Results 231 to 240 of about 1,871,607 (274)

Improved motor imagery BCI performance via task-unaware compression in the BELT Bayesian Edge-Cloud architecture. [PDF]

PLoS One
Danayi A, Soltanian-Zadeh H.
europepmc   +1 more source

‘Location Matters’: Incorporating Spatial Autocorrelation Into Modelling the Drivers of Cyanobacterial Biomass in Reservoirs of Northeastern Brazil

Ecohydrology, Volume 19, Issue 7, November/December 2026.
ABSTRACT Spatial autocorrelation (SAC) is a critical yet often overlooked component of ecological analyses, which can significantly influence the accuracy and interpretation of statistical models. Therefore, this study aimed to evaluate the spatial distribution of cyanobacteria biomass among tropical reservoirs at the watershed scale and to identify ...
Tatiane Medeiros Queiroz, Juliana dos Santos Severiano, Patrícia Silva Cruz, Maria Eduarda Dantas César, Laissa Lima do Nascimento, José Etham de Lucena Barbosa   +5 more
wiley   +1 more source

New Morphological and Molecular Data on <i>Lobatostoma anisotremum</i> Oliva & Carvajal, 1984 (Trematoda: Aspidogastridae) from <i>Anisotremus scapularis</i> (Tschudi, 1846) (Eupercaria <i>incertae sedis</i>: Haemulidae) off the Peruvian Coast, with Insights into the Phylogeny and Systematics of <i>Lobatostoma</i>. [PDF]

Animals (Basel)
Oscco A, Tarmeño N, Cruces CL, Yunis-Aguinaga J, Chero JD.   +4 more
europepmc   +1 more source

Multi‐Trait GWAS Analyses of Blood Pressure Traits Provide Limited Gains in Locus Discovery or Polygenic Risk Score Performance Compared to Single‐Trait GWAS

Genetic Epidemiology, Volume 50, Issue 8, December 2026.
ABSTRACT High blood pressure (BP) represents a major cause of life‐threatening cardiovascular conditions. GWAS have identified >2000 BP‐associated signals. BP‐GWAS usually analyze each BP trait individually. However, multi‐trait GWAS may yield novel loci and improve polygenic risk scores (PRS) prediction. We explored whether multi‐trait BP‐GWAS can: (i)
Yujia Ma, Anna D. Argoty‐Pantoja, Tian Xie, Michael R. Barnes, Patricia B. Munroe, Claudia P. Cabrera, Harold Snieder, Helen R. Warren, UMCG Genetics Lifelines Initiative (UGLI) group author: LifeLines Cohort Study, Raul Aguirre‐Gamboa, Patrick Deelen, Lude Franke, Jan A. Kuivenhoven, Esteban A. Lopera Maya, Ilja M. Nolte, Serena Sanna, Harold Snieder, Morris A. Swertz, Peter M. Visscher, Judith M. Vonk, Cisca Wijmenga, Naomi Wray   +21 more
wiley   +1 more source

Optimize Before You Synthesize-Enhancing the Ionic Conductivity of Li₇SiPS₈ Using Bayesian Optimization. [PDF]

Angew Chem Int Ed Engl
Balzat LG, Calaminus R, Zhao Y, Gjorgjevikj K, Moudrakovski I, Krause S, Koeppe A, Nestler B, Lotsch BV.   +8 more
europepmc   +1 more source

Structural Validity and Measurement Properties of the Tampa Scale for Kinesiophobia in Knee Osteoarthritis: Comparison of Candidate Factor Structures

Musculoskeletal Care, Volume 24, Issue 4, December 2026.
ABSTRACT Purpose Kinesiophobia is clinically relevant in knee osteoarthritis (KOA), but the appropriate Tampa Scale for Kinesiophobia (TSK) structure remains uncertain. We compared candidate TSK factor structures and examined the selected measurement properties of the solution with the most favourable relative fit in this sample.
José Djalma Arrais Junior, Lisa C. Carlesso, Barbara Greco Miura, Bruno Ruocco Verengue, Patrícia Gabrielle dos Santos, Almir Vieira Dibai‐Filho, Cid André Fidelis‐de‐Paula‐Gomes   +6 more
wiley   +1 more source

AI-powered mixed reality acceptance in mining: A PLS-SEM and Bayesian Network modeling

Sustainable Futures
Wecka Imam Yudhistyra, Chalita Srinuan
openaire   +1 more source

Paternal emotional warmth buffers maternal rejection in adolescent NSSI: a network and Bayesian analysis. [PDF]

BMC Psychol
Luo ZY, Guo L, Xu T, Zhi J, Zhang Y, Huang SN, Zheng H, Lu JX, Di JZ.   +8 more
europepmc   +1 more source

Performance Evaluation of Bayesian Network Learning Algorithms in Structural Equation Modeling: A Simulation Study. [PDF]

Entropy (Basel)
Karadag T.
europepmc   +1 more source