Concentric characterization and classification of complex network nodes: Application to an institutional collaboration network

Differently from theoretical scale-free networks, most real networks present multi-scale behavior, with nodes structured in different types of functional groups and communities. While the majority of approaches for classification of nodes in a complex network has relied on local measurements of the topology/connectivity around each node, valuable information about node functionality can be obtained by concentric (or hierarchical) measurements. This paper extends previous methodologies based on concentric measurements, by studying the possibility of using agglomerative clustering methods, in order to obtain a set of functional groups of nodes, considering particular institutional collaboration network nodes, including various known communities (departments of the University of Sao Paulo). Among the interesting obtained findings, we emphasize the scale-free nature of the network obtained, as well as identification of different patterns of authorship emerging from different areas (e.g. human and exact sciences). Another interesting result concerns the relatively uniform distribution of hubs along concentric levels, contrariwise to the non-uniform pattern found in theoretical scale-free networks such as the BA model. (C) 2008 Elsevier B.V. All rights reserved.

The three-dimensional structure of bothropasin, the main hemorrhagic factor from Bothrops jararaca venom: Insights for a new classification of snake venom metalloprotease subgroups

Bothropasin is a 48 kDa hemorrhagic PIII snake venom metalloprotease (SVMP) isolated from Bothrops jararaca, containing disintegrin/cysteine-rich adhesive domains. Here we present the crystal structure of bothropasin complexed with the inhibitor POL647. The catalytic domain consists of a scaffold of two subdomains organized similarly to those described for other SVMPs, including the zinc and calcium-binding sites. The free cysteine residue Cys(189) is located within a hydrophobic core and it is not available for disulfide bonding or other interactions. There is no identifiable secondary structure for the disintegrin domain, but instead it is composed mostly of loops stabilized by seven disulfide bonds and by two calcium ions. The ECD region is in a loop and is structurally related to the RGD region of RGD disintegrins, which are derived from I`ll SVMPs. The ECD motif is stabilized by the Cys(117)_Cys(310) disulfide bond (between the disintegrin and cysteine-rich domains) and by one calcium ion. The side chain of Glu(276) of the ECD motif is exposed to solvent and free to make interactions. In bothropasin, the HVR (hyper-variable region) described for other Pill SVMPs in the cysteine-rich domain, presents a well-conserved sequence with respect to several other Pill members from different species. We propose that this subset be referred to as PIII-HCR (highly conserved region) SVMPs. The differences in the disintegrin-like, cysteine-rich or disintegrin-like cysteine-rich domains may be involved in selecting target binding, which in turn could generate substrate diversity or specificity for the catalytic domain. (C) 2008 Elsevier Ltd. All rights reserved.

CLASSIFICATION OF SUBALGEBRAS OF THE CAYLEY ALGEBRA OVER A FINITE FIELD

We classify all unital subalgebras of the Cayley algebra O(q) over the finite field F(q), q = p(n). We obtain the number of subalgebras of each type and prove that all isomorphic subalgebras are conjugate with respect to the automorphism group of O(q). We also determine the structure of the Moufang loops associated with each subalgebra of O(q).

Classification of the virtually cyclic subgroups of the pure braid groups of the projective plane

We classify the ( finite and infinite) virtually cyclic subgroups of the pure braid groups P(n)(RP(2)) of the projective plane. The maximal finite subgroups of P(n)(RP(2)) are isomorphic to the quaternion group of order 8 if n = 3, and to Z(4) if n >= 4. Further, for all n >= 3, the following groups are, up to isomorphism, the infinite virtually cyclic subgroups of P(n)(RP(2)): Z, Z(2) x Z and the amalgamated product Z(4)*(Z2)Z(4).

Enhanced classification of Chagas serologic results and epidemiologic characteristics of seropositive donors at three large blood centers in Brazil

BACKGROUND: A major problem in Chagas disease donor screening is the high frequency of samples with inconclusive results. The objective of this study was to describe patterns of serologic results among donors to the three Brazilian REDS-II blood centers and correlate with epidemiologic characteristics. STUDY DESIGN AND METHODS: The centers screened donor samples with one Trypanosoma cruzi lysate enzyme immunoassay (EIA). EIA-reactive samples were tested with a second lysate EIA, a recombinant-antigen based EIA, and an immunfluorescence assay. Based on the serologic results, samples were classified as confirmed positive (CP), probable positive (PP), possible other parasitic infection (POPI), and false positive (FP). RESULTS: In 2007 to 2008, a total of 877 of 615,433 donations were discarded due to Chagas assay reactivity. The prevalences (95% confidence intervals [CIs]) among first-time donors for CP, PP, POPI, and FP patterns were 114 (99-129), 26 (19-34), 10 (5-14), and 96 (82-110) per 100,000 donations, respectively. CP and PP had similar patterns of prevalence when analyzed by age, sex, education, and location, suggesting that PP cases represent true T. cruzi infections; in contrast the demographics of donors with POPI were distinct and likely unrelated to Chagas disease. No CP cases were detected among 218,514 repeat donors followed for a total of 718,187 person-years. CONCLUSION: We have proposed a classification algorithm that may have practical importance for donor counseling and epidemiologic analyses of T. cruzi-seroreactive donors. The absence of incident T. cruzi infections is reassuring with respect to risk of window phase infections within Brazil and travel-related infections in nonendemic countries such as the United States.

Use of Metals and Anion Species with Chemometrics Tools for Classification of Unprocessed and Processed Coconut Waters

Coconut water is a natural isotonic, nutritive, and low-caloric drink. Preservation process is necessary to increase its shelf life outside the fruit and to improve commercialization. However, the influence of the conservation processes, antioxidant addition, maturation time, and soil where coconut is cultivated on the chemical composition of coconut water has had few arguments and studies. For these reasons, an evaluation of coconut waters (unprocessed and processed) was carried out using Ca, Cu, Fe, K, Mg, Mn, Na, Zn, chloride, sulfate, phosphate, malate, and ascorbate concentrations and chemometric tools. The quantitative determinations were performed by electrothermal atomic absorption spectrometry, inductively coupled plasma optical emission spectrometry, and capillary electrophoresis. The results showed that Ca, K, and Zn concentrations did not present significant alterations between the samples. The ranges of Cu, Fe, Mg, Mn, PO (4) (3-) , and SO (4) (2-) concentrations were as follows: Cu (3.1-120 A mu g L(-1)), Fe (60-330 A mu g L(-1)), Mg (48-123 mg L(-1)), Mn (0.4-4.0 mg L(-1)), PO (4) (3-) (55-212 mg L(-1)), and SO (4) (2-) (19-136 mg L(-1)). The principal component analysis (PCA) and hierarchical cluster analysis (HCA) were applied to differentiate unprocessed and processed samples. Multivariated analysis (PCA and HCA) were compared through one-way analysis of variance with Tukey-Kramer multiple comparisons test, and p values less than 0.05 were considered to be significant.

Machine vision for the automatic classification of images acquired from Non-destructive tests

This project is based on Artificial Intelligence (A.I) and Digital Image processing (I.P) for automatic condition monitoring of sleepers in the railway track. Rail inspection is a very important task in railway maintenance for traffic safety issues and in preventing dangerous situations. Monitoring railway track infrastructure is an important aspect in which the periodical inspection of rail rolling plane is required.Up to the present days the inspection of the railroad is operated manually by trained personnel. A human operator walks along the railway track searching for sleeper anomalies. This monitoring way is not more acceptable for its slowness and subjectivity. Hence, it is desired to automate such intuitive human skills for the development of more robust and reliable testing methods. Images of wooden sleepers have been used as data for my project. The aim of this project is to present a vision based technique for inspecting railway sleepers (wooden planks under the railway track) by automatic interpretation of Non Destructive Test (NDT) data using A.I. techniques in determining the results of inspection.

Nonstationary feature extraction techniques for automatic classification of impact acoustic signals

Condition monitoring of wooden railway sleepers applications are generallycarried out by visual inspection and if necessary some impact acoustic examination iscarried out intuitively by skilled personnel. In this work, a pattern recognition solutionhas been proposed to automate the process for the achievement of robust results. Thestudy presents a comparison of several pattern recognition techniques together withvarious nonstationary feature extraction techniques for classification of impactacoustic emissions. Pattern classifiers such as multilayer perceptron, learning cectorquantization and gaussian mixture models, are combined with nonstationary featureextraction techniques such as Short Time Fourier Transform, Continuous WaveletTransform, Discrete Wavelet Transform and Wigner-Ville Distribution. Due to thepresence of several different feature extraction and classification technqies, datafusion has been investigated. Data fusion in the current case has mainly beeninvestigated on two levels, feature level and classifier level respectively. Fusion at thefeature level demonstrated best results with an overall accuracy of 82% whencompared to the human operator.

Acoustic Design in Urban Development : analysis of urban soundscapes and acoustic ecology research in New York City

The world is urbanizing rapidly with more than half of the global population now living in cities. Improving urban environments for the well-being of the increasing number of urban citizens is becoming one of the most important challenges of the 21st century. Even though it is common that city planners have visions of a ’good urban milieu’, those visions are concerning visual aesthetics or practical matters. The qualitative perspective of sound, such as sonic diversity and acoustic ecology are neglected aspects in architectural design. Urban planners and politicians are therefore largely unaware of the importance of sounds for the intrinsic quality of a place. Whenever environmental acoustics is on the agenda, the topic is noise abatement or noise legislation – a quantitative attenuation of sounds. Some architects may involve acoustical aspects in their work but sound design or acoustic design has yet to develop to a distinct discipline and be incorporated in urban planning.My aim was to investigate to what extent the urban soundscape is likely to improve if modern architectural techniques merge with principles of acoustics. This is an important, yet unexplored, research area. My study explores and analyses the acoustical aspects in urban development and includes interviews with practitioners in the field of urban acoustics, situated in New York City. My conclusion is that to achieve a better understanding of the human living conditions in mega-cities, there is a need to include sonic components into the holistic sense of urban development.

Classification of Parkinson’s Disease using MultiPass Lvq,Logistic Model Tree,K-Star for Audio Data set : Classification of Parkinson Disease using Audio Dataset

Parkinson's disease (PD) is a degenerative illness whose cardinal symptoms include rigidity, tremor, and slowness of movement. In addition to its widely recognized effects PD can have a profound effect on speech and voice.The speech symptoms most commonly demonstrated by patients with PD are reduced vocal loudness, monopitch, disruptions of voice quality, and abnormally fast rate of speech. This cluster of speech symptoms is often termed Hypokinetic Dysarthria.The disease can be difficult to diagnose accurately, especially in its early stages, due to this reason, automatic techniques based on Artificial Intelligence should increase the diagnosing accuracy and to help the doctors make better decisions. The aim of the thesis work is to predict the PD based on the audio files collected from various patients.Audio files are preprocessed in order to attain the features.The preprocessed data contains 23 attributes and 195 instances. On an average there are six voice recordings per person, By using data compression technique such as Discrete Cosine Transform (DCT) number of instances can be minimized, after data compression, attribute selection is done using several WEKA build in methods such as ChiSquared, GainRatio, Infogain after identifying the important attributes, we evaluate attributes one by one by using stepwise regression.Based on the selected attributes we process in WEKA by using cost sensitive classifier with various algorithms like MultiPass LVQ, Logistic Model Tree(LMT), K-Star.The classified results shows on an average 80%.By using this features 95% approximate classification of PD is acheived.This shows that using the audio dataset, PD could be predicted with a higher level of accuracy.

Speech Assessment for the Classification of Hypokinetic Dysthria in Parkinson Disease

The aim of this thesis is to investigate computerized voice assessment methods to classify between the normal and Dysarthric speech signals. In this proposed system, computerized assessment methods equipped with signal processing and artificial intelligence techniques have been introduced. The sentences used for the measurement of inter-stress intervals (ISI) were read by each subject. These sentences were computed for comparisons between normal and impaired voice. Band pass filter has been used for the preprocessing of speech samples. Speech segmentation is performed using signal energy and spectral centroid to separate voiced and unvoiced areas in speech signal. Acoustic features are extracted from the LPC model and speech segments from each audio signal to find the anomalies. The speech features which have been assessed for classification are Energy Entropy, Zero crossing rate (ZCR), Spectral-Centroid, Mean Fundamental-Frequency (Meanf0), Jitter (RAP), Jitter (PPQ), and Shimmer (APQ). Naïve Bayes (NB) has been used for speech classification. For speech test-1 and test-2, 72% and 80% accuracies of classification between healthy and impaired speech samples have been achieved respectively using the NB. For speech test-3, 64% correct classification is achieved using the NB. The results direct the possibility of speech impairment classification in PD patients based on the clinical rating scale.

Benchmark Evaluation of HOG Descriptors as Features for Classification of Traffic Signs

The purpose of this paper is to analyze the performance of the Histograms of Oriented Gradients (HOG) as descriptors for traffic signs recognition. The test dataset consists of speed limit traffic signs because of their high inter-class similarities.   HOG features of speed limit signs, which were extracted from different traffic scenes, were computed and a Gentle AdaBoost classifier was invoked to evaluate the different features. The performance of HOG was tested with a dataset consisting of 1727 Swedish speed signs images. Different numbers of HOG features per descriptor, ranging from 36 features up 396 features, were computed for each traffic sign in the benchmark testing. The results show that HOG features perform high classification rate as the Gentle AdaBoost classification rate was 99.42%, and they are suitable to real time traffic sign recognition. However, it is found that changing the number of orientation bins has insignificant effect on the classification rate. In addition to this, HOG descriptors are not robust with respect to sign orientation.