Low-activity hotspot investigation method via scanning using deep learning

Small areas of elevated activity are a concern during a final status scan survey of residual radioactivity of decommissioned and contaminated sites. Due to the characteristics of scanning, the lower limit of detection is relatively high because the number of counts is low due to the short measurement time. To overcome this, an algorithm capable of finding hotspots with little information through deep learning was developed. The developed model using an artificial neural network was trained with the scan survey data acquired from a Monte Carlo-based computational simulation. A random mixing method was used to obtain sufficient training data. In order to respond properly to the experimental data, training and verification were conducted in various situations, in this case, in the presence or absence of random background counts and collimators and various source concentrations. Experimental data were obtained using a conventional detector, in this case, the 3″ × 3″ NaI(Tl). The advantages and limitations to the proposed method are as follows. Results were well predicted even in cases at less than 1 Bq/g, which is lower than the scanned minimum detectable concentration (MDC) of the detection system. It is a great advantage that it can detect contaminated areas that are lower than the existing scan’s minimum detectable concentration. However, the limitation is that it cannot be predicted, and the accuracy is low in multi-sourced scans. The source position and size are also important in residual radioactive evaluations, and scanning data images were evaluated in artificial neural network modes with suitable prediction results. The proposed methodology proved the high accuracy of hotspot prediction for low-activity sites and showed that this technology can be used as an efficient and economical hotspot scanning technology and can be extended to an automated system

Separable PINN: Mitigating the Curse of Dimensionality in Physics-Informed Neural Networks

Physics-informed neural networks (PINNs) have emerged as new data-driven PDE solvers for both forward and inverse problems. While promising, the expensive computational costs to obtain solutions often restrict their broader applicability. We demonstrate that the computations in automatic differentiation (AD) can be significantly reduced by leveraging forward-mode AD when training PINN. However, a naive application of forward-mode AD to conventional PINNs results in higher computation, losing its practical benefit. Therefore, we propose a network architecture, called separable PINN (SPINN), which can facilitate forward-mode AD for more efficient computation. SPINN operates on a per-axis basis instead of point-wise processing in conventional PINNs, decreasing the number of network forward passes. Besides, while the computation and memory costs of standard PINNs grow exponentially along with the grid resolution, that of our model is remarkably less susceptible, mitigating the curse of dimensionality. We demonstrate the effectiveness of our model in various PDE systems by significantly reducing the training run-time while achieving comparable accuracy. Project page: https://jwcho5576.github.io/spinn/Comment: To appear in NeurIPS 2022 Workshop on The Symbiosis of Deep Learning and Differential Equations (DLDE) - II, 12 pages, 5 figures, full paper: arXiv:2306.1596

Separable Physics-Informed Neural Networks

Physics-informed neural networks (PINNs) have recently emerged as promising data-driven PDE solvers showing encouraging results on various PDEs. However, there is a fundamental limitation of training PINNs to solve multi-dimensional PDEs and approximate highly complex solution functions. The number of training points (collocation points) required on these challenging PDEs grows substantially, but it is severely limited due to the expensive computational costs and heavy memory overhead. To overcome this issue, we propose a network architecture and training algorithm for PINNs. The proposed method, separable PINN (SPINN), operates on a per-axis basis to significantly reduce the number of network propagations in multi-dimensional PDEs unlike point-wise processing in conventional PINNs. We also propose using forward-mode automatic differentiation to reduce the computational cost of computing PDE residuals, enabling a large number of collocation points (>10^7) on a single commodity GPU. The experimental results show drastically reduced computational costs (62x in wall-clock time, 1,394x in FLOPs given the same number of collocation points) in multi-dimensional PDEs while achieving better accuracy. Furthermore, we present that SPINN can solve a chaotic (2+1)-d Navier-Stokes equation significantly faster than the best-performing prior method (9 minutes vs 10 hours in a single GPU), maintaining accuracy. Finally, we showcase that SPINN can accurately obtain the solution of a highly nonlinear and multi-dimensional PDE, a (3+1)-d Navier-Stokes equation.Comment: arXiv admin note: text overlap with arXiv:2211.0876

Therapeutic monitoring of rivaroxaban in dogs using thromboelastography and prothrombin time

Abstract Background The chromogenic anti‐Xa assay, the gold standard for monitoring the anti‐Xa effect of rivaroxaban, is not available as a cage‐side diagnostic test for use in a clinical setting. Hypothesis/Objectives To evaluate clinical modalities for measuring the anticoagulant effects of rivaroxaban using a point‐of‐care prothrombin time (PT) and thromboelastography (TEG). Animals Six healthy Beagle dogs. Methods Prospective, experimental study. Four different doses of rivaroxaban (0.5, 1, 2, and 4 mg/kg) were administered PO to dogs. Single PO and 3 consecutive dosing regimens also were assessed. Plasma rivaroxaban concentration was determined using a chromogenic anti‐Xa assay, point‐of‐care PT, and TEG analysis with 4 activators (RapidTEG, 1 : 100 tissue factor [TF100], 1 : 3700 tissue factor [TF3700], and kaolin), and results were compared. Spearman correlation coefficients were calculated between ratios (peak to baseline PT; peak reaction time [R] of TEG to baseline [R] of TEG) and anti‐Xa concentration. Results Anti‐Xa concentration had a significant correlation with point‐of‐care PT (R = 0.82, P < .001) and RapidTEG‐TEG, TF100‐TEG, and TF3700‐TEG (R = 0.76, P < .001; R = 0.82, P < .001; and R = 0.83, P < .001, respectively). Conclusions and Clinical Importance Overall, a 1.5‐1.9 × delay in PT and R values of TEG 3 hours after rivaroxaban administration is required to achieve therapeutic anti‐Xa concentrations of rivaroxaban in canine plasma. The R values of TEG, specifically using tissue factors (RapidTEG, TF100, TF3700) and point‐of‐care PT for rivaroxaban can be used practically for therapeutic monitoring of rivaroxaban in dogs

iMGEins: detecting novel mobile genetic elements inserted in individual genomes

Abstract Background Recent advances in sequencing technology have allowed us to investigate personal genomes to find structural variations, which have been studied extensively to identify their association with the physiology of diseases such as cancer. In particular, mobile genetic elements (MGEs) are one of the major constituents of the human genomes, and cause genome instability by insertion, mutation, and rearrangement. Result We have developed a new program, iMGEins, to identify such novel MGEs by using sequencing reads of individual genomes, and to explore the breakpoints with the supporting reads and MGEs detected. iMGEins is the first MGE detection program that integrates three algorithmic components: discordant read-pair mapping, split-read mapping, and insertion sequence assembly. Our evaluation results showed its outstanding performance in detecting novel MGEs from simulated genomes, as well as real personal genomes. In detail, the average recall and precision rates of iMGEins are 96.67 and 100%, respectively, which are the highest among the programs compared. In the testing with real human genomes of the NA12878 sample, iMGEins shows the highest accuracy in detecting MGEs within 20 bp proximity of the breakpoints annotated. Conclusion In order to study the dynamics of MGEs in individual genomes, iMGEins was developed to accurately detect breakpoints and report inserted MGEs. Compared with other programs, iMGEins has valuable features of identifying novel MGEs and assembling the MGEs inserted

Free-Standing Nanocomposite Multilayers with Various Length Scales, Adjustable Internal Structures, and Functionalities

We introduce an innovative and robust method for the preparation of nanocomposite multilayers, which allows accurate control over the placement of functional groups as well as the composition and dimensions of individual layers/internal structure. By employing the photocross-linkable polystyrene (PS-N3, Mn ) 28.0 kg/mol) with 10 wt % azide groups (-N3) for host polymer and/or the PS-N3-SH (Mn ) 6.5 kg/mol) with azide and thiol (-SH) groups for capping ligands of inorganic nanoparticles, nanocomposite multilayers were prepared by an efficient photocross-linking layer-by-layer process, without perturbing underlying layers and nanostructures. The thickness of individual layers could be controlled from a few to hundreds of nanometers producing highly ordered internal structure, and the resulting nanocomposite multilayers, consisting of polymer and inorganic nanoparticles (CdSe@ZnS, Au, and Pt), exhibit a variety of interesting physical properties. These include prolonged photoluminescent durability, facile color tuning, and the ability to prepare functional free-standing films that can have the one-dimensional photonic band gap and furthermore be patterned by photolithography. This robust and tailored method opens a new route for the design of functional film devices based on nanocomposite multilayers.This work was supported by the KOSEF grant funded by the Korea government (MEST) (R01-2008-000-10551-0), the SystemIC2010 project of Korea Ministry of Commerce Industry and Energy (10030559), the ERC Program of KOSEF grant funded by the Korea government (MEST) (R11-2005-048-00000-0), and the Materials Research Laboratory (NSF DMR-0520415) at the University of California, Santa Barbara