Reviewerly/exHarmony huggingface dataset:
Impact of left censoring on longitudinal analyses of dementia using electronic health record data

Abstract Background Longitudinal electronic health record (EHR) data provides an opportunity to evaluate associations between certain risk factors and dementia incidence. In practice, researchers using EHR usually extract data for multiple years and use the first 2‐4 years as a washout period to exclude baseline prevalent cases. However, identifying dementia via clinical diagnostic codes during a washout period likely under‐detect some cases, with time to dementia diagnosis for these cases left censored and estimates for the associations of interest potentially biased. This study aims to assess the amount of left censoring when analyzing time to dementia using EHR data from the Indian Health Service (IHS). Methods We extracted data from the IHS National Data Warehouse and related EHR databases between fiscal year (FY) 2007‐2013. Adults aged 45+ years were identified as having dementia if they had at least one qualifying ICD‐9 diagnostic code for all‐cause dementia. A total of 1,117 AI/AN adults were identified as dementia patients in FY2007. Among these, we calculated the proportions of patients with 1+ ICD‐9 code for dementia within 1‐5 years after FY2007. We used diabetes, identified in a similar manner, as a comparison condition. Results Among the FY2007 dementia patients who used IHS services in FY2008, only 63.7% had 1+ qualifying ICD‐9 code for dementia in FY2008. In contrast, 93.7% of FY2007 diabetes patients had an ICD‐9 code for diabetes in FY2008 (Fig1). If FY2007 data are not included in the analytical dataset and only one year of data (FY2008) is used as the washout period, the remaining 36.3% prevalent dementia cases are classified as incidence after FY2008 with their time to dementia left censored. Even using a 5‐year washout period, 25.0% prevalent dementia patients in FY2007 are classified as incident cases after FY2012 (Fig2a) while 4.6% prevalent diabetes patients in FY2007 are classified as incidence (Fig2b). Conclusions Longitudinal analysis of dementia risk using EHR data might be subject to relatively high amount of left censoring. It is important to recognize the potential biases associated with relatively high proportion of left censored observations when estimating longitudinal associations of risk factors with dementia incidence.
Interpretable Deep Learning in Computational Histopathology for refined identification of Alzheimer’s Disease biomarkers

Abstract Background In this research, an explainable deep‐learning based framework is proposed for the segmentation of tau protein biomarkers like plaques, and tangles in histopathological whole slide images (WSI). The concept is to integrate an explainable deep learning model with collaborative human feedback to improve the model precision of identifying tau biomarkers as well as making the results interpretable to human experts. The final goal of this project is to refine AD patients’ stratification. Method A pilot study has been done on 6 WSIs stained with ALZ50 monoclonal antibody. The preliminary models have been extended to a larger database of 15 WSIs stained with an AT8 monoclonal antibody. All WSIs are post‐mortem brain samples corresponding to the frontal cortex. In the proposed system, firstly, for automatic segmentation of Tau aggregates, deep learning (DL) models are trained and tested using the initial database (6 WSIs). Secondly, we train deep learning models with interpretability features, such as Attention U‐Net, CA‐net and graph neural networks. With these models, two operational loops (Figure 1) are implemented. In the first loop, a technical expert looks at the predictions of the trained model and provides corrections. The corrected predictions are fed back to the training dataset to improve the model performance. In the second loop, a clinical expert looks at the explainability results from the model and provides further corrective feedback. Result A preliminary version of our DL models achieved promising F1 scores for tangles and neuritic plaques as reported in [1]. Currently, a second larger dataset of 15 WSIs (AT8) is studied and used for training an evolved version of the DL model, by including explainable visual features. These new overall results will be presented and discussed during the conference. Conclusion The proposed interactive and interpretable tau protein segmentation framework is envisioned to be impactful towards Alzheimer's patient stratification problems which requires high accuracy identification of AD biomarkers. [1] K Maňoušková, V Abadie, M Ounissi, G Jimenez, L Stimmer, B. Delatour, S. Durrleman, D. Racoceanu. Tau Protein Discrete Aggregates in Alzheimer's Disease: Neuritic Plaques and Tangles Detection and Segmentation using Computational Histopathology. SPIE Medical Imaging 2022, Feb 2022, San Diego, United States.
Synthesizing Images of Tau Pathology from Images of Glucose Utilization

Abstract Background In vivo tau‐positron emission tomography (PET) is an attractive biomarker for Alzheimer’s disease (AD) diagnosis and treatment. However, tau‐PET is less widely available than other modalities. In this study, we tested cross‐modality synthesis of tau‐PET brain images from fluorodeoxyglucose F‐18 (FDG)‐PET using a deep convolutional neural network (CNN). Method Participants (n=1,192) who had brain FDG‐PET with 18 F‐FDG and tau‐PET with Flortaucipir (F‐18‐AV‐1451) were included for training and testing. This cohort spanned normal aging (ages 26‐98), pre‐clinical, and clinical AD and related disorders including the FTD and DLB spectrum. External validation was done using ADNI (n=288). The PET scans were co‐registered to the corresponding MRI and subsequently warped to Mayo Clinic Adult Lifespan Template (MCALT) space. Tau‐PET images were SUVR‐normalized to the cerebellar crus, and FDG to the pons. A 3D dense‐U‐net model was utilized as an architecture. Cross‐validation experiments were conducted using 5‐fold validations (60% training set, 20% validation set, and 20% test set) with mean squared error as the loss function. Result Our dense‐U‐net model successfully synthesized tau‐PET from metabolic images with good correlation and low prediction error for regional SUVRs (Figure 1A‐C). The model showed a robust prediction ability, performing accurately in an independent, external ADNI cohort (Figure 1D‐F). The model‐imputed tau‐PET significantly improved performance in classifying tau positivity (mean AUROC(±SD)=0.78±0.04 and 0.85±0.03 for FDG‐PET and synthesized tau‐PET, respectively) and diagnostic groups (cognitively unimpaired with abnormal amyloid‐PET vs. cognitively impaired with abnormal amyloid‐PET) compared to the original input FDG data (mean AUROC(±SD)=0.89±0.04, 0.85±0.05 0.91±0.04 for actual tau‐PET, FDG‐PET and synthesized tau‐PET, respectively) (Figure 2), suggesting enhanced clinical utility for metabolic images. The ADNI cohort also showed similar results (for tau positivity: AUROC=0.66 and 0.78 for FDG‐PET and synthesized tau‐PET, respectively; for CU A+ vs. CI A+: AUROC=0.86, 0.62, and 0.73 for actual tau‐PET, FDG‐PET, and synthesized tau‐PET; Figure 3). Conclusion We showed that using a CNN model to predict tau‐PET from FDG‐PET is feasible. The synthesized tau‐PET can augment the value of FDG‐PET, facilitating the multi‐modal diagnosis of AD.
Web Based Online Hybrid Teaching Method of Network Music Course

Today, with the rapid development of online course teaching, the demand for online courses is increasing day by day, and the demand for online mixed teaching of online music courses is also increasing rapidly. In the context of big data, the lengthy personalized screening process of users has become one of the problems to be solved. Based on Web data mining, an improved algorithm of hybrid hierarchical recommendation algorithm and genetic algorithm is used in the experiment, and compared with the other two algorithms in the experiment. The experimental results show that the average accuracy of the improved algorithm is 79.63% in the limited training times, and has better adaptability. It can be applied to the online hybrid teaching recommendation and screening of online music courses in dynamic changes.
Attempt to extract features and classify subjective poor physical conditions in facial images using deep metric learning

With the spread of COVID-19, the need for remote detection of physical conditions is increasing, for example, there are several situations wherein the body temperature has to be measured remotely to detect febrile individuals. Aiming to remotely detect physical conditions, the study attempted to investigate anomaly detection based on facial color and skin temperature, which are indicators related to hemodynamics. Triplet loss was used to extract features related to subjective health feelings from facial images to evaluate whether there is a relationship between subjective health feelings and facial images. A classification of subjective health feelings related to poor physical conditions based on these features was also attempted. To obtain the data, an experiment was conducted for approximately 1 year to measure facial visual and thermal images, and subjective feelings related to physical conditions. Anomaly levels were defined based on subjective health feelings. Anomaly detection models were constructed by classifying anomaly and normal data based on subjective health feelings. Facial visible and thermal images were applied to the trained model to quantitatively evaluate the accuracy of the classification of anomaly conditions related to subjective health. At higher levels of anomaly, a combination of facial visible and thermal images resulted in the classification of subjective health feelings with moderate accuracy. Further, the results suggest that the eyes and sides of the nose may indicate subjective health feelings.
Dual-Enhanced-CNN for Few-Shot Object Detection in Remote Sensing Images

Existing convolutional neural networks (CNNs) still perform excellently in object detection over remote sensing images, although remote sensing images are more complicated than natural images. But these approaches are very dependent on the quality and quantity of data. when the number of annotated samples gets smaller, the performance of existing CNNs sharply gets worse. Few-shot object detection (FSOD) can alleviate this problem but still has a lot of improvement space. In this work, to further improve the detection performance, We propose our Dual-Enhanced-CNN model. And the main improvements are as follows: 1) We design a weighted cross image attention to learn the interaction information across both images and channels and then improve the detection capabilities of the query image. 2) We design a new adaptive weight loss to focus more on the targets from novel classes and the targets with poor detection performance. We have conducted multiple experiments on the large-scale remote sensing dataset named DIOR. And the higher detection accuracy and relatively stable experimental performance prove the superiority of our method.
A Dataset and A Lightweight Object Detection Network for Thermal Image-Based Home Surveillance

Due to stringent privacy protection and all-day workability requirements, thermal cameras are more suitable than visible light cameras for analyzing indoor home scenes. However, application of object detection methods with thermal images are hampered by inadequate labeled indoor images and challenges of lightweight implementations. To address this issue, we first create a dataset called THS-DATA with 2,633 images, containing 19,362 person and pet targets, for indoor home surveillance object detection <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The dateset has been made available at: https://github.com/Sohna-Ctrl/A-Dataset-and-A-Lightweight-Object-Detection-Network-for-Thermal-Image ### Based-Home-Surveillance Then, we propose a lightweight object detection architecture called THS-YOLO, where coordinate attention modules and adaptively spatial feature fusion (ASFF) modules are added to pruned YOLOv5s. In addition, pretraining strategies with regular RGB images are discussed. Experiments with the created dataset validate the effectiveness of the proposed network architecture and pretraining method.
PENGGUNAAN MEDIA PERMAINAN EDUKATIF ULAR TANGGA UNTUK MENINGKATKAN HASIL BELAJAR MATEMATIKA MATERI BANGUN RUANG KELAS V MIN 2 MOJOKERTO

The research in this thesis is motivated by several obstacles in the mathematics learning process, including: (1) Students feel bored because learning is more centered on the teacher while students tend to be passive (2) It is easy to forget to remember material because the delivery method is still conventional, namely lectures. and question and answer (3) The media used does not attract students' attention because the teacher only relies on LKS books. Classroom Action Research (CAR) was carried out at MIN 2 Mojokerto, with the object of research being class V students. The approach used in this study was a quantitative descriptive approach with the type of Classroom Action Research. The stages of action research are carried out in 4 stages, namely: 1).Planning, 2).Implementation, 3).Observation, and 4).Reflection. While the data collection using observation techniques, tests and documentation. The results showed that learning by using snakes and ladders game media can improve learning outcomes of mathematics learning materials for class V MIN 2 Mojokerto. The increase in student learning outcomes can be seen in the test results between the pre-cycle, cycle I and cycle II. The percentage increase in learning outcomes in the pre-cycle was 32% with an average of 58.96, then became 56% with an average of 58.20 in the first cycle and increased to 84% with an average of 76.60 in the second cycle.
Description of Increasing the Number of Research and Publication of Lecturers at the Indonesian Christian University, East Jakarta

The purpose of the study was to determine the readiness and difficulty of photographing and to improve research and publications of lecturers in Jakarta through lecturers at the Indonesian Christian University (UKI). Research is one of the indicators of the tridarma that must be carried out by lecturers throughout Indonesia, including lecturers at UKI. One proof of its performance is its publication in international and nationally reputable journals. Because research has a maximum dual effect and contributes to policy making in institutions. Allegedly there was a time when there were absolutely no lecturers who ran the administration of Academic Position. To become a superior university, one of the determining indicators is evidenced by the number of lecturers with the highest Academic Position. Data were obtained from secondary data and primary data as well as filling out questionnaires addressed to lecturers in the UKI environment. Data were collected from 102 respondents from 33 study programs. The results, which include lecturer research data and observations for the last four years from 2017 - 2020, show an increase in the number of publications in reputable and local international and national journals, although the results are comparable between the number of lecturers and publications. The backwardness of the publication of scientific journals of national and international repute can only be achieved if each lecturer conducts a scientific study and is published in accordance with the UKI Research and Community Service (LPPM) program, that each lecturer has the opportunity to conduct research twice a year, both independent research and research research group.
Nurturing “Double-Qualified” Teachers in Vocational Colleges Using Campus Resources

Teachers are an important resource for the development of vocational education and a key force in supporting the reform of vocational education in the new era. Teachers who are capable of teaching theory and guiding students to do hands-on work are called “double-qualified” teachers. Building a high-level and professional double-qualified teacher team is one of the key tasks of vocational institutions. In this paper, we study the double-qualified teacher team from the perspective of using resources in schools. Firstly, the importance of the construction of double-qualified team is explained in terms of national policies and the findings of previous scholars. Secondly, we analyze the objective problem of teachers’ difficulty in learning in enterprises under the new epidemic. Thirdly, the idea of faculty construction based on on-campus resources is proposed. The relevant analysis is then launched from three dimensions: the construction of practical training base projects, school-enterprise research cooperation and skills competition participation. In view of the Covid-19 epidemic, it is necessary to make full use of the campus resources to enhance the practical ability of teachers in vocational institutions. Highly qualified and professional teachers are the important support for high-quality development of vocational institutions and should be highlighted among school leaders.
STAKEHOLDER ENGAGEMENT IN THE DESIGN OF A FALL RISK PREDICTION SYSTEM FOR SOCIALLY VULNERABLE OLDER ADULTS

Abstract Falls and fall-related injuries are significant public health issues for adults 65 years of age and older. Fall-related injuries are among the most expensive medical conditions. Mild cognitive impairment (MCI) and housing conditions are each independent risk factors for multiple falls among older adults and there is increasing evidence of the adverse effect of MCI on balance, stride, gait speed and increasing fall risk. We developed an innovative technology-supported intervention called Sense4Safety to 1) identify escalating risk for falls through real-time in-home passive sensor monitoring (using depth sensors); 2) employ machine learning to inform individualized alerts for fall risk; and 3) link ‘at risk’ socially vulnerable older adults with a tele-coach who guides them in implementing evidence-based individualized plans to reduce fall-risk. The purpose of this study was a) to examine preferences and attitudes of low-income older adults towards an in-home sensor-based system to inform fall prevention strategies; and b) solicit feedback from and validate the intervention protocols by clinical experts in fall prevention, fall risk assessment and geriatrics. We conducted one hour interview sessions with a total of 10 older adults and 10 clinical experts. Sessions were transcribed and analyzed. Findings included perceived benefits and challenges, recommendations for refinement of the intervention (including educational components and different visualization approaches for sensor data) and the role of family members or trusted others in addressing fall risk. We highlight clinical and ethical implications resulting from the use of passive monitoring for socially vulnerable older adults with MCI.
Study finds online microaggressions strongly impact disabled users

A new study conducted by Cornell University researchers found that online microaggressions have a strong impact on users with disabilities. The researchers interviewed 20 volunteers who were active on social media platforms and self‐identified as having disabilities. The researchers then asked participants to describe online discrimination and ableist microaggressions that had impacted their lives. The findings of the study are recorded in a paper entitled “Nothing Micro about It: Examining Ableist Microaggressions on Social Media.”
JoVE Video Dataset

Modeling Oral Esophageal Squamous Cell Carcinoma In 3d Organoids - Video. JoVE publishes peer-reviewed scientific video protocols to accelerate biological, medical, chemical and physical research. Watch our scientific video articles. JoVE is the world-leading producer and provider of science videos with the mission to improve scientific research, scientific journals, and education. Millions of scientists, educators and students at thousands of universities, colleges, hospitals and biopharmaceutical companies worldwide use JoVE for their research, teaching and learning.
Stability of Conditional Invariant Sets of Control Systems

In this paper we attempted to extend the work of study the stability properties of conditional invariant sets for a control system in R n .Here necessary and sufficient conditions for relative to the given control system are determined.Theorems are based on the work of Ladde, an optional control problem maximising a performance inertia is proved.
Syllable Analysis Data Augmentation for Khmer Ancient Palm leaf Recognition

The unique forms and physical conditions of the Khmer palm leaf manuscript recognition system are receiving more attention from researchers. In the state-of-the-art, data augmentation is commonly used for data training; however, grammatical mistakes and data availability in the training process would determine or limit the accuracy rate. The two significant challenges lie in (1) grammar complexity and (2) wording similarity; therefore, this paper presents the Syllable Analysis Data Augmentation (SADA) technique, which aims at boosting the accuracy of the text recognition system for one of Southeast Asia's historical manuscripts from Cambodia. SADA comprises two fundamental modules: (1) formulating a collection of syllables/words to structure glyph patterns and (2) generating patterns from existing data through augmentation techniques and utilizing flexible geometric image transformation to increase similar word/text images. Initially, image collections are established, whereby datasets are interpreted according to the reordered grammatical structures to construct multiple glyph images. Next, we aim at conducting the experiment with a text/word recognition system before regulating attention-based encoder-decoder to enhance the probability of transcriptions of low and high-resolution images. At last, the experiment centers on datasets from various sources, including public datasets from ICFHR 2018 contest and our new augmentation datasets, all of which aim at demonstrating and evaluating the accuracy of the findings.
Authoring tool for creating immersive virtual experiences expeditiously for training

Virtual reality (VR) is still a field that is in constant development, and people are trying to use it to have a close representation of reality by creating immersive environments. However, despite the existence of some tools that have been adapted to work with VR, they require some experience to work with, and there is a considerable amount of resources that need to be spent to create and maintain the VR experiences, which prevents the adoption and use of all the benefits that VR can bring. This work proposes an architecture for an authoring tool that allows users to create their own virtual experiences without the need for an extensive understanding of it and use them to create a virtual training exercise. This paper uses a case study built upon a real training context scenario applied to the agroforestry field.To validate this proposal, a prototype was built and subject to usability and satisfaction tests that demonstrated the ease of understanding and learning of the interfaces and all the functionalities implemented.