DMAS Beamforming with Complementary Subset Transmit for Ultrasound Coherence-Based Power Doppler Detection in Multi-Angle Plane-Wave Imaging

Conventional ultrasonic coherent plane-wave (PW) compounding corresponds to Delay-and-Sum (DAS) beamforming of low-resolution images from distinct PW transmit angles. Nonetheless, the trade-off between the level of clutter artifacts and the number of PW transmit angle may compromise the image quality in ultrafast acquisition. Delay-Multiply-and-Sum (DMAS) beamforming in the dimension of PW transmit angle is capable of suppressing clutter interference and is readily compatible with the conventional method. In DMAS, a tunable p value is used to modulate the signal coherence estimated from the low-resolution images to produce the final high-resolution output and does not require huge memory allocation to record all the received channel data in multi-angle PW imaging. In this study, DMAS beamforming is used to construct a novel coherence-based power Doppler detection together with the complementary subset transmit (CST) technique to further reduce the noise level. For p = 2.0 as an example, simulation results indicate that the DMAS beamforming alone can improve the Doppler SNR by 8.2 dB compared to DAS counterpart. Another 6-dB increase in Doppler SNR can be further obtained when the CST technique is combined with DMAS beamforming with sufficient ensemble averaging. The CST technique can also be performed with DAS beamforming, though the improvement in Doppler SNR and CNR is relatively minor. Experimental results also agree with the simulations. Nonetheless, since the DMAS beamforming involves multiplicative operation, clutter filtering in the ensemble direction has to be performed on the low-resolution images before DMAS to remove the stationary tissue without coupling from the flow signal.

Prediction of intensive care unit mortality based on missing events

BackgroundThe increasing availability of electronic health records has made it possible to construct and implement models for predicting intensive care unit (ICU) mortality using machine learning. However, the algorithms used are not clearly described, and the performance of the model remains low owing to several missing values, which is unavoidable in big databases.MethodsWe developed an algorithm for subgrouping patients based on missing event patterns using the Philips eICU Research Institute (eRI) database as an example. The eRI database contains data associated with 200,859 ICU admissions from many hospitals (>400) and is freely available. We then constructed a model for each subgroup using random forest classifiers and integrated the models. Finally, we compared the performance of the integrated model with the Acute Physiology and Chronic Health Evaluation (APACHE) scoring system, one of the best known predictors of patient mortality, and the imputation approach-based model.ResultsSubgrouping and patient mortality prediction were separately performed on two groups: the sepsis group (the ICU admission diagnosis of which is sepsis) and the non-sepsis group (a complementary subset of the sepsis group). The subgrouping algorithm identified a unique, clinically interpretable missing event patterns and divided the sepsis and non-sepsis groups into five and seven subgroups, respectively. The integrated model, which comprises five models for the sepsis group or seven models for the non-sepsis group, greatly outperformed the APACHE IV or IVa, with an area under the receiver operating characteristic (AUROC) of 0.91 (95% confidence interval 0.89–0.92) compared with 0.79 (0.76–0.81) for the APACHE system in the sepsis group and an AUROC of 0.90 (0.89–0.91) compared with 0.86 (0.85–0.87) in the non-sepsis group. Moreover, our model outperformed the imputation approach-based model, which had an AUROC of 0.85 (0.83–0.87) and 0.87 (0.86–0.88) in the sepsis and non-sepsis groups, respectively.ConclusionsWe developed a method to predict patient mortality based on missing event patterns. Our method more accurately predicts patient mortality than others. Our results indicate that subgrouping, based on missing event patterns, instead of imputation is essential and effective for machine learning against patient heterogeneity.Trial registrationNot applicable.

Prediction of intensive care unit mortality based on missing events

BackgroundThe increasing availability of electronic health records has made it possible to construct and implement models for predicting intensive care unit (ICU) mortality using machine learning. However, the algorithms used are not clearly described, and the performance of the model remains low owing to several missing values, which is unavoidable in big databases.MethodsWe developed an algorithm for subgrouping patients based on missing event patterns using the Philips eICU Research Institute (eRI) database as an example. The eRI database contains data associated with 200,859 ICU admissions from many hospitals (>400) and is freely available. We then constructed a model for each subgroup using random forest classifiers and integrated the models. Finally, we compared the performance of the integrated model with the Acute Physiology and Chronic Health Evaluation (APACHE) scoring system, one of the best known predictors of patient mortality, and the imputation approach-based model.ResultsSubgrouping and patient mortality prediction were separately performed on two groups: the sepsis group (the ICU admission diagnosis of which is sepsis) and the non-sepsis group (a complementary subset of the sepsis group). The subgrouping algorithm identified a unique, clinically interpretable missing event patterns and divided the sepsis and non-sepsis groups into five and seven subgroups, respectively. The integrated model, which comprises five models for the sepsis group or seven models for the non-sepsis group, greatly outperformed the APACHE IV or IVa, with an area under the receiver operating characteristic (AUROC) of 0.91 (95% confidence interval 0.89–0.92) compared with 0.79 (0.76–0.81) for the APACHE system in the sepsis group and an AUROC of 0.90 (0.89–0.91) compared with 0.86 (0.85–0.87) in the non-sepsis group. Moreover, our model outperformed the imputation approach-based model, which had an AUROC of 0.85 (0.83–0.87) and 0.87 (0.86–0.88) in the sepsis and non-sepsis groups, respectively.ConclusionsWe developed a method to predict patient mortality based on missing event patterns. Our method more accurately predicts patient mortality than others. Our results indicate that subgrouping, based on missing event patterns, instead of imputation is essential and effective for machine learning against patient heterogeneity.Trial registrationNot applicable.

Uncovering complementary sets of variants for the prediction of quantitative phenotypes

Recent genome-wide association studies (GWAS) show that mutations in single genetic loci, frequently called single nucleotide polymorphisms (SNPs), alone are not sufficient to explain the phenotypic heritability of complex, quantitative phenotypes. Instead, many methods attempt to deal with this issue by considering a set of loci that can characterize the phenotype together. While the state-of-the-art methods are successful in selecting subsets of SNPs that can achieve high phenotype prediction rates, they are either slow in runtime or have hyper-parameters that require further fine tuning through cross-validation or other similar techniques, which makes such methods inconvenient to use. In this work, we propose a fast and simple algorithm named Macarons to select a small, complementary subset of SNPs by avoiding redundant pairs of SNPs that are likely to be in linkage disequilibrium (LD). Our method features two interpretable parameters that control the time/performance trade-off without requiring any hyper-parameter optimization procedures. In our experiments, we benchmark the performance of the SNP selection methods on the 17 flowering time phenotypes of Arabidopsis Thaliana. Our results consistently show that Macarons has similar or better phenotype prediction performance while being faster and having a simpler premise than other SNP selection methods.

An Automated Refusal Conversion Strategy for Web Surveys

This article presents results from an experiment conducted during the web-based 2017 Federal Employee Viewpoint Survey (FEVS) to evaluate an automated refusal conversion strategy whereby a sample of individuals was given the opportunity to opt out from the survey and stop receiving additional e-mail reminders. Before being added to the unsubscribe list, however, the individual was asked to cite the primary reason for choosing not to take the FEVS. A randomly assigned subset was given a last-moment appeal, tailored to the reason provided, at which point the individual could either confirm desiring to opt out or navigate to the start of the survey. Because the complementary subset did not receive the appeal, we are able to report on the efficacy of the strategy in convincing individuals who may not have been initially inclined to participate to do so.

Triflic acid treatment enables LC-MS/MS analysis of insoluble bacterial biomass

ABSTRACTThe lysis and extraction of soluble bacterial proteins from cells is a common practice for proteomics analyses, but insoluble bacterial biomasses are often left behind. Here, we show that with triflic acid treatment, the insoluble bacterial biomass of Gram- and Gram+ bacteria can be rendered soluble. We use LC-MS/MS shotgun proteomics to show that bacterial proteins in the soluble and insoluble post-lysis fractions differ significantly. Additionally, in the case of Gram-Pseudomonas aeruginosa, triflic acid treatment enables the enrichment of cell envelope-associated proteins. Finally, we apply triflic acid to a human microbiome sample to show that this treatment is robust and enables the identification of a new, complementary subset of proteins from a complex microbial mixture.

Selection of Stratified Core Sets Representing Wild Apple (Malus sieversii)

We estimate the minimum core size necessary to maximally represent a portion of the U.S. Department of Agriculture's National Plant Germplasm System apple (Malus) collection. We have identified a subset of Malus sieversii individuals that complements the previously published core subsets for two collection sites within Kazakhstan. We compared the size and composition of this complementary subset with a core set composed without restrictions. Because the genetic structure of this species has been previously determined, we were able to identify the origin of individuals within this core set with respect to their geographic location and genetic lineage. In addition, this core set is structured in a way that samples all of the major genetic lineages identified in this collection. The resulting panel of genotypes captures a broad range of phenotypic and molecular variation throughout Kazakhstan. These samples will provide a manageable entry point into the larger collection and will be critical in developing a long-term strategy for ex situ wild Malus conservation.

Variational Approach in Transport Theory

In this work we present a variational approach to some methods to solve transport problems of neutral particles. We consider a convex domain X (for example the geometry of slab, or a convex set in the plane, or a convex bounded set in the space) and we use discrete ordinates quadrature to get a system of differential equations derived from the neutron transport equation. The boundary conditions are vacuum for a subset of the boundary, and of specular reflection for the complementary subset of the boundary. Recently some different approximation methods have been presented to solve these transport problems. We introduce in this work the adjoint equations and the conjugate functions obtained by means of the variational approach. First we consider the general formulation, and then some numerical methods such as spherical harmonics and spectral collocation method.