Exploration of Muscle Fatigue Effects in Bioinspired Robot Learning from sEMG Signals by Ning Wang, Yang Xu, Hongbin Ma, Xiaofeng Liu

“…In order to model data from multiple demonstrations, Gaussian mixture models (GMMs) have been employed. According to the identified muscle fatigue factor, a weight has been assigned to each of the demonstration trials in training stage, which is therefore termed as weighted GMMs (W-GMMs) algorithm. …”
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Enhanced spatial analysis assessing the association between PFAS-contaminated water and cancer incidence: rationale, study design, and methods by Resa M. Jones, Erin R. Kulick, Ryan Snead, Robin Taylor Wilson, John Hughes, Ted Lillys

“…Few studies have used rigorous spatiotemporal approaches, and, to our knowledge, none have assessed cumulative exposures given residential histories or incorporated chemical mixture modeling. Thus, spatiotemporal analysis using advanced statistical approaches, accounting for spatially structured and unstructured heterogeneity in risk, can be a highly informative strategy for addressing the potential health effects of PFAS exposure. …”
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Adverse childhood experiences and perceived body size across the life course: a longitudinal study using data from the Canadian Longitudinal Study on Aging (CLSA) by Jean-Eric Tarride, Laura N Anderson, Lauren Griffith, Andrea Gonzalez, Vanessa De Rubeis

“…Body size trajectories were identified using latent class growth mixture modelling. Multinomial logistic regression was used to estimate ORs and 95% CIs for the association between ACEs and perceived body size trajectories. …”
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Gyrfalcon Prey Abundance and Their Habitat Associations in a Changing Arctic by Michaela Gustafson, Jennifer D. McCabe, Brian W. Rolek, Travis L. Booms, Michael T. Henderson, Leah Dunn, David L. Anderson, Jennyffer Cruz

“…We aimed to determine the habitat–abundance relationships for three small herbivores on the Seward Peninsula of Alaska, USA by fitting data from 983 point counts (collected during 2019, 2021, and 2022) with N‐mixture models that account for imperfect detection. These herbivore species, Willow Ptarmigan (Lagopus lagopus), Rock Ptarmigan (L. muta), and Arctic ground squirrels (Urocitellus parryii), are fundamental to tundra food webs, and primary prey for Arctic raptors including Gyrfalcons (Falco rusticolus). …”
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Body Mass Index Trajectories during 6–18 Years Old and the Risk of Hypertension in Young Adult: A Longitudinal Study in Chinese Population by Haoyue Teng, Jia Hu, Wenxin Ge, Qiling Dai, Ji Liu, Chengqi Xiao, Jieyun Yin, Xiaoyan Zhu

“…BMI trajectories were explored using latent class growth mixture models, and associations between identified trajectories with hypertension in young adulthood were examined by logistic regression analyses. …”
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Functional Improvement Trajectories After Surgery (FIT After Surgery) study: protocol for a multicentre prospective cohort study to evaluate significant new disability after major... by E Jacobsohn, Duminda N Wijeysundera, Gerald Lebovic, M Davis, Martine T E Puts, Shabbir M H Alibhai, Karim S Ladha, C Wong, Daniel I McIsaac, C Hanley, T Barnes, G Lorello, N Siddiqui, P Serrano, Tyler R Chesney, S Choi, J Van Vlymen, D Sussman, D Macdonald, P Jüni, Emily Hladkowicz, C David Mazer, M Bosch, CD Mazer, A Jerath, J Pazmino-Canizares, E Kennedy, R Spence, V Lyon, Alice C Wei, Julian F Daza, Sahar Ehtesham, Janet M van Vlymen, DN Wijeysundera, KS Ladha, JF Daza, G Mattina, E Hladkowicz, M Tessier, S Ehtesham, S Nnorom, SMH Alibhai, M Puts, G Lebovic, TR Chesney, AC Wei, S Drozdz, M Louridas, RH Breau, M Lalu, S Abdellatif, S Gagne, E Duceppe, K Zarnke, D Dumerton, M Karizhenskaia, H El Beheiry, F Bonazza, A Zahavich, M Thorleifson, S L Russell, H Bagry, SY MacDonell, J Dale-Gandar, L Kaustov, A Fleet, E Al Azazi, M Parotto, S A McCluskey, H Poonawala, D Dillane, Di McIsaac, S Avramescu, S Shabeen

“…We will use multivariable logistic regression models to determine the association of preoperative characteristics and surgery type with outcomes, joint modelling to characterise longitudinal time trends in WHODAS scores over 12 months after surgery, and longitudinal latent class mixture models to identify clusters following similar trajectories of disability.Ethics and dissemination The FIT After Surgery study has received research ethics board approval at all sites. …”
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Computational Phenotyping of Obstructive Airway Diseases: A Systematic Review by Bashir MBA, Milani GP, De Cosmi V, Mazzocchi A, Zhang G, Basna R, Hedman L, Lindberg A, Ekerljung L, Axelsson M, Vanfleteren LEGW, Rönmark E, Backman H, Kankaanranta H, Nwaru BI

“…Most studies used hierarchical clustering, with some employing latent class modeling, mixture models, and factor analysis. The comprehensiveness of variable reporting was the best quality indicator, while reproducibility measures were often lacking.Conclusion: Variations in phenotyping methods, study settings, participant profiles, and variables contribute to significant differences in characterizing asthma, severe asthma, COPD, ACO, and rhinitis phenotypes across studies. …”
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