Electrical Performance and Reliability Improvement of Amorphous-Indium-Gallium-Zinc-Oxide Thin-Film Transistors with HfO2 Gate Dielectrics by CF4 Plasma Treatment

In this work, amorphous indium-gallium-zinc oxide thin-film transistors (a-IGZO TFTs) with a HfO2 gate insulator and CF4 plasma treatment was demonstrated for the first time. Through the plasma treatment, both the electrical performance and reliability of the a-IGZO TFT with HfO2 gate dielectric were improved. The carrier mobility significantly increased by 80.8%, from 30.2 cm2/V∙s (without treatment) to 54.6 cm2/V∙s (with CF4 plasma treatment), which is due to the incorporated fluorine not only providing an extra electron to the IGZO, but also passivating the interface trap density. In addition, the reliability of the a-IGZO TFT with HfO2 gate dielectric has also been improved by the CF4 plasma treatment. By applying the CF4 plasma treatment to the a-IGZO TFT, the hysteresis effect of the device has been improved and the device’s immunity against moisture from the ambient atmosphere has been enhanced. It is believed that the CF4 plasma treatment not only significantly improves the electrical performance of a-IGZO TFT with HfO2 gate dielectric, but also enhances the device’s reliability

Usability Test for a Digital Archival System in Taiwan: A Case of the Humanities and Social Sciences (HSS) Information System 數位典藏資訊網易用性評估：以人文社會科學相關領域中綱計畫成果資訊網為例

<p>With the advent of the Internet there is now growing expectation of end users to be able have more immediate and functional access to digital archive system. Digitization system affords an opportunity to make collections much more broadly available and save users’ time, but the content and the design of system interface are a vital influence on system satisfaction of end users. The paper focuses on a case study of usability testing done on the Information System for Humanity and Social Science Projects (HSS), a digital archive of research reports, conference papers, books, photographs, audio-visual materials from HSS of The Ministry of Education. Task-based usability testing and questionnaires were taken to capture users’ access behavior and analyze the user’s opinions. According to the result, the color of function zones, the browse of material categories, the download of search results and the integrated search function need to improve. This paper details a survey on the procedure of usability testing and obtains users’ perspectives of the system. As the result, conclusions and suggestions will be proposed for improving the system interface design. pp. 49-68</p><br><p>隨著資訊網路技術之進步，終端使用者對於數位典藏系統之檢索功能與服務期許與日俱增，數位典藏系統有助於資訊廣泛傳播並節省使用者時間，但系統內容與介面設計之良莠，深深影響使用者對於系統的滿意程度。本研究以「人文社會科學相關領域中綱計畫成果資訊網」為例，進行系統易用性評估，該系統收錄教育部推動人文社會科學領域中綱計畫所有產出之研究報告、會議論文、圖書、照片與活動影音記錄等。研究進行任務取向之系統評估，藉由使用者操作系統之實測觀察過程，分析使用者檢索歷程與操作問題，再輔以問卷方式，探知使用者對於操作系統之滿意度。分析結果發現首頁版面功能顏色區隔不清、資料類型瀏覽分類標示不明顯、檢索結果下載標示不明顯、整合查詢功能說明不足等系統介面設計問題。本文主要描述進行系統易用性實測的經驗，並探究使用者對於系統的看法，最終歸納結果與建議，提供系統介面設計改善之參考。頁46-63</p><p> </p><p> </p

數位典藏資訊網易用性評估：以人文社會科學相關領域中綱計畫成果資訊網為例

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Requirement of cyclin-dependent kinase function for hepatitis B virus cccDNA synthesis as measured by digital PCR

Introduction and objectives: HBV covalently closed circular (ccc) DNA is the key player in viral persistence and an important predictive biomarker for hepatitis relapse. Precise quantification of intracellular cccDNA is challenging because cccDNA is present in very low levels in hepatocytes, where it also co-exists with a large excess amount of relaxed circular (rc) DNA. We aimed to develop a highly sensitive cccDNA detection method for cccDNA quantification by digital PCR (dPCR). Patients or materials and methods: A standard plasmid containing the whole HBV genome in the closed circular conformation was employed to characterize the performance of dPCR. rcDNA in the growth medium of HBV-producing HepAD38 cells was used as a matrix for cccDNA detection. Intrahepatic cccDNA measurement by dPCR and qPCR was performed to determine the correlation of the analysis results for the two methods. Results: The limit of detection (LOD) of the cccDNA dPCR was 1.05 copy/μl, and the linear range of detection was 1.02 × 104 copies/μl, achieving a dynamic detection range of 104-fold. cccDNA measurement using excess rcDNA as the matrix did not reveal false-positive detection, indicating that dPCR was highly specific. In the HepAD38 cells, the cccDNA levels measured by dPCR were highly correlated with those measured by qPCR but had a higher sensitivity. The CDK inhibitor AZD-5438 was found to block intracellular cccDNA synthesis. Conclusions: Dpcr greatly improved the sensitivity and specificity of cccDNA detection. Host CDK activities are likely required for cccDNA synthesis. dPCR can potentially be applied for drug screening for effective cccDNA inhibitors

Estimating global burden of COVID-19 with disability-adjusted life years and value of statistical life metrics

Background: Global burden of COVID-19 has not been well studied, disability-adjusted life years (DALYs) and value of statistical life (VSL) metrics were therefore proposed to quantify its impacts on health and economic loss globally. Methods: The life expectancy, cases, and death numbers of COVID-19 until 30th April 2021 were retrieved from open data to derive the epidemiological profiles and DALYs (including years of life lost (YLL) and years loss due to disability (YLD)) by four periods. The VSL estimates were estimated by using hedonic wage method (HWM) and contingent valuation method (CVM). The estimate of willingness to pay using CVM was based on the meta-regression mixed model. Machine learning method was used for classification. Results: Globally, DALYs (in thousands) due to COVID-19 was tallied as 31,930 from Period I to IV. YLL dominated over YLD. The estimates of VSL were US$591 billion and US$5135 billion based on HWM and CVM, respectively. The estimate of VSL increased from US$579 billion in Period I to US$2160 billion in Period IV using CVM. The higher the human development index (HDI), the higher the value of DALYs and VSL. However, there exits the disparity even at the same level of HDI. Machine learning analysis categorized eight patterns of global burden of COVID-19 with a large variation from US$0.001 billion to US$691.4 billion. Conclusion: Global burden of COVID-19 pandemic resulted in substantial health and value of life loss particularly in developed economies. Classifications of such health and economic loss is informative to early preparation of adequate resource to reduce impacts

Applying a Smartwatch to Predict Work-related Fatigue for Emergency Healthcare Professionals: Machine Learning Method

Introduction: Healthcare professionals frequently experience work-related fatigue, which may jeopardize their health and put patient safety at risk. In this study, we applied a machine learning (ML) approach based on data collected from a smartwatch to construct prediction models of work-related fatigue for emergency clinicians.Methods: We conducted this prospective study at the emergency department (ED) of a tertiary teaching hospital from March 10–June 20, 2021, where we recruited physicians, nurses, and nurse practitioners. All participants wore a commercially available smartwatch capable of measuring various physiological data during the experiment. Participants completed the Multidimensional Fatigue Inventory (MFI) web form before and after each of their work shifts. Wecalculated and labeled the before-and-after-shift score differences between each pair of scores. Using several tree-based algorithms, we constructed the prediction models based on features collected from the smartwatch. Records were split into training/validation and testing sets at a 70∶30 ratio, and we evaluated the performances using the area under the curve (AUC) measure of receiver operating characteristic on the test set.Results: In total, 110 participants were included in this study, contributing to a set of 1,542 effective records. Of these records, 85 (5.5%) were labeled as having work-related fatigue when setting the MFI difference between two standard deviations as the threshold. The mean age of the participants was 29.6. Most of the records were collected from nurses (87.7%) and females (77.5%). We selected a union of 31 features to construct the models. For total participants, CatBoost classifier achieved the best performances of AUC (0.838, 95% confidence interval [CI] 0.742–0.918) to identify work-related fatigue. By focusing on a subgroup of nurses &lt;35 years in age, XGBoost classifier obtained excellent performance of AUC (0.928, 95% CI 0.839–0.991) on the test set.Conclusion: By using features derived from a smartwatch, we successfully built ML models capable of classifying the risk of work-related fatigue in the ED. By collecting more data to optimize the models, it should be possible to use smartwatch-based ML models in the future to predict work-related fatigue and adopt preventive measures for emergency clinicians

Applying a Smartwatch to Predict Work-related Fatigue for Emergency Healthcare Professionals: Machine Learning Method

Introduction: Healthcare professionals frequently experience work-related fatigue, which may jeopardize their health and put patient safety at risk. In this study, we applied a machine learning (ML) approach based on data collected from a smartwatch to construct prediction models of work-related fatigue for emergency clinicians. Methods: We conducted this prospective study at the emergency department (ED) of a tertiary teaching hospital from March 10–June 20, 2021, where we recruited physicians, nurses, and nurse practitioners. All participants wore a commercially available smartwatch capable of measuring various physiological data during the experiment. Participants completed the Multidimensional Fatigue Inventory (MFI) web form before and after each of their work shifts. We calculated and labeled the before-and-after-shift score differences between each pair of scores. Using several tree-based algorithms, we constructed the prediction models based on features collected from the smartwatch. Records were split into training/validation and testing sets at a 70:30 ratio, and we evaluated the performances using the area under the curve (AUC) measure of receiver operating characteristic on the test set. Results: In total, 110 participants were included in this study, contributing to a set of 1,542 effective records. Of these records, 85 (5.5%) were labeled as having work-related fatigue when setting the MFI difference between two standard deviations as the threshold. The mean age of the participants was 29.6. Most of the records were collected from nurses (87.7%) and females (77.5%). We selected a union of 31 features to construct the models. For total participants, CatBoost classifier achieved the best performances of AUC (0.838, 95% confidence interval [CI] 0.742–0.918) to identify work-related fatigue. By focusing on a subgroup of nurses <35 years in age, XGBoost classifier obtained excellent performance of AUC (0.928, 95% CI 0.839–0.991) on the test set. Conclusion: By using features derived from a smartwatch, we successfully built ML models capable of classifying the risk of work-related fatigue in the ED. By collecting more data to optimize the models, it should be possible to use smartwatch-based ML models in the future to predict work-related fatigue and adopt preventive measures for emergency clinicians

Relationship between parenting stress and informant discrepancies on symptoms of ADHD/ODD and internalizing behaviors in preschool children

<div><p>Parent and teacher ratings of child behaviors are often discrepant, and these discrepancies may be correlated with parenting stress. The present study explored whether various parenting stress factors are associated with discrepancies between parent and teacher ratings of attention-deficit/hyperactivity disorder and oppositional defiant disorder (ODD) as well as internalizing symptoms in preschool children. We recruited 299 Taiwanese preschool children (aged 4–6 years) from the community or via clinical referrals. A structural equation modeling was used to analyze the relationships among three factors derived from the Parenting Stress Index-Short Form and informant discrepancies on symptoms of inattention, hyperactivity/impulsivity, ODD, and internalizing behaviors. Scores reported by parents were higher for each of the symptoms examined than those reported by teachers, and the degree of agreement between informants ranged from low to moderate. The parental distress factor of parenting stress was associated only with parent ratings, whereas other factors of parenting stress—parent-child dysfunctional interaction and parents’ stress resulted from their child’s temperament—were correlated with both parent and teacher ratings. Only parental distress factor predicted informant discrepancies for all behavioral symptoms assessed. Our findings suggest that parental distress should be considered when parent rating scores show significant discrepancies from that of teacher rating scores.</p></div

LINE-1 Methylation Status Correlates Significantly to Post-Therapeutic Recurrence in Stage III Colon Cancer Patients Receiving FOLFOX-4 Adjuvant Chemotherapy

<div><p>Background</p><p>Methylation levels of long interspersed nucleotide elements (LINE-1) are representative of genome-wide methylation status and crucial in maintaining genomic stability and expression. Their prognostic impact on colon cancer patients receiving adjuvant chemotherapy has not been well established. We evaluated the association between LINE-1 methylation status and clinicopathologic features and postoperative oncological outcomes in stage III colon cancer patients.</p><p>Materials and Methods</p><p>129 UICC stage III colon cancer patients who had received radical resection and FOLFOX adjuvant chemotherapy were enrolled. Global methylation was estimated by analyzing tumor LINE-1 methylation status using bisulfite-polymerase chain reaction (PCR) and pyrosequencing assay. Demographics, clinicopathological data, and postoperative outcomes were recorded by trained abstractors. Outcome measurements included postoperative recurrence and disease-free survival. Univariate, multivariate, and survival analyses were conducted to identify prognostic factors of oncological outcomes.</p><p>Results</p><p>The LINE-1 methylation of all 129 patients was measured on a 0–100 scale (mean 63.3; median 63.7, standard deviation 7.1), LINE-1 hypomethylation was more common in patients aged 65 years and above (61.7%±7.6% vs. 64.6±6.4, <i>p</i>=0.019) and those with post-therapeutic recurrence (61.7±7.4 vs 64.3±6.7, <i>p</i>=0.041). Considering risk adjustment, LINE-1 hypomethylation was found to be an independent risk factor of post-therapeutic recurrence (Adjusted OR=14.1, <i>p</i>=0.012). Kaplan-Meier analysis indicated that patients in the low methylation group had shorter period of disease free survival (<i>p</i>=0.01). In a stratified analysis that included 48 patients with post-therapeutic recurrence, it was found that those who experienced shorter period of disease free survival (≦6 months) appeared to have lower LINE-1 methylation levels than patients who reported of recurrence after 6 months (56.68±15.75 vs. 63.55±7.57, <i>p</i>=0.041)</p><p>Conclusion</p><p>There was a significantly greater risk of early postoperative recurrence and a shorter period of disease-free survival in Stage III colon cancer patients exhibiting LINE-1 hypomethylation status after being treated with radical resection and FOLFOX chemotherapy.</p></div