Spectroscopic Evidence for Multigap Superconductivity of Y at Megabar Pressures

The recent discovery of room-temperature superconductivity (RTSC) at pressures of several megabars has led to intensive efforts to probe the origin of superconducting (SC) electron pairs. Although the signatures of the SC phase transition have been well established, few reports of the SC properties of RTSCs have been published because of the diamond anvil cell (DAC) environments. Here, we report the first direct evidence of two SC gaps in Y metal via point-contact spectroscopy (PCS) in DAC environments, where a sharp peak at the zero-bias voltage in the differential conductance is overlaid with a broad peak owing to Andreev reflection. Analysis based on the Blonder-Tinkham-Klapwijk (BTK) model reveals the existence of two SC gaps: the larger gap is 3.63 meV and the smaller gap is 0.46 meV. The temperature dependence of the two SC gaps is well explained by the BCS theory, indicating that two-band superconductivity is realized in Y metal. The successful application of PCS to Y in DAC environments is expected to guide future research on the SC gap in megabar high-Tc superconductors.Comment: 17 pages, 4 figure

Development and Application of a Metaverse-Based Social Skills Training Program for Children With Autism Spectrum Disorder to Improve Social Interaction: Protocol for a Randomized Controlled Trial

BackgroundAutism spectrum disorder (ASD) is characterized by abnormalities in social communication and limited and repetitive behavioral patterns. Children with ASD who lack social communication skills will eventually not interact with others and will lack peer relationships when compared to ordinary people. Thus, it is necessary to develop a program to improve social communication abilities using digital technology in people with ASD. ObjectiveWe intend to develop and apply a metaverse-based child social skills training program aimed at improving the social interaction abilities of children with ASD aged 7-12 years. We plan to compare and analyze the biometric information collected through wearable devices when applying the metaverse-based social skills training program to evaluate emotional changes in children with ASD in stressful situations. MethodsThis parallel randomized controlled study will be conducted on children aged 7-12 years diagnosed with ASD. A metaverse-based social skills training program using digital technology will be administered to children who voluntarily wish to participate in the research with consent from their legal guardians. The treatment group will participate in the metaverse-based social skills training program developed by this research team once a week for 60 minutes per session for 4 weeks. The control group will not intervene during the experiment. The treatment group will use wearable devices during the experiment to collect real-time biometric information. ResultsThe study is expected to recruit and enroll participants in March 2022. After registering the participants, the study will be conducted from March 2022 to May 2022. This research will be jointly conducted by Yonsei University and Dobrain Co Ltd. Children participating in the program will use the internet-based platform. ConclusionsThe metaverse-based Program for the Education and Enrichment of Relational Skills (PEERS) will be effective in improving the social skills of children with ASD, similar to the offline PEERS program. The metaverse-based PEERS program offers excellent accessibility and is inexpensive because it can be administered at home; thus, it is expected to be effective in many children with ASD. If a method can be applied to detect children's emotional changes early using biometric information collected through wearable devices, then emotional changes such as anxiety and anger can be alleviated in advance, thus reducing issues in children with ASD. Trial RegistrationClinical Research Information Service KCT0006859; https://tinyurl.com/4r3k7cmj International Registered Report Identifier (IRRID)PRR1-10.2196/3596

Artificial intelligence-based iliofemoral deep venous thrombosis detection using a clinical approach

Abstract Early diagnosis of deep venous thrombosis is essential for reducing complications, such as recurrent pulmonary embolism and venous thromboembolism. There are numerous studies on enhancing efficiency of computer-aided diagnosis, but clinical diagnostic approaches have never been considered. In this study, we evaluated the performance of an artificial intelligence (AI) algorithm in the detection of iliofemoral deep venous thrombosis on computed tomography angiography of the lower extremities to investigate the effectiveness of using the clinical approach during the feature extraction process of the AI algorithm. To investigate the effectiveness of the proposed method, we created synthesized images to consider practical diagnostic procedures and applied them to the convolutional neural network-based RetinaNet model. We compared and analyzed the performances based on the model’s backbone and data. The performance of the model was as follows: ResNet50: sensitivity = 0.843 (± 0.037), false positives per image = 0.608 (± 0.139); ResNet152 backbone: sensitivity = 0.839 (± 0.031), false positives per image = 0.503 (± 0.079). The results demonstrated the effectiveness of the suggested method in using computed tomography angiography of the lower extremities, and improving the reporting efficiency of the critical iliofemoral deep venous thrombosis cases

Spectroscopic evidence for the superconductivity of elemental metal Y under pressure

Superconductivity: Emergence in elemental yttrium under pressure The origin of superconductivity in ytterbium has been uncovered by researchers from South Korea and China. Superconductors offer no resistance to the flow of electricity. While superconductivity has been observed in many materials, usually at low temperatures or high pressures, exactly why it arises is not always clear. Tuson Park from Sungkyunkwan University, Suwon, South Korea and colleagues used high-pressure measurement techniques to identify the mechanism driving superconductivity in elemental yttrium. They subjected a yttrium sample to increasing pressures using a diamond anvil cell, and observed superconductivity emerge at 48.6 gigapascals. So-called point-contact spectroscopy measurements performed at this pressure indicated that superconductivity in yttrium is driven by the coupling of pairs of electrons, which can then occupy the same quantum energy state. This mechanism has been seen in other superconductors

Generation and Analysis of End Sequence Database for T-DNA Tagging Lines in Rice

We analyzed 6,749 lines tagged by the gene trap vector pGA2707. This resulted in the isolation of 3,793 genomic sequences flanking the T-DNA. Among the insertions, 1,846 T-DNAs were integrated into genic regions, and 1,864 were located in intergenic regions. Frequencies were also higher at the beginning and end of the coding regions and upstream near the ATG start codon. The overall GC content at the insertion sites was close to that measured from the entire rice (Oryza sativa) genome. Functional classification of these 1,846 tagged genes showed a distribution similar to that observed for all the genes in the rice chromosomes. This indicates that T-DNA insertion is not biased toward a particular class of genes. There were 764, 327, and 346 T-DNA insertions in chromosomes 1, 4 and 10, respectively. Insertions were not evenly distributed; frequencies were higher at the ends of the chromosomes and lower near the centromere. At certain sites, the frequency was higher than in the surrounding regions. This sequence database will be valuable in identifying knockout mutants for elucidating gene function in rice. This resource is available to the scientific community at http://www.postech.ac.kr/life/pfg/risd