Coexisting Innominate Vein Compression Syndrome and May-Thurner Syndrome

AbstractInnominate vein compression syndrome and May-Thurner syndrome (also called iliac vein compression syndrome) are venous compression syndromes caused by normal anatomic structures. Here, we present a case in which these two conditions were found in the same patient using multidetector row computed tomography. This case is significant for two reasons: (1) it is, to the best of our knowledge, the first case study in the literature to report coexisting innominate vein compression syndrome and May-Thurner syndrome; and (2) it shows that multidetector row computed tomography has powerful diagnostic ability for venous diseases

Spin-orbit torques acting upon a perpendicularly-magnetized Py layer

We show that Py, a commonly-used soft ferromagnetic material with weak anisotropy, can become perpendicularly-magnetized while depositing on Ta buffer layer with Hf or Zr insertion layers (ILs) and MgO capping layer. By using two different approaches, namely harmonic voltage measurement and hysteresis loop shift measurement, the dampinglike spin-orbit torque (DL-SOT) efficiencies from Ta/IL/Py/IL/MgO magnetic heterostructures with perpendicular magnetic anisotropy are characterized. We find that though Ta has a significant spin Hall effect, the DL-SOT efficiencies are small in systems with the Ta/Py interface compared to that obtained from the control sample with the traditional Ta/CoFeB interface. Our results indicate that the spin transparency for the Ta/Py interface is much less than that for the Ta/CoFeB interface, which might be related to the variation of spin mixing conductance for different interfaces

PLM-ICD: Automatic ICD Coding with Pretrained Language Models

Automatically classifying electronic health records (EHRs) into diagnostic codes has been challenging to the NLP community. State-of-the-art methods treated this problem as a multilabel classification problem and proposed various architectures to model this problem. However, these systems did not leverage the superb performance of pretrained language models, which achieved superb performance on natural language understanding tasks. Prior work has shown that pretrained language models underperformed on this task with the regular finetuning scheme. Therefore, this paper aims at analyzing the causes of the underperformance and developing a framework for automatic ICD coding with pretrained language models. We spotted three main issues through the experiments: 1) large label space, 2) long input sequences, and 3) domain mismatch between pretraining and fine-tuning. We propose PLMICD, a framework that tackles the challenges with various strategies. The experimental results show that our proposed framework can overcome the challenges and achieves state-of-the-art performance in terms of multiple metrics on the benchmark MIMIC data. The source code is available at https://github.com/MiuLab/PLM-ICDComment: Accepted to the ClinicalNLP 2022 worksho

Biological actions and molecular effects of resveratrol, pterostilbene, and 3′-hydroxypterostilbene

AbstractStilbenes are a class of polyphenolic compounds, naturally found in a wide variety of dietary sources such as grapes, berries, peanuts, red wine, and some medicinal plants. There are several well-known stilbenes including trans-resveratrol, pterostilbene, and 3′-hydroxypterostilbene. The core chemical structure of stilbene compounds is 1,2-diphenylethylene. Recently, stilbenes have attracted extensive attention and interest due to their wide range of health-beneficial effects such as anti-inflammation, -carcinogenic, -diabetes, and -dyslipidemia activities. Moreover, accumulating in vitro and in vivo studies have reported that stilbene compounds act as inducers of multiple cell-death pathways such as apoptosis, cell cycle arrest, and autophagy for chemopreventive and chemotherapeutic agents in several types of cancer cells. The aim of this review is to highlight recent molecular findings and biological actions of trans-resveratrol, pterostilbene, and 3′-hydroxypterostilbene

Analysis and Development of Emergency Management Information System for Railway Systems in Taiwan

Railway is one of the most efficient, convenient, and comfortable ways with maximum mobility to meet people. Railway accidents or disasters often cause delays and service interruptions, resulting in operational and other loss. Despite many railway systems in Taiwan having a variety of monitoring systems for natural disasters, they still need an efficient platform for the emergency management of disasters and accidents since time and efficiency are the keys to emergency management. This study aims to fill in this gap by developing an emergency management information system for Railway Systems in Taiwan, i.e. “Railway Emergency Management Information System”, to support railway emergency management center and its sub-divisions in resource management, communication, messaging, and information sharing among different groups. The system includes many features that will improve communications between emergency management center and the mobile emergency management center to facilitate the progress of the disaster control units and dispatching at the disaster site. The study’s information system has been designated by local railway administration as the core system and starts trial since February 2012. Information requirement analysis, framework and design of the aforementioned information system will be discussed in this paper. It is hoped that the present study's information system research will help improve the emergency response of railway administration and provide safer rail transport service for the passengers