Survivability assessment of Viton in safety-related equipment under simulated severe accident environments

To evaluate equipment survivability of the polymer Viton, used in sealing materials, the effects of its thermal degradation were investigated in severe accident (SA) environment in a nuclear power plant. Viton specimens were prepared and thermally degraded at different SA temperature profiles. Changes in mechanical properties at different temperature profiles in different SA states were investigated. The thermal lag analysis was performed at calculated convective heat transfer conditions to predict the exposure temperature of the polymer inside the safety-related equipment. The polymer that was thermally degraded at postaccident states exhibited the highest change in its mechanical properties, such as tensile strength and elongation

DiffRef3D: A Diffusion-based Proposal Refinement Framework for 3D Object Detection

Denoising diffusion models show remarkable performances in generative tasks, and their potential applications in perception tasks are gaining interest. In this paper, we introduce a novel framework named DiffRef3D which adopts the diffusion process on 3D object detection with point clouds for the first time. Specifically, we formulate the proposal refinement stage of two-stage 3D object detectors as a conditional diffusion process. During training, DiffRef3D gradually adds noise to the residuals between proposals and target objects, then applies the noisy residuals to proposals to generate hypotheses. The refinement module utilizes these hypotheses to denoise the noisy residuals and generate accurate box predictions. In the inference phase, DiffRef3D generates initial hypotheses by sampling noise from a Gaussian distribution as residuals and refines the hypotheses through iterative steps. DiffRef3D is a versatile proposal refinement framework that consistently improves the performance of existing 3D object detection models. We demonstrate the significance of DiffRef3D through extensive experiments on the KITTI benchmark. Code will be available

An efficient multivariate feature ranking method for gene selection in high-dimensional microarray data

Classification of microarray data plays a significant role in the diagnosis and prediction of cancer. However, its high-dimensionality (>tens of thousands) compared to the number of observations (<tens of hundreds) may lead to poor classification accuracy. In addition, only a fraction of genes is really important for the classification of a certain cancer, and thus feature selection is very essential in this field. Due to the time and memory burden for processing the high-dimensional data, univariate feature ranking methods are widely-used in gene selection. However, most of them are not that accurate because they only consider the relevance of features to the target without considering the redundancy among features. In this study, we propose a novel multivariate feature ranking method to improve the quality of gene selection and ultimately to improve the accuracy of microarray data classification. The method can be efficiently applied to high-dimensional microarray data. We embedded the formal definition of relevance into a Markov blanket (MB) to create a new feature ranking method. Using a few microarray datasets, we demonstrated the practicability of MB-based feature ranking having high accuracy and good efficiency. The method outperformed commonly-used univariate ranking methods and also yielded the better result even compared with the other multivariate feature ranking method due to the advantage of data efficiency

PG-RCNN: Semantic Surface Point Generation for 3D Object Detection

One of the main challenges in LiDAR-based 3D object detection is that the sensors often fail to capture the complete spatial information about the objects due to long distance and occlusion. Two-stage detectors with point cloud completion approaches tackle this problem by adding more points to the regions of interest (RoIs) with a pre-trained network. However, these methods generate dense point clouds of objects for all region proposals, assuming that objects always exist in the RoIs. This leads to the indiscriminate point generation for incorrect proposals as well. Motivated by this, we propose Point Generation R-CNN (PG-RCNN), a novel end-to-end detector that generates semantic surface points of foreground objects for accurate detection. Our method uses a jointly trained RoI point generation module to process the contextual information of RoIs and estimate the complete shape and displacement of foreground objects. For every generated point, PG-RCNN assigns a semantic feature that indicates the estimated foreground probability. Extensive experiments show that the point clouds generated by our method provide geometrically and semantically rich information for refining false positive and misaligned proposals. PG-RCNN achieves competitive performance on the KITTI benchmark, with significantly fewer parameters than state-of-the-art models. The code is available at https://github.com/quotation2520/PG-RCNN.Comment: Accepted by ICCV 202

The influence of game genre on Internet gaming disorder

Although Internet gaming disorder (IGD) has been investigated in detail, minimal research has been conducted regarding the influence of different game genres on IGD. The aim of this study is to compare the characteristics of members of game genre-specific groups with IGD and to identify factors associated with IGD status in each group in a large sample of adults. Methods Internet games were categorized into four genres: real-time strategy games, massive multiplayer online role-playing games (MMORPG), sport games, and first-person shooter (FPS) games. Participants (n = 2,923) who usually played one of these games completed an anonymous online survey that collected sociodemographic, game usage pattern, and psychopathological assessment data. Results MMORPG and FPS game players more frequently met the criteria for IGD than participants in the other two groups. Differences between the IGD-suspected gamers within the genre-specific groups were observed for a few items, such as average game-playing time and the subscales of the behavioral activation system; however, the factors that contributed to the development of IGD within each game genre-specific group were found to be considerably different. Discussion and conclusions The findings of this study suggest that IGD is a stable psychiatric diagnosis encompassing users of a broad range of game genres. In addition, the development of strategies for the prevention of and early intervention on individuals at high risk for developing IGD may require consideration of the distinct characteristics identified as effective predictors of IGD in users of each game genre

Directed evolution of CRISPR-Cas9 to increase its specificity

The use of CRISPR-Cas9 as a therapeutic reagent is hampered by its off-target effects. Although rationally designed S. pyogenes Cas9 (SpCas9) variants that display higher specificities than the wild-type SpCas9 protein are available, these attenuated Cas9 variants are often poorly efficient in human cells. Here, we develop a directed evolution approach in E. coli to obtain Sniper-Cas9, which shows high specificities without killing on-target activities in human cells. Unlike other engineered Cas9 variants, Sniper-Cas9 shows WT-level on-target activities with extended or truncated sgRNAs with further reduced off-target activities and works well in a preassembled ribonucleoprotein (RNP) format to allow DNA-free genome editing.

Enhanced knee joint function due to accelerated rehabilitation exercise after anterior cruciate ligament reconstruction surgery in Korean male high school soccer players.

This study was conducted on Korean male high school soccer players who underwent anterior cruciate ligament reconstruction (ACLR) to identify the effects of an accelerated rehabilitation exercise (ARE) program on knee joint isometric strength, thigh circumference, Lysholm score, and active balance agility. We assigned eight test participants each to a physical therapy group (PTG) and an accelerated rehabilitation exercise group (AREG), and compared differences between the groups. Both the PTG and AREG showed significant increases in 30° away and 60° toward isometric strength after treatment. In addition, significant differences were observed in these strength tests between the two groups. Both groups also showed significant increases in thigh circumference, Lysholm score, and active balance agility after treatment, but no significant differences were observed between the two groups. We conclude that the ARE treatment was more effective for improving isometric strength of the knee joint than that of physical therapy, and that an active rehabilitation exercise program after ACLR had positive effects on recovery performance of patients with an ACL injury and their return to the playing field

Ultrasensitive biosensing platform for Mycobacterium tuberculosis detection based on functionalized graphene devices

Tuberculosis (TB) has high morbidity as a chronic infectious disease transmitted mainly through the respiratory tract. However, the conventional diagnosis methods for TB are time-consuming and require specialists, making the diagnosis of TB with point-of-care (POC) detection difficult. Here, we developed a graphene-based field-effect transistor (GFET) biosensor for detecting the MPT64 protein of Mycobacterium tuberculosis with high sensitivity as a POC detection platform for TB. For effective conjugation of antibodies, the graphene channels of the GFET were functionalized by immobilizing 1,5-diaminonaphthalene (1,5-DAN) and glutaraldehyde linker molecules onto the graphene surface. The successful immobilization of linker molecules with spatial uniformity on the graphene surface and subsequent antibody conjugation were confirmed by Raman spectroscopy and X-ray photoelectron spectroscopy. The GFET functionalized with MPT64 antibodies showed MPT64 detection with a detection limit of 1 fg/mL in real-time, indicating that the GFET biosensor is highly sensitive. Compared to rapid detection tests (RDT) and enzyme-linked immunosorbent assays, the GFET biosensor platform developed in this study showed much higher sensitivity but much smaller dynamic range. Due to its high sensitivity, the GFET biosensor platform can bridge the gap between time-consuming molecular diagnostics and low-sensitivity RDT, potentially aiding in early detection or management of relapses in infectious diseases

Efficacy and Safety of a Dexamethasone Implant in Patients with Diabetic Macular Edema at Tertiary Centers in Korea

Purpose. To evaluate the real-world efficacy and safety of the dexamethasone implant (DEX implant) in patients with diabetic macular edema (DME). Methods. Retrospective, multicenter, and noncomparative study of DME patients who were treated with at least one DEX implant. A total of 186 eyes from 165 patients were included. Best-corrected visual acuity (BCVA), central retinal thickness (CRT), complications, and number of retreatments were collected. Data at baseline and monthly for 6 months were analyzed. Results. The average baseline BCVA and CRT were 0.60 LogMAR and 491.6 μm, respectively. The mean BCVA improved until 3 months and then decreased up to 6 months of follow-up (0.53, 0.49, and 0.55 LogMAR at 1, 3, and 6 months; p=0.001, <0.001, and 0.044, resp.). The change of mean CRT was similar to BCVA (345.0, 357.7, and 412.5 μm at 1, 3, and 6 months, p<0.001, <0.001, and <0.001, resp.). 91 eyes (48.9%) received additional treatment with anti-VEGF or DEX implant. The average treatment-free interval was 4.4 months. In group analyses, the DEX implant was more effective in pseudophakic eyes, DME with subretinal fluid (SRF), or diffuse type. Conclusions. Intravitreal dexamethasone implants are an effective treatment for patients with DME, most notably in pseudophakic eyes, DME with SRF, or diffuse type. A half of these patients require additional treatment within 6 months