A Smart TCP Acknowledgment Approach for Multihop Wireless Networks

Reliable data transfer is one of the most difficult tasks to be accomplished in multihop wireless networks. Traditional transport protocols like TCP face severe performance degradation over multihop networks given the noisy nature of wireless media as well as unstable connectivity conditions in place. The success of TCP in wired networks motivates its extension to wireless networks. A crucial challenge faced by TCP over these networks is how to operate smoothly with the 802.11 wireless MAC protocol which also implements a retransmission mechanism at link level in addition to short RTS/CTS control frames for avoiding collisions. These features render TCP acknowledgments (ACK) transmission quite costly. Data and ACK packets cause similar medium access overheads despite the much smaller size of the ACKs. In this paper, we further evaluate our dynamic adaptive strategy for reducing ACK-induced overhead and consequent collisions. Our approach resembles the sender side's congestion control. The receiver is self-adaptive by delaying more ACKs under nonconstrained channels and less otherwise. This improves not only throughput but also power consumption. Simulation evaluations exhibit significant improvement in several scenarios

Spindle burst like spike discharge oscillations in organotypic cultures of neonatal rat cortex

Early network oscillations and spindle bursts are typical patterns of spontaneous rhythmic activity in cortical networks of neonatal rodents in vivo and in vitro. The latter can also be triggered in vivo by stimulation of afferent inputs. The mechanisms underlying such oscillations undergo profound developmental changes in the first postnatal weeks. Their possible role in cortical development is postulated but not known in detail. We have studied spontaneous and evoked patterns of activity in organotypic cultures of slices from neonatal rat cortex grown on multielectrode arrays (MEAs) for extracellular single- and multi-unit recording. Episodes of spontaneous spike discharge oscillations at 7 - 25 Hz lasting for 0.6 - 3 seconds appeared in about half of these cultures spontaneously and could be triggered by electrical stimulation of few distinct electrodes. These oscillations usually covered only restricted areas of the slices. Besides oscillations, single population bursts that spread in a wavelike manner over the whole slice also appeared spontaneously and were triggered by electrical stimulation. In most but not all cultures, population bursts preceded the oscillations. Both population bursts and spike discharge oscillations required intact glutamatergic synaptic transmission since they were suppressed by the AMPA/kainate glutamate receptor antagonist CNQX. The NMDA antagonist d-APV suppressed the oscillations but not the population bursts, suggesting an involvement of NMDA receptors in the oscillations. These findings show that spindle burst like cortical rhythms are reproduced in organotypic cultures of neonatal cortex. The culture model thus allows investigating the role of such rhythms in cortical circuit formation. Supported by SNF grant No. 3100A0-107641/1.

DISSECTION OF STRESS RESPONSE NETWORKS REGULATING MULTIPLE STRESSES IN RICE

Important food crops like rice are constantly exposed to various stresses that can have devastating effect on their survival and productivity. Being sessile, these highly evolved organisms have developed elaborate molecular machineries to sense a mixture of stress signals and elicit a precise response to minimize the damage. However, recent discoveries revealed that the interplay of these stress regulatory and signaling molecules is highly complex and remains largely unknown. In this work, we conducted large scale analysis of differential gene expression using advanced computational methods to dissect regulation of stress response which is at the heart of all molecular changes leading to the observed phenotypic susceptibility. One of the most important stress conditions in terms of loss of productivity is drought. We performed genomic and proteomic analysis of epigenetic and miRNA mechanisms in regulation of drought responsive genes in rice and found subsets of genes with striking properties. Overexpressed genesets included higher number of epigenetic marks, miRNA targets and transcription factors which regulate drought tolerance. On the other hand, underexpressed genesets were poor in above features but were rich in number of metabolic genes with multiple co-expression partners contributing majorly towards drought resistance. Identification and characterization of the patterns exhibited by differentially expressed genes hold key to uncover the synergistic and antagonistic components of the cross talk between stress response mechanisms. We performed meta-analysis on drought and bacterial stresses in rice and Arabidopsis, and identified hundreds of shared genes. We found high level of conservation of gene expression between these stresses. Weighted co-expression network analysis detected two tight clusters of genes made up of master transcription factors and signaling genes showing strikingly opposite expression status. To comprehensively identify the shared stress responsive genes between multiple abiotic and biotic stresses in rice, we performed meta-analyses of microarray studies from seven different abiotic and six biotic stresses separately and found more than thirteen hundred shared stress responsive genes. Various machine learning techniques utilizing these genes classified the stresses into two major classes' namely abiotic and biotic stresses and multiple classes of individual stresses with high accuracy and identified the top genes showing distinct patterns of expression. Functional enrichment and co-expression network analysis revealed the different roles of plant hormones, transcription factors in conserved and non-conserved genesets in regulation of stress response.

Engineered fibrin matrices for functional display of cell membrane-bound growth factor-like activities: study of angiogenic signaling by ephrin-B2

With the rapid increase in approaches to pro- or anti-angiogenic therapy, new and effective methodologies for administration of cell-bound growth factors will be required. We sought to develop the natural hydrogel matrix fibrin as platform for extensive interactions and continuous signaling by the vascular morphogen ephrin-B2 that normally resides in the plasma membrane and requires multivalent presentation for ligation and activation of Eph receptors on apposing endothelial cell surfaces. Using fibrin and protein engineering technology to induce multivalent ligand presentation, a recombinant mutant ephrin-B2 receptor binding domain was covalently coupled to fibrin networks at variably high densities. The ability of fibrin-bound ephrin-B2 to act as ligand for endothelial cells was preserved, as demonstrated by a concomitant, dose-dependent increase of endothelial cell binding to engineered ephrin-B2-fibrin substrates in vitro. The therapeutic relevance of ephrin-B2-fibrin implant matrices was demonstrated by a local angiogenic response in the chick embryo chorioallontoic membrane evoked by the local and prolonged presentation of matrix-bound ephrin-B2 to tissue adjacing the implant. This new knowledge on biomimetic fibrin vehicles for precise local delivery of membrane-bound growth factor signals may help to elucidate specific biological growth factor function, and serve as starting point for development of new treatment strategies.

Neural network based glucose - insulin metabolism models for children with Type 1 diabetes

In this paper two models for the simulation of glucose-insulin metabolism of children with Type 1 diabetes are presented. The models are based on the combined use of Compartmental Models (CMs) and artificial Neural Networks (NNs). Data from children with Type 1 diabetes, stored in a database, have been used as input to the models. The data are taken from four children with Type 1 diabetes and contain information about glucose levels taken from continuous glucose monitoring system, insulin intake and food intake, along with corresponding time. The influences of taken insulin on plasma insulin concentration, as well as the effect of food intake on glucose input into the blood from the gut, are estimated from the CMs. The outputs of CMs, along with previous glucose measurements, are fed to a NN, which provides short-term prediction of glucose values. For comparative reasons two different NN architectures have been tested: a Feed-Forward NN (FFNN) trained with the back-propagation algorithm with adaptive learning rate and momentum, and a Recurrent NN (RNN), trained with the Real Time Recurrent Learning (RTRL) algorithm. The results indicate that the best prediction performance can be achieved by the use of RNN.

A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier

In this paper, a computer-aided diagnostic (CAD) system for the classification of hepatic lesions from computed tomography (CT) images is presented. Regions of interest (ROIs) taken from nonenhanced CT images of normal liver, hepatic cysts, hemangiomas, and hepatocellular carcinomas have been used as input to the system. The proposed system consists of two modules: the feature extraction and the classification modules. The feature extraction module calculates the average gray level and 48 texture characteristics, which are derived from the spatial gray-level co-occurrence matrices, obtained from the ROIs. The classifier module consists of three sequentially placed feed-forward neural networks (NNs). The first NN classifies into normal or pathological liver regions. The pathological liver regions are characterized by the second NN as cyst or "other disease." The third NN classifies "other disease" into hemangioma or hepatocellular carcinoma. Three feature selection techniques have been applied to each individual NN: the sequential forward selection, the sequential floating forward selection, and a genetic algorithm for feature selection. The comparative study of the above dimensionality reduction methods shows that genetic algorithms result in lower dimension feature vectors and improved classification performance.

Understanding Neural Maps with Topographica

The biological function of neurons can often be understood only in the context of large, highly interconnected networks. These networks typically form two-dimensional topographic maps, such as the retinotopic maps in mammalian visual cortex. Computational simulations of these areas have led to valuable insights about how cortical topography develops and functions, but further progress has been hindered due to the lack of appropriate simulation tools. This paper introduces the freely available Topographica maplevel simulator, originally developed at the University of Texas at Austin and now maintained at the University of Edinburgh, UK. Topographica is designed to make large-scale, detailed models practical. The goal is to allow neuroscientists and computational scientists to work together to understand how topographic maps and their connections organize and operate. This understanding will be crucial for integrating experimental observations into a comprehensive theory of brain function.

Simbrain: A visual framework for neural network analysis and education

Simbrain is a visually-oriented framework for building and analyzing neural networks. It emphasizes the analysis of networks which control agents embedded in virtual environments, and visualization of the structures which occur in the high dimensional state spaces of these networks. The program was originally intended to facilitate analysis of representational processes in embodied agents, however it is also well suited to teaching neural networks concepts to a broader audience than is traditional for neural networks courses. Simbrain was used to teach a course at a new university, UC Merced, in its inaugural year. Experiences from the course and sample lessons are provided.

Special Issue: Interactive Educational Media for the Neural and Cognitive Sciences

Understanding the functioning of brains is an extremely challenging endeavour - both for researches as well as for students. Interactive media and tools, like simulations, databases and visualizations or virtual laboratories proved to be not only indispensable in research but also in education to help understanding brain function. Accordingly, a wide range of such media and tools are now available and it is getting increasingly difficult to see an overall picture. Written by researchers, tool developers and experienced academic teachers, this special issue of Brains, Minds & Media covers a broad range of interactive research media and tools with a strong emphasis on their use in neural and cognitive sciences education. The focus lies not only on the tools themselves, but also on the question of how research tools can significantly enhance learning and teaching and how a curricular integration can be achieved. This collection gives a comprehensive overview of existing tools and their usage as well as the underlying educational ideas and thus provides an orientation guide not only for teaching researchers but also for interested teachers and students.

Shifted Coupling of EEG Driving Frequencies and fMRI Resting State Networks in Schizophrenia Spectrum Disorders

INTRODUCTION: The cerebral resting state in schizophrenia is altered, as has been demonstrated separately by electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) resting state networks (RSNs). Previous simultaneous EEG/fMRI findings in healthy controls suggest that a consistent spatiotemporal coupling between neural oscillations (EEG frequency correlates) and RSN activity is necessary to organize cognitive processes optimally. We hypothesized that this coupling is disorganized in schizophrenia and related psychotic disorders, in particular regarding higher cognitive RSNs such as the default-mode (DMN) and left-working-memory network (LWMN). METHODS: Resting state was investigated in eleven patients with a schizophrenia spectrum disorder (n = 11) and matched healthy controls (n = 11) using simultaneous EEG/fMRI. The temporal association of each RSN to topographic spectral changes in the EEG was assessed by creating Covariance Maps. Group differences within, and group similarities across frequencies were estimated for the Covariance Maps. RESULTS: The coupling of EEG frequency bands to the DMN and the LWMN respectively, displayed significant similarities that were shifted towards lower EEG frequencies in patients compared to healthy controls. CONCLUSIONS: By combining EEG and fMRI, each measuring different properties of the same pathophysiology, an aberrant relationship between EEG frequencies and altered RSNs was observed in patients. RSNs of patients were related to lower EEG frequencies, indicating functional alterations of the spatiotemporal coupling. SIGNIFICANCE: The finding of a deviant and shifted coupling between RSNs and related EEG frequencies in patients with a schizophrenia spectrum disorder is significant, as it might indicate how failures in the processing of internal and external stimuli, as commonly seen during this symptomatology (i.e. thought disorders, hallucinations), arise.

Investigation on the evolution of an indoor robotic localization system based on wireless networks

This work addresses the evolution of an artificial neural network (ANN) to assist in the problem of indoor robotic localization. We investigate the design and building of an autonomous localization system based on information gathered from wireless networks (WN). The article focuses on the evolved ANN, which provides the position of a robot in a space, as in a Cartesian coordinate system, corroborating with the evolutionary robotic research area and showing its practical viability. The proposed system was tested in several experiments, evaluating not only the impact of different evolutionary computation parameters but also the role of the transfer functions on the evolution of the ANN. Results show that slight variations in the parameters lead to significant differences on the evolution process and, therefore, in the accuracy of the robot position.

"When we like what we know – A parametric fMRI analysis of beauty and familiarity"

This paper presents a neuroscientific study of aesthetic judgments on written texts. In an fMRI experiment participants read a number of proverbs without explicitly evaluating them. In a post-scan rating they rated each item for familiarity and beauty. These individual ratings were correlated with the functional data to investigate the neural correlates of implicit aesthetic judgments. We identified clusters in which BOLD activity was correlated with individual post-scan beauty ratings. This indicates that some spontaneous aesthetic evaluation takes place during reading, even if not required by the task. Positive correlations were found in the ventral striatum and in medial prefrontal cortex, likely reflecting the rewarding nature of sentences that are aesthetically pleasing. On the contrary, negative correlations were observed in the classic left frontotemporal reading network. Midline structures and bilateral temporo-parietal regions correlated positively with familiarity, suggesting a shift from the task-network towards the default network with increasing familiarity.

The effects of catecholamine depletion on the neural response to fearful faces in remitted depression

Recent evidence suggests that increased psychophysiological response to negatively valenced emotional stimuli found in major depressive disorder (MDD) may be associated with reduced catecholaminergic neurotransmission. Fourteen unmedicated, remitted subjects with MDD (RMDD) and 13 healthy control subjects underwent catecholamine depletion with oral α-methyl-para-tyrosine (AMPT) in a randomized, placebo-controlled, double-blind crossover trial. Subjects were exposed to fearful (FF) and neutral faces (NF) during a scan with [15O]H2O positron emission tomography to assess the brain-catecholamine interaction in brain regions previously associated with emotional face processing. Treatment with AMPT resulted in significantly increased, normalized cerebral blood flow (CBF) in the left inferior temporal gyrus (ITG) and significantly decreased CBF in the right cerebellum across conditions and groups. In RMDD, flow in the left posterior cingulate cortex (PCC) increased significantly in the FF compared to the NF condition after AMPT, but remained unchanged after placebo, whereas healthy controls showed a significant increase under placebo and a significant decrease under AMPT in this brain region. In the left dorsolateral prefrontal cortex (DLPFC), flow decreased significantly in the FF compared to the NF condition under AMPT, and increased significantly under placebo in RMDD, whereas healthy controls showed no significant differences. Differences between AMPT and placebo of within-session changes in worry-symptoms were positively correlated with the corresponding changes in CBF in the right subgenual prefrontal cortex in RMDD. In conclusion, this study provided evidence for a catecholamine-related modulation of the neural responses to FF expressions in the left PCC and the left DLPFC in subjects with RMDD that might constitute a persistent, trait-like abnormality in MDD.

The structural substrate of brain function: analysis of brain structural networks based on diffusion-weighted imaging

The brain is a complex neural network with a hierarchical organization and the mapping of its elements and connections is an important step towards the understanding of its function. Recent developments in diffusion-weighted imaging have provided the opportunity to reconstruct the whole-brain structural network in-vivo at a large scale level and to study the brain structural substrate in a framework that is close to the current understanding of brain function. However, methods to construct the connectome are still under development and they should be carefully evaluated. To this end, the first two studies included in my thesis aimed at improving the analytical tools specific to the methodology of brain structural networks. The first of these papers assessed the repeatability of the most common global and local network metrics used in literature to characterize the connectome, while in the second paper the validity of further metrics based on the concept of communicability was evaluated. Communicability is a broader measure of connectivity which accounts also for parallel and indirect connections. These additional paths may be important for reorganizational mechanisms in the presence of lesions as well as to enhance integration in the network. These studies showed good to excellent repeatability of global network metrics when the same methodological pipeline was applied, but more variability was detected when considering local network metrics or when using different thresholding strategies. In addition, communicability metrics have been found to add some insight into the integration properties of the network by detecting subsets of nodes that were highly interconnected or vulnerable to lesions. The other two studies used methods based on diffusion-weighted imaging to obtain knowledge concerning the relationship between functional and structural connectivity and about the etiology of schizophrenia. The third study integrated functional oscillations measured using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) as well as diffusion-weighted imaging data. The multimodal approach that was applied revealed a positive relationship between individual fluctuations of the EEG alpha-frequency and diffusion properties of specific connections of two resting-state networks. Finally, in the fourth study diffusion-weighted imaging was used to probe for a relationship between the underlying white matter tissue structure and season of birth in schizophrenia patients. The results are in line with the neurodevelopmental hypothesis of early pathological mechanisms as the origin of schizophrenia. The different analytical approaches selected in these studies also provide arguments for discussion of the current limitations in the analysis of brain structural networks. To sum up, the first studies presented in this thesis illustrated the potential of brain structural network analysis to provide useful information on features of brain functional segregation and integration using reliable network metrics. In the other two studies alternative approaches were presented. The common discussion of the four studies enabled us to highlight the benefits and possibilities for the analysis of the connectome as well as some current limitations.

Neural correlates of wishful thinking.

Wishful thinking (WT) implies the overestimation of the likelihood of desirable events. It occurs for outcomes of personal interest, but also for events of interest to others we like. We investigated whether WT is grounded on low-level selective attention or on higher level cognitive processes including differential weighting of evidence or response formation. Participants in our MRI study predicted the likelihood that their favorite or least favorite team would win a football game. Consistent with expectations, favorite team trials were characterized by higher winning odds. Our data demonstrated activity in a cluster comprising parts of the left inferior occipital and fusiform gyri to distinguish between favorite and least favorite team trials. More importantly, functional connectivities of this cluster with the human reward system were specifically involved in the type of WT investigated in our study, thus supporting the idea of an attention bias generating WT. Prefrontal cortex activity also distinguished between the two teams. However, activity in this region and its functional connectivities with the human reward system were altogether unrelated to the degree of WT reflected in the participants' behavior and may rather be related to social identification, ensuring the affective context necessary for WT to arise.