Reviewerly/exHarmony huggingface dataset:
Machine Learning Models Using Routinely Collected Clinical Data Offer Robust and Interpretable Predictions of 90-Day Unplanned Acute Care Use for Cancer Immunotherapy Patients

PURPOSE Clinical management of patients receiving immune checkpoint inhibitors (ICIs) could be informed using accurate predictive tools to identify patients at risk of short-term acute care utilization (ACU). We used routinely collected data to develop and assess machine learning (ML) algorithms to predict unplanned ACU within 90 days of ICI treatment initiation. METHODS We used aggregated electronic health record data from 7,960 patients receiving ICI treatments to train and assess eight ML algorithms. We developed the models using pre–SARS-COV-19 COVID-19 data generated between January 2016 and February 2020. We validated our algorithms using data collected between March 2020 and June 2022 (peri–COVID-19 sample). We assessed performance using area under the receiver operating characteristic curves (AUROC), sensitivity, specificity, and calibration plots. We derived intuitive explanations of predictions using variable importance and Shapley additive explanation analyses. We assessed the marginal performance of ML models compared with that of univariate and multivariate logistic regression (LR) models. RESULTS Most algorithms significantly outperformed the univariate and multivariate LR models. The extreme gradient boosting trees (XGBT) algorithm demonstrated the best overall performance (AUROC, 0.70; sensitivity, 0.53; specificity, 0.74) on the peri–COVID-19 sample. The algorithm performance was stable across both pre– and peri–COVID-19 samples, as well as ICI regimen and cancer groups. Type of ICI agents, oxygen saturation, diastolic blood pressure, albumin level, platelet count, immature granulocytes, absolute monocyte, chloride level, red cell distribution width, and alcohol intake were the top 10 key predictors used by the XGBT algorithm. CONCLUSION Machine learning algorithms trained using routinely collected data outperformed traditional statistical models when predicting 90-day ACU. The XGBT algorithm has the potential to identify high–ACU risk patients and enable preventive interventions to avoid ACU.
PipCKG-BS: A Method to Build Cybersecurity Knowledge Graph for Blockchain Systems via the Pipeline Approach

The increasing sophistication of cyberattacks on blockchain systems has significantly disrupted security experts from gaining immediate insight into the security situation. The Cybersecurity Knowledge Graph (CKG) currently provides a novel technical solution for blockchain system situational awareness by integrating massive fragmented Cyber Threat Intelligence (CTI) about blockchain technology. However, the existing literature does not provide a solution for building CKG appropriate for blockchain systems. Therefore, designing a method to construct a CKG for blockchain systems by efficiently extracting information from the CTI is mandatory. This paper proposes PipCKG-BS, a pipeline-based approach that builds CKG for blockchain systems. The PipCKG-BS incorporates contextual features and Pre-trained Language Models (PLMs) to improve the performance of the information extraction process. Precisely, we develop the Named Entity Recognition (NER) and Relation Extraction (RE) models for cybersecurity text in PipCKG-BS. In the NER model, we apply the prompt-based learning paradigm to cybersecurity text by constructing prompt templates. In the RE model, we employ external features and prior knowledge of sentences to improve entity relationship extraction accuracy. Several experimental results demonstrate that PipCKG-BS is better than advanced methods in extracting CTI information and is an appealing solution to build high-quality CKG for blockchain systems.
An advanced system with advanced user interfaces for image-guided intervention applications

Augmented Reality (AR) is becoming a more common addition to physicians' repertoire for aiding in resident training and patient interactions. However, the use of augmented reality in clinical settings is still beset with many complications, including the lack of physician control over the systems, set modes of interactions within the system, and physician's lack of familiarity with such AR systems. In this paper, we plan to expand on our previous prostate biopsy AR system by adding in improved user interface systems within the virtual world in order to allow the user to more accurately visualize only parts of the system which they consider to be useful at that time. To accomplish this, we have incorporated three-dimensional virtual sliders built from the ground up, using Unity to afford control over each model's RGB values, as well as their transparency. This means that the user would be able to fully edit the color, and transparency of each individual model in real time as they see fit quickly and easily while still being immersed in the augmented space. This would allow users to view internal holograms while not sacrificing the capability to view the external structure. Such leeway could be invaluable when visualizing a tumor within a prostate and would provide the physician with the capability to view as much or as little of the surrounded virtual models as desired, while providing the option to reinstate the surrounding models at will. The AR system can provide a new approach for potential uses in image-guided interventions including targeted biopsy of the prostate.
LEARNING AS A TRANSFORMATION OF THE INTELLECT: NEURAL NETWORK APPROACH

The article analyzes the transformation of the human intellect as a connection between the collective conscious and the collective unconscious. The phenomenon of the collective professional unconscious is examined. Learning is defined as a directed transformation of the intellect of the individual, providing the possibility of effective communication with the information environment. The authors substantiate the hypothesis that human intelligence is dual in nature and is determined by the nature of interpersonal communications, as a result of which a common neural network is formed. The professional collective unconscious, as a segment of the collective unconscious, is responsible for the highest levels of professionalism - for the presence of professional intuition, for flashes of creative activity, the so-called “insights”, etc. The authors come to the conclusion that the use of the collective professional unconscious makes it possible to move to a different level of interaction with transpersonal information structures. This approach now opens up new opportunities in learning.
Review of hierarchical database access control for E-medicine systems

Key management schemes for hierarchical access control enable users who have hierarchical relationships with each other to manage their secret keys efficiently. In these schemes, the users are divided into several groups, and all groups have their own central authorities. Each central authority is responsible for setting parameters and generating user’s secret keys in a hierarchical structure such that all users efficiently derive their secret keys and solve dynamic access control problems. Several key management schemes with Health Insurance Portability Accountability Act regulations were recently proposed for hierarchical access control in e-medicine systems. However, these schemes either are insecure or require a large amount of storage and heavy computations. Therefore, this study reviews and discusses hierarchical access control schemes with privacy/security regulations for medical record databases.
PDC: a highly compact file format to store protein 3D coordinates

Abstract Recent improvements in computational and experimental techniques for obtaining protein structures have resulted in an explosion of 3D coordinate data. To cope with the ever-increasing sizes of structure databases, this work proposes the Protein Data Compression (PDC) format, which compresses coordinates and temperature factors of full-atomic and Cα-only protein structures. Without loss of precision, PDC results in 69% to 78% smaller file sizes than Protein Data Bank (PDB) and macromolecular Crystallographic Information File (mmCIF) files with standard GZIP compression. It uses ∼60% less space than existing compression algorithms specific to macromolecular structures. PDC optionally performs lossy compression with minimal sacrifice of precision, which allows reduction of file sizes by another 79%. Conversion between PDC, mmCIF and PDB formats is typically achieved within 0.02 s. The compactness and fast reading/writing speed of PDC make it valuable for storage and analysis of large quantity of tertiary structural data. Database URL https://github.com/kad-ecoli/pdc
Abstract 5407: A pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis

Abstract Patient-derived xenografts (PDXs) model human intra-tumoral heterogeneity in the context of the intact tissue of immunocompromised mice. Histological imaging via hematoxylin and eosin (H&amp;E) staining is performed on PDX samples for routine assessment and, in principle, captures the complex interplay between tumor and stromal cells. Deep learning (DL)-based analysis of large human H&amp;E image repositories has extracted inter-cellular and morphological signals correlated with disease phenotype and therapeutic response. Here, we present an extensive, pan-cancer repository of nearly 1,000 PDX and paired human progenitor H&amp;E images. These images, curated from the PDXNet consortium, are associated with genomic and transcriptomic data, clinical metadata, pathological assessment of cell composition, and, in several cases, detailed pathological annotation of tumor, stroma, and necrotic regions. We demonstrate that DL can be applied to these images to classify tumor regions with an accuracy of 0.87. Further, we show that DL can predict xenograft-transplant lymphoproliferative disorder, the unintended outgrowth of human lymphocytes at the transplantation site, with an accuracy of 0.97. This repository enables PDX-specific investigations of cancer biology through histopathological analysis and contributes important model system data that expand on existing human histology repositories. We expect the PDXNet Image Repository to be valuable for controlled digital pathology analysis, both for the evaluation of technical issues such as stain normalization and for development of novel computational methods based on spatial behaviors within cancer tissues. Citation Format: Brian S. White, Xing Yi Woo, Soner Koc, Todd Sheridan, Steven B. Neuhauser, Shidan Wang, Yvonne A. Evrard, John David Landua, R Jay Mashl, Sherri R. Davies, Bingliang Fang, Maria Gabriela Raso, Kurt W. Evans, Matthew H. Bailey, Yeqing Chen, Min Xiao, Jill Rubinstein, Ali Foroughi pour, Lacey Elizabeth Dobrolecki, Maihi Fujita, Junya Fujimoto, Guanghua Xiao, Ryan C. Fields, Jacqueline L. Mudd, Xiaowei Xu, Melinda G. Hollingshead, Shahanawaz Jiwani, PDXNet consortium, Tiffany A. Wallace, Jeffrey A. Moscow, James H. Doroshow, Nicholas Mitsiades, Salma Kaochar, Chong-xian Pan, Moon S. Chen, Luis G. Carvajal-Carmona, Alana L. Welm, Bryan E. Welm, Ramaswamy Govindan, Shunqiang Li, Michael A. Davies, Jack A. Roth, Funda Meric-Bernstam, Yang Xie, Meenhard Herlyn, Li Ding, Michael T. Lewis, Carol J. Bolt, Dennis A. Dean, Jeffrey H. Chuang. A pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5407.
Start-End Writing Integration with Convolutional Neural Network for Bengali Handwritten Numeral Recognition

Convolutional neural network (CNN) based methods have been very successful for handwritten numeral recognition (HNR) applications. However, CNN seems to misclassify similar shaped numerals (i.e., the silhouette of the numerals that look same). This paper presents an enhanced HNR system to improve the classification accuracy of the similar shaped handwritten numerals incorporating the terminals points (i.e., the start and end positions) with CNN’s recognition. Start-End Writing Measure (SEWM) and its integration with CNN is the main contribution of this research. Traditionally, CNN’s classification (i.e., $\mathrm{C}\mathrm{L}_{\mathrm{C}\mathrm{N}\mathrm{N}}\in\{0,1, \ldots,9\})$ for highest probability is the outcome of CNN-based system. In the proposed system, along with classification in CLCNN, its probability value (say CNN’s confidence level 0CNN) is also used as a regulating element. Parallel to CNN’s classification operation, SEWM measures start-end points of the numeral image, suggests CLSEWM numeral category for which measured start-end points are found closed to reference start-end points of the numeral class. Finally, output label or system’s classification $(\mathrm{C}\mathrm{L}\mathrm{s}_{\mathrm{y}\mathrm{s}})$ of the given numeral image is provided comparing 0CNN with predefined threshold value (o0): CNN’s outcome is considered as system’s outcome $(i.e., \mathrm{C}\mathrm{L}\mathrm{s}_{\mathrm{y}\mathrm{s}}=CLcNN)$ if $0CNN = \gt \sigma 0$; otherwise, SEWM rectifies CNN’s decision (i.e., $\mathrm{C}\mathrm{L}\mathrm{s}_{\mathrm{y}\mathrm{s}}=$CLsEWM) as CNN’s confidence is low. The proposed method is tested on rich benchmark numeral datasets of Bengali and revealed itself as a suitable HNR method.
IndoGuruji Indian Language Teacher

Abstract: The IndoGuruji Indian Language Teacher application is a great application for anyone wanting to learn an Indian regional language. It provides an easy and convenient way to learn text from one language to another. The application is extremely user-friendly and can be used by anyone, regardless of their level of expertise. The Indian Language Teacher is an essential tool for anyone wanting to communicate in an Indian regional language. It is a revolutionary new application that makes learning Indian languages easy and fun. With its user-friendly interface and comprehensive language learning tools, the Indian Language Teacher is the perfect application for anyone interested in learning an Indian language. The IndoGuruji Application is the one and only kind to teach the Indian Language in an interactive way and also covers all the flaws of the other Indian Language Teaching application which is available in the market. The app contains lessons and quizzes to help users learn the basics of the Indian language of their choice.
Entangled Photon Pair Source Demonstrator Using the Quantum Instrumentation Control Kit System

We report the first demonstration of using the Quantum Instrumentation and Control Kit (QICK) system on RFSoC-FPGA technology to drive the electro-optic intensity modulator that generate time-bin entangled photon pairs and to detect the photon signals. With the QICK system, we achieve high levels of performance metrics including coincidence-to-accidental ratio exceeding 150, and entanglement visibility exceeding 95%, consistent with performance metrics achieved using conventional waveform generators. We also demonstrate simultaneous detector readout using the digitization functional of QICK, achieving internal system synchronization time resolution of 3.2 ps. The work reported in this paper represents an explicit demonstration of the feasibility for replacing commercial waveform generators and time taggers with RFSoC-FPGA technology in the operation of a quantum network, representing a cost reduction of more than an order of magnitude.
Supplementary Figure 1 from Gene Methylation and Cytological Atypia in Random Fine-Needle Aspirates for Assessment of Breast Cancer Risk

&lt;p&gt;Figure S1: Performance of a minimal 5-gene marker panel.&lt;/p&gt;
Foundations of Statistics for Data Scientists: With R and Python <b>Foundations of Statistics for Data Scientists: With R and Python</b> , by AlanAgresti and MariaKateri. Boca Raton, FL: CRC Press, 2022, xvii + 467 pp., $99.95 USD, ISBN: 978-0-367-74845-6.

"Foundations of Statistics for Data Scientists: With R and Python." Journal of Quality Technology, ahead-of-print(ahead-of-print), pp. 1–5
Prediction of Brain Metastases Development in Patients With Lung Cancer by Explainable Artificial Intelligence From Electronic Health Records

PURPOSE Early detection of brain metastases (BMs) is critical for prompt treatment and optimal control of the disease. In this study, we seek to predict the risk of developing BM among patients diagnosed with lung cancer on the basis of electronic health record (EHR) data and to understand what factors are important for the model to predict BM development through explainable artificial intelligence approaches accurately. MATERIALS AND METHODS We trained a recurrent neural network model, REverse Time AttentIoN (RETAIN), to predict the risk of developing BM using structured EHR data. To interpret the model's decision process, we analyzed the attention weights in the RETAIN model and the SHAP values from a feature attribution method, Kernel SHAP, to identify the factors contributing to BM prediction. RESULTS We developed a high-quality cohort with 4,466 patients with BM from the Cerner Health Fact database, which contains over 70 million patients from more than 600 hospitals. RETAIN uses this data set to achieve the best area under the receiver operating characteristic curve at 0.825, a significant improvement over the baseline model. We also extended a feature attribution method, Kernel SHAP, to structured EHR data for model interpretation. Both RETAIN and Kernel SHAP can identify important features related to BM prediction. CONCLUSION To the best of our knowledge, this is the first study to predict BM using structured EHR data. We achieved decent prediction performance for BM prediction and identified factors highly relevant to BM development. The sensitivity analysis demonstrated that both RETAIN and Kernel SHAP could discriminate unrelated features and put more weight on the features important to BM. Our study explored the potential of applying explainable artificial intelligence for future clinical applications.
ENGAGING SIMPLER APPLICATIONS TO DEVELOP STUDENTS’ ENGLISH VOCABULARY AT THE SECONDARY LEVEL

Considering the importance of vocabulary in learning English, it was necessary to make efforts to improve students' abilities in developing English vocabulary. The existence of the Simpler application as a medium was expected to make a positive contribution to students. Students' interest in learning while playing can motivate students to actively learn English. This study aimed to improve the development of English vocabulary. This research was experimental research using the pre-experimental method carried out by following the Cresswell research procedure including a pre-test, treatment, and post-test activities. The research was carried out in the first grade of SMPN 7 Tarakan for the academic year 2022/23 consisting of 31 students. The results showed that before giving students treatment using the Simpler application, the average result of the test of students was 48.19, and after 6 meetings using the Simpler application, the result of the final test was an average value of 78.00. The researchers also obtained the results of hypothesis testing using SPSS, whose significance value was smaller than 0.005 and the t value obtained was higher than the t table, 8.488&gt; 1.697, meaning that Ha was accepted. It can be highlighted that the use of the Simpler application in the development of first graders' English vocabulary was very useful.
Research on the Construction of Intelligent Information Network system in Library

In order to continuously explore the construction content of intelligent library and improve the level of literature information resources guarantee of library, this paper takes the library of Dalian Naval Academy as an example, analyzes the present situation of the construction of information network system of military school library, and sums up some problems, such as backward business management system, imperfect service function of digital book management and service platform, incomplete construction of intelligent book management system, and so on. In view of the above problems, this paper puts forward that the military school library should constantly strengthen the construction of hardware and software, improve the level of intelligent service and management of the library, and constantly promote the process of the construction of the intelligent library.
SMARTPHONE ROLES IN TRIGGERING STUDENTS’ READING SKILL

The role of smartphone in the field of education has become a major support for teachers and students in learning and teaching areas. Teachers and students have been helped a lot to access any sources to collect many information as much as they want through this technology. Smartphone provides many contents that can be read everywhere not only in the classroom such as journal, articles, e-books and any others. As a recent technology, smartphone offers valuable opportunities for teachers to create an effective strategy in teaching especially to attract students’ interest to read via smartphone. In this literature review, I would like to find and share the role of smartphone in triggering students reading skill. And the methodology that I would like to use is the qualitative method in which there will be some interviews in collecting the data. And reading through all the result of the interviews and drawing some connection between smartphone and reading skill to analyze the data, the participants are from VIII grade students of Makassar Adventist Junior High School. As a result, those students who are reading through digital text (smartphone) have unlimited access of information to enhance their reading skills than those who are not. Here the writer found out that smartphone plays a role in triggering students’ reading skill.
A Needs Analysis Regarding the Development of Interactive Multimedia for Learning Japanese in High Schools

This study aims to analyze what indicators are needed in developing interactive multimedia for Japanese language subjects to provide solutions to one of the problems in learning Japanese at the high school level. This research uses descriptive research method. Respondents were students and teachers in one of private schools in Bandung. Data collection was carried out through questionnaires and interviews. The results showed that the use of information technology in learning Japanese at the high school level is still not optimal, so the use of interactive multimedia is expected by students in learning Japanese to increase student motivation and learning outcomes in learning Japanese. Therefore, the development of interactive Japanese learning media is required.
The Advance of Distributed Computing Methods

As data computing techniques continue to advance and change, distributed computing has become more and more mature and widely used, and it has become an important and effective method for computing data in today's era. Since human beings entered the information age, effective data processing has always been a topic of concern, in the face of complex and huge data, distributed computing has always played its own role, and gradually has a significant impact on other fields such as the Internet of Things, medical care, artificial intelligence and other fields, and has a positive effect on making today's human life more convenient and faster. Starting from the development background of distributed computing, starting from the three typical distributed frameworks of distributed computing, this paper not only introduces distributed computing as a data processing method, but also uses divergent thinking, describes different computing methods, and analyzes them together. In addition, this paper briefly points out the advantages and disadvantages of distributed computing, as well as the challenges and challenges that still exist in distributed computing and puts forward expectations for the future development of distributed computing.