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BACKGROUND - Active learning (AL) has shown the promising potential to minimize the annotation cost while maximizing the performance in building statistical natural language processing (NLP) models. However, very few studies have investigated AL in a real-life setting in medical domain.
METHODS - In this study, we developed the first AL-enabled annotation system for clinical named entity recognition (NER) with a novel AL algorithm. Besides the simulation study to evaluate the novel AL algorithm, we further conducted user studies with two nurses using this system to assess the performance of AL in real world annotation processes for building clinical NER models.
RESULTS - The simulation results show that the novel AL algorithm outperformed traditional AL algorithm and random sampling. However, the user study tells a different story that AL methods did not always perform better than random sampling for different users.
CONCLUSIONS - We found that the increased information content of actively selected sentences is strongly offset by the increased time required to annotate them. Moreover, the annotation time was not considered in the querying algorithms. Our future work includes developing better AL algorithms with the estimation of annotation time and evaluating the system with larger number of users.
Summary - A major barrier to the practical utilization of large, complex models of biochemical systems is the lack of open-source computational tools to evaluate model behaviors over high-dimensional parameter spaces. This is due to the high computational expense of performing thousands to millions of model simulations required for statistical analysis. To address this need, we have implemented a user-friendly interface between cupSODA, a GPU-powered kinetic simulator, and PySB, a Python-based modeling and simulation framework. For three example models of varying size, we show that for large numbers of simulations PySB/cupSODA achieves order-of-magnitude speedups relative to a CPU-based ordinary differential equation integrator.
Availability and implementation - The PySB/cupSODA interface has been integrated into the PySB modeling framework (version 1.4.0), which can be installed from the Python Package Index (PyPI) using a Python package manager such as pip. cupSODA source code and precompiled binaries (Linux, Mac OS/X, Windows) are available at github.com/aresio/cupSODA (requires an Nvidia GPU; developer.nvidia.com/cuda-gpus). Additional information about PySB is available at pysb.org.
Contact - email@example.com or firstname.lastname@example.org.
Supplementary information - Supplementary data are available at Bioinformatics online.
© The Author(s) 2017. Published by Oxford University Press.
Chemical exchange saturation transfer (CEST) imaging of amine protons exchanging at intermediate rates and whose chemical shift is around 2 ppm may provide a means of mapping creatine. However, the quantification of this effect may be compromised by the influence of overlapping CEST signals from fast-exchanging amines and hydroxyls. We aimed to investigate the exchange rate filtering effect of a variation of CEST, named chemical exchange rotation transfer (CERT), as a means of isolating creatine contributions at around 2 ppm from other overlapping signals. Simulations were performed to study the filtering effects of CERT for the selection of transfer effects from protons of specific exchange rates. Control samples containing the main metabolites in brain, bovine serum albumin (BSA) and egg white albumen (EWA) at their physiological concentrations and pH were used to study the ability of CERT to isolate molecules with amines at 2 ppm that exchange at intermediate rates, and corresponding methods were used for in vivo rat brain imaging. Simulations showed that exchange rate filtering can be combined with conventional filtering based on chemical shift. Studies on samples showed that signal contributions from creatine can be separated from those of other metabolites using this combined filter, but contributions from protein amines may still be significant. This exchange filtering can also be used for in vivo imaging. CERT provides more specific quantification of amines at 2 ppm that exchange at intermediate rates compared with conventional CEST imaging.
Copyright © 2017 John Wiley & Sons, Ltd.
DDIT4 gene encodes a protein whose main action is to inhibit mTOR under stress conditions whilst several in vitro studies indicate that its expression favors cancer progression. We have previously described that DDIT4 expression is an independent prognostic factor for tripe negative breast cancer resistant to neoadjuvant chemotherapy. We herein report that high DDIT4 expression is related to the outcome (recurrence-free survival, time to progression and overall survival) in several cancer types. We performed in silico analysis in online platforms, in pooled datasets from KM Plotter and meta-analysis of individual datasets from SurvExpress. High levels of DDIT4 were significantly associated with a worse prognosis in acute myeloid leukemia, breast cancer, glioblastoma multiforme, colon, skin and lung cancer. Conversely, a high DDIT4 expression was associated with an improved prognostic in gastric cancer. DDIT4 was not associated with the outcome of ovarian cancers. Analysis with data from the Cell Miner Tool in 60 cancer cell lines indicated that although rapamycin activity was correlated with levels of MTOR, it is not influenced by DDIT4 expression. In summary, DDIT4 might serve as a novel prognostic biomarker in several malignancies. DDIT4 activity could be responsible for resistance to mTOR inhibitors and is a potential candidate for the development of targeted therapy.
Goodpasture's disease is closely associated with HLA, particularly DRB1*1501. Other susceptible or protective HLA alleles are not clearly elucidated. The presentation models of epitopes by susceptible HLA alleles are also unclear. We genotyped 140 Chinese patients and 599 controls for four-digit HLA II genes, and extracted the encoding sequences from the IMGT/HLA database. T-cell epitopes of α3(IV)NC1 were predicted and the structures of DR molecule-peptide-T-cell receptor were constructed. We confirmed DRB1*1501 (OR = 4·6, P = 5·7 × 10 ) to be a risk allele for Goodpasture's disease. Arginine at position 13 (ARG13) (OR = 4·0, P = 1·0 × 10 ) and proline at position 11 (PRO11) (OR = 4·0, P = 2·0 × 10 ) on DRβ1, encoded by DRB1*1501, were associated with disease susceptibility. α (HGWISLWKGFSFIMF) was predicted as a T-cell epitope presented by DRB1*1501. Isoleucine , tryptophan , glycine , phenylalanine and phenylalanine , were presented in peptide-binding pockets 1, 4, 6, 7 and 9 of DR2b, respectively. ARG13 in pocket 4 interacts with tryptophan and forms a hydrogen bond. In conclusion, we propose a mechanism for DRB1*1501 susceptibility for Goodpasture's disease through encoding ARG13 and PRO11 on MHC-DRβ1 chain and presenting T-cell epitope, α , with five critical residues.
© 2017 John Wiley & Sons Ltd.
Over the last decade, Electronic Health Records (EHR) systems have been increasingly implemented at US hospitals. Despite their great potential, the complex and uneven nature of clinical documentation and data quality brings additional challenges for analyzing EHR data. A critical challenge is the information bias due to the measurement errors in outcome and covariates. We conducted empirical studies to quantify the impacts of the information bias on association study. Specifically, we designed our simulation studies based on the characteristics of the Electronic Medical Records and Genomics (eMERGE) Network. Through simulation studies, we quantified the loss of power due to misclassifications in case ascertainment and measurement errors in covariate status extraction, with respect to different levels of misclassification rates, disease prevalence, and covariate frequencies. These empirical findings can inform investigators for better understanding of the potential power loss due to misclassification and measurement errors under a variety of conditions in EHR based association studies.
To assess the effect of chemotherapy on mitochondrial genome mutations in cancer survivors and their offspring, a study sequenced the full mitochondrial genome and determined the mitochondrial DNA heteroplasmic (mtDNA) mutation rate. To build a model for counts of heteroplasmic mutations in mothers and their offspring, bivariate Poisson regression was used to examine the relationship between mutation count and clinical information while accounting for the paired correlation. However, if the sequencing depth is not adequate, a limited fraction of the mtDNA will be available for variant calling. The classical bivariate Poisson regression model treats the offset term as equal within pairs; thus, it cannot be applied directly. In this research, we propose an extended bivariate Poisson regression model that has a more general offset term to adjust the length of the accessible genome for each observation. We evaluate the performance of the proposed method with comprehensive simulations, and the results show that the regression model provides unbiased parameter estimations. The use of the model is also demonstrated using the paired mtDNA dataset.
RF arrays with a large number of independent coil elements are advantageous for parallel transmission (pTx) and reception at high fields. One of the main challenges in designing RF arrays is to minimize the electromagnetic (EM) coupling between the coil elements. The induced current elimination (ICE) method, which uses additional resonator elements to cancel coils' mutual EM coupling, has proven to be a simple and efficient solution for decoupling microstrip, L/C loop, monopole and dipole arrays. However, in previous embodiments of conventional ICE decoupling, the decoupling elements acted as "magnetic-walls" with low transmit fields and consequently low MR signal near them. To solve this problem, new resonator geometries including overlapped and perpendicular decoupling loops are proposed. The new geometries were analyzed theoretically and validated in EM simulations, bench tests and MR experiments. The isolation between two closely-placed loops could be improved from about -5dB to <-45dB by using the new geometries.
Copyright © 2017 Elsevier Inc. All rights reserved.
Traveling-wave MRI, which uses relatively small and simple RF antennae, has robust matching performance and capability for large field-of-view (FOV) imaging. However, the power efficiency of traveling-wave MRI is much lower than conventional methods, which limits its application. One simple approach to improve the power efficiency is to place passive resonators around the subject being imaged. The feasibility of this approach has been demonstrated in previous works using a single small resonant loop. In this work, we aim to explore how much the improvements can be maintained in human imaging using an array design, and whether electric dipoles can be used as local elements. First, a series of electromagnetic (EM) simulations were performed on a human model. Then RF coils were constructed and the simulation results using the best setup for head imaging were validated in MR experiments. By using the passive local loop and transverse dipole arrays, respectively, the transmit efficiency (B) of traveling-wave MRI can be improved by 3-fold in the brain and 2-fold in the knee. The types of passive elements (loops or dipoles) should be carefully chosen for brain or knee imaging to maximize the improvement, and the enhancement depends on the local body configuration.
Copyright © 2017 Elsevier Inc. All rights reserved.
Dysregulation of iron metabolism in cancer is well documented and it has been suggested that there is interdependence between excess iron and increased cancer incidence and progression. In an effort to better understand the linkages between iron metabolism and breast cancer, a predictive mathematical model of an expanded iron homeostasis pathway was constructed that includes species involved in iron utilization, oxidative stress response and oncogenic pathways. The model leads to three predictions. The first is that overexpression of iron regulatory protein 2 (IRP2) recapitulates many aspects of the alterations in free iron and iron-related proteins in cancer cells without affecting the oxidative stress response or the oncogenic pathways included in the model. This prediction was validated by experimentation. The second prediction is that iron-related proteins are dramatically affected by mitochondrial ferritin overexpression. This prediction was validated by results in the pertinent literature not used for model construction. The third prediction is that oncogenic Ras pathways contribute to altered iron homeostasis in cancer cells. This prediction was validated by a combination of simulation experiments of Ras overexpression and catalase knockout in conjunction with the literature. The model successfully captures key aspects of iron metabolism in breast cancer cells and provides a framework upon which more detailed models can be built.