Towards accurate and efficient live cell imaging data analysis

Electronic health records (EHRs) are an increasingly important source of information for healthcare professionals and researchers. However, EHRs are often fragmented, unstructured, and difficult to analyze due to the heterogeneity of the data sources and the sheer volume of information. Knowledge graphs have emerged as a powerful tool for capturing and representing complex relationships within large datasets. In this study, we explore the use of knowledge graphs to capture and represent complex relationships within EHRs. Specifically, we address the following research question: Can a knowledge graph created using the MIMIC III dataset and GraphDB effectively capture semantic relationships within EHRs and enable more efficient and accurate data analysis? We map the MIMIC III dataset to an ontology using text refinement and Protege; then, we create a knowledge graph using GraphDB and use SPARQL queries to retrieve and analyze information from the graph. Our results demonstrate that knowledge graphs can effectively capture semantic relationships within EHRs, enabling more efficient and accurate data analysis. We provide examples of how our implementation can be used to analyze patient outcomes and identify potential risk factors. Our results demonstrate that knowledge graphs are an effective tool for capturing semantic relationships within EHRs, enabling a more efficient and accurate data analysis. Our implementation provides valuable insights into patient outcomes and potential risk factors, contributing to the growing body of literature on the use of knowledge graphs in healthcare. In particular, our study highlights the potential of knowledge graphs to support decision-making and improve patient outcomes by enabling a more comprehensive and holistic analysis of EHR data. Overall, our research contributes to a better understanding of the value of knowledge graphs in healthcare and lays the foundation for further research in this area.

The development of a process approach to cost management in a digital environment has great prospects. Firstly, the use of digital technologies allows for greater accuracy in cost accounting, which is particularly important for enterprises with large production volumes and numerous processes. Secondly, digitization enables the automation of many processes and reduces the human factor in cost management. Thirdly, digital technologies enable faster and more efficient data analysis and decisionmaking based on this data. One promising direction for the development of a process approach to cost management in enterprises is the use of blockchain technologies. Blockchain can provide transparency and security in storing and transmitting information about cost accounting between enterprises and their partners. This can be particularly important in areas where it is important to accurately track the origin and distribution of costs, such as in agriculture or food production. It is also worth noting that the development and use of artificial intelligence and machine learning can improve cost management processes and allow for faster and more accurate data analysis and decision-making. Overall, the development of a process approach to cost management in enterprises in a digital environment will continue, and its successful implementation will enable enterprises to optimize their costs, increase efficiency, and competitiveness in the market.

The performance of two quality control (QC) tests for aerodynamic particle size distributions (APSD) of orally inhaled drug products (OIPs) is compared. One of the tests is based on the fine particle dose (FPD) metric currently expected by the European regulators. The other test, called efficient data analysis (EDA), uses the ratio of large particle mass to small particle mass (LPM/SPM), along with impactor sized mass (ISM), to detect changes in APSD for QC purposes. The comparison is based on analysis of APSD data from four products (two different pressurized metered dose inhalers (MDIs) and two dry powder inhalers (DPIs)). It is demonstrated that in each case, EDA is able to detect shifts and abnormalities that FPD misses. The lack of sensitivity on the part of FPD is due to its "aggregate" nature, since FPD is a univariate measure of all particles less than about 5μm aerodynamic diameter, and shifts or changes within the range encompassed by this metric may go undetected. EDA is thus shown to be superior to FPD for routine control of OIP quality. This finding augments previously reported superiority of EDA compared with impactor stage groupings (favored by US regulators) for incorrect rejections (type I errors) when incorrect acceptances (type II errors) were adjusted to the same probability for both approaches. EDA is therefore proposed as a method of choice for routine quality control of OIPs in both European and US regulatory environments.

With the development of information technology, the function of software is becoming more and more powerful, and people pay more attention to the software performance health status and adaptability. It is indispensable to analyze the efficiency data of information system to realize the evaluation of software, but the traditional method for efficiency data acquisition is difficult to meet the new challenges of distributed deployment and unfixed location in the environment of maneuver information system. Fortunately, the smart mobile terminal technology based on Android is getting mature, which gives a new direction for the efficiency data acquisition conveniently. An efficiency data acquisition and analysis platform based on Android is proposed via combining the study and research on both Android development tools and efficiency data. The platform also makes a detailed design on key functional modules, which provides support for the development of efficiency data acquisition tools.

The basic analysis steps of spatial transcriptomics require obtaining gene expression information from both space and cells. The existing tools for these analyses incur performance issues when dealing with large datasets. These issues involve computationally intensive spatial localization, RNA genome alignment, and excessive memory usage in large chip scenarios. These problems affect the applicability and efficiency of the analysis. Here, a high-performance and accurate spatial transcriptomics data analysis workflow, called Stereo-seq Analysis Workflow (SAW), was developed for the Stereo-seq technology developed at BGI. SAW includes mRNA spatial position reconstruction, genome alignment, gene expression matrix generation, and clustering. The workflow outputs files in a universal format for subsequent personalized analysis. The execution time for the entire analysis is ∼148 min with 1 GB reads 1 × 1 cm chip test data, 1.8 times faster than with an unoptimized workflow.

Recent advances in high-throughput RNA sequencing (RNA-seq) have enabled tremendous leaps forward in our understanding of bacterial transcriptomes. However, computational methods for analysis of bacterial transcriptome data have not kept pace with the large and growing data sets generated by RNA-seq technology. Here, we present new algorithms, specific to bacterial gene structures and transcriptomes, for analysis of RNA-seq data. The algorithms are implemented in an open source software system called Rockhopper that supports various stages of bacterial RNA-seq data analysis, including aligning sequencing reads to a genome, constructing transcriptome maps, quantifying transcript abundance, testing for differential gene expression, determining operon structures and visualizing results. We demonstrate the performance of Rockhopper using 2.1 billion sequenced reads from 75 RNA-seq experiments conducted with Escherichia coli, Neisseria gonorrhoeae, Salmonella enterica, Streptococcus pyogenes and Xenorhabdus nematophila. We find that the transcriptome maps generated by our algorithms are highly accurate when compared with focused experimental data from E. coli and N. gonorrhoeae, and we validate our system’s ability to identify novel small RNAs, operons and transcription start sites. Our results suggest that Rockhopper can be used for efficient and accurate analysis of bacterial RNA-seq data, and that it can aid with elucidation of bacterial transcriptomes.

In order to realize the poverty alleviation of China's current standards to achieve total poverty alleviation by 2020, the state has proposed a strategic plan for precision poverty alleviation. Based on the multidimensional poverty analysis methods and latest research results proposed by Alklre and Foster [1]. Taking the existing poverty data sources in Fengqing County of Yunnan Province as the research object, the selection of poverty dimension indicators is carried out. The multi-dimensional poverty threshold deprivation index system of five dimensions and seven indicators is designed, the multidimensional poverty measurement model is designed, and the RBF (radial basis function) neural network technology is used to measure the poverty depth of poor households. Using Python programming to achieve model programming, embedding the platform to achieve poverty measurement function, and constructing a scientific and accurate data analysis model for poverty alleviation, providing decision support for the government to carry out accurate poverty alleviation.

With the rapidly increasing popularity of economic activities, a large amount of economic data is being collected. Although such data offers super opportunities for economic analysis, its low-quality, high-dimensionality and huge-volume pose great challenges on efficient analysis of economic big data. The existing methods have primarily analyzed economic data from the perspective of econometrics, which involves limited indicators and demands prior knowledge of economists. When embracing large varieties of economic factors, these methods tend to yield unsatisfactory performance. To address the challenges, this paper presents a new framework for efficient analysis of high-dimensional economic big data based on innovative distributed feature selection. Specifically, the framework combines the methods of economic feature selection and econometric model construction to reveal the hidden patterns for economic development. The functionality rests on three pillars: (i) novel data pre-processing techniques to prepare high-quality economic data, (ii) an innovative distributed feature identification solution to locate important and representative economic indicators from multidimensional data sets, and (iii) new econometric models to capture the hidden patterns for economic development. The experimental results on the economic data collected in Dalian, China, demonstrate that our proposed framework and methods have superior performance in analyzing enormous economic data.

Structural degradation of many components of a railway system leads to many rail failures on rail networks. Therefore, there is an obvious need to use more innovative approaches by applying efficient remote survey technologies, data analysis and fast decision making. Thermography is an advanced NDT technique based on the detection of infrared radiation. This thermal technique for test and diagnostic, provides a fast, 2D and real-time inspection as well as a non-destructive testing. Unlike the passive thermography, the active thermography is a technique requiring external thermal excitation of the tested object. The two modes of thermography are based on infrared waves generated from the surface of the specimen captured by thermal imager. Effective approaches by using thermography technology for successfully studying of hidden defects in materials as well as in electrical and mechanical units of railway systems is developed. The main features of passive and different actives modes of the infrared thermography technique, important for application in railway transport field are discussed in the paper. The requirements of thermography survey of railway transport, including on components of the rolling stock and the infrastructure, the safety of railway systems and transport as well as the preventive maintenance are analysed. Experimental results from infrared monitoring of some electrical and mechanical units of railway systems are presented. Thermal imaging and innovative thermal data analysis of problem areas in catenaries connection, hidden crack detection in rail and some components of the rolling stock are shown and discussed.

In high-energy physics, particles produced in collision events decay in a format of a hierarchical tree structure, where only the final decay products can be observed using detectors. However, the large combinatorial space of possible tree structures makes it challenging to recover the actual decay process given a set of final particles. To better analyse the hierarchical tree structure, we propose a graph-based deep learning model to infer the tree structure to reconstruct collision events. In particular, we use a compact matrix representation termed as lowest common ancestor generations matrix, to encode the particle decay tree structure. Then, we introduce a perturbative augmentation technique applied to node features, aiming to mimic experimental uncertainties and increase data diversity. We further propose a supervised graph contrastive learning algorithm to utilize the information of inter-particle relations from multiple decay processes. Extensive experiments show that our proposed supervised graph contrastive learning with perturbative augmentation method outperforms state-of-the-art baseline models on an existing physics-based dataset, significantly improving the reconstruction accuracy. This method provides a more effective training strategy for models with the same parameters and makes way for more accurate and efficient high-energy particle physics data analysis.

Exploration on Data Collection and Analysis System Based on Integrated SIFT Algorithm

The ability to measure similarity or distance between data points is critical for various analytical tasks, including classification, clustering, and anomaly detection. However, traditional distance metrics such as Euclidean, Manhattan, and Hamming often struggle with mixed data types, varying attribute scales, and noise, limiting their robustness in diverse datasets. This paper introduces the Standard Deviation Score (SD-score), a novel similarity metric designed to address these challenges. By transforming traditional distance values into standard deviation units relative to a target point, the SD-score enables robust and interpretable similarity assessments. Extensive experimental evaluations demonstrate that the SD-score consistently outperforms conventional metrics in accuracy, precision, recall, and F-score within the k-Nearest Neighbors classification framework. Also, a comprehensive evaluation of the SD-score’s performance across Gaussian, skewed, and multimodal distributions showed promising results in the cluster coherence experiment, in which the Silhouette score was measured through the K-means clustering algorithm, emphasizing its adaptability to real-world data complexities. Additionally, the experiments detail improved handling of mixed numerical, ordinal, and categorical data types through a unified framework. The proposed metric incorporates inherent normalization mechanisms, reducing sensitivity to outliers and ensuring consistency across varying data scales and distributions, making it a versatile tool for real-world applications. This advancement in similarity measurement paves the way for more accurate and efficient data analysis across multiple domains.

The Internet of Medical Things (IoMT) is reforming healthcare by enabling interconnected medical devices and systems to facilitate efficient data collection, transmission, and analysis. While IoMT has significantly improved real-time monitoring and personalized care, the transmission of high-resolution medical images remains a challenge due to bandwidth constraints, latency issues, data loss, and computational overhead. Efficient and secure medical image transmission is critical to ensuring reliable diagnostics and timely patient care in this ecosystem. This research presents an optimized Deep Learning (DL) architecture developed to overcome the limitations of medical image transmission in IoMT environments. The proposed solution incorporates Convolutional Neural Networks (CNNs) for spatial feature extraction and dimensionality reduction while preserving diagnostic-critical information, and Long Short-Term Memory (LSTM) networks to manage sequential data and mitigate transmission issues such as packet loss and latency. The framework incorporates robust encryption mechanisms to ensure data security without significantly increasing computational overhead. Once predictions are made, the data is securely transferred to the cloud for further analysis and storage. Furthermore, Hippopotamus Optimization is utilised to enhance the model's performance and fine-tune hyperparameters, improving both efficiency and accuracy. Performance evaluations were conducted using real-world medical image datasets under varying IoMT network conditions. The results demonstrate that the proposed CNN-LSTM framework delivers superior performance across key metrics, like Peak Signal-to-Noise Ratio (PSNR), accuracy, F1 score, specificity, and sensitivity. Additionally, the framework optimizes encryption and decryption times and reduces bandwidth consumption, ensuring efficient and secure data transmission. This approach showcases a significant advancement in IoMT-based medical imaging, paving the way for enhanced reliability and efficiency in healthcare delivery systems.

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTElectron population analysis of accurate diffraction data. II. Application of one-center formalisms to some organic and inorganic moleculesPhilip Coppens, D. Pautler, and J. F. GriffinCite this: J. Am. Chem. Soc. 1971, 93, 5, 1051–1058Publication Date (Print):March 1, 1971Publication History Published online1 May 2002Published inissue 1 March 1971https://pubs.acs.org/doi/10.1021/ja00734a001https://doi.org/10.1021/ja00734a001research-articleACS PublicationsRequest reuse permissionsArticle Views99Altmetric-Citations36LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose Get e-Alerts

300 Background: The National Radiation Oncology Registry (NROR) is developing an IT infrastructure to support the efficient collection, aggregation, and analysis of data concerning cancer patients treated with radiotherapy across the United States. Methods: Detailed requirements were developed to emphasize the following distinguishing goals for the system: 1) streamline the process of data collection by minimizing manual data entry; 2) support the collection of patient-reported outcomes (PROs) and their linkage with clinician-observed outcomes; 3) facilitate data integration within the NROR and between the NROR and other registries; 4) allow for evolution of the system in response to new learning; and 5) promote sustainability and ongoing improvement through an open standard of interoperability. Results: An innovative technical architecture has been designed to meet the goals described. The system maximizes efficiency through automated extraction and electronic transfer of data from radiation oncology information systems. PROs are collected directly from patients via a web-based portal and linked to data submitted from participating institutions. A data dictionary has been developed based on national standards and controlled vocabularies to facilitate data integration. Contemporary forms-based database technology is used in combination with traditional relational technology to permit both flexible evolution of the system and efficient data analysis. A unique “gateway” architecture and open transmission standard makes it possible for commercial products to interoperate with the registry in the future. Conclusions: The NROR prototype system will be launched later this year to collect data for a pilot registry of prostate cancer patients. Its performance and usability will be important factors in the success of the pilot. If effective, the platform will support the expansion of the NROR to benefit patients, clinicians, researchers, payors, device manufacturers, and policy-makers by capturing reliable, population-based information on radiation treatment delivery and health outcomes.