Miracle-dz/UniFAR-FTUs huggingface dataset:
Label-Free and Homogeneous Electrochemical Biosensor for Flap Endonuclease 1 Based on the Target-Triggered Difference in Electrostatic Interaction between Molecular Indicators and Electrode Surface

Target-induced differences in the electrostatic interactions between methylene blue (MB) and indium tin oxide (ITO) electrode surface was firstly employed to develop a homogeneous electrochemical biosensor for flap endonuclease 1 (FEN1) detection. In the absence of FEN1, the positively charged methylene blue (MB) is free in the solution and can diffuse onto the negatively charged ITO electrode surface easily, resulting in an obvious electrochemical signal. Conversely, with the presence of FEN1, a 5′-flap is cleaved from the well-designed flapped dumbbell DNA probe (FDP). The remained DNA fragment forms a closed dumbbell DNA probe to trigger hyperbranched rolling circle amplification (HRCA) reaction, generating plentiful dsDNA sequences. A large amount of MB could be inserted into the produced dsDNA sequences to form MB-dsDNA complexes, which contain a large number of negative charges. Due to the strong electrostatic repulsion between MB-dsDNA complexes and the ITO electrode surface, a significant signal drop occurs. The signal change (ΔCurrent) shows a linear relationship with the logarithm of FEN1 concentration from 0.04 to 80.0 U/L with a low detection limit of 0.003 U/L (S/N = 3). This study provides a label-free and homogeneous electrochemical platform for evaluating FEN1 activity.
Differential effects of metalinguistic awareness components in early foreign language acquisition of English vocabulary and grammar

Abstract Metalinguistic awareness (MLA) is a predictor of adult foreign language (FL) learning in instructed settings. Following Bialystok and Ryan (1985) two-component model of MLA, we distinguish ML analysis as the ability to compare and select language items from ML control as the ability to detect and manipulate rule-based linguistic patterns. So far, there is little evidence how components of MLA interact, and how they affect learning outcomes in early FL learning. In this paper, we investigate the effects of MLA on FL learning of English in primary school, where learning is mostly oral and proficiency in the target language is still rudimentary. We tested 200 students in German public primary schools on English vocabulary and grammar. Metalinguistic (ML) analysis was assessed in an interview, while ML control was operationalized in phoneme manipulation and letter fluency tasks. In addition, we assessed personal, cognitive, and social covariates. Results from linear mixed effects regression analyses indicate that (a) ML control predicts ML analysis skills, (b) ML analysis fosters FL vocabulary outcomes, and (c) ML control benefits grammar skills. We discuss theoretical and practical implications. Plain language summary In this paper, we studied how primary-school-age children’s knowledge about language helps them learning a foreign language. Specifically, we examined two aspects of what has been termed ‘metalinguistic awareness’. Theory assumes that metalinguistic awareness consists of (a) knowledge of how languages work and how they are similar or different (metalinguistic analysis) and (b) skills to manipulate or rearrange elements of language and play with them (metalinguistic control). For instance, speakers know how words are ordered in a sentence and how to add endings to make words rhyme. We were interested in how these two aspects of awareness are connected and how they help learners in different areas of foreign language learning. To answer these questions, we collected data from 200 4th-graders in Germany with different linguistic and social backgrounds learning English as a foreign language. We were particularly interested in the early stages of learning a foreign language, where proficiency is still limited. Results show that metalinguistic awareness indeed consists of an analysis and a control component affecting different parts of foreign language learning. Whereas the ability to compare linguistic structures (metalinguistic analysis) is important for English vocabulary learning, the manipulation of language elements (metalinguistic control) helps learning English grammar. Since different types of metalinguistic awareness are important factors during the learning process, teachers should incorporate activities that foster both in the classroom. Overall, comparing and reflecting on language(s) on a metalinguistic level can be a fruitful approach even at the beginning stages of foreign language learning.
A Socio-Ecological Approach to Addressing Digital Redlining in the United States: A Call to Action for Health Equity

Physical distancing requirements due to the coronavirus (COVID-19) pandemic has increased the need for broadband internet access. The World Health Organization defines social determinants of health as non-medical factors that impact health outcomes by affecting the conditions in which people are born, grow, work, live, and age. By this definition broadband internet access is a social determinant of health. Digital redlining—the systematic process by which specific groups are deprived of equal access to digital tools such as the internet—creates inequities in access to educational and employment opportunities, as well as healthcare and health information. Although it is known that internet service providers systematically exclude low-income communities from broadband service, little has been done to stop this discriminatory practice. In this paper, we seek to amplify the call to action against the practice of digital redlining in the United States, describe how it contributes to health disparities broadly and within the context of the COVID-19 pandemic, and use a socio-ecological framework to propose short- and long-term actions to address this inequity.
Online Semantic-Assisted Topological Map Building With LiDAR in Large-Scale Outdoor Environments: Toward Robust Place Recognition

The building of map model is crucial to the efficiency of unmanned ground vehicles’ (UGVs) navigation and robust place recognition. This article presents an online semantic-assisted topological map building approach using 3-D Light Detection And Ranging (LiDAR) point clouds in large-scale outdoor environments. To achieve global consistency while switching between global navigation satellite system (GNSS)-denied and GNSS-fixed scenes, a pose estimation module composed of semantic-assisted LiDAR-simultaneous localization and mapping (SLAM) and the GNSS positioning is designed and integrated seamlessly, which can supply both the UGV’s global 6-degrees of freedom (DoF) poses and the local semantic-metric map. On the basis of semantic-metric map output, an online topological map building framework is implemented aiming to construct the topological map with robust place recognition performance, in which a novel local node density adjustment (LoNDA) algorithm is proposed to online adjust the distance between adjacent nodes based on both the targeted node density and appearance similarity. Field experiments are conducted on both self-collected datasets and public datasets. Experimental results demonstrate that the proposed approach can perform the online LiDAR-based topological map building with high accuracy, and the proposed LoNDA algorithm can achieve superior place recognition performance than the density-based scheme.
Using the Virtual Institute of Training and Learning (VITAL) for online modules in biomechanics - an effort by the Physical Education and Sports Science Academic Group at the National lnstitute of Education, Singapore

This article describes the development of an online education biomechanics module that is based on two pedagogical theories, namely, information processing and constructivism. A hybrid (blended) pedagogical approach was planned for the course so as to facilitate a supportive and afirmative learning environment, as well as to enable participants to have hands-on, real-life experiences in the applications of biomechanical concepts to human motion. Learners may then be able to tap on such experiences when perusing the online content as well as to actively engage in the electronic discussion forums. The module is offered for the first time in the Virtual lnstitute of Training and Learning (VITAL) management system of the Ministry of Education (MOE), Singapore. In subsequent implementations, refinements would be made to the pedagogical stance of the course based on students' feedback and research findings.
Differences in Help-Seeking Behavior in Adolescents with Anxiety Disorder During the COVID-19 Pandemic

Adolescents represent one of the vulnerable groups that could suffer from mental and psychosocial health during the COVID-19 pandemic. This study aims to analyze behavioral differences in adolescents who have experienced anxiety disorders during the COVID-19 pandemic. The study used a cross-sectional design on 122 adolescents in East Java during the pandemic. Data was collected through a questionnaire via Google Forms in May 2021. Data analysis in this study used the Mann–Whitney test on SPSS Statistics 26. The current study found that male respondents (80%) show a higher tendency to have anxiety disorders compared with female respondents (74%). Meanwhile, there was no difference in the help-seeking behavior based on the gender of adolescent respondents’ who experienced anxiety disorders during the COVID-19 pandemic (p=0.291). The current study also found that there is no difference in help-seeking behavior between adolescents who experience anxiety disorders or those who do not experience anxiety disorder (p=0.449). Most adolescents with anxiety disorders tend to discuss their issues with friends and family to relieve anxiety, whereas some do not seek help because they can cope with anxiety disorders on their own, they think they do not really need help, they feel that no one cares about their problems, and there is a lack of supporting health facilities in the youth environment. Teenagers in East Java are advised to consult their mental health problems with experts to find relevant solutions.
A survey on green unmanned aerial vehicles‐based fog computing: Challenges and future perspective

In the area of unmanned aerial vehicles (UAVs) and the Internet of Things (IoT), enormous technological advancement has transformed the way we choose to live. UAVs link everything in the modern world, and it is an attractive area for academia to focus on. UAVs are proven to be a boon for the whole world as it provides flying services in critical and medical emergencies. UAVs are futuristic technology that enriches human needs by providing numerous benefits or services to proceed toward the smart world. In UAV‐assisted traditional networks, multiple sensors and flying nodes consume a lot of energy resources, and Fifth Generation (5G)/Beyond 5G enabled devices to excrete high radiations, which may cause damage to our environment as well as our society. So, it is very important to maintain a balance between the environment and modern technologies for a better lifestyle. To maintain this, many steps need to be put further to reduce the energy consumption of nodes, consume fewer data resources, and encourage effective energy‐use techniques. Green UAV‐based fog computing is a promising solution to balance environmental issues and provide energy‐efficient data computation and aerial to ground network communication for building an intelligent world with improved Quality of Services (QoS). This article provides a comprehensive survey on green technologies and applications for UAV‐Fog. We also highlight research issues, current challenges, lessons learned, and the future direction of the UAV‐Fog network.
The Effectiveness of Stress Ball Therapy to Reduce Anxiety Level in COVID-19 Patients in Pati, Central Java

Background: COVID-19 has become a pandemic in all countries, including Indonesia. COVID-19 cases are increasing concern for the public because of the possibility of exposure and the anxiety experienced by confirmed COVID-19 patients undergoing self-isolation. This study aimed to analyze the effectiveness of stress ball therapy on reducing anxiety levels among COVID-19 patients in a self-isolation room. Subjects and Method: This was a randomized controlled trial (RCT) conducted at Self-Isolation Room, RAA Soewondo General Hospital, Pati, Central Java, in June 2021. A total of 56 COVID-19 patients was selected and divided into 2 groups: (1) Experimental group consisting of 28 patients receiving stress ball therapy; (2) Control group consisting of 28 patients not receiving stress ball therapy. The dependent variable was anxiety level. The independent variable was stress ball therapy. The anxiety level was measured by Hamilton Anxiety Rating Scale (HARS). The Mann-Whitney test analyzed the data. Results: Anxiety level after stress ball therapy intervention was lower in the experimental group (Mean= 14.17; SD= 4.36) than the control group (Mean= 19.93; SD=0.62), and it was statistically significant (p<0.001). Conclusion: Stress ball therapy effectively lowers anxiety levels in COVID-19 patients.
Real-time Dynamic Reconfiguration for IEC 61499

Reconfiguration is an important feature for industrial automation systems to provide flexibility, adaptability, and resilience. Dynamic reconfiguration of real-time systems requires both functional and temporal correctness. A lengthy disruption of real-time behaviors caused by a reconfiguration can cause the system to fail. We describe the scheduling problem for a component-based real-time industrial control system on the basis of the IEC 61499 and extend this problem to handle disruptions introduced by a dynamic reconfiguration. We show that the disruption can be quantified by applying the Priority Ceiling Protocol (PCP) and calculating the blocking time. Further, we show that the order of operations within a reconfiguration sequence can be optimized using the blocking time as objective function. An evaluation on two example systems shows that our model allows the application of common schedulability tests for Rate Monotonic scheduling. In our examples, the optimization reduces the blocking time of a task during reconfiguration compared to a heuristic topological ordering by up to 85%. This makes previously infeasible reconfigurations feasible. The results imply that timing analysis of dynamic reconfiguration for component-based real-time systems is attainable, and further research is necessary to extend it for distributed systems.
Relationship between Dietary Patterns and Subjectively Measured Physical Activity in Japanese Individuals 85 Years and Older: A Cross-Sectional Study

High-quality diets and regular physical activity (PA) are considered healthy behaviors (HBs). HBs are associated with many health outcomes and are expected to improve quality of life. Although implementing HBs is important, the relationship between dietary patterns (DPs) and PA has not been well investigated, especially among those aged ≥ 85. This study used data from the Tokyo Oldest Old survey on Total Health study to examine the relationship between DPs and PA in a cross-sectional study. The dietary survey used the brief self-administered diet history questionnaire to estimate the intake of 58 foods. After energy adjustment, principal component analysis was performed to identify major DPs. A validated questionnaire was used to evaluate PA, and linear regression analysis was used to investigate the association between DPs and PA, considering confounders. A total of 519 participants were included. Three major DPs (‘Various plant foods’, ‘Fish and mushrooms’, ‘Cooked rice and miso soup’) were identified. ‘Various plant foods’ was similar to DPs previously named ‘Healthy’ or ‘Prudent’, and its trend was positively associated with higher PA. This study observed the implementation of HBs even among those aged ≥ 85, suggesting that a trend toward a healthier diet is associated with higher PA.
A Bregman-Split-Based Compressive Sensing Method for Dynamic Harmonic Estimation

In order to overcome the spectral interference of the conventional Fourier transform in the International Electrotechnical Commission framework, this paper introduces a Bregman-split-based compressive sensing (BSCS) method to estimate the Taylor–Fourier coefficients in a multi-frequency dynamic phasor model. Considering the DDC component estimation, this paper transforms the phasor problem into a compressive sensing model based on the regularity and sparsity of the dynamic harmonic signal distribution. It then derives an optimized hybrid regularization algorithm with the Bregman split method to reconstruct the dynamic phasor estimation. The accuracy of the model was verified by using the cross entropy to measure the distribution differences of values. Composite tests derived from the dynamic phasor test conditions were then used to verify the potentialities of the BSCS method. Simulation results show that the algorithm can alleviate the impact of dynamic signals on phasor estimation and significantly improve the estimation accuracy, which provides a theoretical basis for P-class phasor measurement units (PMUs).
CT-Only Radiotherapy: An Exploratory Study for Automatic Dose Prediction on Rectal Cancer Patients Via Deep Adversarial Network

Purpose Current deep learning methods for dose prediction require manual delineations of planning target volume (PTV) and organs at risk (OARs) besides the original CT images. Perceiving the time cost of manual contour delineation, we expect to explore the feasibility of accelerating the radiotherapy planning by leveraging only the CT images to produce high-quality dose distribution maps while generating the contour information automatically. Materials and Methods We developed a generative adversarial network (GAN) with multi-task learning (MTL) strategy to produce accurate dose distribution maps without manually delineated contours. To balance the relative importance of each task (i.e., the primary dose prediction task and the auxiliary tumor segmentation task), a multi-task loss function was employed. Our model was trained, validated and evaluated on a cohort of 130 rectal cancer patients. Results Experimental results manifest the feasibility and improvements of our contour-free method. Compared to other mainstream methods (i.e., U-net, DeepLabV3+, DoseNet, and GAN), the proposed method produces the leading performance with statistically significant improvements by achieving the highest HI of 1.023 (3.27E-5) and the lowest prediction error with ΔD95 of 0.125 (0.035) and ΔDmean of 0.023 (4.19E-4), respectively. The DVH differences between the predicted dose and the ideal dose are subtle and the errors in the difference maps are minimal. In addition, we conducted the ablation study to validate the effectiveness of each module. Furthermore, the results of attention maps also prove that our CT-only prediction model is capable of paying attention to both the target tumor (i.e., high dose distribution area) and the surrounding healthy tissues (i.e., low dose distribution areas). Conclusion The proposed CT-only dose prediction framework is capable of producing acceptable dose maps and reducing the time and labor for manual delineation, thus having great clinical potential in providing accurate and accelerated radiotherapy. Code is available at https://github.com/joegit-code/DoseWithCT
Clustering, Classification, and Factor Analysis in High Dimensional Data Analysis

Clustering, classification, and factor analysis are three popular data mining techniques. In this dissertation, we investigate these methods in high dimensional data analysis. Since there are much more features than the sample sizes and most of the features are non-informative in high dimensional data, dimension reduction is necessary before clustering or classification can be made. In the first part of this dissertation, we reinvestigate an existing clustering procedure, optimal discriminant clustering (ODC; Zhang and Dai, 2009), and propose to use cross-validation to select the tuning parameter. Then we develop a variation of ODC, sparse optimal discriminant clustering (SODC) for high dimensional data, by adding a group-lasso type of penalty to ODC. We also demonstrate that both ODC and SDOC can be used as a dimension reduction tool for data visualization in cluster analysis. In the second part, three existing sparse principal component analysis (SPCA) methods, Lasso-PCA (L-PCA), Alternative Lasso PCA (ALPCA), and sparse principal component analysis by choice of norm (SPCABP) are applied to a real data set the International HapMap Project for AIM selection to genome-wide SNP data, the classification accuracy is compared for them and it is demonstrated that SPCABP outperforms the other two SPCA methods. Third, we propose a novel method called sparse factor analysis by projection (SFABP) based on SPCABP, and propose to use cross-validation method for the selection of the tuning parameter and the number of factors. Our simulation studies show that SFABP has better performance than the unpenalyzed factor analysis when they are applied to classification problems. INDEX WORDS: Cluster analysis, Classification, Cross-validation, High-dimensional data, Optimal score, Principal components analysis, Tuning parameter, Variable selection, Factor Analysis CLUSTERING, CLASSIFICATION, AND FACTOR ANALYSIS IN HIGH DIMENSIONAL DATA ANALYSIS
Effectiveness of Outpatient Pulmonary Rehabilitation in Patients with Surgically Resected Lung Cancer: A Retrospective Real-World Analysis

Simple Summary Patients with lung cancer often suffer from low exercise capacity and reduced quality of life. An outpatient pulmonary rehabilitation program may improve exercise performance and reduce symptom load in these patients. We performed an analysis on lung cancer patients after surgery who completed 6 weeks of an outpatient pulmonary rehabilitation. After the rehabilitation program, there was a meaningful improvement in different exercise and strength tests. Patients with surgically resected lung cancer may benefit from an outpatient pulmonary rehabilitation program. Abstract Patients with lung cancer frequently suffer from physical deconditioning, low exercise capacity, and reduced quality of life. There is little evidence on the effects of a structured outpatient pulmonary rehabilitation program (OPR) on exercise capacity and symptom load in these patients. We performed a retrospective, single-center analysis of surgically resected lung cancer patients, who underwent a multiprofessional 6-week OPR. The primary endpoint was a change in the six-minute walk test distance (6 MWT). Secondary endpoints included changes in maximal workload and constant work-rate test results during cycle-ergometry, upper and lower extremity strength, and inspiratory muscle strength. The COPD Assessment Test (CAT) was used to assess symptom burden. Fifty-seven patients were included. Of those, fifty-two (91.2%) completed the full 6 weeks of OPR. The mean age was 56.4 (SD 9.2) years, and 58% were female. At completion of OPR, there was a statistically significant mean of a 50 m (95% CI, 29.6–70.7; p < 0.001) increase in 6 MWT. Significant improvements were also seen in all other exercise and strength tests (p < 0.001), accompanied by a significant reduction in the CAT score (mean difference −3.1, p = 0.001). No adverse effects were reported. OPR for surgically resected lung cancer patients was safe and effective and showed high adherence in the current study.