn(ScPt). The corresponding group-subgroup scheme and the differences in chemical bonding with respect to other AlB2-derived REPdZn and REPtZn compounds are discussed. 45Sc solid state NMR spectra confirm the single crystallographic scandium sites. From electronic band structure calculations the two compounds are found metallic with free electron like behavior at the Fermi level. A larger cohesive energy for ScPtZn suggests a more strongly bonded intermetallic than ScPdZn. Electron localization and overlap population analyses identify the largest bonding for scandium with the transition metal (Pd, Pt).