Using a cost-effective approach to increase background ensemble member size within the GSI-based EnVar system for improved radar analyses and forecasts of convective systems

A Valid Time Shifting (VTS) method is explored for the GSI-based ensemble variational (EnVar) system modified to directly assimilate radar reflectivity at convective scales. VTS is a cost-efficient method to increase ensemble size by including subensembles before and after the central analysis time. Additionally, VTS addresses common time and phase model error uncertainties within the ensemble. VTS is examined here for assimilating radar reflectivity in a continuous hourly analysis system for a case study of 1-2 May 2019. The VTS implementation is compared against a 36-member control experiment (ENS-36), to increase ensemble size (3×36 VTS), and as a cost-savings method (3×12 VTS), with time-shifting intervals τ between 15 and 120 min. The 3×36 VTS experiments increased the ensemble spread, with largest subjective benefits in early cycle analyses during convective development. The 3×12 VTS experiments captured analysis with similar accuracy as ENS-36 by the third hourly analysis. Control forecasts launched from hourly EnVar analyses show significant skill increases in 1-h precipitation over ENS-36 out to hour 12 for 3×36 VTS experiments, subjectively attributable to more accurate placement of the convective line. For 3×12 VTS, experiments with τ ≥ 60 min met and exceeded the skill of ENS-36 out to forecast hour 15, with VTS-3×12τ90 maximizing skill. Sensitivity results demonstrate preference to τ = 30–60 min for 3x36 VTS and 60 – 120 min for 3×12 VTS. The best 3×36 VTS experiments add a computational cost of 45-67%, compared to the near tripling of costs when directly increasing ensemble size, while best 3×12 VTS experiments save about 24-41% costs over ENS-36.

Accelerated cardiac T1 mapping in four heartbeats with inline MyoMapNet: a deep learning-based T1 estimation approach

Purpose To develop and evaluate MyoMapNet, a rapid myocardial T1 mapping approach that uses fully connected neural networks (FCNN) to estimate T1 values from four T1-weighted images collected after a single inversion pulse in four heartbeats (Look-Locker, LL4). Method We implemented an FCNN for MyoMapNet to estimate T1 values from a reduced number of T1-weighted images and corresponding inversion-recovery times. We studied MyoMapNet performance when trained using native, post-contrast T1, or a combination of both. We also explored the effects of number of T1-weighted images (four and five) for native T1. After rigorous training using in-vivo modified Look-Locker inversion recovery (MOLLI) T1 mapping data of 607 patients, MyoMapNet performance was evaluated using MOLLI T1 data from 61 patients by discarding the additional T1-weighted images. Subsequently, we implemented a prototype MyoMapNet and LL4 on a 3 T scanner. LL4 was used to collect T1 mapping data in 27 subjects with inline T1 map reconstruction by MyoMapNet. The resulting T1 values were compared to MOLLI. Results MyoMapNet trained using a combination of native and post-contrast T1-weighted images had excellent native and post-contrast T1 accuracy compared to MOLLI. The FCNN model using four T1-weighted images yields similar performance compared to five T1-weighted images, suggesting that four T1 weighted images may be sufficient. The inline implementation of LL4 and MyoMapNet enables successful acquisition and reconstruction of T1 maps on the scanner. Native and post-contrast myocardium T1 by MOLLI and MyoMapNet was 1170 ± 55 ms vs. 1183 ± 57 ms (P = 0.03), and 645 ± 26 ms vs. 630 ± 30 ms (P = 0.60), and native and post-contrast blood T1 was 1820 ± 29 ms vs. 1854 ± 34 ms (P = 0.14), and 508 ± 9 ms vs. 514 ± 15 ms (P = 0.02), respectively. Conclusion A FCNN, trained using MOLLI data, can estimate T1 values from only four T1-weighted images. MyoMapNet enables myocardial T1 mapping in four heartbeats with similar accuracy as MOLLI with inline map reconstruction.

Symptom Dimension Breakpoints for the Obsessive-Compulsive Inventory-Child Version (OCI-CV)

Pediatric obsessive-compulsive disorder (OCD) clusters around three major symptom dimensions: contamination/cleaning, symmetry/ordering, and disturbing thoughts/checking. The Obsessive-Compulsive Inventory-Child Version (OCI-CV) is a self-report questionnaire that provides scores along six theory-based OCD dimensions, but no study has evaluated how well OCI-CV identifies clinically significant symptoms within each of the three major symptom dimensions of OCD. We examined this question using data from 197 Swedish and Spanish youth with OCD. All youth completed the OCI-CV and clinically significant symptom severity within each major OCD dimension was established with a validated interview-based measure. Results showed that a score ≥ 3 on the OCI-CV washing scale excellently captured those with clinically significant contamination/cleaning symptoms (AUC = 0.85 [0.80–0.90], 79% accuracy). A score ≥ 4 on the obsessing scale adequately captured those with disturbing thoughts/checking symptoms (AUC = 0.71 [0.64–0.78], 67% accuracy) and a score ≥ 3 on the ordering scale adequately captured those with symmetry/ordering symptoms (AUC = 0.72 [0.65–0.79], 70% accuracy). Similar accuracy of the breakpoints was found in the Swedish and Spanish samples. OCI-CV works well to identify youth with pediatric OCD that have clinically significant contamination/cleaning symptoms. The measure can also with adequate precision identify those with clinically significant disturbing thoughts/checking and symmetry/ordering symptoms. The breakpoints provided in this study can be used to examine differences in clinical presentation and treatment outcome for youth with different types of OCD.

Frailty and In-Hospital Mortality Risk Using EHR Nursing Data

Purpose The purpose of this study was to evaluate four definitions of a Frailty Risk Score (FRS) derived from EHR data that includes combinations of biopsychosocial risk factors using nursing flowsheet data or International Classification of Disease, 10th revision (ICD-10) codes and blood biomarkers and its predictive properties for in-hospital mortality in adults ≥50 years admitted to medical-surgical units. Methods In this retrospective observational study and secondary analysis of an EHR dataset, survival analysis and Cox regression models were performed with sociodemographic and clinical covariates. Integrated area under the ROC curve (iAUC) across follow-up time based on Cox modeling was estimated. Results The 46,645 patients averaged 1.5 hospitalizations (SD = 1.1) over the study period and 63.3% were emergent admissions. The average age was 70.4 years (SD = 11.4), 55.3% were female, 73.0% were non-Hispanic White (73.0%), mean comorbidity score was 3.9 (SD = 2.9), 80.5% were taking 1.5 high risk medications, and 42% recorded polypharmacy. The best performing FRS-NF-26-LABS included nursing flowsheet data and blood biomarkers (Adj. HR = 1.30, 95% CI [1.28, 1.33]), with good accuracy (iAUC = .794); the reduced model with age, sex, and FRS only demonstrated similar accuracy. The poorest performance was the ICD-10 code-based FRS. Conclusion The FRS captures information about the patient that increases risk for in-hospital mortality not accounted for by other factors. Identification of frailty enables providers to enhance various aspects of care, including increased monitoring, applying more intensive, individualized resources, and initiating more informed discussions about treatments and discharge planning.

Adolescent-specific memory effects: Evidence from working memory, immediate and 24-hour recognition memory performance in 8- to 30-year-olds

Working memory and long-term memory develop from childhood to adulthood, but the relationship between them is not fully understood, especially during adolescence. We investigated associations between n-back task performance and subsequent recognition memory in a community sample (8-30 years, n=150) using tasks from the Adolescent Brain Cognitive Development Study (ABCD Study®). We added a 24-hour delay condition to assess long-term memory and assessed ages that overlap with those to be assessed in the 10-year ABCD study. Overall working memory, immediate, and long-term recognition memory performance peaked during adolescence. Age effects in recognition memory varied by items (i.e., old targets and distractors and new items) and delay. For immediate recognition, accuracy was higher for new items and targets than distractors, with the highest accuracy for new items emerging by the mid-teens. For long-term recognition, adolescents were more accurate in identifying new items than children and adults and adolescents showed more long-term forgetting of distractors relative to targets. In contrast, adults showed similar accuracy for targets and distractors, while children showed long-term forgetting of both. The results suggest that working memory processes may facilitate long-term storage of task-relevant items over irrelevant items and may benefit the detection of novel information during adolescence.

Endorectal Ultrasonography and Pelvic Magnetic Resonance Imaging Show Similar Diagnostic Accuracy in Local Staging of Rectal Cancer: An Update Meta-Analysis

Background: Endorectal Ultrasonography (EUS-ERUS) and pelvic magnetic resonance imaging (MRI) are world-wide performed for the local staging of rectal cancer (RC), but no clear consensus on their indications is present, there being literature in support of both. The aim of this meta-analysis is to give an update regarding the diagnostic test accuracy of ERUS and pelvic MRI about the local staging of RC. Materials and methods: A systematic literature search from November 2020 to October 2021 was performed to select studies in which head-to-head comparison between ERUS and MRI was reported for the local staging of rectal cancer. Quality and risk of bias were assessed with the QUADAS-2 tool. Our primary outcome was the T staging accuracy of ERUS and MRI for which pooled accuracy indices were calculated using a bivariable random-effects model. In addition, a hierarchical summary receiver operating characteristic curve (hSROC) was created to characterize the accuracy of ERUS and MRI for the staging of T and N parameters. The area under the hSROC curve (AUChSROC) was determined as a measure of diagnostic accuracy. Results: Seven studies and 331 patients were included in our analysis. ERUS and MRI showed a similar accuracy for the T staging, with AUChSROC curves of 0.91 (95% C.I., 0.89 to 0.93) and 0.87 (95% C.I., 0.84 to 0.89), respectively (p = 0.409). For T staging, ERUS showed a pooled sensitivity of 0.82 (95% C.I. 0.72 to 0.89) and pooled specificity of 0.91 (95% C.I. 0.77–0.96), while MRI had pooled sensitivity and specificity of 0.69 (95% C.I. 0.55–0.81) and 0.88 (95% C.I. 0.79–0.93), respectively. ERUS and MRI showed a similar accuracy in the N staging too, with AUChSROC curves of 0.92 (95% C.I., 0.89 to 0.94) and 0.93 (95% C.I., 0.90 to 0.95), respectively (p = 0.389). Conclusions: In conclusion, ERUS and MRI are comparable imaging techniques for the local staging of rectal cancer.

Insights from the IronTract challenge: optimal methods for mapping brain pathways from multi-shell diffusion MRI

Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more time-consuming, Cartesian-grid scheme. Importantly, we show that simple pre- and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods.

Online Monitoring of Power Converter Degradation Using Deep Neural Network

Power semiconductor devices in the power converters used for motor drives are susceptible to wear-out and failure, especially when operated in harsh environments. Therefore, detection of degradation of power devices is crucial for ensuring the reliable performance of power converters. In this paper, a deep learning approach for online classification of the health states of the snubber resistors in the Insulated Gate Bipolar Transistors (IGBTs) in a three-phase Brushless DC (BLDC) motor drive is proposed. The method can locate one out of the six IGBTs experiencing a snubber resistor degradation problem by measuring the voltage waveforms of the three shunt resistors using voltage sensors. The range of the degradation of the snubber resistors for successful classification is also investigated. The off-the-shelf deep Convolutional Neural Network (CNN) architecture ResNet50 is used for transfer learning to determine which snubber resistor has degraded. The dataset for evaluating the above classification scheme of IGBT degradation is obtained by measuring the shunt voltage waveforms with varying snubber resistance and reference current. Then, the three-phase voltage waveforms are converted into greyscale images and RGB spectrogram images, which are later fed into the deep CNN. Experiments are carried out on the greyscale image dataset and the spectrogram image dataset using four-fold cross-validation. The results show that the proposed scheme can classify seven classes (one class for normal condition and six classes for abnormal condition in one of the six IGBTs in a three-phase BLDC drive) with over 95% average accuracy within a specific range of snubber resistance. Using grayscale images and using spectrogram-based RGB images yields similar accuracy.

Direction Finding and Likelihood Ratio Detection for Oceanographic HF Radars

Previous work with simulations of oceanographic HF radars has identified possible improvements when using Maximum Likelihood Estimation (MLE) for directional-of-arrival (DOA), however methods for determining the number of emitters (here defined as spatially distinct patches of the ocean surface) have not realized these improvements. Here we describe and evaluate the use of the Likelihood Ratio (LR) for emitter detection, demonstrating its application to oceanographic HF radar data. The combined detection-estimation methods MLE-LR are compared with MUSIC and MUSIC parameters for SeaSonde HF radars, along with a method developed for 8-channel systems known as MUSIC-Highest. Results show that the use of MLE-LR produces similar accuracy in terms of the RMS difference and correlation coefficients squared, as previous methods. We demonstrate that improved accuracy can be obtained for both methods, at the cost of fewer velocity observations and decreased spatial coverage. For SeaSondes, accuracy improvements are obtained with less commonly used parameter sets. The MLE-LR is shown to be able to resolve simultaneous closely spaced emitters, which has the potential to improve observations obtained by HF radars operating in complex current environments.