Article
Multidisciplinary Sciences
Amir Bahmani, Arash Alavi, Thore Buergel, Sushil Upadhyayula, Qiwen Wang, Srinath Krishna Ananthakrishnan, Amir Alavi, Diego Celis, Dan Gillespie, Gregory Young, Ziye Xing, Minh Hoang Huynh Nguyen, Audrey Haque, Ankit Mathur, Josh Payne, Ghazal Mazaheri, Jason Kenichi Li, Pramod Kotipalli, Lisa Liao, Rajat Bhasin, Kexin Cha, Benjamin Rolnik, Alessandra Celli, Orit Dagan-Rosenfeld, Emily Higgs, Wenyu Zhou, Camille Lauren Berry, Katherine Grace Van Winkle, Kevin Contrepois, Utsab Ray, Keith Bettinger, Somalee Datta, Xiao Li, Michael P. Snyder
Summary: The rapid growth of biomedical data presents enormous opportunities for improving health outcomes, as well as new challenges. The authors have developed the Personal Health Dashboard, an end-to-end solution for big biomedical data analytics that utilizes state-of-the-art security and scalability technologies.
NATURE COMMUNICATIONS
(2021)
Article
Biochemical Research Methods
Marta Moreno, Ricardo Vilaca, Pedro G. Ferreira
Summary: This paper reviews the main steps and concepts in machine learning pipelines and scalable data science for gene expression analysis. It discusses the benefits of using the Dask framework and provides case studies to demonstrate its effectiveness in boosting data science applications.
BMC BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Simone Pallotta, Silvia Cascianelli, Marco Masseroli
Summary: RGMQL is a specialized R/Bioconductor package designed to extract, combine, process, and compare heterogeneous omics datasets. By leveraging GMQL's capabilities and overcoming its limitations, RGMQL provides full interoperability with other packages in the R/Bioconductor framework.
BMC BIOINFORMATICS
(2022)
Article
Computer Science, Hardware & Architecture
Claudia Campolo, Giacomo Genovese, Gurtaj Singh, Antonella Molinaro
Summary: This paper presents a comprehensive framework based on MQTT and LwM2M semantics to address the challenges of Federated Learning in low communication footprint, robustness and interoperability for IoT devices acting as FL clients. The viability of the proposal and its communication efficiency compared to a literature solution are evaluated through a realistic Proof-of-Concept (PoC) under different link settings and for different datasets.
Article
Multidisciplinary Sciences
Nathan C. Sheffield, Vivien R. Bonazzi, Philip E. Bourne, Tony Burdett, Timothy Clark, Robert L. Grossman, Ola Spjuth, Andrew D. Yates
Summary: The biomedical research community is heavily investing in biomedical cloud platforms, which hold great promise for addressing big data challenges and ensuring reproducibility in biology. However, cloud platforms themselves do not automatically support FAIRness, and various challenges, such as platform lock-in and difficulty integrating across platforms, have emerged. To alleviate these difficulties, the prioritization of microservices and modular data access in smaller chunks or summarized form is proposed to improve interoperability.
Article
Computer Science, Information Systems
Khulud Salem Alshudukhi, Maher Ali Khemakhem, Fathy Elbouraey Eassa, Kamal Mansur Jambi
Summary: In this research, we propose a blockchain security manager based on microservice technology for federated cloud systems in an IoT environment. This security manager enables transaction exchange between different cloud providers and ensures security and access control protection. Interoperability is achieved through smart contracts.
Review
Chemistry, Analytical
Maryam Khoubnasabjafari, Mohamad Reza Afshar Mogaddam, Elaheh Rahimpour, Jafar Soleymani, Amir Ata Saei, Abolghasem Jouyban
Summary: Metabolomics research, particularly in breathomics, is advancing rapidly in disease diagnosis, focusing on the collection and analysis methods of exhaled breath. Analyzing metabolites in exhaled breath is valuable for disease diagnosis, indicating the promising future of breathomics as a noninvasive discipline. Wide variations in reported metabolite concentrations from breathomics studies should be addressed with more accurate analytical methods and sophisticated numerical algorithms.
CRITICAL REVIEWS IN ANALYTICAL CHEMISTRY
(2022)
Article
Computer Science, Information Systems
Alixandra Taylor, Austin Kugler, Praneeth Babu Marella, Gaby G. Dagher
Summary: Achieving interoperability between healthcare providers is a major challenge. Current systems for managing prescription records suffer from data siloing, unnecessary record duplication, and slow record transfers. Solving these problems requires creating a patient-centric and interoperable prescription management system.
Article
Computer Science, Theory & Methods
Andrea Cimmino, Juan Cano-Benito, Alba Fernandez-Izquierdo, Christos Patsonakis, Apostolos C. Tsolakis, Raul Garcia-Castro, Dimosthenis Ioannidis, Dimitrios Tzovaras
Summary: Demand Response (DR) is essential for the EU energy markets, and standardising demand response data models has been a significant effort. However, existing proposals are usually centralised and hindered by heterogeneous data formats and models. This article presents a tool called CIM that allows DR systems to decentralise their architectures and exchange data transparently, even with systems following different DR standards. CIM provides a solid security framework and semantic interoperability layer for data exchange.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Li-Minn Ang, Kah Phooi Seng
Summary: Recent advances in hyperspectral sensing technology have enabled the acquisition of hundreds of spectral bands in a single capture, containing a large volume of spatial-spectral information. This hyperspectral data, especially when combined with temporal information, poses challenges in terms of data generation speed and volume, leading to Big data challenges in remote sensing applications.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Deliya B. Wesley, Joseph Blumenthal, Shrenikkumar Shah, Robin A. Littlejohn, Zoe Pruitt, Ram Dixit, Chun-Ju Hsiao, Christine Dymek, Raj M. Ratwani
Summary: Despite the proliferation of apps for collecting patient-reported outcomes, seamless integration with EHRs remains a challenge. This study utilized PRO standards to develop a multitiered architecture for integrating standardized PRO data with EHRs. The results demonstrated the feasibility, security, and efficiency of this approach for real-time patient data access in clinical encounters.
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
(2021)
Article
Computer Science, Theory & Methods
Jacopo Soldani, Antonio Brogi
Summary: This paper provides a structured overview and qualitative analysis of currently available techniques for anomaly detection and root cause analysis in modern multi-service applications. It also discusses some open challenges and research directions stemming out from the analysis.
ACM COMPUTING SURVEYS
(2023)
Article
Multidisciplinary Sciences
Niek F. de Jonge, Joris J. R. Louwen, Elena Chekmeneva, Stephane Camuzeaux, Femke J. Vermeir, Robert S. Jansen, Florian Huber, Justin J. J. van der Hooft
Summary: The authors have developed a machine learning approach, MS2Query, to facilitate chemical discovery in mass spectral libraries. This tool increases the annotation rate and aids in assessing novelty in metabolomics datasets. By integrating mass spectral embedding-based chemical similarity predictors and detected precursor masses, MS2Query offers a more reliable and efficient alternative for searching structurally related molecules in metabolomics studies.
NATURE COMMUNICATIONS
(2023)
Article
Chemistry, Analytical
Ioannis Tzanettis, Christina-Maria Androna, Anastasios Zafeiropoulos, Eleni Fotopoulou, Symeon Papavassiliou
Summary: This paper presents a modern observability approach and pilot implementation for tackling data fusion aspects in edge and cloud computing orchestration platforms. By integrating signals from multiple open-source monitoring and observability frameworks, it improves observability and enables insights and root cause analyses related to performance issues.
Review
Biochemical Research Methods
Sandra Taylor, Matthew Ponzini, Machelle Wilson, Kyoungmi Kim
Summary: Missing values are common in high-throughput mass spectrometry data. Two strategies are available to address missing values: imputation and imputation-free methods. This study reviews the impact of sample size and percentage of missing values on statistical inference, comparing the performance of different methods.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Medical Laboratory Technology
Niclas Rollborn, Jenny Jakobsson, Andrew Campbell, Gunnar Nordin, Mathias Karlsson, Anders Larsson, Kim Kultima
Summary: The study found significant deviations in FLC measurements among different methods, with lower deviation when only nephelometry was used. There were significant differences in coefficient of variation between the two main FLC assays, and generally good coefficient of determination between reagents and instrument platforms.
CLINICAL BIOCHEMISTRY
(2023)
Article
Chemistry, Analytical
Thomas Hankemeier, Luojiao Huang, Nicolas Drouin, Jason Causon, Agnieszka Wegrzyn, Jose Castro-Perez, Ronan Fleming, Amy Harms
Summary: Accurate reconstruction of metabolic pathways is crucial for understanding metabolomics changes and biological processes in diseases. A tracer-based metabolomics strategy using stable isotope labeled precursors can trace pathways by measuring the transformation of metabolites. By quantifying labeled metabolite substructures, a new method achieves simultaneous isotopic labeling information at the intact metabolite and moiety level. This method was applied to trace the fate of labeled atoms in human-induced pluripotent stem cell-derived neurons, revealing the pathway reconstruction of de novo glutathione synthesis and its alteration under oxidative stress and neurodegeneration.
ANALYTICAL CHEMISTRY
(2023)
Article
Behavioral Sciences
Fiona A. Hagenbeek, Jenny van Dongen, Rene Pool, Peter J. Roetman, Amy C. Harms, Jouke Jan Hottenga, Cornelis Kluft, Olivier F. Colins, Catharina E. M. van Beijsterveldt, Vassilios Fanos, Erik A. Ehli, Thomas Hankemeier, Robert R. J. M. Vermeiren, Meike Bartels, Sebastien Dejean, Dorret Boomsma
Summary: This study introduces and demonstrates the potential of an integrated multi-omics approach in investigating the biology of childhood aggressive behavior. By using single- and integrative multi-omics models, the researchers identified biomarkers for subclinical aggression and studied the connections among these biomarkers. The study found strong associations between DNA methylation, amino acids, and parental non-transmitted polygenic scores with traits like ADHD, Autism Spectrum Disorder, intelligence, smoking initiation, and self-reported health. Aggression-related omics traits were also linked to known and novel risk factors such as inflammation, carcinogens, and smoking.
Article
Clinical Neurology
Sonja Kosek, Barbro Persson, Rui Rodrigues, Clas Malmestrom, Anna Rostedt Punga, Joachim Burman
Summary: This study aimed to estimate the 5-year incidence rate of autoimmune encephalitis (AE) and paraneoplastic neurological syndrome (PNS) in Sweden. The results showed that the incidence rate of AE and PNS doubled from 2015 to 2019.
ACTA NEUROLOGICA SCANDINAVICA
(2023)
Article
Biochemistry & Molecular Biology
Henning Otto Brinkhaus, Kohulan Rajan, Jonas Schaub, Achim Zielesny, Christoph Steinbeck
Summary: Recent years have witnessed a significant growth in the development of deep learning and AI-based molecular informatics. Although there is increasing interest in applying deep learning to various aspects of molecular informatics, the lack of FAIR and open data poses a constraint on the application of AI in this field. However, with the rise of open science practices and initiatives supporting open data and software, researchers in molecular informatics are encouraged to embrace open science and contribute to open repositories. The combination of open-source deep learning frameworks, cloud computing platforms, and a culture promoting open science provides opportunities for the continued growth of AI-driven molecular informatics.
CURRENT OPINION IN STRUCTURAL BIOLOGY
(2023)
Review
Plant Sciences
Kanchana Pandian, Minami Matsui, Thomas Hankemeier, Ahmed Ali, Emiko Okubo-Kurihara
Summary: Single-cell metabolomics is a powerful tool for understanding cellular heterogeneity and unraveling the mechanisms of biological phenomena. It holds great promise in plant research, especially when cellular heterogeneity affects different biological processes. Metabolomics, as a detailed phenotypic analysis, is expected to provide answers to previously unaddressed questions, leading to improved crop production, better understanding of disease resistance, and other applications.
Article
Multidisciplinary Sciences
Lida Zafeiri, Torbjoern akerfeldt, Andreas Tolf, Kristina Carlson, Alkistis Skalkidou, Joachim Burman
Summary: This study investigated the relationship between AMH levels and age and reproductive potential in MS patients treated with AHSCT. The results showed that although AMH concentration significantly decreased after AHSCT, six patients successfully conceived despite low concentrations, suggesting that high-dose cyclophosphamide treatment may not negatively impact fertility.
Article
Chemistry, Multidisciplinary
Mahnoor Zulfiqar, Luiz Gadelha, Christoph Steinbeck, Maria Sorokina, Kristian Peters
Summary: Mapping the chemical space of compounds to chemical structures remains a challenge in metabolomics. Many novel computational methods and tools have been developed to enable chemical structure annotation to known and unknown compounds. Here, we present an automated and reproducible Metabolome Annotation Workflow (MAW) for untargeted metabolomics data. MAW combines tandem mass spectrometry input data pre-processing, spectral and compound database matching, computational classification, and in silico annotation to facilitate and automate complex annotation.
JOURNAL OF CHEMINFORMATICS
(2023)
Article
Multidisciplinary Sciences
Stephanie Herman, Staffan Arvidsson McShane, Christina Zjukovskaja, Payam Emami Khoonsari, Anders Svenningsson, Joachim Burman, Ola Spjuth, Kim Kultima
Summary: Currently, there is a need for biomarkers to assist in early diagnosis of progressive multiple sclerosis (PMS). Research has shown that a selection of cerebrospinal fluid metabolites can differentiate between PMS and its preceding phenotype. By using predictive methods, highly confident predictions can be made for patients who will develop PMS within three years. In a clinical trial, the methodology was applied to PMS patients receiving intrathecal treatment, and it was found that 68% of the patients decreased their similarity to the PMS phenotype after one year of treatment.
Article
Biology
Pedro de Atauri, Carles Foguet, Marta Cascante
Summary: Metabolic Control Analysis (MCA) has revealed that control of metabolic pathways is distributed among many enzymes and depends on kinetic determinants in addition to stoichiometric structure. By incorporating kinetic determinants and ruling out enzymes with low control coefficients, MCA can improve the prediction and identification of therapeutic targets in drug discovery.
Article
Biochemical Research Methods
Zhengzheng Zhang, Madhulika Singh, Alida Kindt, Agnieszka B. Wegrzyn, Mackenzie J. Pearson, Ahmed Ali, Amy C. Harms, Paul Baker, Thomas Hankemeier
Summary: The importance of lipidomics research in understanding various diseases, including metabolism, cancer, and the recent COVID-19 pandemic, has led to the development of a targeted HILIC-MS/MS method that allows for comprehensive analysis of lipid species. This method overcomes the challenges posed by the diverse structures and properties of lipids in biological systems, and provides accurate quantitation of lipid concentrations at the fatty acyl chain level. The applicability of this method has been demonstrated through the discovery of differential lipid features related to COVID-19 severity, highlighting its potential for future investigations of the lipidome in different disease contexts.
JOURNAL OF CHROMATOGRAPHY A
(2023)
Article
Clinical Neurology
Elisa Longinetti, Simon Englund, Joachim Burman, Katharina Fink, Anna Fogdell-Hahn, Martin Gunnarsson, Jan Hillert, Annette Magdalene Langer-Gould, Jan Lycke, Petra Nilsson, Jonatan Salzer, Anders Svenningsson, Johan Mellergard, Tomas Olsson, Fredrik Piehl, Thomas Frisell
Summary: This study analyzed a Swedish nationwide observational study on RRMS to identify trajectories of processing speed and physical disability after DMT start. The results showed that patients' processing speed remained stable over time, while those with moderate physical disability experienced deterioration in physical function. However, there was a strong association between processing speed and disability.
JOURNAL OF NEUROLOGY NEUROSURGERY AND PSYCHIATRY
(2023)
Article
Clinical Neurology
Faisal Hayat Nazir, Anna Wiberg, Malin Mueller, Sara Mangsbo, Joachim Burman
Summary: Multiple sclerosis is a complex and heterogeneous disease that often starts as a clinically isolated syndrome. Autoantibodies play an important role in its pathogenesis, but their target has been difficult to identify. Cell-based methods have been developed as an alternative strategy for detecting autoantibodies. This study explored differences in antibody binding to oligodendroglial and neuronal cell-lines in serum and CSF samples from multiple sclerosis patients and controls, and found that the binding of immunoglobulin G from CSF to the human oligodendroglioma cell-line was the best discriminator between patients and controls, with a high sensitivity and specificity. The cell-based ELISA showed a high degree of accuracy in discriminating between multiple sclerosis patients and controls, with the disease course being the major determinant for antibody binding.
BRAIN COMMUNICATIONS
(2023)
Article
Chemistry, Multidisciplinary
Stefan Kuhn, Heinz Kolshorn, Christoph Steinbeck, Nils Schloerer
Summary: NMRshiftDB, now renamed as nmrshiftdb2, is a long-running open-source and open-content database in the field of open data in chemistry. After 20 years, the success of the project is evaluated, and lessons learned are presented for similar projects.
MAGNETIC RESONANCE IN CHEMISTRY
(2023)
