The link between partial melting, granitization and granulite development in central Ribeira Fold Belt, SE Brazil: New evidence from elemental and Sr-Nd isotopic geochemistry

Elemental and Sr-Nd isotopic data on metatexites, diatexites, orthogneisses and charnockites from the central Ribeira Fold Belt indicate that they are LILE-enriched weakly peraluminous granodiorites. Harker and Th-Hf-La correlation trends suggest that these rocks represent a co-genetic sequence, whereas variations on CaO, MnO, Y and HREE for charnockites can be explained by garnet consumption during granulitic metamorphism. Similar REE patterns and isotopic results of epsilon(565)(Nd) = -5.4 to -7.3 and (87)Sr/(86)Sr(565) = 0.706-0.711 for metatexites, diatexites, orthogneisses and charnockites, as well as similar T(DM) ages between 2.0 and 1.5 Ga are consistent with evolution from a relatively homogeneous and enriched common crustal (metasedimentary) protolith. Results suggest a genetic link between metatexites, diatexites, orthogneisses and charnockites and a two-step process for charnockite development: (a) generation of the hydrated igneous protoliths by anatexis of metasedimentary rocks; (b) continuous high-grade metamorphism that transformed the ""S-type granitoids"" (leucosomes and diatexites) into orthogneisses and, as metamorphism and dehydration progressed, into charnockites. (C) 2011 Elsevier Ltd. All rights reserved.

Thermochronology of central Ribeira Fold Belt, SE Brazil: Petrological and geochronological evidence for long-term high temperature maintenance during Western Gondwana amalgamation

The studied sector of the central Ribeira Fold Belt (SE Brazil) comprises metatexites, diatexites, charnockites and blastomylonites. This study integrates petrological and thermochronological data in order to constrain the thermotectonic and geodynamic evolution of this Neoproterozoic-Ordovician mobile belt during Western Gondwana amalgamation. New data indicate that after an earlier collision stage at similar to 610 Ma (zircon, U-Pb age), peak metamorphism and lower crust partial melting, coeval with the main regional high grade D(1) thrust deformation, occurred at 572-562 Ma (zircon, U-Pb ages). The overall average cooling rate was low (<5 degrees C/Ma) from 750 to 250 degrees C (at similar to 455 Ma; biotite-WR Rb-Sr age), but disparate cooling paths indicate differential uplift between distinct lithotypes: (a) metatexites and blastomylonites show a overall stable 3-5 degrees C/Ma cooling rate; (b) charnockites and associated rocks remained at T>650 degrees C during sub-horizontal D(2) shearing until similar to 510-470 Ma (garnet-WR Sm-Nd ages) (1-2 degrees C/Ma), being then rapidly exhumed/cooled (8-30 degrees C/Ma) during post-orogenic D(3) deformation with late granite emplacement at similar to 490 Ma (zircon, U-Pb age). Cooling rates based on garnet-biotite Fe-Mg diffusion are broadly consistent with the geochronological cooling rates: (a) metatexites were cooled faster at high temperatures (6 degrees C/Ma) and slowly at low temperatures (0.1 degrees C/Ma), decreasing cooling rates with time; (b) charnockites show low cooling rates (2 degrees C/Ma) near metamorphic peak conditions and high cooling rates (120 degrees C/Ma) at lower temperatures, increasing cooling rates during retrogression. The charnockite thermal evolution and the extensive production of granitoid melts in the area imply that high geothermal gradients were sustained fora long period of time (50-90 Ma). This thermal anomaly most likely reflects upwelling of asthenospheric mantle and magma underplating coupled with long-term generation of high HPE (heat producing elements) granitoids. These factors must have sustained elevated crustal geotherms for similar to 100 Ma, promoting widespread charnockite generation at middle to lower crustal levels. (C) 2010 Elsevier B.V. All rights reserved.

Metamorphic P-T evolution of granulites in the central Ribeira Fold Belt, SE Brazil

Pseudosections, geothermobarometric estimates and careful petrographic observations of gneissic migmatites and granulites from Neoproterozoic central Ribeira Fold Belt (SE Brazil) were performed in order to quantify the metamorphic P-T conditions during prograde and retrograde evolution of the Brasiliano Orogeny. Results establish a prograde metamorphic trajectory from amphibolite facies conditions to metamorphic peak (T = 850 +/- 50 A degrees C; P = 8 +/- 1 kbar) that promoted widespread dehydrationmelting of 30 to 40% of the gneisses and high-grade granitization. After the metamorphic peak, migmatites evolved with cooling and decompression to T a parts per thousand 500 A degrees C and P a parts per thousand 5 kbar coupled with aH2O increase, replacing the high-grade paragenesis plagioclase-quartz-K-feldspar-garnet by quartz-biotite-sillimanite-(muscovite). Cordierite absence, microtextural observations and P-T results constrain the migmatite metamorphic evolution in the pseudosections as a clockwise P-T path with retrograde cooling and decompression. High-temperature conditions further dehydrated the lower crust with biotite and amphibole-dehydration melting and granulite formation coupled with 10% melt generation. Granulites can thus be envisaged as middle to lower crust dehydrated restites. Granulites were slowly (nearly isobarically) cooled, followed by late exhumation/retrograde rapid decompression and cooling, reflecting a two step P-T path. This retrograde evolution, coupled with water influx, chemically reequilibrated the rocks from granulite to amphibolite/greenschist facies, promoting the replacement of the plagioclase-quartz-garnet-hypersthene peak assemblage by quartz-biotite- K-feldspar symplectites.

P-T-Fluid evolution and graphite deposition during retrograde metamorphism in Ribeira Fold Belt, SE Brazil: Oxygen fugacity, fluid inclusions and C-O-H isotopic evidence

Combined fluid inclusion (FI) microthermometry, Raman spectroscopy, X-ray diffraction, C-O-H isotopes and oxygen fugacities of granulites from central Ribeira Fold Belt, SE Brazil, provided the following results: i) Magnetite-Hematite fO(2) estimates range from 10(-11.5) bar (QFM + 1) to 10(-18.3) bar (QFM - 1) for the temperature range of 896 degrees C-656 degrees C, implying fO(2) decrease from metamorphic peak temperatures to retrograde conditions; ii) 5 main types of fluid inclusions were observed: a) CO(2) and CO(2)-N(2) (0-11 mol%) high to medium density (1.01-0.59 g/cm(3)) FI; b) CO(2) and CO(2)-N(2) (0-36 mol%) low density (0.19-0.29 g/cm(3)) FI; c) CO(2) (94-95 mol%)-N(2) (3 mol%)-CH(4) (2-3 mol%)-H(2)O (water phi(v) (25 degrees C) = 0.1) FI; d) low-salinity H(2)O-CO(2) FI; and e) late low-salinity H(2)O FI; iii) Raman analyses evidence two graphite types in khondalites: an early highly ordered graphite (T similar to 450 degrees C) overgrown by a disordered kind (T similar to 330 degrees C); iv) delta(18)O quartz results of 10.3-10.7 parts per thousand, imply high-temperature CO(2) delta(18)O values of 14.4-14.8 parts per thousand, suggesting the involvement of a metamorphic fluid, whereas lower temperature biotite delta(18)O and delta D results of 7.5-8.5 parts per thousand and -54 to -67 parts per thousand respectively imply H(2)O delta(18)O values of 10-11 parts per thousand and delta D(H2O) of -23 to -36 parts per thousand suggesting delta(18)O depletion and increasing fluid/rock ratio from metamorphic peak to retrograde conditions. Isotopic results are compatible with low-temperature H(2)O influx and fO(2) decrease that promoted graphite deposition in retrograde granulites, simultaneous with low density CO(2), CO(2)-N(2) and CO(2)-N(2)-CH(4)-H(2)O fluid inclusions at T = 450-330 degrees C. Graphite delta(13)C results of -10.9 to -11.4 parts per thousand imply CO(2) delta(13)C values of -0.8 to -1.3 parts per thousand suggesting decarbonation of Cambrian marine carbonates with small admixture of lighter biogenic or mantle derived fluids. Based on these results, it is suggested that metamorphic fluids from the central segment of Ribeira Fold Belt evolved to CO(2)-N(2) fluids during granulitic metamorphism at high fO(2), followed by rapid pressure drop at T similar to 400-450 degrees C during late exhumation that caused fO(2) reduction induced by temperature decrease and water influx, turning carbonic fluids into CO(2)-H(2)O (depleting biotite delta(18)O and delta D values), and progressively into H(2)O. When fO(2) decreased substantially by mixture of carbonic and aqueous fluids, graphite deposited forming khondalites. (C) 2010 Elsevier Ltd. All rights reserved.

Dynamic Changes in the Mental Rotation Network Revealed by Pattern Recognition Analysis of fMRI Data

We investigated the temporal dynamics and changes in connectivity in the mental rotation network through the application of spatio-temporal support vector machines (SVMs). The spatio-temporal SVM [Mourao-Miranda, J., Friston, K. J., et al. (2007). Dynamic discrimination analysis: A spatial-temporal SVM. Neuroimage, 36, 88-99] is a pattern recognition approach that is suitable for investigating dynamic changes in the brain network during a complex mental task. It does not require a model describing each component of the task and the precise shape of the BOLD impulse response. By defining a time window including a cognitive event, one can use spatio-temporal fMRI observations from two cognitive states to train the SVM. During the training, the SVM finds the discriminating pattern between the two states and produces a discriminating weight vector encompassing both voxels and time (i.e., spatio-temporal maps). We showed that by applying spatio-temporal SVM to an event-related mental rotation experiment, it is possible to discriminate between different degrees of angular disparity (0 degrees vs. 20 degrees, 0 degrees vs. 60 degrees, and 0 degrees vs. 100 degrees), and the discrimination accuracy is correlated with the difference in angular disparity between the conditions. For the comparison with highest accuracy (08 vs. 1008), we evaluated how the most discriminating areas (visual regions, parietal regions, supplementary, and premotor areas) change their behavior over time. The frontal premotor regions became highly discriminating earlier than the superior parietal cortex. There seems to be a parcellation of the parietal regions with an earlier discrimination of the inferior parietal lobe in the mental rotation in relation to the superior parietal. The SVM also identified a network of regions that had a decrease in BOLD responses during the 100 degrees condition in relation to the 0 degrees condition (posterior cingulate, frontal, and superior temporal gyrus). This network was also highly discriminating between the two conditions. In addition, we investigated changes in functional connectivity between the most discriminating areas identified by the spatio-temporal SVM. We observed an increase in functional connectivity between almost all areas activated during the 100 degrees condition (bilateral inferior and superior parietal lobe, bilateral premotor area, and SMA) but not between the areas that showed a decrease in BOLD response during the 100 degrees condition.

Fabrication of disposable voltammetric electronic tongues by using Prussian Blue films electrodeposited onto CD-R gold surfaces and recognition of milk adulteration

A new approach to fabricate a disposable electronic tongue is reported. The fabrication of the disposable sensor aimed the integration of all electrodes necessary for measurement in the same device. The disposable device was constructed with gold CD-R and copper sheets substrates and the sensing elements were gold, copper and a gold surface modified with a layer of Prussian Blue. The relative standard deviation for signals obtained from 20 different disposable gold and 10 different disposable copper electrodes was below 3.5%. The performance, electrode materials and the capability of the device to differentiate samples were evaluated for taste substances model, milk with different pasteurization processes (homogenized/pasteurized, ultra high temperature (UHT) pasteurized and UHT pasteurized with low fat content) and adulterated with hydrogen peroxide. In all analysed cases, a good separation between different samples was noticed in the score plots obtained from the principal component analysis (PCA). Crown Copyright (C) 2008 Published by Elsevier B.V. All rights reserved.

Human endogenous RNAs: Implications for the immunomodulation of Toll-like receptor 3

Toll-like receptors (TLRs), a family of mammalian receptors, are able to recognize nucleic acids. TLR3 recognizes double-stranded (ds)RNA, a product of the replication of certain viruses. Polyinosinic-polycytidylic acid, referred to as poly(I:C), an analog of viral dsRNA, interacts with TLR3 thereby eliciting immunoinflammatory responses characteristic of viral infection or down-regulating the expression of chemokine receptor CXCR4. It is known that dsRNA also directly activates interferon (IFN)-induced enzymes, such as the RNA-dependent protein kinase (PKR). In the present study, the mRNA expression of TLR3, CXCR4, IFN gamma and PKR was investigated in a culture of peripheral blood mononuclear cells (PBMCs) stimulated with poly(I:C) and endogenous RNA from human PBMCs. No cytotoxic effect on the cells or on the proliferation of CD3(+), CD4(+) and CD8(+) cells was observed. TLR3 expression in the PBMCs in the presence of poly(I:C) was up-regulated 9.5-fold, and TLR3 expression in the PBMCs treated with endogenous RNA was down-regulated 1.8-fold (p=0.002). The same trend was observed for IFN gamma where in the presence of poly(I:C) an 8.7-fold increase was noted and in the presence of endogenous RNA a 3.1-fold decrease was observed. In the culture activated with poly(1:C), mRNA expression of CXCR4 increased 8.0-fold and expression of PKR increased 33.0-fold. Expression of these genes decreased in the culture treated with endogenous RNA when compared to the culture without stimulus. Thus, high expression of mRNA for TLR3, IFN gamma, CXCR4 and PKR was observed in the presence of poly(I:C) and low expression was observed in the cells cultured with endogenous RNA. In conclusion, TLR3 may play major physiological roles that are not in the context of viral infection. It is possible that RNA released from cells could contain enough double-stranded structures to regulate cell activation. The involvement of endogenous RNA in endogenous gene expression and its implications in the regulation thereof, are still being studied, and will have significant implications in the future.

Road and Traffic Signs Recognition using Vector Machines

Intelligent Transportation System (ITS) is a system that builds a safe, effective and integrated transportation environment based on advanced technologies. Road signs detection and recognition is an important part of ITS, which offer ways to collect the real time traffic data for processing at a central facility.This project is to implement a road sign recognition model based on AI and image analysis technologies, which applies a machine learning method, Support Vector Machines, to recognize road signs. We focus on recognizing seven categories of road sign shapes and five categories of speed limit signs. Two kinds of features, binary image and Zernike moments, are used for representing the data to the SVM for training and test. We compared and analyzed the performances of SVM recognition model using different features and different kernels. Moreover, the performances using different recognition models, SVM and Fuzzy ARTMAP, are observed.

Road Sign Recognition based onInvariant Features using SupportVector Machine

Since last two decades researches have been working on developing systems that can assistsdrivers in the best way possible and make driving safe. Computer vision has played a crucialpart in design of these systems. With the introduction of vision techniques variousautonomous and robust real-time traffic automation systems have been designed such asTraffic monitoring, Traffic related parameter estimation and intelligent vehicles. Among theseautomatic detection and recognition of road signs has became an interesting research topic.The system can assist drivers about signs they don’t recognize before passing them.Aim of this research project is to present an Intelligent Road Sign Recognition System basedon state-of-the-art technique, the Support Vector Machine. The project is an extension to thework done at ITS research Platform at Dalarna University [25]. Focus of this research work ison the recognition of road signs under analysis. When classifying an image its location, sizeand orientation in the image plane are its irrelevant features and one way to get rid of thisambiguity is to extract those features which are invariant under the above mentionedtransformation. These invariant features are then used in Support Vector Machine forclassification. Support Vector Machine is a supervised learning machine that solves problemin higher dimension with the help of Kernel functions and is best know for classificationproblems.

Real-Time Recognition System for Traffic Signs

The aim of this thesis project is to develop the Traffic Sign Recognition algorithm for real time. Inreal time environment, vehicles move at high speed on roads. For the vehicle intelligent system itbecomes essential to detect, process and recognize the traffic sign which is coming in front ofvehicle with high relative velocity, at the right time, so that the driver would be able to pro-actsimultaneously on instructions given in the Traffic Sign. The system assists drivers about trafficsigns they did not recognize before passing them. With the Traffic Sign Recognition system, thevehicle becomes aware of the traffic environment and reacts according to the situation.The objective of the project is to develop a system which can recognize the traffic signs in real time.The three target parameters are the system’s response time in real-time video streaming, the trafficsign recognition speed in still images and the recognition accuracy. The system consists of threeprocesses; the traffic sign detection, the traffic sign recognition and the traffic sign tracking. Thedetection process uses physical properties of traffic signs based on a priori knowledge to detect roadsigns. It generates the road sign image as the input to the recognition process. The recognitionprocess is implemented using the Pattern Matching algorithm. The system was first tested onstationary images where it showed on average 97% accuracy with the average processing time of0.15 seconds for traffic sign recognition. This procedure was then applied to the real time videostreaming. Finally the tracking of traffic signs was developed using Blob tracking which showed theaverage recognition accuracy to 95% in real time and improved the system’s average response timeto 0.04 seconds. This project has been implemented in C-language using the Open Computer VisionLibrary.

An Upgrade of Network Traffic Recognition System for SIP/VoIP Traffic Recognition

The purpose of this project is to update the tool of Network Traffic Recognition System (NTRS) which is proprietary software of Ericsson AB and Tsinghua University, and to implement the updated tool to finish SIP/VoIP traffic recognition. Basing on the original NTRS, I analyze the traffic recognition principal of NTRS, and redesign the structure and module of the tool according to characteristics of SIP/VoIP traffic, and then finally I program to achieve the upgrade. After the final test with our SIP data trace files in the updated system, a satisfactory result is derived. The result presents that our updated system holds a rate of recognition on a confident level in the SIP session recognition as well as the VoIP call recognition. In the comparison with the software of Wireshark, our updated system has a result which is extremely close to Wireshark’s output, and the working time is much less than Wireshark. In the aspect of practicability, the memory overflow problem is avoided, and the updated system can output the specific information of SIP/VoIP traffic recognition, such as SIP type, SIP state, VoIP state, etc. The upgrade fulfills the demand of this project.

Hand Gesture Detection & Recognition System

The project introduces an application using computer vision for Hand gesture recognition. A camera records a live video stream, from which a snapshot is taken with the help of interface. The system is trained for each type of count hand gestures (one, two, three, four, and five) at least once. After that a test gesture is given to it and the system tries to recognize it.A research was carried out on a number of algorithms that could best differentiate a hand gesture. It was found that the diagonal sum algorithm gave the highest accuracy rate. In the preprocessing phase, a self-developed algorithm removes the background of each training gesture. After that the image is converted into a binary image and the sums of all diagonal elements of the picture are taken. This sum helps us in differentiating and classifying different hand gestures.Previous systems have used data gloves or markers for input in the system. I have no such constraints for using the system. The user can give hand gestures in view of the camera naturally. A completely robust hand gesture recognition system is still under heavy research and development; the implemented system serves as an extendible foundation for future work.

Motion Cues Analysis for Parkinson Gait Recognition

Background: Previous assessment methods for PG recognition used sensor mechanisms for PG that may cause discomfort. In order to avoid stress of applying wearable sensors, computer vision (CV) based diagnostic systems for PG recognition have been proposed. Main constraints in these methods are the laboratory setup procedures: Novel colored dresses for the patients were specifically designed to segment the test body from a specific colored background. Objective: To develop an image processing tool for home-assessment of Parkinson Gait(PG) by analyzing motion cues extracted during the gait cycles. Methods: The system is based on the idea that a normal body attains equilibrium during the gait by aligning the body posture with the axis of gravity. Due to the rigidity in muscular tone, persons with PD fail to align their bodies with the axis of gravity. The leaned posture of PD patients appears to fall forward. Whereas a normal posture exhibits a constant erect posture throughout the gait. Patients with PD walk with shortened stride angle (less than 15 degrees on average) with high variability in the stride frequency. Whereas a normal gait exhibits a constant stride frequency with an average stride angle of 45 degrees. In order to analyze PG, levodopa-responsive patients and normal controls were videotaped with several gait cycles. First, the test body is segmented in each frame of the gait video based on the pixel contrast from the background to form a silhouette. Next, the center of gravity of this silhouette is calculated. This silhouette is further skeletonized from the video frames to extract the motion cues. Two motion cues were stride frequency based on the cyclic leg motion and the lean frequency based on the angle between the leaned torso tangent and the axis of gravity. The differences in the peaks in stride and lean frequencies between PG and normal gait are calculated using Cosine Similarity measurements. Results: High cosine dissimilarity was observed in the stride and lean frequencies between PG and normal gait. High variations are found in the stride intervals of PG whereas constant stride intervals are found in the normal gait. Conclusions: We propose an algorithm as a source to eliminate laboratory constraints and discomfort during PG analysis. Installing this tool in a home computer with a webcam allows assessment of gait in the home environment.

What affects recognition most – wrong wordstress or wrong word accent?

In an attempt to find out which of the two Swedish prosodic contrasts of 1) wordstress pattern and 2) tonal word accent category has the greatest communicative weight, a lexical decision experiment was conducted: in one part word stress pattern was changed from trochaic to iambic, and in the other part trochaic accentII words were changed to accent I.Native Swedish listeners were asked to decide whether the distorted words werereal words or ‘non-words’. A clear tendency is that listeners preferred to give more‘non-word’ responses when the stress pattern was shifted, compared to when wordaccent category was shifted. This could have implications for priority of phonological features when teaching Swedish as a second language.

Traffic and Road Sign Recognition

This thesis presents a system to recognise and classify road and traffic signs for the purpose of developing an inventory of them which could assist the highway engineers’ tasks of updating and maintaining them. It uses images taken by a camera from a moving vehicle. The system is based on three major stages: colour segmentation, recognition, and classification. Four colour segmentation algorithms are developed and tested. They are a shadow and highlight invariant, a dynamic threshold, a modification of de la Escalera’s algorithm and a Fuzzy colour segmentation algorithm. All algorithms are tested using hundreds of images and the shadow-highlight invariant algorithm is eventually chosen as the best performer. This is because it is immune to shadows and highlights. It is also robust as it was tested in different lighting conditions, weather conditions, and times of the day. Approximately 97% successful segmentation rate was achieved using this algorithm.Recognition of traffic signs is carried out using a fuzzy shape recogniser. Based on four shape measures - the rectangularity, triangularity, ellipticity, and octagonality, fuzzy rules were developed to determine the shape of the sign. Among these shape measures octangonality has been introduced in this research. The final decision of the recogniser is based on the combination of both the colour and shape of the sign. The recogniser was tested in a variety of testing conditions giving an overall performance of approximately 88%.Classification was undertaken using a Support Vector Machine (SVM) classifier. The classification is carried out in two stages: rim’s shape classification followed by the classification of interior of the sign. The classifier was trained and tested using binary images in addition to five different types of moments which are Geometric moments, Zernike moments, Legendre moments, Orthogonal Fourier-Mellin Moments, and Binary Haar features. The performance of the SVM was tested using different features, kernels, SVM types, SVM parameters, and moment’s orders. The average classification rate achieved is about 97%. Binary images show the best testing results followed by Legendre moments. Linear kernel gives the best testing results followed by RBF. C-SVM shows very good performance, but ?-SVM gives better results in some case.