A sparse regulatory network of copy-number driven expression reveals putative breast cancer oncogenes

The influence of DNA cis-regulatory elements on a gene's expression has been intensively studied. However, little is known about expressions driven by trans-acting DNA hotspots. DNA hotspots harboring copy number aberrations are recognized to be important in cancer as they influence multiple genes on a global scale. The challenge in detecting trans-effects is mainly due to the computational difficulty in detecting weak and sparse trans-acting signals amidst co-occuring passenger events. We propose an integrative approach to learn a sparse interaction network of DNA copy-number regions with their downstream targets in a breast cancer dataset. Information from this network helps distinguish copy-number driven from copy-number independent expression changes on a global scale. Our result further delineates cis- and trans-effects in a breast cancer dataset, for which important oncogenes such as ESR1 and ERBB2 appear to be highly copy-number dependent. Further, our model is shown to be efficient and in terms of goodness of fit no worse than other state-of the art predictors and network reconstruction models using both simulated and real data.Comment: Accepted at IEEE International Conference on Bioinformatics & Biomedicine (BIBM 2010

Renal Replacement Therapy in Austere Environments

Myoglobinuric renal failure is the classically described acute renal event occurring in disaster environments—commonly after an earthquake—which most tests the ingenuity and flexibility of local and regional nephrology resources. In recent decades, several nephrology organizations have developed response teams and planning protocols to address disaster events, largely focusing on patients at risk for, or with, acute kidney injury (AKI). In this paper we briefly review the epidemiology and outcomes of patients with dialysis-requiring AKI after such events, while providing greater focus on the management of the end-stage renal disease population after a disaster which incapacitates a pre-existing nephrologic infrastructure (if it existed at all). “Austere” dialysis, as such, is defined as the provision of renal replacement therapy in any setting in which traditional, first-world therapies and resources are limited, incapacitated, or nonexistent

Graph complexity analysis identifies an ETV5 tumor-specific network in human and murine low-grade glioma

Conventional differential expression analyses have been successfully employed to identify genes whose levels change across experimental conditions. One limitation of this approach is the inability to discover central regulators that control gene expression networks. In addition, while methods for identifying central nodes in a network are widely implemented, the bioinformatics validation process and the theoretical error estimates that reflect the uncertainty in each step of the analysis are rarely considered. Using the betweenness centrality measure, we identified Etv5 as a potential tissue-level regulator in murine neurofibromatosis type 1 (Nf1) low-grade brain tumors (optic gliomas). As such, the expression of Etv5 and Etv5 target genes were increased in multiple independently-generated mouse optic glioma models relative to non-neoplastic (normal healthy) optic nerves, as well as in the cognate human tumors (pilocytic astrocytoma) relative to normal human brain. Importantly, differential Etv5 and Etv5 network expression was not directly the result of Nf1 gene dysfunction in specific cell types, but rather reflects a property of the tumor as an aggregate tissue. Moreover, this differential Etv5 expression was independently validated at the RNA and protein levels. Taken together, the combined use of network analysis, differential RNA expression findings, and experimental validation highlights the potential of the computational network approach to provide new insights into tumor biology

Blueprint for the Dissemination of Evidence-Based Practices in Health Care

Proposes strategies for better dissemination of best practices through quality improvement campaigns, including campaigns aligned with adopting organizations' goals, practical implementation tools and guides, and networks to foster learning opportunities

Signatures of unresolved binaries in stellar spectra: implications for spectral fitting

The observable spectrum of an unresolved binary star system is a superposition of two single-star spectra. Even without a detectable velocity offset between the two stellar components, the combined spectrum of a binary system is in general different from that of either component, and fitting it with single-star models may yield inaccurate stellar parameters and abundances. We perform simple experiments with synthetic spectra to investigate the effect of unresolved main-sequence binaries on spectral fitting, modeling spectra similar to those collected by the APOGEE, GALAH, and LAMOST surveys. We find that fitting unresolved binaries with single-star models introduces systematic biases in the derived stellar parameters and abundances that are modest but certainly not negligible, with typical systematic errors of $300\,\rm K$ in $T_{\rm eff}$, 0.1 dex in $\log g$, and 0.1 dex in $[\rm Fe/H]$ for APOGEE-like spectra of solar-type stars. These biases are smaller for spectra at optical wavelengths than in the near-infrared. We show that biases can be corrected by fitting spectra with a binary model, which adds only two labels to the fit and includes single-star models as a special case. Our model provides a promising new method to constrain the Galactic binary population, including systems with single-epoch spectra and no detectable velocity offset between the two stars.Comment: Accept to MNRAS with minor revisions since v1. 7 pages, 5 figure

The solar wind ionic charge states during the Ulysses pole-to-pole pass

We analyze and compare the ionic charge composition data for different types of the solar wind which the Solar Wind Ion Composition Spectrometer on Ulysses observed during the pole-to-pole pass of its primary mission. The implications on the electron temperature, electron density and ion outflow velocity from the corresponding solar wind source regions are also discussed. We find that the electron temperature in the source region of the slow solar wind is higher than that in the coronal hole. We also find a possible north-south asymmetry in the electron temperature that may be correlated to the north-south asymmetry in the solar wind speed found in the SWOOPS/Ulysses data. Based on our data without clear constraints from other coronal observations, it is found that the electron density may be higher, or the heavy ion outflow velocities may be lower toward lower heliographic latitude. © 1999 American Institute of Physics.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87726/2/491_1.pd

Deep Learning -- A first Meta-Survey of selected Reviews across Scientific Disciplines, their Commonalities, Challenges and Research Impact

Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. Similar to the basic structure of a brain, a deep learning algorithm consists of an artificial neural network, which resembles the biological brain structure. Mimicking the learning process of humans with their senses, deep learning networks are fed with (sensory) data, like texts, images, videos or sounds. These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years. This growth resulted in way over 10,000 publications per year in the last years. For example, the search engine PubMed alone, which covers only a sub-set of all publications in the medical field, provides already over 11,000 results in Q3 2020 for the search term 'deep learning', and around 90% of these results are from the last three years. Consequently, a complete overview over the field of deep learning is already impossible to obtain and, in the near future, it will potentially become difficult to obtain an overview over a subfield. However, there are several review articles about deep learning, which are focused on specific scientific fields or applications, for example deep learning advances in computer vision or in specific tasks like object detection. With these surveys as a foundation, the aim of this contribution is to provide a first high-level, categorized meta-survey of selected reviews on deep learning across different scientific disciplines. The categories (computer vision, language processing, medical informatics and additional works) have been chosen according to the underlying data sources (image, language, medical, mixed). In addition, we review the common architectures, methods, pros, cons, evaluations, challenges and future directions for every sub-category.Comment: 83 pages, 22 figures, 9 tables, 100 reference

Multiparametric MRI and [18F]fluorodeoxyglucose positron emission tomography imaging is a potential prognostic imaging biomarker in recurrent glioblastoma

Purpose/objectivesMultiparametric advanced MR and [18F]fluorodeoxyglucose (FDG)-positron emission tomography (PET) imaging may be important biomarkers for prognosis as well for distinguishing recurrent glioblastoma multiforme (GBM) from treatment-related changes.Methods/materialsWe retrospectively evaluated 30 patients treated with chemoradiation for GBM and underwent advanced MR and FDG-PET for confirmation of tumor progression. Multiparametric MRI and FDG-PET imaging metrics were evaluated for their association with 6-month overall (OS) and progression-free survival (PFS) based on pathological, radiographic, and clinical criteria.Results17 males and 13 females were treated between 2001 and 2014, and later underwent FDG-PET at suspected recurrence. Baseline FDG-PET and MRI imaging was obtained at a median of 7.5 months [interquartile range (IQR) 3.7–12.4] following completion of chemoradiation. Median follow-up after FDG-PET imaging was 10 months (IQR 7.2–13.0). Receiver-operator characteristic curve analysis identified that lesions characterized by a ratio of the SUVmax to the normal contralateral brain (SUVmax/NB index) >1.5 and mean apparent diffusion coefficient (ADC) value of ≤1,400 × 10−6 mm2/s correlated with worse 6-month OS and PFS. We defined three patient groups that predicted the probability of tumor progression: SUVmax/NB index >1.5 and ADC ≤1,400 × 10−6 mm2/s defined high-risk patients (n = 7), SUVmax/NB index ≤1.5 and ADC >1,400 × 10−6 mm2/s defined low-risk patients (n = 11), and intermediate-risk (n = 12) defined the remainder of the patients. Median OS following the time of the FDG-PET scan for the low, intermediate, and high-risk groups were 23.5, 10.5, and 3.8 months (p < 0.01). Median PFS were 10.0, 4.4, and 1.9 months (p = 0.03). Rates of progression at 6-months in the low, intermediate, and high-risk groups were 36, 67, and 86% (p = 0.04).ConclusionRecurrent GBM in the molecular era is associated with highly variable outcomes. Multiparametric MR and FDG-PET biomarkers may provide a clinically relevant, non-invasive and cost-effective method of predicting prognosis and improving clinical decision making in the treatment of patients with suspected tumor recurrence