Implementation of the submarine diving simulation in a distributed environment

ABSTRACTTo implement a combined discrete event and discrete time simulation such as submarine diving simulation in a distributed environment, e.g., in the High Level Architecture (HLA)/Run-Time Infrastructure (RTI), a HLA interface, which can easily connect combined models with the HLA/RTI, was developed in this study. To verify the function and performance of the HLA interface, it was applied to the submarine dive scenario in a distributed environment, and the distributed simulation shows the same results as the stand-alone simulation. Finally, by adding a visualization model to the simulation and by editing this model, we can confirm that the HLA interface can provide user-friendly functions such as adding new model and editing a model

Use of signal sequences as an in situ removable sequence element to stimulate protein synthesis in cell-free extracts

This study developed a method to boost the expression of recombinant proteins in a cell-free protein synthesis system without leaving additional amino acid residues. It was found that the nucleotide sequences of the signal peptides serve as an efficient downstream box to stimulate protein synthesis when they were fused upstream of the target genes. The extent of stimulation was critically affected by the identity of the second codons of the signal sequences. Moreover, the yield of the synthesized protein was enhanced by as much as 10 times in the presence of an optimal second codon. The signal peptides were in situ cleaved and the target proteins were produced in their native sizes by carrying out the cell-free synthesis reactions in the presence of Triton X-100, most likely through the activation of signal peptidase in the S30 extract. The amplification of the template DNA and the addition of the signal sequences were accomplished by PCR. Hence, elevated levels of recombinant proteins were generated within several hours

Artificial intelligence for the detection of sacroiliitis on magnetic resonance imaging in patients with axial spondyloarthritis

BackgroundMagnetic resonance imaging (MRI) is important for the early detection of axial spondyloarthritis (axSpA). We developed an artificial intelligence (AI) model for detecting sacroiliitis in patients with axSpA using MRI.MethodsThis study included MRI examinations of patients who underwent semi-coronal MRI scans of the sacroiliac joints owing to chronic back pain with short tau inversion recovery (STIR) sequences between January 2010 and December 2021. Sacroiliitis was defined as a positive MRI finding according to the ASAS classification criteria for axSpA. We developed a two-stage framework. First, the Faster R-CNN network extracted regions of interest (ROIs) to localize the sacroiliac joints. Maximum intensity projection (MIP) of three consecutive slices was used to mimic the reading of two adjacent slices. Second, the VGG-19 network determined the presence of sacroiliitis in localized ROIs. We augmented the positive dataset six-fold. The sacroiliitis classification performance was measured using the sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). The prediction models were evaluated using three-round three-fold cross-validation.ResultsA total of 296 participants with 4,746 MRI slices were included in the study. Sacroiliitis was identified in 864 MRI slices of 119 participants. The mean sensitivity, specificity, and AUROC for the detection of sacroiliitis were 0.725 (95% CI, 0.705–0.745), 0.936 (95% CI, 0.924–0.947), and 0.830 (95%CI, 0.792–0.868), respectively, at the image level and 0.947 (95% CI, 0.912–0.982), 0.691 (95% CI, 0.603–0.779), and 0.816 (95% CI, 0.776–0.856), respectively, at the patient level. In the original model, without using MIP and dataset augmentation, the mean sensitivity, specificity, and AUROC were 0.517 (95% CI, 0.493–0.780), 0.944 (95% CI, 0.933–0.955), and 0.731 (95% CI, 0.681–0.780), respectively, at the image level and 0.806 (95% CI, 0.729–0.883), 0.617 (95% CI, 0.523–0.711), and 0.711 (95% CI, 0.660–0.763), respectively, at the patient level. The performance was improved by MIP techniques and data augmentation.ConclusionAn AI model was developed for the detection of sacroiliitis using MRI, compatible with the ASAS criteria for axSpA, with the potential to aid MRI application in a wider clinical setting

Non-Vitamin K Antagonist Oral Anticoagulants in Patients with Atrial Fibrillation and Valvular Heart Disease

Background: There are limited data for non-vitamin K antagonist oral anticoagulants (NOACs) impact on outcomes for patients with atrial fibrillation (AF) and valvular heart diseases (VHDs). Methods: We identified patients with AF and associated Evaluated Heartvalves, Rheumatic or Artificial (EHRA) type 2 VHDs, and who had been naïve from the oral anticoagulants in the Korean National Health Insurance Service database between 2014 and 2016 (warfarin: n = 2671; NOAC: n = 3058). For analyzing the effect of NOAC on primary prevention, we excluded those with a previous history of ischemic stroke, intracranial hemorrhage (ICH), and gastrointestinal (GI) bleeding events. To balance covariates, we used the propensity score weighting method. Ischemic stroke, ICH, GI bleeding, major bleeding, all-cause death, and their composite outcome and fatal clinical events were evaluated. Results: During a follow-up with a mean duration of 1.4 years, NOACs were associated with lower risks of ischemic stroke (hazard ratio (HR): 0.71, 95% confidence interval (CI): 0.53–0.96), GI bleeding (HR: 0.50, 95% CI: 0.35–0.72), fatal ICH (HR: 0.28, 95% CI: 0.07–0.83), and major bleeding (HR: 0.61, 95% CI: 0.45–0.80) compared with warfarin. Overall, NOACs were associated with a lower risk of the composite outcome (HR: 0.68, 95% CI: 0.58–0.80). Conclusions: In this nationwide Asian AF population with EHRA type 2 VHDs, NOAC use was associated with lower risks of ischemic stroke, major bleeding, all-cause death, and the composite outcome compared to warfarin use

Temporal Trends of Emergency Department Visits of Patients with Atrial Fibrillation:A Nationwide Population-Based Study

The question of list decoding error-correcting codes over finite fields (under the Hamming metric) has been widely studied in recent years. Motivated by the similar discrete linear structure of linear codes and point lattices in R N, and their many shared applications across complexity theory, cryptography, and coding theory, we initiate the study of list decoding for lattices. Namely: for a lattice L ⊆ R N, given a target vector r ∈ R N and a distance parameter d, output the set of all lattice points w ∈ L that are within distance d of r. In this work we focus on combinatorial and algorithmic questions related to list decoding for the well-studied family of Barnes-Wall lattices. Our main contributions are twofold: 1. We give tight (up to polynomials) combinatorial bounds on the worst-case list size, showing it to be polynomial in the lattice dimension for any error radius bounded away from the lattice’s minimum distance (in the Euclidean norm). 2. Building on the unique decoding algorithm of Micciancio and Nicolosi (ISIT ’08), we give a list-decoding algorithm that runs in time polynomial in the lattice dimension and worst-case list size, for any error radius. Moreover, our algorithm is highly parallelizable, and with sufficiently many processors can run in parallel time only poly-logarithmic in the lattice dimension. In particular, our results imply a polynomial-time list-decoding algorithm for any error radius bounded away from the minimum distance, thus beating a typical barrier for natural error-correcting codes posed by the Johnson radius

Differentiation of the right versus left outflow tract ventricular arrhythmias using local activation time at the His bundle electrogram

Background Although multiple algorithms based on surface electrocardiographic criteria have been introduced to localize idiopathic ventricular arrhythmia (VA) origins from the outflow tract (OT), their diagnostic accuracy and clinical usefulness remain limited. We evaluated whether local activation time of the His bundle region could differentiate left and right ventricular OT VA origins in the early stage of electrophysiology study. Methods We studied 30 patients who underwent catheter ablation for OT VAs with a left bundle branch block pattern and inferior axis QRS morphology. The interval between the local V signal on the mapping catheter placed in the RVOT and His bundle region (V(RVOT)-V(HB) interval) and the interval from QRS complex onset to the local V signal on the His bundle region (QRS-V(HB) interval) were measured during VAs. Results The V(RVOT)-V(HB) and QRS-V(HB) intervals were significantly shorter in patients with LVOT VAs. The area under the curve (AUC) for the V(RVOT)-V(HB) interval by receiver operating characteristic analysis was 0.865. A cutoff value of ≤ 50 ms predicted an LVOT origin of VA with sensitivity, specificity, and positive and negative predictive values of 100%, 62.5%, 40%, and 100%, respectively. The QRS-V(HB) interval showed similar diagnostic accuracy (AUC, 0.840), and a cutoff value of ≤ 15 ms predicted an LVOT origin of VA with a sensitivity, specificity, and positive and negative predictive values of 100%, 70.8%, 45.2%, and 100%, respectively. Conclusion The V(RVOT)-V(HB) and QRS-V(HB) intervals could differentiate left from right OT origins of VA with high sensitivity and negative predictive values