Review
Biochemistry & Molecular Biology
Alessia Paganelli, Valeria Righi, Elisabetta Tarentini, Cristina Magnoni
Summary: Metabolomic profiling is a promising field for studying metabolites in biological systems. Skin metabolomics has potential applications in the diagnosis, prognosis, and therapy of skin disorders. This review provides a comprehensive overview of published studies in skin metabolomics, with a focus on inflammatory dermatoses and immune-mediated cutaneous disorders.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Computer Science, Information Systems
Minho Kim, Hyuk-Chul Kwon
Summary: This study proposes an unsupervised disambiguation method based on the Korean WordNet, which outperforms supervised disambiguation methods by calculating the chi(2) statistic between related words to resolve the data deficiency problem.
Article
Biochemistry & Molecular Biology
Supratim Mukherjee, Dimitri Stamatis, Jon Bertsch, Galina Ovchinnikova, Jagadish Chandrabose Sundaramurthi, Janey Lee, Mahathi Kandimalla, I-Min A. Chen, Nikos C. Kyrpides, T. B. K. Reddy
Summary: The Genomes OnLine Database (GOLD) is a manually curated collection of genome projects and their metadata, with over 1.17 million entries. Users can browse, search, and input project details in GOLD, ensuring accurate metadata documentation for analysis. The database also imports projects from public repositories to maintain a reference dataset for the scientific community.
NUCLEIC ACIDS RESEARCH
(2021)
Article
Telecommunications
Sarat Chandra Bobbili, Sanidhay Bhambay, Parimal Parag
Summary: Timely reception of information is crucial in cyber-physical systems, and a scheme utilizing temporal correlation in source messages to send differential information can improve timeliness performance even without feedback when codeword lengths are chosen judiciously.
IEEE COMMUNICATIONS LETTERS
(2021)
Article
Computer Science, Information Systems
Cheng-Hsiung Hsieh, Kuan-Yu Chen, Meng-Yuan Jiang, Jiun-Jian Liaw, Jungpil Shin
Summary: This paper proposes a low-cost and effective approach using image processing schemes to estimate the concentration of PM2.5. The approach consists of four stages and utilizes features extracted from images and relative humidity to build a support vector regression model. Experimental results demonstrate that the proposed method outperforms other methods.
Article
Statistics & Probability
Majid Noroozi, Ramchandra Rimal, Marianna Pensky
Summary: The paper investigates the Popularity Adjusted Block model (PABM) for modeling networks in biological sciences, providing estimators and upper bounds for estimation and clustering errors. It uses the Sparse Subspace Clustering (SSC) approach for community partitioning, showing advantages for modeling similarity and functional networks.
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
(2021)
Article
Engineering, Electrical & Electronic
Jing Zhang, Shuguang Zhang
Summary: This paper discusses the block sparse signal recovery when partial prior support information is available, and establishes a high order block RIP condition for the proposed weighted l(2)/l(1-2) minimization. Numerical experiments demonstrate the excellent recovery performance of this method.
DIGITAL SIGNAL PROCESSING
(2022)
Article
Medicine, Legal
Emanuela Locci, Matteo Stocchero, Rossella Gottardo, Fabio De-Giorgio, Roberto Demontis, Matteo Nioi, Alberto Chighine, Franco Tagliaro, Ernesto D'Aloja
Summary: Estimation of the post-mortem interval (PMI) remains a concern in forensic science, with eye compartments such as vitreous humour being studied for their resistance to post-mortem modifications. Potassium concentration in the vitreous humour has been found to better correlate with PMI estimation. Recent research suggests that potassium and metabolite concentrations may rise due to similar biological mechanisms, with the metabolomic profile showing greater predictive power than potassium behavior in estimating the time since death within the first 24 hours PMI window.
INTERNATIONAL JOURNAL OF LEGAL MEDICINE
(2021)
Article
Environmental Sciences
Xingdi Chen, Peng Kong, Peng Jiang, Yanlan Wu
Summary: This study proposed a deep Bayesian PM2.5 estimation model that considers multiple scales and incorporates satellite and reanalysis data to improve model generalization ability and accuracy. The model demonstrated higher accuracy and better generalization compared to other models, with a focus on reducing overfitting and effectively estimating PM2.5 concentrations.
Article
Computer Science, Software Engineering
A. Ulmer, D. Sessler, J. Kohlhammer
Summary: The study introduces a web-based progressive approach, ProBGP, to visually analyze BGP update routes by highlighting routing changes between autonomous systems through geographic visualization. Researchers created a novel data processing algorithm and combined it with progressively updating visualization, allowing queries of all log records since 1999.
COMPUTER GRAPHICS FORUM
(2021)
Editorial Material
Environmental Sciences
A. Gupta, R. S. Govindaraju, R. Morbidelli, C. Corradini
Summary: Bayes theorem provides a framework for parameter estimation by combining prior and sample information, but the availability and vagueness of prior knowledge may require the use of a reference prior for objective analysis. This study pursues an information-theoretic approach to derive reference priors and compares them to results obtained using a uniform prior.
WATER RESOURCES RESEARCH
(2022)
Article
Engineering, Biomedical
Yihua Zhong, Xu Zhang, Jonathan Beckel, William C. de Groat, Changfeng Tai
Summary: A new axonal conduction model was used to analyze the interaction between intracellular sodium concentration and membrane potential oscillation in axonal conduction block induced by high-frequency biphasic stimulation. The results show that the block duration can be shortened by increasing the HFBS intensity, and the block can be maintained if the intensity is above a certain threshold.
JOURNAL OF NEURAL ENGINEERING
(2022)
Article
Automation & Control Systems
Yan Chen, Zhiyu Lyu, Yimin Hou
Summary: Dehazing based on deep learning neural networks has achieved remarkable results, but most existing models only work well on synthetic images and struggle with realistic hazy images. To tackle this challenge, a novel Image Prior Dehazing Network (IPDNet) is developed, which consists of two sub-networks and a learnable fusion block. IPDNet offers benefits such as effective generation of high-quality dehazed images at low computational cost, enhancement of dehazing performance on realistic hazy images through an image preprocessing block, and flexible feature extraction on limited datasets. Extensive experiments show that IPDNet outperforms other state-of-the-art methods on synthetic and realistic datasets, contributing to improving traffic safety in adverse weather conditions.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Nutrition & Dietetics
Joana Sampaio, Joana Carvalho, Andreia Pizarro, Joana Pinto, Andre Moreira, Patricia Padrao, Paula Guedes de Pinho, Pedro Moreira, Renata Barros
Summary: The MED-E project aims to assess the effect of sustainable healthy diet (SHD) and multicomponent training (MT) interventions on various health outcomes in the elderly. The study assigned older adults into four groups and evaluated blood biomarkers, metabolic profile alterations, dietary intake, physical fitness, body composition, cognitive function, quality of life, and geographical data. The results of the MED-E project will provide valuable information about the importance and benefits of combined SHD and MT interventions for healthy aging policies.
Article
Biochemical Research Methods
Zhuoran Xu, Luigi Marchionni, Shuang Wang
Summary: The study focuses on the application of network information to prioritize candidate disease-associated omics profiles. The metabolome, which connects genotypes and phenotypes, has gained attention. A multi-omics network approach was developed to simultaneously prioritize disease-associated metabolites and gene expressions. The framework, called MultiNEP, effectively addresses the issue of network imbalances and outperforms competing methods in simulation studies and human cancer cohorts.
Article
Ecology
Maxwell J. Farrell, Mohamad Elmasri, David A. Stephens, T. Jonathan Davies
Summary: This study predicts missing links in global mammal-parasite networks using available data and demonstrates how these predictions can guide the collection of interaction data, ultimately increasing the completeness of global species interaction networks. The study provides insights into the use of phylogenies for predicting host-parasite interactions and highlights the importance of iterated prediction and targeted search in collecting information on host-parasite interactions.
JOURNAL OF ANIMAL ECOLOGY
(2022)
Article
Hematology
Kate Downes, Xuefei Zhao, Nicholas S. Gleadall, Harriet McKinney, Carly Kempster, Joana Batista, Patrick L. Thomas, Matthew Cooper, James Michael, Roman Kreuzhuber, Katherine Wedderburn, Kathryn Waller, Bianca Varney, Hippolyte Verdier, Neline Kriek, Sofie E. Ashford, Kathleen E. Stirrups, Joanne L. Dunster, Steven E. McKenzie, Willem H. Ouwehand, Jonathan M. Gibbins, Jing Yang, William J. Astle, Peisong Ma
Summary: Interindividual variation in platelet response to activation is heritable, with a common intronic variant, rs10886430, associated with increased sensitivity of platelets to activate through PAR-1.
Article
Mathematical & Computational Biology
Daniel Rodriguez Duque, David A. Stephens, Erica E. M. Moodie, Marina B. Klein
Summary: This article discusses the use of Bayesian methods for inference of dynamic treatment regimes, which allows for individual-level quantification of uncertainty and personalized decision-making.
Article
Computer Science, Theory & Methods
Willem van den Boom, Ajay Jasra, Maria De Iorio, Alexandros Beskos, Johan G. Eriksson
Summary: Markov chain Monte Carlo (MCMC) is a powerful method for approximating posterior distributions. However, MCMC is not naturally compatible with modern highly parallel computing environments. In this study, we propose a new method that couples MCMC chains derived from sequential Monte Carlo (SMC) algorithms, achieving fully parallel unbiased Monte Carlo estimation. This method has desirable theoretical properties and can handle more challenging target distributions.
STATISTICS AND COMPUTING
(2022)
Article
Statistics & Probability
Erica E. M. Moodie, David A. Stephens
Summary: This article reviews the core principles and methods of causal inference and important developments in the field, highlighting connections with traditional associational statistical methods.
CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE
(2022)
Article
Statistics & Probability
Marco Molinari, Andrea Cremaschi, Maria De Iorio, Nishi Chaturvedi, Alun Hughes, Therese Tillin
Summary: This paper proposes a novel approach to estimate multiple graphical models for analyzing temporal patterns of association among a set of metabolites across different groups of patients. The study focuses on a tri-ethnic cohort study conducted in the UK, aiming to identify potential ethnic differences in metabolite levels and associations and understand the different risks of cardio-metabolic disorders across ethnicities. Within a Bayesian framework, the authors employ a nodewise regression approach to infer the structure of the graphs, taking into account information across time and ethnicities. The proposed method is implemented using the software Stan, which performs posterior inference using Hamiltonian Monte Carlo sampling.
JOURNAL OF APPLIED STATISTICS
(2022)
Article
Biology
Armando Turchetta, Erica E. M. Moodie, David A. Stephens, Sylvie D. Lambert
Summary: This study provides a Bayesian approach for calculating sample size, allowing for more accurate and robust estimates that account for uncertainty in inputs through the "two priors" method. Compared to standard frequentist formulae, this methodology relies on fewer assumptions, incorporates pre-trial knowledge, and shifts the focus to the MDD.
Article
Biochemical Research Methods
Cecilia Wieder, Rachel P. J. Lai, Timothy M. D. Ebbels
Summary: This study evaluates the applicability of single sample pathway analysis methods in metabolomics, demonstrating the potential of ssPA methods through benchmarking with semi-synthetic metabolomics data and a case study on inflammatory bowel disease. Clustering/dimensionality reduction-based methods provide higher precision at moderate-to-high effect sizes, offering a deeper level of interpretation that conventional methods cannot provide.
BMC BIOINFORMATICS
(2022)
Article
Statistics & Probability
Shengxiao Vincent Feng, Willem van den Boom, Maria De Iorio, Gladi J. Thng, Jerry K. Y. Chan, Helen Y. Chen, Kok Hian Tan, Michelle Z. L. Kee
Summary: Maternal depression and anxiety during pregnancy have long-term societal impacts. This study proposes a Bayesian framework to jointly model seven outcomes of maternal mental health over time, revealing distinct trajectories and cautioning against the use of hair corticosteroids as a biomarker for mental health progression.
JOURNAL OF APPLIED STATISTICS
(2022)
Review
Biochemistry & Molecular Biology
Timothy M. D. Ebbels, Justin J. J. van der Hooft, Haley Chatelaine, Corey Broeckling, Nicola Zamboni, Soha Hassoun, Ewy A. Mathe
Summary: The computational metabolomics field brings together experts from various disciplines to maximize the impact of metabolomics research. Advances in technology have generated complex datasets that require processing, annotation, modeling, and interpretation. Techniques for visualization, integration, and interpretation of metabolomics data have evolved alongside the development of databases and knowledge resources. This review highlights recent advances and discusses opportunities and innovations in response to challenges in the field.
CURRENT OPINION IN CHEMICAL BIOLOGY
(2023)
Article
Nutrition & Dietetics
Alexis C. Wood, Goncalo Graca, Meghana Gadgil, Mackenzie K. Senn, Matthew A. Allison, Ioanna Tzoulaki, Philip Greenland, Timothy Ebbels, Paul Elliott, Mark O. Goodarzi, Russell Tracy, Jerome I. Rotter, David Herrington
Summary: This study investigated the relationship between red meat intake and inflammation. The results showed no significant association between processed or unprocessed red meat and markers of inflammation. However, unprocessed red meat intake was inversely associated with the plasma metabolite glutamine, which was also inversely associated with C-reactive protein levels.
AMERICAN JOURNAL OF CLINICAL NUTRITION
(2023)
Article
Nutrition & Dietetics
Alexis C. Wood, Mark O. Goodarzi, Mackenzie K. Senn, Meghana D. Gadgil, Goncalo Graca, Matthew A. Allison, Ioanna Tzoulaki, Michael Y. Mi, Philip Greenland, Timothy Ebbels, Paul Elliott, Russell P. Tracy, David M. Herrington, Jerome I. Rotter
Summary: Avocado intake is associated with metabolomic biomarkers related to glycemia. These biomarkers are strongly associated with lower fasting glucose, lower fasting insulin, and lower incidence of type 2 diabetes. However, the association between avocado intake and fasting insulin is attenuated when controlling for body mass index.
JOURNAL OF NUTRITION
(2023)
Article
Mathematical & Computational Biology
Armando Turchetta, Nicolas Savy, David A. A. Stephens, Erica E. M. Moodie, Marina B. B. Klein
Summary: Forecasting recruitments is crucial in the monitoring phase of multicenter studies. The Poisson-Gamma recruitment model is a popular technique based on the doubly stochastic Poisson process. However, the assumption of constant recruitment rates is often unrealistic in real studies. This paper presents a flexible generalization of the model, allowing varying enrollment rates over time using B-splines. The approach is shown to be suitable for a wide range of recruitment behaviors in simulations and is applied to estimate recruitment progression in a Canadian Co-infection Cohort.
STATISTICS IN MEDICINE
(2023)
Article
Multidisciplinary Sciences
Parsa Akbari, Dragana Vuckovic, Luca Stefanucci, Tao Jiang, Kousik Kundu, Roman Kreuzhuber, Erik L. Bao, Janine H. Collins, Kate Downes, Luigi Grassi, Jose A. Guerrero, Stephen Kaptoge, Julian C. Knight, Stuart Meacham, Jennifer Sambrook, Denis Seyres, Oliver Stegle, Jeffrey M. Verboon, Klaudia Walter, Nicholas A. Watkins, John Danesh, David J. Roberts, Emanuele Di Angelantonio, Vijay G. Sankaran, Mattia Frontini, Stephen Burgess, Taco Kuijpers, James E. Peters, Adam S. Butterworth, Willem H. Ouwehand, Nicole Soranzo, William J. Astle
Summary: The authors identify genetic variation associated with properties of the internal biological structures of blood cells and demonstrate how this can contribute to our understanding of the cellular mechanisms causing disease.
NATURE COMMUNICATIONS
(2023)
Review
Endocrinology & Metabolism
Michael T. Judge, Timothy M. D. Ebbels
Summary: This review aims to broaden the application of automated annotation tools by discussing the key ideas of spectral matching and describing a set of terms for classifying this information, thus advancing standards for communicating annotation confidence. Additionally, it hopes to facilitate collaboration between chemical data scientists, software developers, and the NMR metabolomics community for long-term software solutions.
