Article
Automation & Control Systems
Xiaolong Chen, Yi Chai, Qie Liu, Pengfei Huang, Linchuan Fan
Summary: In this paper, a novel Bayesian sparse multiple kernel-based identification method (BSMKM) for multiple-input single-output (MISO) Hammerstein system is proposed. The method represents the nonlinear part and the linear part using basis-function model and finite impulse response model respectively and estimates all unknown model parameters through hierarchical prior distribution and full Bayesian method based on variational Bayesian inference.
Article
Genetics & Heredity
Yuqing Yang, Xin Wang, Kaikun Xie, Congmin Zhu, Ning Chen, Ting Chen
Summary: The study proposes a computational model, the kLDM model, which estimates multiple association networks corresponding to specific environmental conditions in microbial ecosystems. Results demonstrate the effectiveness of the kLDM model on various datasets, showing better performance compared to other methods in analyzing microbial relationships. The model was able to reveal complex associations within microbial ecosystems, particularly showing advantages in studies regarding gut microbes and cancer patients.
GENOMICS PROTEOMICS & BIOINFORMATICS
(2021)
Article
Environmental Sciences
Wanzhou Wang, Shujian Li, Jineng Sun, Yanan Huang, Fengpeng Han, Zhi Li
Summary: The loess-covered region, accounting for about 10% of global land surface, has a complex and controversial groundwater recharge mechanism. This study on the tablelands of China's Loess Plateau reveals the involvement of both regional-scale piston flow and local-scale preferential flow in groundwater recharge, with piston flow dominating. The recharge forms are controlled by vadose zone thickness, and precipitation is the main driver of recharge rates. The findings are important for groundwater modeling and studying recharge mechanisms in thick aquifers.
SCIENCE OF THE TOTAL ENVIRONMENT
(2023)
Article
Environmental Sciences
Gregory L. Britten, Yara Mohajerani, Louis Primeau, Murat Aydin, Catherine Garcia, Wei-Lei Wang, Benoit Pasquier, B. B. Cael, Francois W. Primeau
Summary: Hierarchical Bayesian modeling is increasingly used in environmental science to describe statistical complexities in large compiled datasets, offering benefits such as flexibility, reduction of uncertainty, and incorporation of prior scientific information. Its versatility and feasibility for diverse environmental applications are highlighted, enhanced by recent developments in Markov Chain Monte Carlo algorithms and user-friendly software implementations.
FRONTIERS IN ENVIRONMENTAL SCIENCE
(2021)
Article
Mathematical & Computational Biology
Daniel Kang, Christopher Coffey, Brian Smith, Ying Yuan, Qian Shi, Jun Yin
Summary: HCOMBS is a hierarchical Bayesian clustering design for multiple biomarker subgroups, aimed at reducing sample size, differentiating effect sizes, and controlling operating characteristics. Compared to Simon's Optimal two-stage design, simulations show that HCOMBS requires fewer participants per treatment arm with better controlled family-wise error rate and desirable power.
STATISTICS IN MEDICINE
(2021)
Article
Engineering, Civil
Carlos H. R. Lima, Hyun-Han Kwon, Yong-Tak Kim
Summary: A hierarchical Bayesian mixture model was developed for daily rainfall forecasts using stochastic weather models, improving forecast skills through the inclusion of predictors and reduction of parameter uncertainties. The model structure allows for better understanding and estimation of regional parameters, tested with 47 years of data from 60 gauges in South Korea. The model showed improvements in skill scores over climatology and persistence reference models up to a three days lead time, with potential applications in real-time daily rainfall forecasts globally.
JOURNAL OF HYDROLOGY
(2021)
Article
Biochemistry & Molecular Biology
Ben Galvin, Jay Jones, Michaela Powell, Katherine Olin, Matthew Jones, Thomas Robbins
Summary: This research proposes a method to expand the taxonomic resolution of PCR diagnostic systems for pathogen identification by leveraging known genetic variations and post-PCR melting curve analysis. The approach can be used to monitor outbreaks, observe circulation patterns, and guide testing practices.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Biochemical Research Methods
Teng Zhang, Shao-wu Zhang, Jian Feng, Bei Zhang
Summary: This study proposes a Bayesian hierarchical mixture model (m(6)Aexpress-BHM) to predict m(6)A regulation of gene expression in multiple groups of MeRIP-seq experiments. The model demonstrates high predicting precision and robustness based on evaluations on simulated data. The application of m(6)Aexpress-BHM on real-world datasets reveals the regulatory function of m(6)A in immune response and suggests its potential role in influencing the expression of PD-1/PD-L1 through regulating interacting genes.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Engineering, Electrical & Electronic
Jun Xiao, Rui Zhao, Kin-Man Lam
Summary: Sparse models have been successful in image denoising and have advantages over deep-learning-based methods, such as not requiring a large amount of training data and having better generalization capability.
SIGNAL PROCESSING-IMAGE COMMUNICATION
(2021)
Article
Chemistry, Analytical
Zi-Chun Fan, Zhuang Li, Xian-Yong Wei, Qian-Qian Kong, Zhong-Qiu Liu, Li Li, Jia-Hao Li, Fan Yin, Kun-Lang Lu, Zhi-Min Zong
Summary: This study proposed a method to simultaneously solve the clean utilization of lignite and the detection of catechol (CC) and hydroquinone (HQ). By preparing porous carbon materials with well-developed microporous and mesoporous structures, an electrochemical sensor with excellent electrocatalytic activity was constructed. The prepared sensor exhibited a wider linear concentration range and lower detection limits compared to graphene-based sensors, and showed good stability and applicability in environmental water samples.
MICROCHEMICAL JOURNAL
(2022)
Article
Multidisciplinary Sciences
Mark E. J. Newman
Summary: Explored the problem of computing rankings when there are multiple conflicting types of comparison and proposed a fast method based on an expectation-maximization algorithm and a modified Bradley-Terry model.
PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
(2022)
Article
Engineering, Industrial
Salman Jahani, Shiyu Zhou, Dharmaraj Veeramani
Summary: This paper introduces a method utilizing Brownian motion process and penalized splines to model multiple time-varying environmental covariates and the drift relationship of the degradation process. The approach does not assume a functional form for the degradation process drift and takes into account the effects of multiple environmental factors on the degradation process.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Computer Science, Artificial Intelligence
Berkan Kadioglu, Peng Tian, Jennifer Dy, Deniz Erdogmus, Stratis Ioannidis
Summary: This paper focuses on the rank regression setting, where a dataset of N samples with features is ranked through pairwise comparisons by a noisy oracle. By observing the comparison dataset, the learner aims to regress sample ranks and learn the model parameters with a certain level of accuracy.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Berkan Kadioglu, Peng Tian, Jennifer Dy, Deniz Erdogmus, Stratis Ioannidis
Summary: In this research, the ranking regression problem is discussed, and it is shown that learning through paired comparisons can regress the rank of samples.
PATTERN RECOGNITION
(2022)
Review
Clinical Neurology
M. Puthenparampil, P. Perini, R. Bergamaschi, M. Capobianco, M. Filippi, P. Gallo
Summary: Italy is considered a high-risk country for multiple sclerosis (MS), with the incidence and prevalence gradually increasing over the past years. Research suggests that environmental factors may better explain this trend than genetic heterogeneity.
JOURNAL OF NEUROLOGY
(2022)
Article
Environmental Sciences
Michele Carugno, Dario Consonni, Giorgia Randi, Dolores Catelan, Laura Grisotto, Pier Alberto Bertazzi, Annibale Biggeri, Michela Baccini
ENVIRONMENTAL RESEARCH
(2016)
Editorial Material
Health Care Sciences & Services
Annibale Biggeri, Dolores Catelan, David Conesa, Penelope Vounatsou
Article
Health Care Sciences & Services
Laura Grisotto, Dario Consonni, Lorenzo Cecconi, Dolores Catelan, Corrado Lagazio, Pier Alberto Bertazzi, Michela Baccini, Annibale Biggeri
Article
Health Care Sciences & Services
Lorenzo Cecconi, Anna Busolin, Fabio Barbone, Diego Serraino, Alessandra Chiarugi, Annibale Biggeri, Dolores Catelan
Article
Public, Environmental & Occupational Health
Alessandra Binazzi, Alessandro Marinaccio, Marisa Corfiati, Caterina Bruno, Lucia Fazzo, Roberto Pasetto, Roberta Pirastu, Annibale Biggeri, Dolores Catelan, Pietro Comba, Amerigo Zona
SCANDINAVIAN JOURNAL OF WORK ENVIRONMENT & HEALTH
(2017)
Article
Environmental Sciences
Fabio Barbone, Dolores Catelan, Riccardo Pistelli, Gabriele Accetta, Daniele Grechi, Franca Rusconi, Annibale Biggeri
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2019)
Article
Public, Environmental & Occupational Health
Maria Puopolo, Dolores Catelan, Sabina Capellari, Anna Ladogana, Antonio Sanguedolce, Alberto Fedele, Valerio Aprile, Giuseppa Lucia Turco, Elisa Colaizzo, Dorina Tiple, Luana Vaianella, Piero Parchi, Annibale Biggeri, Maurizio Pocchiari
Article
Environmental Sciences
Dolores Catelan, Dario Consonni, Annibale Biggeri, Barbara Dallari, Angela C. Pesatori, Luciano Riboldi, Carolina Mensi
ENVIRONMENTAL RESEARCH
(2020)
Review
Public, Environmental & Occupational Health
Luigi Castriotta, Valentina Rosolen, Annibale Biggeri, Luca Ronfani, Dolores Catelan, Marika Mariuz, Maura Bin, Liza Vecchi Brumatti, Milena Horvat, Fabio Barbone
INTERNATIONAL JOURNAL OF HYGIENE AND ENVIRONMENTAL HEALTH
(2020)
Article
Environmental Sciences
Luca Ferrari, Francesca Borghi, Simona Iodice, Dolores Catelan, Stefano Rossi, Ilaria Giusti, Laura Grisotto, Sabrina Rovelli, Andrea Spinazze, Rossella Alinovi, Silvana Pinelli, Laura Cantone, Laura Dioni, Benedetta Ischia, Irene Rota, Jacopo Mariani, Federica Rota, Mirjam Hoxha, Giorgia Stoppa, Damiano Monticelli, Domenico Cavallo, Enrico Bergamaschi, Marco Vicenzi, Nicola Persico, Annibale Biggeri, Andrea Cattaneo, Vincenza Dolo, Michele Miragoli, Paola Mozzoni, Valentina Bollati
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2020)
Article
Environmental Sciences
Dolores Catelan, Annibale Biggeri, Francesca Russo, Dario Gregori, Gisella Pitter, Filippo Da Re, Tony Fletcher, Cristina Canova
Summary: A study was conducted in the Veneto Region's Red Zone to assess COVID-19 mortality rates, revealing a higher risk possibly linked to PFAS exposure.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2021)
Article
Environmental Sciences
Dolores Catelan, Manuela Giangreco, Annibale Biggeri, Fabio Barbone, Lorenzo Monasta, Giuseppe Ricci, Federico Romano, Valentina Rosolen, Gabriella Zito, Luca Ronfani
Summary: The study investigated new cases of endometriosis in women aged 15-50 years in the Friuli Venezia Giulia region from 2004 to 2017, finding geographic variability in incidence rates with the highest in the 31-35 age group. The geographical distribution of endometriosis incidence showed a strong north-south spatial gradient in the region, and a cluster of five neighboring municipalities at higher risk was consistently identified. Individual studies, including biomonitoring and life-course studies, are deemed necessary for further evaluation of the findings due to the ecologic nature of the present study.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2021)
Article
Environmental Sciences
Giorgia Stoppa, Carolina Mensi, Lucia Fazzo, Giada Minelli, Valerio Manno, Dario Consonni, Annibale Biggeri, Dolores Catelan
Summary: This study uses Bayesian models to explore the geographical ecological association between ovarian cancer and malignant mesothelioma, and finds evidence of a shared risk factor between the two diseases.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2022)
Article
Oncology
Dolores Catelan, Annibale Biggeri, Lauro Bucchi, Valerio Manno, Marilena Pappagallo, Giorgia Stoppa, Francesco Grippo, Luisa Frova, Federica Zamagni, Roberta Crialesi, Giada Minelli
Summary: The study analyzed the mortality risk of lung cancer in Italy from 1995 to 2016, focusing on the spatial-temporal evolution by sex and province of residence. It found that the peak mortality of lung cancer among males occurred in the 1920-1929 cohort, followed by a decline. Among females, a peak was observed in the 1955-1964 cohort, with a downward trend thereafter. The risk of lung cancer has shifted to the southwest of Italy, raising concerns.
INTERNATIONAL JOURNAL OF CANCER
(2023)
Letter
Critical Care Medicine
Giulia Lorenzoni, Corrado Lanera, Danila Azzolina, Paola Berchialla, Dario Gregori