Associations between Chest CT Abnormalities and Clinical Features in Patients with the Severe Fever with Thrombocytopenia Syndrome

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease caused by the SFTS virus. It involves multiple organ systems, including the lungs. However, the significance of the lung involvement in SFTS remains unclear. In the present study, we aimed to investigate the relationship between the clinical findings and abnormalities noted in the chest computed tomography (CT) of patients with SFTS. The medical records of 22 confirmed SFTS patients hospitalized in five hospitals in Nagasaki, Japan, between April 2013 and September 2019, were reviewed retrospectively. Interstitial septal thickening and ground-glass opacity (GGO) were the most common findings in 15 (68.1%) and 12 (54.5%) patients, respectively, and lung GGOs were associated with fatalities. The SFTS patients with a GGO pattern were elderly, had a disturbance of the conscious and tachycardia, and had higher c-reactive protein levels at admission (p = 0.009, 0.006, 0.002, and 0.038, respectively). These results suggested that the GGO pattern in patients with SFTS displayed disseminated inflammation in multiple organs and that cardiac stress was linked to higher mortality. Chest CT evaluations may be useful for hospitalized patients with SFTS to predict their severity and as early triage for the need of intensive care

Eth2Vec: Learning Contract-Wide Code Representations for Vulnerability Detection on Ethereum Smart Contracts

Ethereum smart contracts are programs that run on the Ethereum blockchain, and many smart contract vulnerabilities have been discovered in the past decade. Many security analysis tools have been created to detect such vulnerabilities, but their performance decreases drastically when codes to be analyzed are being rewritten. In this paper, we propose Eth2Vec, a machine-learning-based static analysis tool for vulnerability detection, with robustness against code rewrites in smart contracts. Existing machine-learning-based static analysis tools for vulnerability detection need features, which analysts create manually, as inputs. In contrast, Eth2Vec automatically learns features of vulnerable Ethereum Virtual Machine (EVM) bytecodes with tacit knowledge through a neural network for language processing. Therefore, Eth2Vec can detect vulnerabilities in smart contracts by comparing the code similarity between target EVM bytecodes and the EVM bytecodes it already learned. We conducted experiments with existing open databases, such as Etherscan, and our results show that Eth2Vec outperforms the existing work in terms of well-known metrics, i.e., precision, recall, and F1-score. Moreover, Eth2Vec can detect vulnerabilities even in rewritten codes

Eth2Vec: Learning contract-wide code representations for vulnerability detection on Ethereum smart contracts

Ethereum smart contracts are computer programs that are deployed and executed on the Ethereum blockchain to enforce agreements among untrusting parties. Being the most prominent platform that supports smart contracts, Ethereum has been targeted by many attacks and plagued by security incidents. Consequently, many smart contract vulnerabilities have been discovered in the past decade. To detect and prevent such vulnerabilities, different security analysis tools, including static and dynamic analysis tools, have been created, but their performance decreases drastically when codes to be analyzed are constantly being rewritten. In this paper, we propose Eth2Vec, a machine-learning-based static analysis tool that detects smart contract vulnerabilities. Eth2Vec maintains its robustness against code rewrites; i.e., it can detect vulnerabilities even in rewritten codes. Other machine-learning-based static analysis tools require features, which analysts create manually, as inputs. In contrast, Eth2Vec uses a neural network for language processing to automatically learn the features of vulnerable contracts. In doing so, Eth2Vec can detect vulnerabilities in smart contracts by comparing the similarities between the codes of a target contract and those of the learned contracts. We performed experiments with existing open databases, such as Etherscan, and Eth2Vec was able to outperform a recent model based on support vector machine in terms of well-known metrics, i.e., precision, recall, and F1-score

Survey of Global Personnel Development and Long-term Impact of Study Abroad -Summary Report: March, 2016-

International Comparative Research into Global Personnel Development and Long-term Impact of Study Abroad: JSPS Grants-in-Aid for Scientific Research, Scientific Research (A), Research Project Number 25245078JSPS Grants-in-Aid for Scientific Research, Scientific Research (A), Research Project Number 25245078Principal Investigator: Professor Masahiro Yokota, School of Global Japanese Studies, Meiji University1. Changes in perceptions and values through study abroad2. Improvement in abilities through study abroad3. Current income and position after study abroad, frequency of foreign language usage at work4. Career impact of study abroad5. Proactivity towards classes and extracurricular activities6. Satisfaction with life and work7. Changes in behavior as a result of study abroad20 p

Associations between Chest CT Abnormalities and Clinical Features in Patients with the Severe Fever with Thrombocytopenia Syndrome

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease caused by the SFTS virus. It involves multiple organ systems, including the lungs. However, the significance of the lung involvement in SFTS remains unclear. In the present study, we aimed to investigate the relationship between the clinical findings and abnormalities noted in the chest computed tomography (CT) of patients with SFTS. The medical records of 22 confirmed SFTS patients hospitalized in five hospitals in Nagasaki, Japan, between April 2013 and September 2019, were reviewed retrospectively. Interstitial septal thickening and ground-glass opacity (GGO) were the most common findings in 15 (68.1%) and 12 (54.5%) patients, respectively, and lung GGOs were associated with fatalities. The SFTS patients with a GGO pattern were elderly, had a disturbance of the conscious and tachycardia, and had higher c-reactive protein levels at admission (p = 0.009, 0.006, 0.002, and 0.038, respectively). These results suggested that the GGO pattern in patients with SFTS displayed disseminated inflammation in multiple organs and that cardiac stress was linked to higher mortality. Chest CT evaluations may be useful for hospitalized patients with SFTS to predict their severity and as early triage for the need of intensive care