Reviewerly/exHarmony huggingface dataset:
Predicting Molecular Alterations from Blast Morphology of Acute Myeloid Leukemia Bone Marrow Aspirate Smears Using Machine Learning

Introduction Acute Myeloid Leukemia (AML) has enormous molecular and cytogenetic heterogeneity. Evaluation of bone marrow morphology is essential in the diagnosis of AML, however, it requires significant training and there is wide inter-observer variability. Genomic testing has been progressively relied on for AML classification, ultimately helping to dictate prognosis and guide clinical management. However, genomic testing, if performed, can be subject to prolonged turnaround times and lacks clinical penetration in low-resource settings. To overcome these challenges, we aimed to evaluate the feasibility of predicting clinically relevant genomic alterations from bone marrow cell morphology using a machine learning approach. Methods We scanned 799 Wright-stained bone marrow aspirate smear slides, made for routine patient care, for subjects diagnosed with AML. These subjects were linked to clinical data, including pertinent cytogenetic/molecular alterations and subclassified as cases with mutated Nucleophosmin 1 ( NPM1), those with TP53 mutations and those with myelodysplastic-related changes (AML-MRC). Whole slide images were screened for containing spicules and blasts, and were analyzed to detect 180 million cells (blasts and non-blasts) excluding hemodilute and particle regions. We developed a multi-instance model to predict patient molecular subtype by repeatedly sampling 512 cells from each slide and using these to train a classifier that weights cells with morphology relevant to subtype. We also trained a model to predict NPM1 and TP53 mutations independently, as well as other frequent variants reported to impact blast morphology. Results Performance in predicting NPM1/TP53/AML-MRC subtype was moderate (0.80 AUROC). Performance of the single-gene model varied, with NPM1 having the highest performance (0.85 AUROC) followed by IDH (0.80 AUROC), FLT3 (0.79 AUROC) and TP53 (0.62 AUROC). Conclusions This study provides promising preliminary results for prediction of genetic alterations in AML in a large-scale single-institution dataset. Interestingly, this method did not rely on the classification of individual cells, avoiding the need for manually collecting annotations to train a blast cell classifier. Future work will evaluate the morphology of cells attended by the multi-instance model, and explore the value of enriching sampled cells with blasts using cell classification methods.
Deep Learning for Morphology-Based, Bone Marrow Cell Classification

The morphological classification of cells in bone marrow aspirate (BMA) is central to the diagnosis of hematologic diseases, including leukemias. Despite being a critical task, its monotonous, time-consuming nature and dependency on highly skilled clinical experts makes it prone to human error. Such errors can lead to delays and misdiagnoses that negatively impact patient care. To counter these challenges, we curated an expansive dataset of more than 40,000 hematopathologist consensus-annotated single-cell images, extracted from BMA whole slide images (WSIs), each annotated into one of 23 distinct morphologic classes. We then utilized this data to develop DeepHeme, a convolutional neural network classifier designed for bone marrow cell typing tasks. DeepHeme achieves state-of-the-art performance in both the breadth of differentiable classes and accuracy across these classes. By comparing its performance to that of individual hematopathologists from three premier academic medical centers, using our gold standard consensus-labelled images, we found our AI algorithm either matched or surpassed the average performance across all classes. In addition, we integrated DeepHeme with internally developed region classifier and cell detection algorithms, culminating in a comprehensive diagnostic pipeline for whole slide cell differential. We next tested DeepHeme on slides from an external hospital system at a major cancer center to evaluate the generalizability of our model, a necessary precondition to widespread application. DeepHeme demonstrated a high level of generalizability, evidenced by a decrease of only 4% in the mean F-1 score, from 0.89 to 0.85, across all 23 cell classes. Lastly, to improve access to the DeepHeme algorithm results and encourage further real-world generalizability testing, we developed a web application that allows scientists and clinicians to test the DeepHeme algorithm on either test images from our study or their own user-uploaded aspirates.
Deep Learning for Circulating Multiple Myeloma Cell (CMMC) Enumeration with the Cellsearch System

Background Circulating Multiple Myeloma Cell (CMMC) enumeration in blood samples with the CELLSEARCH® system is a minimally invasive assay used to quantify plasma cells in patients with Multiple Myeloma (MM) and in precursor stages MGUS and SMM 1 particularly during disease stages when a bone marrow aspirate is not desirable (Foulk et al. 2017). Currently, CMMC identification is performed by human reviewers through visual assessment, which is a time-consuming procedure that could be affected by subjective interpretations. Modern Deep Learning (DL) methods can provide a solution by automating the process of CMMC identification, thus eliminating subjectivity and producing faster and more reproducible results. The purpose of this study was to train and test an automated algorithm (for research use only) based on DL models for CMMC identification in CELLSEARCH® fluorescent images. Methods The DL algorithm was designed to analyze images using a segmentation and a classification network as presented in recent studies (Zeune et al. Nature MI 2020; Coumans et al. ACTC 2021). The DL classification model was trained on 5139 CMMC and 16235 non-CMMC images (training set) of fully anonymized blood samples from the CoMMpass study (NCT01454297 sponsored by the Multiple Myeloma Research Foundation) scanned by CELLTRACKS ANALYZER II® (CTAII). The test set was composed of 75 samples from the CoMMpass study with 1000 images per sample reviewed by an operator (with training and experience of reviewing CTAII images) and processed by the algorithm, for a total of 75000 images. The algorithm was compared with the human reviewer in terms of overall agreement and Cohen's Kappa of the image classification results and of correlation for the CMMC enumeration results per sample. Results In the test dataset, out of 75000 images in total the reviewer identified 32403 CMMCs and the algorithm 32842 CMMCs. The DL algorithm and the reviewer provided the same classification for 71711 images, thus showing very high agreement = 95.6% (Cohen's Kappa = 0.91). CMMC counts per sample and linear regression are shown in the figure: slope = 0.95 (95% CI = 0.02), intercept = 26.99 (95% CI = 11.56) and coefficient of determination R 2 = 0.99. Conclusion These preliminary results show the potential of applying automated algorithms to CMMC identification with the CELLSEARCH® system in order to remove human subjectivity from the review process, thus maximizing standardization among different research centers. 1) The CMMC assay is run in a CLIA/CAP certified laboratory in the US and is also New York State CLEP approved. In the EU, the CMMC assay is research use only (RUO).
Implementation of Blockchain in Automotive Industry to Secure Connected Vehicle Data: Study and Analysis

The automotive industry is undergoing rapid transformation because of the collaboration between technology companies and automakers. To support the present trend of connected cars, several stakeholders like insurance firms, car buyers and sellers and government agencies need different kinds of vehicle data. Data gathering is currently either manual or unverified, which raises trust, validity, and exactness concerns such as tampered safety checks and false or duplicate vehicle data records, among others. A robust instrument that is capable of safeguarding vehicle data is essential. It is necessary to record the changes for the purpose of auditing and ultimately establishing faith in the system. To meet these requirements, we propose utilizing blockchain technology. Healthcare, transportation, finance, cybersecurity, and supply chain management are just a few of the fields in which blockchain technology has recently gained popularity and been implemented. The determination of this survey is to deliver a comprehensive investigation of the advantages and uses of the Blockchain application in the automotive segment.
Performance Evaluation of Neural Networks-Based Virtual Machine Placement Algorithm for Server Consolidation in Cloud Data Centres

Cloud computing, an on-demand model that provides IT services as a utility, has become increasingly popular in recent years. To the contrary, the cloud's resources are housed in data centres that have a major impact on the environment due to their high energy consumption. As a result, consolidating servers into fewer physical locations is a significant method for increasing data centre efficiency and reducing energy waste. In this work, we propose a technique for selecting and deploying servers and virtual machines using artificial neural networks. It forecasts user demand, evenly distributes work if the server is overloaded, trains a feed forward neural network with back propagation learning, uses a selection algorithm to determine which virtual machine to use, and finally uses a cross-validation algorithm to ensure that the placement was accurate. Cloud computing providers like Amazon (EC2), Microsoft Azure, and Google Cloud Storage provide the data used to measure the efficacy of server consolidation efforts. The algorithm's experimental results are compared to those of other algorithms published by the same author, both in exact and heuristic optimization methods, that aim to achieve the same goals.
DEVELOPMENT OF MEDIA COMPETENCE IN THE CONTEXT OF PERSONALITY EDUCATION

The article analyzes modern approaches to the development of media and human potential. It presents a modern European approach to digital tools in the educational process, especially in the professional activities of teachers, supporting new levels of communication and interaction for all participants in the educational process, as well as digital subjects of the educational process aimed at developing competencies. The study used the theoretical method of historical and pedagogical analysis, generalization, comparison, study of curricula, other documentation and practices of educational activities, and factual generalization, which allowed us to explore the origins of the concept of "media. Foreign experience in domestic practice is determined by the empirical method - observation and analysis. The results reflect the general state of digital literacy in Ukrainian society, it was determined that the most prepared in this regard are representatives of the age group of students. The categories of media and information and communication competence, structural analysis allowed us to consider the system of competencies based on their basic components. The article focuses on the importance of teachers and students acquiring the knowledge, skills, and abilities to realize and protect their democratic rights and responsibilities on the Internet. According to researches, media literate students should be able to critically and consciously evaluate media texts, maintain a critical distance from mass culture and resist manipulation. Media-literate students should be able to critically and consciously evaluate media texts, maintain a critical distance from mass culture, and resist manipulation.
Blockchain Technology: The Future of Decentralized Applications

The advances in computer science &amp;amp; technology have gone to new levels. The process of computational technology and ubiquitous computing is expanding exponentially daily. The computer system &amp;amp; its related inventions have become an integral part of our lives. People &amp;amp; services are migrating towards clouds and decentralized platforms. Nowadays, commerce &amp;amp; finance on the Internet rely on trusted third parties like banks to process electronic online payments. Though it fits well with most transactions, it still suffers from the inherent weakness of trust-based models. Blockchain Technology- the technology behind the cryptocurrency Bitcoin is buzzing a lot nowadays to invent and witness the future of data storage &amp;amp; processing in an efficient, decentralized, tamper-proof, and peer-to-peer (P2P) network. Nowadays, the use of blockchain is not confined to crypto-currencies only. This revolutionary technology is drawing its impact &amp;amp; use in many fields and applications. Primarily, a blockchain network can be used for storing, sharing &amp;amp; transparently transferring data without the intervention of third parties and other intermediaries. In a blockchain network, blocks are added using a consensus algorithm &amp;amp; connected cryptographically. Hence it is merely impossible to delete and modify the content of blocks stored in a blockchain network. Other advantages of this technology include consistency, security, transparency, distributed ledger, decentralized network, etc. This paper systematically reviews the work accomplished by many researchers for the past 11 years. Specifically, the application &amp;amp; impact of blockchain technology in various fields like crypto currencies, the health sector, e-governance, banking, finance, and food supply management systems have been discussed, and a projection of a novel application of the same technology has been proposed.
Modern Strategies of Full-Time Students’ Employment (On the Example of University Youth in Volgograd)

The article is touches upon the issue of employment strategies for full-time students. Modern socio-economic processes allow young people to combine educational and work activities, focusing on earning money, acquir-ing practical skills, gaining independence, personal and professional development. The lack of work and life experience among young Volgograd residents raises the issue of choosing a job search strategy, selecting offers, and self-presentation on the labor market. Based on theoretical analysis and empirical research con-ducted by questionnaire, the authors identify the five most popular employment strategies. Focusing on one strategy or another allows for optimal use of available resources to achieve the goal of employment. Based on the results of the study, the authors identify and analyze positive and negative factors that are characteristic of the strategies under consideration. At the end of the article, according to the criteria presented by the authors and the subjective assessments of students, situations are explicated where each strategy will help achieve the goal of employment and reduce dissatisfaction and disappointment with the received vacancy.
SISTEM PENDUKUNG KEPUTUSAN UNTUK MENENTUKAN HARGA BAHAN POKOK MENGGUNAKAN REGRESI LINIERi

The Kediri City Government has provided an application that can provide updates on the prices of necessities in traditional markets everyday. This is useful for the community to get updates on the latest prices of necessities needed. After getting the price of the necessities needed at each market, the community is still doing calculations to get the cheapest transportation costs based on the location to the market. This makes it difficult for the community because people have to do calculations to compare the cost of obtaining necessities based on price updates on the application and manual calculations for transportation costs that can be measured by distance. Website-based application development is equipped with a Decision Support System that can provide recommendations for obtaining basic commodities at the lowest cost that takes into account two variables, namely distance and prices of necessities. The use of the linear regression method is needed to get the lowest value of the two variables whose coefficients have been determined, namely 70 percent for distance and 30 percent for price.
A Haskell-Embedded DSL for Secure Information-Flow

This paper presents a domain specific language, embedded in Haskell (EDSL), for enforcing the information flow policy Delimited Release. To build this language we use Haskell extensions that will allow some kind of dependently-typed programming. Considering the effort it takes to build a language from scratch, we decided to provide an information-flow security language as an EDSL, using the infrastructure of the host language to support it. The decision of using Haskell as the implementation language is because it has a powerful type system that makes it possible to encode the security type systems of the embedded language at the type level and also because it is a general purpose language. The implementation follows an approach where the type of the abstract syntax of the embedded language was decorated with security type information. This way, typed programs will correspond to secure programs, and the verification of the security invariants of programs will be reduced to type-checking. The embedded security language is designed in a way that is easy to use. We illustrate its use through two examples: an electronic purchase and a secure reading of database information.
What Data Scientists (Care To) Recall

To maintain and evolve a software system, developers need to gain new or recover lost knowledge about that system. Thus, program comprehension is a crucial activity in software development and maintenance processes. We know from previous work that developers prioritize what information they want to remember about a system based on the perceived importance of that information. However, AI-based software systems as a special case are not developed by software developers alone, but also by data scientists who deal with other concepts and have a different educational background than most developers. In this paper, we study what information data scientists (aim to) recall about their systems. For this purpose, we replicated our previous work by interviewing 11 data scientists, investigating the knowledge they consider important to remember, and whether they can remember parts of their systems correctly. Our results suggest that data scientists consider knowledge about the AI-project settings to be the most important to remember and that they perform best when remembering knowledge they consider important. Contrary to software developers, data scientists’ self-assessments increase when reflecting on their systems. Our findings indicate similarities and differences between developers and data scientists that are important for managing the processes surrounding a system.
The use of blockchain technology in the scientific research workflow

This paper is based upon a lightening talk that was given on February 14, 2023 at the 2023 NISO Plus conference. The presentation described the research for the creation of a white paper on the use of blockchain technology along the scientific research workflow that will be released in early 2024 by the International Union of Pure and Applied Chemistry (IUPAC). What the three-year study found is that the technology is indeed being used in almost all the steps in the scientific research workflow - from hypothesis development through to publication - by commercial organizations as well as by non-profits and across all market sectors, even governments. For example, the U.S Department of Health and Human Services (HHS) uses blockchain technology in a pilot program, the Grant-recipient Digital Dossier (GDD), to manage their grant program more efficiently. As of July 2021, GDD had reduced the time required to complete grant assessment tasks from four-plus-hours to a fifteen-minute process. This paper briefly summarizes the findings, discusses the pros/cons of the technology, and provides a glimpse of how the technology is impacting the future of scientific research.
Design of Oil Field Data Fusion Integration Platform

With the deepening of oilfield digitization process, oilfield data sharing can improve data utilization and is conducive to mining oilfield production laws. Due to historical reasons, there are great differences in the formats of databases and data tables used in different oil fields. Under the condition of keeping the data storage environment and format of each oil field unchanged, the establishment of an integrated data fusion platform can improve the access speed of data and enhance the utilization effect. The platform integrates the data in Oracle, SQL Server 2008, SQL Server 2012 and other databases in the form of data mapping. The integrated platform adopts SQL Server 2012. In the process of data fusion, data integrity and legitimacy directly affect the accuracy of data mining and data analysis. The system belong to B / S architecture, uses HTML and C# language to jointly carry out front and back-end development in vs 2015 environment, and realizes data fusion of different databases. Taking the information of 10000 wells of Chuanqing drilling company as the test data, the system can extract and display the data as required, and remind the error of unreasonable data, so that users can check and calibrate the data, so as to achieve the goal of data availability.
An overview of the 2023 NISO Plus Conference: Global conversations/global connections

This paper offers an overview of some of the highlights of the 2023 NISO Plus Annual Conference that was held virtually from February 14–February 16, 2023. This was the fourth such conference and the third to be held in a completely virtual format due to the global impact of COVID-19. These conferences have resulted from the merger of NISO and the National Federation of Abstracting and Information Services (NFAIS) in June 2019, replacing the NFAIS Annual Conferences and offering a new, more interactive format. As with prior years, there was no general topical theme (although the importance of metadata was a common thread throughout), but there were topics of interest for everyone working in the information ecosystem - from the practical subjects of standards, archiving, digital preservation, Open Science, and Open Access. With speakers and attendees from around the world and across multiple time zones and continents, it truly was a global conversation!
PENGARUH METODE TPR (TOTAL PHYSICAL RESPONSE) TERHADAP PENINGKATAN KECERDASAN LINGUSITIK ANAK USIA DINI PADA KELOMPOK B TK MELATI DESA SIKUR

The purpose of this study was to see the effect of the TPR (total physical response) method on increasing linguistic intelligence in group B early childhood TK Melati Sikur Village. This study used a quantitative research approach with a pre-experimental type and a one group pretest-posttest research design. The research subjects were 27 children. Methods of data collection used observation, test and documentation. Data analysis used a statistical non-parametric level test marked Wilcoxon (Wilcoxon match pairs test). Based on the results of data analysis on linguistic-verbal intelligence at the time of initial observation (pre-test) and post-test Physical Response) shows that the average value of children's linguistic intelligence before using the TPR method is included in the sufficient category. Whereas after using the TPR method in group B early childhood TK Melati Sikur Village experienced an increase to a good category. This can be interpreted that there is an influence of the TPR (Total Physical Response) method on increasing the linguistic intelligence of early childhood group B TK Melati Sikur Village.