Post-processing Strategy Research Articles

An intelligent nonlinear meta element for elastoplastic continua: deep learning using a new Time-distributed Residual U-Net architecture

Substructuring is a model order reduction technique that accelerates the finite element method in solid mechanics. In this improved hybrid substructuring approach, methods from computational intelligence empower a reduced-order meta element. We propose a nonlinear and inelastic intelligent meta element for history-dependent boundary value problems. Fully compatible with conventional finite elements, it can be used to assemble larger structures. Within the intelligent meta element, a new deep neural network architecture composed of convolutions and recursions, the Time-distributed Residual U-Net (TRUNet), learns to solve the history-dependent spatial regression problem. The TRUNet automatically creates and updates the internal history variables necessary for the mechanical problem. Based on a new data generation strategy, data from a wide variety of use-cases train the neural network. An interface connects the neural network and the finite element method using a new data pre- and post-processing strategy. In three numerical demonstrations of elastoplastic continua, the intelligent meta element performs well, exhibiting low errors on a separate test dataset of several thousand samples. The intelligent reduced-order models compute considerably faster and achieve excellent approximations of the displacements, stresses, and forces.

SAT-based models for overlapping community detection in networks

Communities in social networks or graphs are sets of well-connected, overlapping vertices. Network community detection is a hot research topic in social, biological and information networks analysis. The effectiveness of a community detection algorithm is determined by accuracy in finding the ground-truth communities. In this article, we present two models to detect overlapping communities in large complex networks. In the first model, we introduce a parametrized notion of community, called k-linked community, that allows us to characterize a vertex/edge centered k-linked community with bounded diameter. Such community possesses a vertex/edge with a distance at most $$\frac{k}{2}$$ from any other vertex of that community. Next, we show how the problem of detecting vertex/edge centered k-linked communities can be expressed as a Partial Max-SAT optimization problem. Then, we propose a post-processing strategy to further enhance the overlaps between the final communities. Our second model called preference-based centroid model aims to constrain the choice of centroids communities in the first model. This new framework allows to integrate more easily the user preferences in order to discover high quality communities by selecting the most central vertices. For this, we exploit Weighted Partial Max-SAT to solve the underlying optimization problem. We evaluate the proposed frameworks empirically against several high-performing methods, with respect to three evaluation metrics and scalability, on a number of real-life datasets. The experimental results show that our algorithms outperform existing state-of-the-art methods in detecting relevant communities.

Temporal rainfall disaggregation using a micro-canonical cascade model: possibilities to improve the autocorrelation

Abstract. In urban hydrology rainfall time series of high resolution in time are crucial. Such time series with sufficient length can be generated through the disaggregation of daily data with a micro-canonical cascade model. A well-known problem of time series generated in this way is the inadequate representation of the autocorrelation. In this paper two cascade model modifications are analysed regarding their ability to improve the autocorrelation in disaggregated time series with 5 min resolution. Both modifications are based on a state-of-the-art reference cascade model (method A). In the first modification, a position dependency is introduced in the first disaggregation step (method B). In the second modification the position of a wet time step is redefined in addition by taking into account the disaggregated finer time steps of the previous time step instead of the previous time step itself (method C). Both modifications led to an improvement of the autocorrelation, especially the position redefinition (e.g. for lag-1 autocorrelation, relative errors of −3 % (method B) and 1 % (method C) instead of −4 % for method A). To ensure the conservation of a minimum rainfall amount in the wet time steps, the mimicry of a measurement device is simulated after the disaggregation process. Simulated annealing as a post-processing strategy was tested as an alternative as well as an addition to the modifications in methods B and C. For the resampling, a special focus was given to the conservation of the extreme rainfall values. Therefore, a universal extreme event definition was introduced to define extreme events a priori without knowing their occurrence in time or magnitude. The resampling algorithm is capable of improving the autocorrelation, independent of the previously applied cascade model variant (e.g. for lag-1 autocorrelation the relative error of −4 % for method A is reduced to 0.9 %). Also, the improvement of the autocorrelation by the resampling was higher than by the choice of the cascade model modification. The best overall representation of the autocorrelation was achieved by method C in combination with the resampling algorithm. The study was carried out for 24 rain gauges in Lower Saxony, Germany.

A Multi-Target SNR-Progressive Learning Approach to Regression Based Speech Enhancement

We propose a multi-target, signal-to-noise-ratio (SNR)-progressive learning (SNR-PL) framework for regression based speech enhancement (SE). At low SNR levels, it is often not easy to directly learn the complicated regression required in SE. We therefore decompose the original SE problem of mapping noisy to clean speech features, with a large SNR gap, into a series of sub-problems, each with a small SNR increment and presumably easier to learn. In our configurations, each hidden layer of the proposed regression neural network is guided to explicitly learn an intermediate target with a specified but small SNR gain. Tested on both deep neural network (DNN) and long short-term memory (LSTM) architectures, SNR-PL consistently outperforms the conventional “black box” DNN framework in terms of both objective measure superiority and network model compactness. Furthermore, with the best configured LSTM-based SNR-PL model, we often observe that the performance is easily saturated or even degraded when increasing the number of intermediate targets, due to the fact that useful information is lost in dimension reduction when involving more target layers. Accordingly, to address this information loss issue, we explore densely connected networks on top of the LSTM structure where the input and the preceding intermediate targets are concatenated together to learn the next target. Finally, to fully utilize the rich and complementary information of intermediate targets, a simple post-processing strategy is adopted to further improve the performance. Evaluated on the simulation speech data, experimental results in unseen noises cases demonstrate that the proposed approach consistently performs better than the conventional LSTM approach in terms of objective speech enhancement measures for speech intelligibility and quality. Furthermore, when evaluated on real data provided by the CHiME-4 Challenge for automatic speech recognition (ASR) of noisy microphone array speech, we show that the proposed approach with intermediate outputs can directly improve the ASR performance, while the conventional LSTM approach increases the word error rate.

MR brain image segmentation using elite kinetic-molecular theory optimisation algorithm

This paper explores the use of the elite kinetic-molecular theory optimisation algorithm (EKMTOA) to compute threshold selection for MR brain image segmentation. EKMTOA is inspired by learning and collaboration among the elites individual. To solve the limitation of Otsu method based on projection of cross section that single threshold segmentation. Firstly, this paper proposes a multi-threshold cross section projection Otsu method. Secondly, adopt the post-processing strategy to further improve anti-noise capability. Finally, the EKMTOA is used to optimise the objective function, find the best segmentation threshold of the MR brain image. Experimental results demonstrate over multiple brain image with different of noise validate the efficiency of the proposed technique with regard to segmentation accuracy, anti-noise capability and robustness.

Enlarged Training Dataset by Pairwise GANs for Molecular-Based Brain Tumor Classification

This paper addresses issues of brain tumor subtype classification using Magnetic Resonance Images (MRIs) from different scanner modalities like T1 weighted, T1 weighted with contrast-enhanced, T2 weighted and FLAIR images. Currently most available glioma datasets are relatively moderate in size, and often accompanied with incomplete MRIs in different modalities. To tackle the commonly encountered problems of insufficiently large brain tumor datasets and incomplete modality of image for deep learning, we propose to add augmented brain MR images to enlarge the training dataset by employing a pairwise Generative Adversarial Network (GAN) model. The pairwise GAN is able to generate synthetic MRIs across different modalities. To achieve the patient-level diagnostic result, we propose a post-processing strategy to combine the slice-level glioma subtype classification results by majority voting. A two-stage course-to-fine training strategy is proposed to learn the glioma feature using GAN-augmented MRIs followed by real MRIs. To evaluate the effectiveness of the proposed scheme, experiments have been conducted on a brain tumor dataset for classifying glioma molecular subtypes: isocitrate dehydrogenase 1 (IDH1) mutation and IDH1 wild-type. Our results on the dataset have shown good performance (with test accuracy 88.82%). Comparisons with several state-of-the-art methods are also included.

Tensor Alignment Based Domain Adaptation for Hyperspectral Image Classification

This paper presents a tensor alignment (TA) based domain adaptation method for hyperspectral image (HSI) classification. To be specific, HSIs in both domains are first segmented into superpixels and tensors of both domains are constructed to include neighboring samples from single superpixel. Then we consider the subspace invariance between two domains as projection matrices and original tensors are projected as core tensors with lower dimensions into the invariant tensor subspace by applying Tucker decomposition. To preserve geometric information in original tensors, we employ a manifold regularization term for core tensors into the decomposition progress. The projection matrices and core tensors are solved in an alternating optimization manner and the convergence of TA algorithm is analyzed. In addition, a post-processing strategy is defined via pure samples extraction for each superpixel to further improve classification performance. Experimental results on four real HSIs demonstrate that the proposed method can achieve better performance compared with the state-of-the-art subspace learning methods when a limited amount of source labeled samples are available.

A Multi-Organ Nucleus Segmentation Challenge.

Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated. Contestants were given a training set with 30 images from seven organs with annotations of 21,623 individual nuclei. A test dataset with 14 images taken from seven organs, including two organs that did not appear in the training set was released without annotations. Entries were evaluated based on average aggregated Jaccard index (AJI) on the test set to prioritize accurate instance segmentation as opposed to mere semantic segmentation. More than half the teams that completed the challenge outperformed a previous baseline. Among the trends observed that contributed to increased accuracy were the use of color normalization as well as heavy data augmentation. Additionally, fully convolutional networks inspired by variants of U-Net, FCN, and Mask-RCNN were popularly used, typically based on ResNet or VGG base architectures. Watershed segmentation on predicted semantic segmentation maps was a popular post-processing strategy. Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics.

Scalable classification of nanoparticles: A proof of principle for process design

Nanoparticles like quantum confined ZnS semiconductor nanocrystals, exhibit unique structure-property relationships. Narrow particle size distributions (PSDs) become one of the most important factors to tailor product performance. Size selective precipitation has already been proven to be an effective post processing strategy for ZnS nanoparticles. It is based on the titration of a poor liquid into a stable dispersion, which leads to the preferred flocculation of larger particles. Afterwards, these flocks must be separated from the continuous phase. While on lab scale the formed flocks can be easily separated by centrifugation from the fine fraction, for larger scale production using continuous processes, new concepts are urgently needed. Herein we developed a filtration process for flock removal that allows the handling of larger quantities. For process design, we first investigated the flock properties in order to know how stable the generated flocks are and how the flock properties can be controlled. Then, we replaced the classical flock separation by centrifugation through separation by surface filtration under the constraint that the underlying separation efficiency was not affected. By the future use of properly controlled, alternating filtration modules, our work opens the door for establishing an urgently needed, scalable post-processing for sub-10 nm nanoparticles.

Early prediction of epileptic seizures using a long-term recurrent convolutional network

BackgroundA seizure prediction system can detect seizures prior to their occurrence and allow clinicians to provide timely treatment for patients with epilepsy. Research on seizure prediction has progressed from signal processing analyses to machine learning. However, most prediction methods are hand-engineered and have high computational complexity, increasing the difficulty of obtaining real-time predictions. Some forecasting and early warning methods have achieved good results in the short term but have low applicability in practical situations over the long term. New methodsFirst, electroencephalogram (EEG) time series were converted into two-dimensional images for multichannel fusion. A feasible method, a long-term recurrent convolutional network (LRCN), was proposed to create a spatiotemporal deep learning model for predicting epileptic seizures. The convolutional network block was used to automatically extract deep features from the data. The long short-term memory (LSTM) block was incorporated into learning a time sequence for identifying the preictal segments. New network settings and a postprocessing strategy were proposed in the seizure prediction model. ResultsThe deep seizure prediction model achieved an accuracy of 93.40%, prediction sensitivity of 91.88% and specificity of 86.13% in segment-based evaluations. For the event-based evaluations, 164 seizures were predicted. The proposed method provides high sensitivity and a low false prediction rate (FPR) of 0.04 F P/h. Comparison with existing methodsWe employed different methods, including the LRCN, deep learning and traditional machine learning methods, and compared them using the same data in this paper. Overall, the LRCN offers approximately 5–9% increased sensitivity and specificity. ConclusionThis study describes the LRCN network for analyzing EEG data to predict epileptic seizures, thereby enabling the implementation of an early warning system that detects epileptic seizures before they occur in clinical applications.

An improved Otsu method for threshold segmentation based on set mapping and trapezoid region intercept histogram

The line intercept histogram Otsu method enjoys a good segmentation performance but relatively poor computational efficiency and anti-noise capability. This paper analyzes its set mapping essence, and then proposes a new Otsu method for threshold segmentation based on set mapping and trapezoid region intercept histogram. Firstly, in order to improve its computational efficiency, a mapping rule for the mapping of the pixels in the two-dimensional (2D) histogram onto different trapezoid regions is established to compress its threshold space sufficiently. Then the post-processing strategy is introduced to modify the mapping rule, so that the algorithm can better maintain the image details and resist noises. The experimental results show that the algorithm has a better performance in computational efficiency, and can achieve a balance between the noise elimination and the detail retention in image segmentation.

A data‐driven approach to determining freeway incident impact areas with fuzzy and graph theory‐based clustering

AbstractDetermining spatiotemporal impact areas of incidents plays a significant role in incident impact analysis. Although existing empirical methods have proven to be promising, they suffer from the drawbacks that limit their wide applications in automated freeway safety management. This study presents a data‐driven approach to automatically determining the spatiotemporal impact areas of freeway incidents. The spatiotemporal contour plots were first constructed using three representative traffic measures. Next, a nonrecurrent congestion area identification method based on fuzzy clustering was developed. To distinguish possible multiple independent blocks in the nonrecurrent congestion area, a clustering algorithm based on graph theory was adopted. The incident impact areas were then determined by conducting a postprocessing strategy. The incident records and the associated traffic flow data, collected on I‐5 freeway segments in San Diego Region, CA, were used to evaluate the proposed approach. Experimental results show the proposed approach can automatically and properly determine incident impact areas while accounting for the uncertainty resulting from traffic variations.

Dynamic Measurements of 1000 Microstrains Using Chirped-Pulse Phase-Sensitive Optical Time-Domain Reflectometry

This paper extends the capabilities of chirped-pulse phase-sensitive optical time-domain reflectometry to the measurement of large dynamic strains over hundreds of meters of standard single-mode fiber. Benefitting from single-shot strain measurements, this technique has already demonstrated dynamic strains of the order of submicrostrains with a sensitivity of picostrains-per-root-Hertz. Yet, for large dynamic strains, it relies on the accumulation of incremental measurements, where each trace is cross correlated with its predecessor to determine the relative change of strain. However, practical time records of measured high slew-rate applied perturbations contain disturbing outliers. We then detail and analyze a post-processing strategy to mitigate this limitation. Through this strategy, we are able to achieve for the first time (to our knowledge) high signal-to-noise Rayleigh-backscattering-based distributed measurements of large and fast dynamic strains of a longitudinally vibrating 4 m section at the end of 210 m of a single-mode fiber: from peak to peak 150-1190 με at vibration frequency of 400 Hz and 50 Hz, respectively.

A post processing technique to predict primary particle size of sooting flames based on a chemical discrete sectional model: Application to diluted coflow flames

A numerical post-processing strategy to reconstruct the size of soot primary particles is presented in this work in the context of the chemical sectional models. The proposed technique is based on solving the transport equation of the primary particle number density for each considered aggregate size. The chemical source terms are derived from the corresponding chemical reactions.The validity of the proposed approach is tested on target flames of the International Sooting Flame (ISF) Workshop. In particular, first, a laminar premixed ethylene flame is considered. Profiles of soot volume fraction and mean primary particle size are compared between simulation and measurements and a satisfactory agreement is observed, validating the proposed post-processing strategy. Second, a laminar coflow ethylene flame is put under the scope. Numerical results are compared to experimental data once again in terms of soot volume fraction and primary particle size. The sensitivity to model parameters, such as accounting for surface rounding and the choice of the smallest aggregating particle size, is explored.Once validated, the effect of dilution on the mean primary particle diameter in laminar diffusion flames is examined. The general trends observed experimentally are recovered. The correlation between temperature, precursor concentrations, soot volume fraction and primary particle diameter is explored. Finally, formation rates and residence time along the particle trajectories are investigated to explain the effect of dilution on the spatial localization of the biggest particles along the flame. The relation between the soot volume fraction and the mean primary particle diameter is examined.

An Approach to a Clock Offsets Model for Real-Time PPP Time and Frequency Transfer During Data Discontinuity

To resolve the dilemma in any post-processing strategy, i.e., the difficulty of monitoring the real-time time and frequency signals in a timely manner, real-time GPS time and the frequency transfer have recently become trending topics. Unfortunately, data interruption occurs when conducting real-time time transfer, sometimes from unexpected reasons. In this study, to ensure the stability and precision of real-time time transfer, an adaptive prediction model and a between-epoch constraint receiver clock model are applied as the mathematic models. The purpose of prediction is to solve the ambiguity from re-convergence when the data reappear. Moreover, compared to the conventional method, the between-epoch constraint receiver clock model is employed in this study to consider the correlation of epoch-wise clock parameters to avoid wasting useful information. The simulation data and real data are compared to verify the performance of the new approach. The simulation data for 165 days are designed with random daily interruptions of 10, 30, 60 and 90 min. Real data from 12 days is captured from the incomplete data in routine observation records. Ignoring the simulation data and real data, the investigation of six stations shows that the results with the between-epoch constraint receiver clock model were smoother than those with a white noise model. With an adaptive prediction model and the between-epoch constraint receiver clock model, the simulation results illustrate that the average root mean squares (RMS) values of all the stations are significantly reduced, i.e., by 66.03% from 0.43 to 0.14 ns, by 64.91% from 0.44 to 0.15 ns, by 57.47% from 0.43 to 0.18 ns, and by 51.67% from 0.44 to 0.21 ns for the 10, 30, 60 and 90 min data interruptions, respectively. The stability of all the stations is improved by at least 50%. The improvement increases to 100% for short-term stability. The real results show that the stability of four links is boosted by at least 5%. The model proposed in this paper is more effective in producing short-term stability than long-term stability.

Comparing GEFS, ECMWF, and Postprocessing Methods for Ensemble Precipitation Forecasts over Brazil

AbstractThis study compares the performance of Global Ensemble Forecast System (GEFS) and European Centre for Medium-Range Weather Forecasts (ECMWF) precipitation ensemble forecasts in Brazil and evaluates different analog-based methods and a logistic regression method for postprocessing the GEFS forecasts. The numerical weather prediction (NWP) forecasts were evaluated against the Physical Science Division South America Daily Gridded Precipitation dataset using both deterministic and probabilistic forecasting evaluation metrics. The results show that the ensemble precipitation forecasts performed commonly well in the east and poorly in the northwest of Brazil, independent of the models and the postprocessing methods. While the raw ECMWF forecasts performed better than the raw GEFS forecasts, analog-based GEFS forecasts were more skillful and reliable than both raw ECMWF and GEFS forecasts. The choice of a specific postprocessing strategy had less impact on the performance than the postprocessing itself. Nonetheless, forecasts produced with different analog-based postprocessing strategies were significantly different and were more skillful and as reliable and sharp as forecasts produced with the logistic regression method. The approach considering the logarithm of current and past reforecasts as the measure of closeness between analogs was identified as the best strategy. The results also indicate that the postprocessing using analog methods with long-term reforecast archive improved raw GEFS precipitation forecasting skill more than using logistic regression with short-term reforecast archive. In particular, the postprocessing dramatically improves the GEFS precipitation forecasts when the forecasting skill is low or below zero.

Oxidation Removal of CO from Flue Gas Using Two Fenton-like Wet Scrubbing Systems

Advanced oxidation removal of carbon monoxide (CO) from flue gas using two Fenton-like wet scrubbing systems was studied in a bubbling scrubber for the first time. Influence of several technological parameters (e.g., H2O2 concentration, Fe3+/Cu2+ concentration, temperature, solution pH, CO inlet concentration, and gas flow rate) on the oxidation removal of CO was evaluated. Free radicals, products, and mechanism of CO oxidation removal were also studied. The results demonstrate that pH and temperature of the solution and concentration of H2O2 have double influence on the removal efficiency of CO. The increase of Fe3+/Cu2+ concentration elevates the removal efficiency of CO. The CO removal efficiency is reduced by raising the CO inlet concentration and the gas flow rate. Hydroxyl radical (•OH) oxidation is proved to be responsible for the oxidation removal of CO in the two studied Fenton-like wet scrubbing systems (H2O2/Cu2+ and H2O2/Fe3+). The main products of CO oxidation removal are bicarbonate, carbonate, and CO2 and can be effectively treated using a simple postprocessing strategy. The new removal process will not produce subsequent pollution, and may even provide a new option for green emission reduction of CO from exhaust gas.

Direct Hot-Injection Synthesis of Lead Halide Perovskite Nanocubes in Acrylic Monomers for Ultrastable and Bright Nanocrystal-Polymer Composite Films.

In recent years, lead halide perovskite nanocrystals (NCs) have attracted significant attention in both fundamental research and commercial applications because of their excellent optical and optoelectrical properties. However, the protective ligands on the surface of the perovskite NCs could be easily removed after the tedious process of centrifugation, separation, and dispersion, which greatly hampers their stability against light, heat, moisture, and oxygen and limits their practical applications. Here, we report a new post-processing free strategy (i.e., without centrifugation, separation, and dispersion process) of using an ultraviolet (UV)-polymerizable acrylic monomer of lauryl methacrylate as the solvent to synthesize CsPbBr3 NCs, and then adding polyester polyurethane acrylate oligomer, monomer (IBOA), and initiator for direct UV polymerization to fabricate NC-polymer composite films. These films exhibited an improved photoluminescence quantum yield (85-90%) than classic NC films (40-50%), which were processed using octadecene (ODE) as the solvent for NC synthesis and postprocessed for UV polymerization. Significantly, the as-fabricated films by post-processing free strategy exhibited excellent photostability against strong Xe lamp illumination; while the other films using classic methods were quickly photodegraded. Meanwhile, these NC-polymer composite films showed good stability against moisture and heating when aging in water at 50 °C for over 200 h. These films, along with K2SiF6:Mn4+ (KSF) phosphor emitters, were used as downconverters for blue light-emitting diodes in liquid-crystal displays with a wide color gamut of 115% in the International Commission on Illumination (CIE) 1931 color space. This work provides a facile and effective strategy for the preparation of ultrastable and bright color-conversion NC films for the development of the next-generation wide color gamut displays.

Comprehensive Multi-Omics Analysis of Gene Fusions in a Large Multiple Myeloma Cohort

Introduction: Gene fusions are the result of genomic rearrangements that create hybrid protein products or bring the regulatory elements of one gene into close proximity of another. Fusions often dysregulate gene function or expression through oncogene overexpression or tumor suppressor underexpression (Gao, Liang, Foltz, et al. Cell Rep 2018). Some fusions such as EML4–ALK in lung adenocarcinoma are known druggable targets. Fusion detection algorithms utilize discordantly mapped RNA-seq reads. Careful consideration of detection and filtering procedures is vital for large-scale fusion detection because current methods are prone to reporting false positives and show poor concordance.Multiple myeloma (MM) is a blood cancer in which rapidly expanding clones of plasma cells spread in the bone marrow. Translocations that juxtapose the highly-expressed IGH enhancer with potential oncogenes are associated with overexpression of partner genes, although they may not lead to a detectable gene fusion in RNA-seq data. Previous studies have explored the fusion landscape of multiple myeloma cohorts (Cleynen, et al. Nat Comm 2017; Nasser, et al. Blood 2017). In this study, we developed a novel gene fusion detection pipeline and post-processing strategy to analyze 742 patient samples at the primary time point and 64 samples at follow-up time points (806 total samples) from the Multiple Myeloma Research Foundation (MMRF) CoMMpass Study using RNA-seq, WGS, and clinical data.Methods and Results: We overlapped five fusion detection algorithms (EricScript, FusionCatcher, INTEGRATE, PRADA, and STAR-Fusion) to report fusion events. Our filtered call set consisted of 2,817 fusions with a median of 3 fusions per sample (mean 3.8), similar to glioblastoma, breast, ovarian, and prostate cancers in TCGA. Major recurrent fusions involving immunoglobulin genes included IGH–WHSC1 (88 primary samples), IGL–BMI1 (29), and the upstream neighbor of MYC, PVT1, paired with IGH (6), IGK (3), and IGL (11).For each event, we used WGS data when available to determine if there was genomic support of the gene fusion (based on discordant WGS reads, SV event detection, and MMRF CoMMpass Seq-FISH WGS results) (Miller, et al. Blood 2016). WGS validation rates varied by the level of RNA-seq evidence supporting each fusion, with an overall rate of 24.1%, which is comparable to previously observed pan-cancer validation rates using low-pass WGS.We calculated the association between fusion status and gene expression and identified genes such as BCL2L11, CCND1/2, LTBR, and TXNDC5 that showed significant overexpression (t-test). We explored the clinical connections of fusion events through survival analysis and clinical data correlations, and by mining potentially druggable targets from our Database of Evidence for Precision Oncology (dinglab.wustl.edu/depo) (Sun, Mashl, Sengupta, et al. Bioinformatics 2018). Major examples of upregulated fusion kinases that could potentially be targeted with off-label drug use include FGFR3 and NTRK1.We examined the evolution of fusion events over multiple time points. In one MMRF patient with a t(8;14) translocation joining the IGH locus and transcription factor MAFA, we observed IGH fusions with TOP1MT (neighbor of MAFA) at all four time points with corresponding high expression of TOP1MT and MAFA. Using non-MMRF single-cell RNA data from different patients, we were able to track cell-type composition over time as well as detect subpopulations of cells harboring fusions at different time points with potential treatment implications.Discussion: Gene fusions offer potential targets for alternative MM therapies. Careful implementation of gene fusion detection algorithms and post-processing are essential in large cohort studies to reduce false positives and enrich results for clinically relevant information. Clinical fusion detection from untargeted RNA-seq remains a challenge due to poor sensitivity, specificity, and usability. By combining MMRF CoMMpass data from multiple platforms, we have produced a comprehensive fusion profile of 742 MM patients. We have shown novel gene fusion associations with gene expression and clinical data, and we identified candidates for druggability studies. DisclosuresVij:Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Jazz Pharmaceuticals: Honoraria, Membership on an entity's Board of Directors or advisory committees; Jansson: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Karyopharma: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding.

Reducing the communication complexity of quantum private database queries by subtle classical post-processing with relaxed quantum ability

Existing protocols for private database queries in the quantum context are classified into two-dimensional protocols and d-dimensional ones. For the two-dimensional protocols, to obtain a meaningful n-bit message from the database, the user Alice has to query n times to get all the n bits. However, each query may leak Alice's privacy once the server Bob obtains the address she queried, since each queried bit contributes to the whole queried message. Apparently, the longer the queried message is, the worse the user privacy becomes. By contrast, the d-dimensional protocol allows the user to query a multi-bit block with length no more than logd from the database via only one query. In this paper, we propose a novel d-dimensional quantum protocol for private database query. In contrast to existing d-dimensional protocols, the proposed protocol allows the user to retrieve a message of theoretically arbitrary length without the limitation of the dimension d by using a subtle classical post-processing strategy. Furthermore, the server's outcomes of measurement can be irrelevant for the implementation of the protocol thus relaxing his/her quantum ability. Finally, the security of the proposed protocol in the zero-error case has been proved in terms of the user privacy and database security.