Brain responses associated with consciousness of breathlessness (air hunger)

Little is known about the physiological mechanisms subserving the experience of air hunger and the affective control of breathing in humans. Acute hunger for air after inhalation of CO2 was studied in nine healthy volunteers with positron emission tomography. Subjective breathlessness was manipulated while end-tidal CO2- was held constant. Subjects experienced a significantly greater sense of air hunger breathing through a face mask than through a mouthpiece. The statistical contrast between the two conditions delineated a distributed network of primarily limbic/paralimbic brain regions, including multiple foci in dorsal anterior and middle cingulate gyrus, insula/claustrum, amygdala/periamygdala, lingual and middle temporal gyrus, hypothalamus, pulvinar, and midbrain. This pattern of activations was confirmed by a correlational analysis with breathlessness ratings. The commonality of regions of mesencephalon, diencephalon and limbic/paralimbic areas involved in primal emotions engendered by the basic vegetative systems including hunger for air, thirst, hunger, pain, micturition, and sleep, is discussed with particular reference to the cingulate gyrus. A theory that the phylogenetic origin of consciousness came from primal emotions engendered by immediate threat to the existence of the organism is discussed along with an alternative hypothesis by Edelman that primary awareness emerged with processes of ongoing perceptual categorization giving rise to a scene [Edelman, G. M. (1992) Bright Air, Brilliant Fire (Penguin, London)].

Neural fate of seen and unseen faces in visuospatial neglect: A combined event-related functional MRI and event-related potential study

To compare neural activity produced by visual events that escape or reach conscious awareness, we used event-related MRI and evoked potentials in a patient who had neglect and extinction after focal right parietal damage, but intact visual fields. This neurological disorder entails a loss of awareness for stimuli in the field contralateral to a brain lesion when stimuli are simultaneously presented on the ipsilateral side, even though early visual areas may be intact, and single contralateral stimuli may still be perceived. Functional MRI and event-related potential study were performed during a task where faces or shapes appeared in the right, left, or both fields. Unilateral stimuli produced normal responses in V1 and extrastriate areas. In bilateral events, left faces that were not perceived still activated right V1 and inferior temporal cortex and evoked nonsignificantly reduced N1 potentials, with preserved face-specific negative potentials at 170 ms. When left faces were perceived, the same stimuli produced greater activity in a distributed network of areas including right V1 and cuneus, bilateral fusiform gyri, and left parietal cortex. Also, effective connectivity between visual, parietal, and frontal areas increased during perception of faces. These results suggest that activity can occur in V1 and ventral temporal cortex without awareness, whereas coupling with dorsal parietal and frontal areas may be critical for such activity to afford conscious perception.

A systematic exploration of perceptual and semantic differences in category-specific object-processing using magnetoencephalography

In a series of experiments, we tested category-specific activation in normal parti¬cipants using magnetoencephalography (MEG). Our experiments explored the temporal processing of objects, as MEG characterises neural activity on the order of milliseconds. Our experiments explored object-processing, including assessing the time-course of ob¬ject naming, early differences in processing living compared with nonliving objects and processing objects at the basic compared with the domain level, and late differences in processing living compared with nonliving objects and processing objects at the basic compared with the domain level. In addition to studies using normal participants, we also utilised MEG to explore category-specific processing in a patient with a deficit for living objects. Our findings support the cascade model of object naming (Humphreys et al., 1988). In addition, our findings using normal participants demonstrate early, category-specific perceptual differences. These findings are corroborated by our patient study. In our assessment of the time-course of category-specific effects as well as a separate analysis designed to measure semantic differences between living and nonliving objects, we found support for the sensory/motor model of object naming (Martin, 1998), in addition to support for the cascade model of object naming. Thus, object processing in normal participants appears to be served by a distributed network in the brain, and there are both perceptual and semantic differences between living and nonliving objects. A separate study assessing the influence of the level at which you are asked to identify an object on processing in the brain found evidence supporting the convergence zone hypothesis (Damasio, 1989). Taken together, these findings indicate the utility of MEG in exploring the time-course of object processing, isolating early perceptual and later semantic effects within the brain.

Organisational change and safety culture: the impact of communication

This research examines and explains the links between safety culture and communication. Safety culture is a concept that in recent years has gained prominence but there has been little applied research conducted to investigate the meaning of the concept in 'real life' settings. This research focused on a Train Operating Company undergoing change in a move towards privatisation. These changes were evident in the management of safety, the organisation of the industry and internally in their management. The Train Operating Company's management took steps to improve their safety culture and communications through the development of a cascade communication structure. The research framework employed a qualitative methodology in order to investigate the effect of the new system on safety culture. Findings of the research were that communications in the organisation failed to be effective for a number of reasons, including both cultural and logistical problems. The cultural problems related to a lack of trust in the organisation by the management and the workforce, the perception of communications as management propaganda, and asyntonic communications between those involved, whilst logistical problems related to the inherent difficulties of communicating over a geographically distributed network. An organisational learning framework was used to explain the results. It is postulated that one of the principal reasons why change, either to the safety culture or to communications, did not occur was because of the organisation's inability to learn. The research has also shown the crucial importance of trust between the members of the organisation, as this was one of the fundamental reasons why the safety culture did not change, and why safety management systems were not fully implemented. This is consistent with the notion of mutual trust in the HSC (1993) definition of safety culture. This research has highlighted its relevance to safety culture and its importance for organisational change.

A multimodal investigation of dynamic face perception using functional magnetic resonance imaging and magnetoencephalography

Motion is an important aspect of face perception that has been largely neglected to date. Many of the established findings are based on studies that use static facial images, which do not reflect the unique temporal dynamics available from seeing a moving face. In the present thesis a set of naturalistic dynamic facial emotional expressions was purposely created and used to investigate the neural structures involved in the perception of dynamic facial expressions of emotion, with both functional Magnetic Resonance Imaging (fMRI) and Magnetoencephalography (MEG). Through fMRI and connectivity analysis, a dynamic face perception network was identified, which is demonstrated to extend the distributed neural system for face perception (Haxby et al.,2000). Measures of effective connectivity between these regions revealed that dynamic facial stimuli were associated with specific increases in connectivity between early visual regions, such as inferior occipital gyri and superior temporal sulci, along with coupling between superior temporal sulci and amygdalae, as well as with inferior frontal gyri. MEG and Synthetic Aperture Magnetometry (SAM) were used to examine the spatiotemporal profile of neurophysiological activity within this dynamic face perception network. SAM analysis revealed a number of regions showing differential activation to dynamic versus static faces in the distributed face network, characterised by decreases in cortical oscillatory power in the beta band, which were spatially coincident with those regions that were previously identified with fMRI. These findings support the presence of a distributed network of cortical regions that mediate the perception of dynamic facial expressions, with the fMRI data providing information on the spatial co-ordinates paralleled by the MEG data, which indicate the temporal dynamics within this network. This integrated multimodal approach offers both excellent spatial and temporal resolution, thereby providing an opportunity to explore dynamic brain activity and connectivity during face processing.

Physics-based modeling of power system components for the evaluation of low-frequency radiated electromagnetic fields

The low-frequency electromagnetic compatibility (EMC) is an increasingly important aspect in the design of practical systems to ensure the functional safety and reliability of complex products. The opportunities for using numerical techniques to predict and analyze system's EMC are therefore of considerable interest in many industries. As the first phase of study, a proper model, including all the details of the component, was required. Therefore, the advances in EMC modeling were studied with classifying analytical and numerical models. The selected model was finite element (FE) modeling, coupled with the distributed network method, to generate the model of the converter's components and obtain the frequency behavioral model of the converter. The method has the ability to reveal the behavior of parasitic elements and higher resonances, which have critical impacts in studying EMI problems. For the EMC and signature studies of the machine drives, the equivalent source modeling was studied. Considering the details of the multi-machine environment, including actual models, some innovation in equivalent source modeling was performed to decrease the simulation time dramatically. Several models were designed in this study and the voltage current cube model and wire model have the best result. The GA-based PSO method is used as the optimization process. Superposition and suppression of the fields in coupling the components were also studied and verified. The simulation time of the equivalent model is 80-100 times lower than the detailed model. All tests were verified experimentally. As the application of EMC and signature study, the fault diagnosis and condition monitoring of an induction motor drive was developed using radiated fields. In addition to experimental tests, the 3DFE analysis was coupled with circuit-based software to implement the incipient fault cases. The identification was implemented using ANN for seventy various faulty cases. The simulation results were verified experimentally. Finally, the identification of the types of power components were implemented. The results show that it is possible to identify the type of components, as well as the faulty components, by comparing the amplitudes of their stray field harmonics. The identification using the stray fields is nondestructive and can be used for the setups that cannot go offline and be dismantled

AS INOVAÇÕES TECNOLÓGICAS E OS INTERMEDIÁRIOS DA PRODUÇÃO E DO ACESSO À INFORMAÇÃO JORNALÍSTICA

O jornalismo é um dos principais meios de oferta de temas para a discussão e formação da opinião pública, porém depende de um sistema técnico para ser transmitido. Durante mais de cem anos as informações produzidas pela imprensa foram emitidas, armazenadas, transmitidas e recebidas pelos chamados veículos de comunicação de massa que utilizam a rede centralizada cujas características estão na escassez material, produção em série e massificação. Esse sistema separa no tempo e no espaço emissores e receptores criando uma relação desigual de força em que as grandes empresas controlaram o fluxo informativo, definindo quais fatos seriam veiculados como notícia. Em 1995, a internet cuja informação circula sob a tecnologia da rede distribuída, foi apropriada pela sociedade, alterando a forma de produção, armazenamento e transmissão de informação. A tecnologia despertou a esperança de que esta ferramenta poderia proporcionar uma comunicação mais dialógica e democrática. Mas aos poucos pode-se perceber novas empresas se apropriando da tecnologia da rede distribuída sob a qual circula a internet, gerando um novo controle do fluxo informativo. Realizou-se nessa pesquisa um levantamento bibliográfico para estabelecer uma reflexão crítica dos diferentes intermediários entre fato e a notícia tanto da rede centralizada como na rede distribuída, objetivando despertar uma discussão que possa oferecer novas ideias para políticas, bem como alternativas para uma comunicação mais democrática e mais libertária.

Numerical encoding to tame SQL injection attacks.

Recent years have seen an astronomical rise in SQL Injection Attacks (SQLIAs) used to compromise the confidentiality, authentication and integrity of organisations’ databases. Intruders becoming smarter in obfuscating web requests to evade detection combined with increasing volumes of web traffic from the Internet of Things (IoT), cloud-hosted and on-premise business applications have made it evident that the existing approaches of mostly static signature lack the ability to cope with novel signatures. A SQLIA detection and prevention solution can be achieved through exploring an alternative bio-inspired supervised learning approach that uses input of labelled dataset of numerical attributes in classifying true positives and negatives. We present in this paper a Numerical Encoding to Tame SQLIA (NETSQLIA) that implements a proof of concept for scalable numerical encoding of features to a dataset attributes with labelled class obtained from deep web traffic analysis. In the numerical attributes encoding: the model leverages proxy in the interception and decryption of web traffic. The intercepted web requests are then assembled for front-end SQL parsing and pattern matching by applying traditional Non-Deterministic Finite Automaton (NFA). This paper is intended for a technique of numerical attributes extraction of any size primed as an input dataset to an Artificial Neural Network (ANN) and statistical Machine Learning (ML) algorithms implemented using Two-Class Averaged Perceptron (TCAP) and Two-Class Logistic Regression (TCLR) respectively. This methodology then forms the subject of the empirical evaluation of the suitability of this model in the accurate classification of both legitimate web requests and SQLIA payloads.

Preventing encrypted traffic analysis

Many existing encrypted Internet protocols leak information through packet sizes and timing. Though seemingly innocuous, prior work has shown that such leakage can be used to recover part or all of the plaintext being encrypted. The prevalence of encrypted protocols as the underpinning of such critical services as e-commerce, remote login, and anonymity networks and the increasing feasibility of attacks on these services represent a considerable risk to communications security. Existing mechanisms for preventing traffic analysis focus on re-routing and padding. These prevention techniques have considerable resource and overhead requirements. Furthermore, padding is easily detectable and, in some cases, can introduce its own vulnerabilities. To address these shortcomings, we propose embedding real traffic in synthetically generated encrypted cover traffic. Novel to our approach is our use of realistic network protocol behavior models to generate cover traffic. The observable traffic we generate also has the benefit of being indistinguishable from other real encrypted traffic further thwarting an adversary's ability to target attacks. In this dissertation, we introduce the design of a proxy system called TrafficMimic that implements realistic cover traffic tunneling and can be used alone or integrated with the Tor anonymity system. We describe the cover traffic generation process including the subtleties of implementing a secure traffic generator. We show that TrafficMimic cover traffic can fool a complex protocol classification attack with 91% of the accuracy of real traffic. TrafficMimic cover traffic is also not detected by a binary classification attack specifically designed to detect TrafficMimic. We evaluate the performance of tunneling with independent cover traffic models and find that they are comparable, and, in some cases, more efficient than generic constant-rate defenses. We then use simulation and analytic modeling to understand the performance of cover traffic tunneling more deeply. We find that we can take measurements from real or simulated traffic with no tunneling and use them to estimate parameters for an accurate analytic model of the performance impact of cover traffic tunneling. Once validated, we use this model to better understand how delay, bandwidth, tunnel slowdown, and stability affect cover traffic tunneling. Finally, we take the insights from our simulation study and develop several biasing techniques that we can use to match the cover traffic to the real traffic while simultaneously bounding external information leakage. We study these bias methods using simulation and evaluate their security using a Bayesian inference attack. We find that we can safely improve performance with biasing while preventing both traffic analysis and defense detection attacks. We then apply these biasing methods to the real TrafficMimic implementation and evaluate it on the Internet. We find that biasing can provide 3-5x improvement in bandwidth for bulk transfers and 2.5-9.5x speedup for Web browsing over tunneling without biasing.

Physics-Based Modeling of Power System Components for the Evaluation of Low-Frequency Radiated Electromagnetic Fields

The low-frequency electromagnetic compatibility (EMC) is an increasingly important aspect in the design of practical systems to ensure the functional safety and reliability of complex products. The opportunities for using numerical techniques to predict and analyze system’s EMC are therefore of considerable interest in many industries. As the first phase of study, a proper model, including all the details of the component, was required. Therefore, the advances in EMC modeling were studied with classifying analytical and numerical models. The selected model was finite element (FE) modeling, coupled with the distributed network method, to generate the model of the converter’s components and obtain the frequency behavioral model of the converter. The method has the ability to reveal the behavior of parasitic elements and higher resonances, which have critical impacts in studying EMI problems. For the EMC and signature studies of the machine drives, the equivalent source modeling was studied. Considering the details of the multi-machine environment, including actual models, some innovation in equivalent source modeling was performed to decrease the simulation time dramatically. Several models were designed in this study and the voltage current cube model and wire model have the best result. The GA-based PSO method is used as the optimization process. Superposition and suppression of the fields in coupling the components were also studied and verified. The simulation time of the equivalent model is 80-100 times lower than the detailed model. All tests were verified experimentally. As the application of EMC and signature study, the fault diagnosis and condition monitoring of an induction motor drive was developed using radiated fields. In addition to experimental tests, the 3DFE analysis was coupled with circuit-based software to implement the incipient fault cases. The identification was implemented using ANN for seventy various faulty cases. The simulation results were verified experimentally. Finally, the identification of the types of power components were implemented. The results show that it is possible to identify the type of components, as well as the faulty components, by comparing the amplitudes of their stray field harmonics. The identification using the stray fields is nondestructive and can be used for the setups that cannot go offline and be dismantled

Stress-testing centralised model stores

One of the current challenges in model-driven engineering is enabling effective collaborative modelling. Two common approaches are either storing the models in a central repository, or keeping them under a traditional file-based version control system and build a centralized index for model-wide queries. Either way, special attention must be paid to the nature of these repositories and indexes as networked services: they should remain responsive even with an increasing number of concurrent clients. This paper presents an empirical study on the impact of certain key decisions on the scalability of concurrent model queries, using an Eclipse Connected Data Objects model repository and a Hawk model index. The study evaluates the impact of the network protocol, the API design and the internal caching mechanisms and analyzes the reasons for their varying performance.

ROLE OF THE FRONTAL EYE FIELD, SUPERIOR COLLICULUS, AND BASAL GANGLIA IN FLEXIBLE SACCADE BEHAVIOR

A distributed network of cortical and subcortical brain regions mediates the control of voluntary behavior, but it is unclear how this complex system may flexibly shift between different behavioral events. This thesis describes the neurophysiological changes in several key nuclei across the brain during flexible behavior, using saccadic eye movements in rhesus macaque monkeys. We examined five nuclei critical for saccade initiation and modulation: the frontal eye field (FEF) in the cerebral cortex, the subthalamic nucleus (STN), caudate nucleus (CD), and substantia nigra pars reticulata (SNr) in the basal ganglia (BG), and the superior colliculus (SC) in the midbrain. The first study tested whether a ‘threshold’ theory of how neuronal activity cues saccade initiation is consistent with the flexible control of behavior. The theory suggests there is a fixed level of FEF and SC neuronal activation at which saccades are initiated. Our results provide strong evidence against a fixed saccade threshold in either structure during flexible behavior, and indicate that threshold variability might depend on the level of inhibitory signals applied to the FEF or SC. The next two studies investigated the BG network as a likely candidate to modulate a saccade initiation mechanism, based on strong inhibitory output signals from the BG to the FEF and SC. We investigated the STN and CD (BG input), and the SNr (BG oculomotor output) to examine changes across the BG network. This revealed robust task-contingent shifts in BG signaling (Chapter 3), which uniquely impacted saccade initiation according to behavioral condition (Chapters 3 and 4). The thesis concludes with a published short review of the mechanistic effects of BG deep brain stimulation (Chapter 5), and a general discussion including proof of concept saccade behavioral changes in an MPTP-induced Parkinsonian model (Chapter 6). The studies presented here demonstrate that the conditions for saccade initiation by the FEF and SC vary according to behavioral condition, while simultaneously, large-scale task dependent shifts occur in BG signaling consistent with the observed modulation of FEF and SC activity. Taken together, these describe a mechanistic framework by which the cortico-BG loop may contribute to the flexible control of behavior.

Mathematical methods to analyze and interpret calcium signals of astrocytes

Astrocytes are the most numerous glial cell type in the mammalian brain and permeate the entire CNS interacting with neurons, vasculature, and other glial cells. Astrocytes display intracellular calcium signals that encode information about local synaptic function, distributed network activity, and high-level cognitive functions. Several studies have investigated the calcium dynamics of astrocytes in sensory areas and have shown that these cells can encode sensory stimuli. Nevertheless, only recently the neuro-scientific community has focused its attention on the role and functions of astrocytes in associative areas such as the hippocampus. In our first study, we used the information theory formalism to show that astrocytes in the CA1 area of the hippocampus recorded with 2-photon fluorescence microscopy during spatial navigation encode spatial information that is complementary and synergistic to information encoded by nearby "place cell" neurons. In our second study, we investigated various computational aspects of applying the information theory formalism to astrocytic calcium data. For this reason, we generated realistic simulations of calcium signals in astrocytes to determine optimal hyperparameters and procedures of information measures and applied them to real astrocytic calcium imaging data. Calcium signals of astrocytes are characterized by complex spatiotemporal dynamics occurring in subcellular parcels of the astrocytic domain which makes studying these cells in 2-photon calcium imaging recordings difficult. However, current analytical tools which identify the astrocytic subcellular regions are time consuming and extensively rely on user-defined parameters. Here, we present Rapid Astrocytic calcium Spatio-Temporal Analysis (RASTA), a novel machine learning algorithm for spatiotemporal semantic segmentation of 2-photon calcium imaging recordings of astrocytes which operates without human intervention. We found that RASTA provided fast and accurate identification of astrocytic cell somata, processes, and cellular domains, extracting calcium signals from identified regions of interest across individual cells and populations of hundreds of astrocytes recorded in awake mice.

Holistic Analysis for Fork-Join Distributed Tasks supported by the FTT-SE Protocol

11th IEEE World Conference on Factory Communication Systems (WFCS 2015). 27 to 29, May, 2015, TII-SS-2: Scheduling and Performance Analysis. Palma de Mallorca, Spain.

XDense: A Dense Grid Sensor Network for Distributed Feature Extraction

XXXIII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC 2015). 15 to 19, May, 2015, III Workshop de Comunicação em Sistemas Embarcados Críticos. Vitória, Brasil.