Implications of the IRAS data for galactic gamma-ray astronomy and EGRET /

"To appear in the Proceeding of the EGRET Science Symposium."

Master station list for solar-terrestrial physics data at WDC-A for Solar-terrestrial Physics /

Mode of access: Internet.

Sediment deposition in U.S. reservoirs : summary of data reported through 1975 /

Issued Feb. 1978.

The HIPASS catalogue - I. Data presentation

The H I Parkes All-Sky Survey (HIPASS) catalogue forms the largest uniform catalogue of H I sources compiled to date, with 4315 sources identified purely by their H I content. The catalogue data comprise the southern region delta < + 2&DEG; of HIPASS, the first blind H I survey to cover the entire southern sky. The rms noise for this survey is 13 mJy beam(-1) and the velocity range is -1280 to 12 700 km s(-1). Data search, verification and parametrization methods are discussed along with a description of measured quantities. Full catalogue data are made available to the astronomical community including positions, velocities, velocity widths, integrated fluxes and peak flux densities. Also available are on-sky moment maps, position-velocity moment maps and spectra of catalogue sources. A number of local large-scale features are observed in the space distribution of sources, including the super-Galactic plane and the Local Void. Notably, large-scale structure is seen at low Galactic latitudes, a region normally obscured at optical wavelengths.

Mathematical Morphology: Star/Galaxy Differentiation & Galaxy Morphology Classication

We present an application of Mathematical Morphology (MM) for the classification of astronomical objects, both for star/galaxy differentiation and galaxy morphology classification. We demonstrate that, for CCD images, 99.3 +/- 3.8% of galaxies can be separated from stars using MM, with 19.4 +/- 7.9% of the stars being misclassified. We demonstrate that, for photographic plate images, the number of galaxies correctly separated from the stars can be increased using our MM diffraction spike tool, which allows 51.0 +/- 6.0% of the high-brightness galaxies that are inseparable in current techniques to be correctly classified, with only 1.4 +/- 0.5% of the high-brightness stars contaminating the population. We demonstrate that elliptical (E) and late-type spiral (Sc-Sd) galaxies can be classified using MM with an accuracy of 91.4 +/- 7.8%. It is a method involving fewer 'free parameters' than current techniques, especially automated machine learning algorithms. The limitation of MM galaxy morphology classification based on seeing and distance is also presented. We examine various star/galaxy differentiation and galaxy morphology classification techniques commonly used today, and show that our MM techniques compare very favourably.

Data Independence in the Multi-dimensional Numbered Information Spaces

The concept of data independence designates the techniques that allow data to be changed without affecting the applications that process it. The different structures of the information bases require corresponded tools for supporting data independence. A kind of information bases (the Multi-dimensional Numbered Information Spaces) are pointed in the paper. The data independence in such information bases is discussed.

Photographic Plate Libraries at European Astronomical Observatories

ACM Computing Classification System (1998): J.2.

Potsdam Library of Astronomical Photographic Plates

ACM Computing Classification System (1998): J.2.

Software Tools for Digitization of Astronomical Photographic Plates

ACM Computing Classification System (1998): J.2.

AstroWeb – A Graphical Representation of an Astronomical WFPDB Database Using a Web-Based Open Source Geographical Information System

ACM Computing Classification System (1998): H.5.2, H.2.8, J.2, H.5.3.

Adaptive Document Image Binarization with Application in Processing Astronomical Logbooks

ACM Computing Classification System (1998): I.7, I.7.5.

Statistical analysis and meta-analysis of microarray data

The microarray technology provides a high-throughput technique to study gene expression. Microarrays can help us diagnose different types of cancers, understand biological processes, assess host responses to drugs and pathogens, find markers for specific diseases, and much more. Microarray experiments generate large amounts of data. Thus, effective data processing and analysis are critical for making reliable inferences from the data. ^ The first part of dissertation addresses the problem of finding an optimal set of genes (biomarkers) to classify a set of samples as diseased or normal. Three statistical gene selection methods (GS, GS-NR, and GS-PCA) were developed to identify a set of genes that best differentiate between samples. A comparative study on different classification tools was performed and the best combinations of gene selection and classifiers for multi-class cancer classification were identified. For most of the benchmarking cancer data sets, the gene selection method proposed in this dissertation, GS, outperformed other gene selection methods. The classifiers based on Random Forests, neural network ensembles, and K-nearest neighbor (KNN) showed consistently god performance. A striking commonality among these classifiers is that they all use a committee-based approach, suggesting that ensemble classification methods are superior. ^ The same biological problem may be studied at different research labs and/or performed using different lab protocols or samples. In such situations, it is important to combine results from these efforts. The second part of the dissertation addresses the problem of pooling the results from different independent experiments to obtain improved results. Four statistical pooling techniques (Fisher inverse chi-square method, Logit method. Stouffer's Z transform method, and Liptak-Stouffer weighted Z-method) were investigated in this dissertation. These pooling techniques were applied to the problem of identifying cell cycle-regulated genes in two different yeast species. As a result, improved sets of cell cycle-regulated genes were identified. The last part of dissertation explores the effectiveness of wavelet data transforms for the task of clustering. Discrete wavelet transforms, with an appropriate choice of wavelet bases, were shown to be effective in producing clusters that were biologically more meaningful. ^

Phylogenetic analysis of within -host serially-sampled viral data

The primary goal of this dissertation is the study of patterns of viral evolution inferred from serially-sampled sequence data, i.e., sequence data obtained from strains isolated at consecutive time points from a single patient or host. RNA viral populations have an extremely high genetic variability, largely due to their astronomical population sizes within host systems, high replication rate, and short generation time. It is this aspect of their evolution that demands special attention and a different approach when studying the evolutionary relationships of serially-sampled sequence data. New methods that analyze serially-sampled data were developed shortly after a groundbreaking HIV-1 study of several patients from which viruses were isolated at recurring intervals over a period of 10 or more years. These methods assume a tree-like evolutionary model, while many RNA viruses have the capacity to exchange genetic material with one another using a process called recombination. ^ A genealogy involving recombination is best described by a network structure. A more general approach was implemented in a new computational tool, Sliding MinPD, one that is mindful of the sampling times of the input sequences and that reconstructs the viral evolutionary relationships in the form of a network structure with implicit representations of recombination events. The underlying network organization reveals unique patterns of viral evolution and could help explain the emergence of disease-associated mutants and drug-resistant strains, with implications for patient prognosis and treatment strategies. In order to comprehensively test the developed methods and to carry out comparison studies with other methods, synthetic data sets are critical. Therefore, appropriate sequence generators were also developed to simulate the evolution of serially-sampled recombinant viruses, new and more through evaluation criteria for recombination detection methods were established, and three major comparison studies were performed. The newly developed tools were also applied to "real" HIV-1 sequence data and it was shown that the results represented within an evolutionary network structure can be interpreted in biologically meaningful ways. ^

Possibilidades de enfoque CTS para o ensino superior de química: proposta de uma abordagem para ácidos e bases

Scientific education has been passing by redefinitions, contestations and new contributions from the research on science teaching. One contribution is the idea of science and technology literacy, allowing the citizens not only knowing science but also understand aspects on the construction and motivation of scientific and technological research. In accordance with this idea, there is the Science-Technology-Society (STS) studies which, since the 1970s, has been contributing for science teaching and learning according to the comprehension of the relationships with society in the Western countries of the North. In Brazil, this approach began to gain projection from the 1990s when the first essays on the theme were published. Currently, there is a clear influence of this approach on the national curriculum guidelines, especially for the area of Natural Sciences, and also on the textbooks chosen by the High School National Program (Programa Nacional do Ensino Médio). However, there seems to be a gap in relation to the discussion on the specific curricular component seen in college on this approach. Thus, this study aims at adopting the approach STS, face to the preparation of complimentary educational material on acid and bases concepts studied in the course of General Chemistry of the Natural Sciences graduation program. To this end, it was performed a bibliographical research aiming at making the state-of-the-art in in these concepts in specific literature to science teaching. It is divided in two stages: systematic study (with sixteen journals chosen according to Qualis-Capes and an unsystematic study with direct search in databases and references in the papers of the systematic study. The studies had their content analyzed and the categories chosen a priori were the level of education, the acid-base theory adopted, and the strategy/theoretical frame of reference adopted. A second stage aimed at identifying attitudes and beliefs on STS (Science-Technology-Society) and CSE (Chemistry-Society-Environment) of students in the teacher and technologist training course in three diferent institutions: UTFPR, UFRN and IFRN. In this study, it was used two questionnaires, composed of a Likert scale, semantic differential scale and open questions. The quantitative data reliability was estimated through Cronbach’s alpha method, and tha data were treated according to classic statistics, using the mean as the centrality measures, and the mean deviation as dispersion. The qualitative data were treated according to the content analysis with categories taken from the reading of answers. In the third stage, it was analyzed the presence of STS and CSE content in chapters on acid and bases concepts of nine General Chemistry textbooks, frequently used in graduation programs in public institutions of the state of Rio Grande do Norte. The results showed that there are few proposals of acid and bases teaching, and they are generally aimed at High School or at instrumentation for teaching courses, and no course for General Chemistry. The student’s attitudes and beliefs show the presence of a positivist point of view based on the concept of Science and Technology neutrality and the salvation of its mediation. The books analysis showed just a few content on STS and CSE are found in the studied chapters, and they are generally presented disjointedly in relation to the rest of the main text. In the end, as suggestion to solve the absence of proposals STS in General Chemistry books, as well as the student’s positivist attitudes, it was developed some educational material to be used in the course of General Chemistry at College. The material is structured to introduce a historical view of the concepts preparation, present the use of materials, the industrial and technological processes, and social and environmental consequences of this activities

Labeling large scale social media data using budget-driven One-class SVM classification

The social media classification problems draw more and more attention in the past few years. With the rapid development of Internet and the popularity of computers, there is astronomical amount of information in the social network (social media platforms). The datasets are generally large scale and are often corrupted by noise. The presence of noise in training set has strong impact on the performance of supervised learning (classification) techniques. A budget-driven One-class SVM approach is presented in this thesis that is suitable for large scale social media data classification. Our approach is based on an existing online One-class SVM learning algorithm, referred as STOCS (Self-Tuning One-Class SVM) algorithm. To justify our choice, we first analyze the noise-resilient ability of STOCS using synthetic data. The experiments suggest that STOCS is more robust against label noise than several other existing approaches. Next, to handle big data classification problem for social media data, we introduce several budget driven features, which allow the algorithm to be trained within limited time and under limited memory requirement. Besides, the resulting algorithm can be easily adapted to changes in dynamic data with minimal computational cost. Compared with two state-of-the-art approaches, Lib-Linear and kNN, our approach is shown to be competitive with lower requirements of memory and time.