ベンゼン、ポルフィリン、アゾベンゼンおよびトリチアスマネンπ骨格を元にした強誘電性超分子集合体に関する研究

Tohoku University芥川智行課

Follow-up of patients with COVID-19 by the Delta variant after hospital discharge in Guangzhou, Guandong, China

The B.1.617.2 (Delta) variant of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has contributed to a new increment in cases across the globe. We conducted a prospective follow-up of COVID-19 cases to explore the recurrence and potential propagation risk of the Delta variant and discuss potential explanations for the infection recurrence. A prospective, non-interventional follow-up of discharged patients who had SARS-CoV-2 infections by the Delta variant in Guangdong, China, from May 2021 to June 2021 was conducted. The subjects were asked to complete a physical health examination and undergo nucleic acid testing and antibody detection for the laboratory diagnosis of COVID-19. In total, 20.33% (25/123) of patients exhibited recurrent positive results after discharge. All patients with infection recurrence were asymptomatic and showed no abnormalities in the pulmonary computed tomography. The time from discharge to the recurrent positive testing was usually between 1-33 days, with a mean time of 9.36 days. The cycle threshold from the real-time polymerase chain reaction assay that detected the recurrence of positivity ranged from 27.48 to 39.00, with an average of 35.30. The proportion of vaccination in the non-recurrent group was higher than that in the recurrently positive group (26% vs. 4%; χ2 = 7.902; P < 0.05). Two months after discharge, the most common symptom was hair loss and 59.6% of patients had no long-term symptoms at all. It is possible for the Delta variant SARS-CoV-2 patients after discharge to show recurrent positive results of nucleic acid detection; however, there is a low risk of continuous community transmission. Both, the physical and mental quality of life of discharged patients were significantly affected. Our results suggest that it makes sense to implement mass vaccination against the Delta variant of SARS-CoV-2

Cloud-Magnetic Resonance Imaging System: In the Era of 6G and Artificial Intelligence

Magnetic Resonance Imaging (MRI) plays an important role in medical diagnosis, generating petabytes of image data annually in large hospitals. This voluminous data stream requires a significant amount of network bandwidth and extensive storage infrastructure. Additionally, local data processing demands substantial manpower and hardware investments. Data isolation across different healthcare institutions hinders cross-institutional collaboration in clinics and research. In this work, we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing, 6G bandwidth, edge computing, federated learning, and blockchain technology. This system is called Cloud-MRI, aiming at solving the problems of MRI data storage security, transmission speed, AI algorithm maintenance, hardware upgrading, and collaborative work. The workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw Data (ISMRMRD) format. Then, the data are uploaded to the cloud or edge nodes for fast image reconstruction, neural network training, and automatic analysis. Then, the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other services. The Cloud-MRI system will save the raw imaging data, reduce the risk of data loss, facilitate inter-institutional medical collaboration, and finally improve diagnostic accuracy and work efficiency.Comment: 4pages, 5figures, letter

Study on multi-period palaeotectonic stress fields simulation and fractures distribution prediction in Lannigou gold mine, Guizhou

A significant controlling factor for gold mineralisation is the tectonic stress field, and the fractures formed under its action are the migration channels and ore-holding spaces of ore-forming fluids, which often directly control the migration and accumulation of ore-forming fluids. Therefore, performing quantitative prediction research on the distribution of fractures in the Guizhou, Lannigou gold deposit in order to identify potential fluid flow pathways is of utmost importance for ore prospecting in practical. In this study, a 3D geological entity model was generated based on the GOCAD platform by analysing and processing the geological data of the studied area, as well achieved is the accurate characterisation of the study area’s geometric model. By integrating regional tectonic evolution history analysis, geological interpretation, rock mechanics experiments and acoustic emission testing, the finite element method was utilised to create a 3D geomechanical model of the research area, the paleotectonic stress field after the Indosinian and Yanshanian movements were superimposed was simulated, in associated with the rock failure criterion, the comprehensive fracture rate parameter (Iz) is introduced to predict the fracture distribution. The results show that the research area’s maximum principal stress is primarily distributed between 153.85 and 189.53 MPa, and the maximum shear stress is between 83.53 and 98.42 MPa. The spatial distribution of faults influences the stress distribution characteristics significantly, and the stress level is relatively high at the intersection of the fault, the end of the fault and the vicinity of the fault zone, and the stress value between the faults is relatively low. The tectonic stress field primarily controls the distribution and development of fractures, which is usually consistent with the areas with high values of maximum principal stress and maximum shear stress. Using the combined modeling technique of GOCAD and midas GTS to realize the conversion from 3D geological model to geomechanical model, a set of comprehensive fracture distribution prediction technique for the superposition of multi-stage tectonic stress fields of mineral deposits in complex tectonic areas has been formed, and provide a reference for the prediction of fracture distribution in similar complex structural areas.This study was supported by the program of China Scholarships Council (No. 202006670005); the National Natural Science Foundation of China (Project Nos. 51964007, 52264004, 52104080, 41962008); the Guizhou Province Science and Technology Support Program Project (Number: QIANKEHE Support [2021] General 516); Scientific and Technological Innovation Talents Team in Guizhou Province (Project No. [2019]5619); the Guizhou Province Highlevel Innovative Talents Training Project (Grant No. JZ2016-4011). Major Collaborative Innovation Project for Strategic Action of Mineral Search Breakthrough in Guizhou Province ([2022] ZD005); Natural Science Special (Special Post) Scientific Research Fund Project of Guizhou University (Project No. Guizhou University Special Post (2021) 51).Peer ReviewedPostprint (published version

Inlet and Outlet Boundary Conditions and Uncertainty Quantification in Volumetric Lattice Boltzmann Method for Image-Based Computational Hemodynamics

Inlet and outlet boundary conditions (BCs) play an important role in newly emerged image-based computational hemodynamics for blood flows in human arteries anatomically extracted from medical images. We developed physiological inlet and outlet BCs based on patients’ medical data and integrated them into the volumetric lattice Boltzmann method. The inlet BC is a pulsatile paraboloidal velocity profile, which fits the real arterial shape, constructed from the Doppler velocity waveform. The BC of each outlet is a pulsatile pressure calculated from the three-element Windkessel model, in which three physiological parameters are tuned by the corresponding Doppler velocity waveform. Both velocity and pressure BCs are introduced into the lattice Boltzmann equations through Guo’s non-equilibrium extrapolation scheme. Meanwhile, we performed uncertainty quantification for the impact of uncertainties on the computation results. An application study was conducted for six human aortorenal arterial systems. The computed pressure waveforms have good agreement with the medical measurement data. A systematic uncertainty quantification analysis demonstrates the reliability of the computed pressure with associated uncertainties in the Windkessel model. With the developed physiological BCs, the image-based computation hemodynamics is expected to provide a computation potential for the noninvasive evaluation of hemodynamic abnormalities in diseased human vessels

A Two-Year Surveillance of 2009 Pandemic Influenza A (H1N1) in Guangzhou, China: From Pandemic to Seasonal Influenza?

In this two-years surveillance of 2009 pandemic influenza A (H1N1) (pH1N1) in Guangzhou, China, we reported here that the scale and duration of pH1N1 outbreaks, severe disease and fatality rates of pH1N1 patients were significantly lower or shorter in the second epidemic year (May 2010-April 2011) than those in the first epidemic year (May 2009-April 2010) (P<0.05), but similar to those of seasonal influenza (P>0.05). Similar to seasonal influenza, pre-existing chronic pulmonary diseases was a risk factor associated with fatal cases of pH1N1 influenza. Different from seasonal influenza, which occurred in spring/summer seasons annually, pH1N1 influenza mainly occurred in autumn/winter seasons in the first epidemic year, but prolonged to winter/spring season in the second epidemic year. The information suggests a tendency that the epidemics of pH1N1 influenza may probably further shift to spring/summer seasons and become a predominant subtype of seasonal influenza in coming years in Guangzhou, China

Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications

Sensitivity analysis (SA) aims to identify the key parameters that affect model performance and it plays important roles in model parameterization, calibration, optimization, and uncertainty quantification. However, the increasing complexity of hydrological models means that a large number of parameters need to be estimated. To better understand how these complex models work, efficient SA methods should be applied before the application of hydrological modeling. This study provides a comprehensive review of global SA methods in the field of hydrological modeling. The common definitions of SA and the typical categories of SA methods are described. A wide variety of global SA methods have been introduced to provide a more efficient evaluation framework for hydrological modeling. We review, analyze, and categorize research into global SA methods and their applications, with an emphasis on the research accomplished in the hydrological modeling field. The advantages and disadvantages are also discussed and summarized. An application framework and the typical practical steps involved in SA for hydrological modeling are outlined. Further discussions cover several important and often overlooked topics, including the relationship between parameter identification, uncertainty analysis, and optimization in hydrological modeling, how to deal with correlated parameters, and time-varying SA. Finally, some conclusions and guidance recommendations on SA in hydrological modeling are provided, as well as a list of important future research directions that may facilitate more robust analyses when assessing hydrological modeling performance