Field measurements of tracer gas transport by barometric pumping

Vertical gas motions induced by barometric pressure variations can carry radioactive gases out of the rubblized region produced by an underground nuclear explosion, through overburden rock, into the atmosphere. To better quantify transit time and amount of transport, field experiments were conducted at two sites on Pahute Mesa, Kapelli and Tierra, where radioactive gases had been earlier detected in surface cracks. At each site, two tracer gases were injected into the rubblized chimney 300-400 m beneath the surface and their arrival was monitored by concentration measurements in gas samples extracted from shallow collection holes. The first ``active`` tracer was driven by a large quantity of injected air; the second ``passive`` tracer was introduced with minimal gas drive to observe the natural transport by barometric pumping. Kapelli was injected in the fall of 1990, followed by Tierra in the fall of 1991. Data was collected at both sites through the summer of 1993. At both sites, no surface arrival of tracer was observed during the active phase of the experiment despite the injection of several million cubic feet of air, suggesting that cavity pressurization is likely to induce horizontal transport along high permeability layers rather than vertical transport to the surface. In contrast, the vertical pressure gradients associated with barometric pumping brought both tracers to the surface in comparable concentrations within three months at Kapelli, whereas 15 months elapsed before surface arrival at Tierra. At Kapelli, a quasisteady pumping regime was established, with tracer concentrations in effluent gases 1000 times smaller than concentrations thought to exist in the chimney. Tracer concentrations observed at Tierra were typically an order of magnitude smaller. Comparisons with theoretical calculations suggest that the gases are traveling through {approximately}1 millimeter vertical fractures spaced 2 to 4 meters apart. 6 refs., 18 figs., 3 tabs

Методическая работа в дошкольной образовательной организации как условие повышения информационно-коммуникационной компетентности педагогов

Тема работы актуальна. В ВКР представлены условия, способствующие развитию компонентов ИКК педагогов. Работа имеет практическую значимост

Acceptability of Condom Promotion and Distribution Among 10-19 Year-Old Adolescents in Mpwapwa and Mbeya Rural Districts, Tanzania.

\ud The HIV/AIDS pandemic remains a leading challenge for global health. Although condoms are acknowledged for their key role on preventing HIV transmission, low and inappropriate use of condoms persists in Tanzania and elsewhere in Africa. This study assesses factors affecting acceptability of condom promotion and distribution among adolescents in Mpwapwa and Mbeya rural districts of Tanzania. Data were collected in 2011 as part of a larger cross-sectional survey on condom use among 10-19 year-olds in Mpwapwa and Mbeya rural districts of Tanzania using a structured questionnaire. Associations between acceptability of condom promotion and distribution and each of the explanatory variables were tested using Chi Square. Multivariate logistic regression model was used to examine independent predictors of the acceptability of condom promotion and distribution using STATA (11) statistical software at 5% significance level. Mean age of the 1,327 adolescent participants (50.5% being males) was 13.5 years (SD = 1.4). Acceptance of condom promotion and distribution was found among 37% (35% in Mpwapwa and 39% in Mbeya rural) of the adolescents. Being sexually active and aged 15-19 was the strongest predictor of the acceptability of condom promotion and distribution (OR = 7.78, 95% CI 4.65-12.99). Others were; not agreeing that a condom is effective in preventing transmissions of STIs including HIV (OR = 0.34, 95% CI 0.20-0.56), being a resident of Mbeya rural district (OR = 1.67, 95% CI 1.28-2.19), feeling comfortable being seen by parents/guardians holding/buying condoms (OR = 2.20, 95% CI 1.40-3.46) and living with a guardian (OR = 1.48, 95% CI 1.08-2.04). Acceptability of condom promotion and distribution among adolescents in Mpwapwa and Mbeya rural is low. Effect of sexual activity on the acceptability of condom promotion and distribution is age-dependent and was the strongest. Feeling comfortable being seen by parents/guardians buying or holding condoms, perceived ability of condoms to offer protection against HIV/AIDS infections, district of residence and living arrangements also offered significant predictive effect. Knowledge of these factors is vital in designing successful and sustainable condom promotion and distribution programs in Tanzania.\u

Prediction of catalytic residues using Support Vector Machine with selected protein sequence and structural properties

BACKGROUND: The number of protein sequences deriving from genome sequencing projects is outpacing our knowledge about the function of these proteins. With the gap between experimentally characterized and uncharacterized proteins continuing to widen, it is necessary to develop new computational methods and tools for functional prediction. Knowledge of catalytic sites provides a valuable insight into protein function. Although many computational methods have been developed to predict catalytic residues and active sites, their accuracy remains low, with a significant number of false positives. In this paper, we present a novel method for the prediction of catalytic sites, using a carefully selected, supervised machine learning algorithm coupled with an optimal discriminative set of protein sequence conservation and structural properties. RESULTS: To determine the best machine learning algorithm, 26 classifiers in the WEKA software package were compared using a benchmarking dataset of 79 enzymes with 254 catalytic residues in a 10-fold cross-validation analysis. Each residue of the dataset was represented by a set of 24 residue properties previously shown to be of functional relevance, as well as a label {+1/-1} to indicate catalytic/non-catalytic residue. The best-performing algorithm was the Sequential Minimal Optimization (SMO) algorithm, which is a Support Vector Machine (SVM). The Wrapper Subset Selection algorithm further selected seven of the 24 attributes as an optimal subset of residue properties, with sequence conservation, catalytic propensities of amino acids, and relative position on protein surface being the most important features. CONCLUSION: The SMO algorithm with 7 selected attributes correctly predicted 228 of the 254 catalytic residues, with an overall predictive accuracy of more than 86%. Missing only 10.2% of the catalytic residues, the method captures the fundamental features of catalytic residues and can be used as a "catalytic residue filter" to facilitate experimental identification of catalytic residues for proteins with known structure but unknown function

Evaluations of People Depicted With Facial Disfigurement Compared to Those With Mobility Impairment

There are few extant studies of stereotyping of people with facial disfigurement. In the present study, two experiments (both within-participants) showed positive evaluations of people depicted as wheelchair users and, from the same participants, negative evaluations of people with facial disfigurements, compared to controls. The results of Experiment 2 suggested that implicit affective attitudes were more negative toward people with facial disfigurement than wheelchair users and were correlated with evaluation negativity. Social norms were perceived to permit more discrimination against people with facial disfigurement than against wheelchair users. These factors could help to explain the evaluative differences between the two disadvantaged groups

Information retrieval and text mining technologies for chemistry

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European Community’s Horizon 2020 Program (project reference: 654021 - OpenMinted). M.K. additionally acknowledges the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology. O.R. and J.O. thank the Foundation for Applied Medical Research (FIMA), University of Navarra (Pamplona, Spain). This work was partially funded by Consellería de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684). We thank Iñigo Garciá -Yoldi for useful feedback and discussions during the preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Predicting Positive p53 Cancer Rescue Regions Using Most Informative Positive (MIP) Active Learning

Many protein engineering problems involve finding mutations that produce proteins with a particular function. Computational active learning is an attractive approach to discover desired biological activities. Traditional active learning techniques have been optimized to iteratively improve classifier accuracy, not to quickly discover biologically significant results. We report here a novel active learning technique, Most Informative Positive (MIP), which is tailored to biological problems because it seeks novel and informative positive results. MIP active learning differs from traditional active learning methods in two ways: (1) it preferentially seeks Positive (functionally active) examples; and (2) it may be effectively extended to select gene regions suitable for high throughput combinatorial mutagenesis. We applied MIP to discover mutations in the tumor suppressor protein p53 that reactivate mutated p53 found in human cancers. This is an important biomedical goal because p53 mutants have been implicated in half of all human cancers, and restoring active p53 in tumors leads to tumor regression. MIP found Positive (cancer rescue) p53 mutants in silico using 33% fewer experiments than traditional non-MIP active learning, with only a minor decrease in classifier accuracy. Applying MIP to in vivo experimentation yielded immediate Positive results. Ten different p53 mutations found in human cancers were paired in silico with all possible single amino acid rescue mutations, from which MIP was used to select a Positive Region predicted to be enriched for p53 cancer rescue mutants. In vivo assays showed that the predicted Positive Region: (1) had significantly more (p<0.01) new strong cancer rescue mutants than control regions (Negative, and non-MIP active learning); (2) had slightly more new strong cancer rescue mutants than an Expert region selected for purely biological considerations; and (3) rescued for the first time the previously unrescuable p53 cancer mutant P152L

Clustering More than Two Million Biomedical Publications: Comparing the Accuracies of Nine Text-Based Similarity Approaches

We investigate the accuracy of different similarity approaches for clustering over two million biomedical documents. Clustering large sets of text documents is important for a variety of information needs and applications such as collection management and navigation, summary and analysis. The few comparisons of clustering results from different similarity approaches have focused on small literature sets and have given conflicting results. Our study was designed to seek a robust answer to the question of which similarity approach would generate the most coherent clusters of a biomedical literature set of over two million documents.We used a corpus of 2.15 million recent (2004-2008) records from MEDLINE, and generated nine different document-document similarity matrices from information extracted from their bibliographic records, including titles, abstracts and subject headings. The nine approaches were comprised of five different analytical techniques with two data sources. The five analytical techniques are cosine similarity using term frequency-inverse document frequency vectors (tf-idf cosine), latent semantic analysis (LSA), topic modeling, and two Poisson-based language models--BM25 and PMRA (PubMed Related Articles). The two data sources were a) MeSH subject headings, and b) words from titles and abstracts. Each similarity matrix was filtered to keep the top-n highest similarities per document and then clustered using a combination of graph layout and average-link clustering. Cluster results from the nine similarity approaches were compared using (1) within-cluster textual coherence based on the Jensen-Shannon divergence, and (2) two concentration measures based on grant-to-article linkages indexed in MEDLINE.PubMed's own related article approach (PMRA) generated the most coherent and most concentrated cluster solution of the nine text-based similarity approaches tested, followed closely by the BM25 approach using titles and abstracts. Approaches using only MeSH subject headings were not competitive with those based on titles and abstracts