949 research outputs found
lp-Recovery of the Most Significant Subspace among Multiple Subspaces with Outliers
We assume data sampled from a mixture of d-dimensional linear subspaces with
spherically symmetric distributions within each subspace and an additional
outlier component with spherically symmetric distribution within the ambient
space (for simplicity we may assume that all distributions are uniform on their
corresponding unit spheres). We also assume mixture weights for the different
components. We say that one of the underlying subspaces of the model is most
significant if its mixture weight is higher than the sum of the mixture weights
of all other subspaces. We study the recovery of the most significant subspace
by minimizing the lp-averaged distances of data points from d-dimensional
subspaces, where p>0. Unlike other lp minimization problems, this minimization
is non-convex for all p>0 and thus requires different methods for its analysis.
We show that if 0<p<=1, then for any fraction of outliers the most significant
subspace can be recovered by lp minimization with overwhelming probability
(which depends on the generating distribution and its parameters). We show that
when adding small noise around the underlying subspaces the most significant
subspace can be nearly recovered by lp minimization for any 0<p<=1 with an
error proportional to the noise level. On the other hand, if p>1 and there is
more than one underlying subspace, then with overwhelming probability the most
significant subspace cannot be recovered or nearly recovered. This last result
does not require spherically symmetric outliers.Comment: This is a revised version of the part of 1002.1994 that deals with
single subspace recovery. V3: Improved estimates (in particular for Lemma 3.1
and for estimates relying on it), asymptotic dependence of probabilities and
constants on D and d and further clarifications; for simplicity it assumes
uniform distributions on spheres. V4: minor revision for the published
versio
Infectious Disease Ontology
Technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data and information. In this chapter, we describe different types of vocabulary resources and emphasize those features of formal ontologies that make them most useful for computational applications. We describe current uses of ontologies and discuss future goals for ontology-based computing, focusing on its use in the field of infectious diseases. We review the largest and most widely used vocabulary resources relevant to the study of infectious diseases and conclude with a description of the Infectious Disease Ontology (IDO) suite of interoperable ontology modules that together cover the entire infectious disease domain
Bridging the reality gap in quantum devices with physics-aware machine learning
The discrepancies between reality and simulation impede the optimization and scalability of solid-state quantum devices. Disorder induced by the unpredictable distribution of material defects is one of the major contributions to the reality gap. We bridge this gap using physics-aware machine learning, in particular, using an approach combining a physical model, deep learning, Gaussian random field, and Bayesian inference. This approach enables us to infer the disorder potential of a nanoscale electronic device from electron-transport data. This inference is validated by verifying the algorithm’s predictions about the gate-voltage values required for a laterally defined quantum-dot device in AlGaAs/GaAs to produce current features corresponding to a double-quantum-dot regime
A dynamical model reveals gene co-localizations in nucleus
Co-localization of networks of genes in the nucleus is thought to play an important role in determining gene expression patterns. Based upon experimental data, we built a dynamical model to test whether pure diffusion could account for the observed co-localization of genes within a defined subnuclear region. A simple standard Brownian motion model in two and three dimensions shows that preferential co-localization is possible for co-regulated genes without any direct interaction, and suggests the occurrence may be due to a limitation in the number of available transcription factors. Experimental data of chromatin movements demonstrates that fractional rather than standard Brownian motion is more appropriate to model gene mobilizations, and we tested our dynamical model against recent static experimental data, using a sub-diffusion process by which the genes tend to colocalize more easily. Moreover, in order to compare our model with recently obtained experimental data, we studied the association level between genes and factors, and presented data supporting the validation of this dynamic model. As further applications of our model, we applied it to test against more biological observations. We found that increasing transcription factor number, rather than factory number and nucleus size, might be the reason for decreasing gene co-localization. In the scenario of frequency-or amplitude-modulation of transcription factors, our model predicted that frequency-modulation may increase the co-localization between its targeted genes
Argumentation in school science : Breaking the tradition of authoritative exposition through a pedagogy that promotes discussion and reasoning
The value of argumentation in science education has become internationally recognised and has been the subject of many research studies in recent years. Successful introduction of argumentation activities in learning contexts involves extending teaching goals beyond the understanding of facts and concepts, to include an emphasis on cognitive and metacognitive processes, epistemic criteria and reasoning. The authors focus on the difficulties inherent in shifting a tradition of teaching from one dominated by authoritative exposition to one that is more dialogic, involving small-group discussion based on tasks that stimulate argumentation. The paper builds on previous research on enhancing the quality of argument in school science, to focus on how argumentation activities have been designed, with appropriate strategies, resources and modelling, for pedagogical purposes. The paper analyses design frameworks, their contexts and lesson plans, to evaluate their potential for enhancing reasoning through foregrounding the processes of argumentation. Examples of classroom dialogue where teachers adopt the frameworks/plans are analysed to show how argumentation processes are scaffolded. The analysis shows that several layers of interpretation are needed and these layers need to be aligned for successful implementation. The analysis serves to highlight the potential and limitations of the design frameworks
Multilocation Corn Stover Harvest Effects on Crop Yields and Nutrient Removal
Corn (Zea mays L.) stover was identified as an important feedstock for cellulosic bioenergy production because of the extensive area upon which the crop is already grown. This report summarizes 239 site-years of field research examining effects of zero, moderate, and high stover removal rates at 36 sites in seven different states. Grain and stover yields from all sites as well as N, P, and K removal from 28 sites are summarized for nine longitude and six latitude bands, two tillage practices (conventional vs no tillage), two stover-harvest methods (machine vs calculated), and two crop rotations {continuous corn (maize) vs corn/soybean [Glycine max (L.) Merr.]}. Mean grain yields ranged from 5.0 to 12.0 Mg ha−1 (80 to 192 bu ac−1). Harvesting an average of 3.9 or 7.2 Mg ha−1(1.7 or 3.2 tons ac−1) of the corn stover resulted in a slight increase in grain yield at 57 and 51 % of the sites, respectively. Average no-till grain yields were significantly lower than with conventional tillage when stover was not harvested, but not when it was collected. Plant samples collected between physiological maturity and combine harvest showed that compared to not harvesting stover, N, P, and K removal was increased by 24, 2.7, and 31 kg ha−1, respectively, with moderate (3.9 Mg ha−1) harvest and by 47, 5.5, and 62 kg ha−1, respectively, with high (7.2 Mg ha−1) removal. This data will be useful for verifying simulation models and available corn stover feedstock projections, but is too variable for planning site-specific stover harvest
Expression of the transcription factor, TFII-I, during post-implantation mouse embryonic development
<p>Abstract</p> <p>Background</p> <p>General transcription factor (TFII-I) is a multi-functional transcription factor encoded by the Gtf2i gene, that has been demonstrated to regulate transcription of genes critical for development. Because of the broad range of genes regulated by TFII-I as well as its potential role in a significant neuro-developmental disorder, developing a comprehensive expression profile is critical to the study of this transcription factor. We sought to define the timing and pattern of expression of TFII-I in post-implantation embryos at a time during which many putative TFII-I target genes are expressed.</p> <p>Findings</p> <p>Antibodies to the N-terminus of TFII-I were used to probe embryonic mouse sections. TFII-I protein is widely expressed in the developing embryo. TFII-I is expressed throughout the period from E8-E16. However, within this period there are striking shifts in localization from cytoplasmic predominant to nuclear. TFII-I expression varies in both a spatial and temporal fashion. There is extensive expression in neural precursors at E8. This expression persists at later stages. TFII-I is expressed in developing lung, heart and gut structures. There is no evidence of isoform specific expression. Available data regarding expression patterns at both an RNA and protein level throughout development are also comprehensively reviewed.</p> <p>Conclusions</p> <p>Our immunohistochemical studies of the temporal and spatial expression patterns of TFII-I in mouse embryonic sections are consistent with the hypothesis that hemizygous deletion of <it>GTF2I </it>in individuals with Williams-Beuren Syndrome contributes to the distinct cognitive and physiological symptoms associated with the disorder.</p
Electromagnetic counterparts to gravitational wave events from Gaia
The recent discoveries of gravitational wave events and in one case also its electromagnetic (EM) counterpart allow us to study the Universe in a novel way. The increased sensitivity of the LIGO and Virgo detectors has opened the possibility for regular detections of EM transient events from mergers of stellar remnants. Gravitational wave sources are expected to have sky localisation up to a few hundred square degrees, thus Gaia as an all-sky multi-epoch photometric survey has the potential to be a good tool to search for the EM counterparts. In this paper we study the possibility of detecting EM counterparts to gravitational wave sources using the Gaia Science Alerts system. We develop an extension to current used algorithms to find transients and test its capabilities in discovering candidate transients on a sample of events from the observation periods O1 and O2 of LIGO and Virgo. For the gravitational wave events from the current run O3 we expect that about 16 (25) per cent should fall in sky regions observed by Gaia 7 (10) days after gravitational wave. The new algorithm will provide about 21 candidates per day from the whole sky
Improving statistical inference on pathogen densities estimated by quantitative molecular methods: malaria gametocytaemia as a case study
BACKGROUND: Quantitative molecular methods (QMMs) such as quantitative real-time polymerase chain reaction (q-PCR), reverse-transcriptase PCR (qRT-PCR) and quantitative nucleic acid sequence-based amplification (QT-NASBA) are increasingly used to estimate pathogen density in a variety of clinical and epidemiological contexts. These methods are often classified as semi-quantitative, yet estimates of reliability or sensitivity are seldom reported. Here, a statistical framework is developed for assessing the reliability (uncertainty) of pathogen densities estimated using QMMs and the associated diagnostic sensitivity. The method is illustrated with quantification of Plasmodium falciparum gametocytaemia by QT-NASBA. RESULTS: The reliability of pathogen (e.g. gametocyte) densities, and the accompanying diagnostic sensitivity, estimated by two contrasting statistical calibration techniques, are compared; a traditional method and a mixed model Bayesian approach. The latter accounts for statistical dependence of QMM assays run under identical laboratory protocols and permits structural modelling of experimental measurements, allowing precision to vary with pathogen density. Traditional calibration cannot account for inter-assay variability arising from imperfect QMMs and generates estimates of pathogen density that have poor reliability, are variable among assays and inaccurately reflect diagnostic sensitivity. The Bayesian mixed model approach assimilates information from replica QMM assays, improving reliability and inter-assay homogeneity, providing an accurate appraisal of quantitative and diagnostic performance. CONCLUSIONS: Bayesian mixed model statistical calibration supersedes traditional techniques in the context of QMM-derived estimates of pathogen density, offering the potential to improve substantially the depth and quality of clinical and epidemiological inference for a wide variety of pathogens
Outcome of ATP-based tumor chemosensitivity assay directed chemotherapy in heavily pre-treated recurrent ovarian carcinoma
BACKGROUND: We wished to evaluate the clinical response following ATP-Tumor Chemosensitivity Assay (ATP-TCA) directed salvage chemotherapy in a series of UK patients with advanced ovarian cancer. The results are compared with that of a similar assay used in a different country in terms of evaluability and clinical endpoints. METHODS: From November 1998 to November 2001, 46 patients with pre-treated, advanced ovarian cancer were given a total of 56 courses of chemotherapy based on in-vitro ATP-TCA responses obtained from fresh tumor samples or ascites. Forty-four patients were evaluable for results. Of these, 18 patients had clinically platinum resistant disease (relapse < 6 months after first course of chemotherapy). There was evidence of cisplatin resistance in 31 patients from their first ATP-TCA. Response to treatment was assessed by radiology, clinical assessment and tumor marker level (CA 125). RESULTS: The overall response rate was 59% (33/56) per course of chemotherapy, including 12 complete responses, 21 partial responses, 6 with stable disease, and 15 with progressive disease. Two patients were not evaluable for response having received just one cycle of chemotherapy: if these were excluded the response rate is 61%. Fifteen patients are still alive. Median progression free survival (PFS) was 6.6 months per course of chemotherapy; median overall survival (OAS) for each patient following the start of TCA-directed therapy was 10.4 months (95% confidence interval 7.9-12.8 months). CONCLUSION: The results show similar response rates to previous studies using ATP-TCA directed therapy in recurrent ovarian cancer. The assay shows high evaluability and this study adds weight to the reproducibility of results from different centre
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