Associations of maternal quitting, reducing, and continuing smoking during pregnancy with longitudinal fetal growth: Findings from Mendelian randomization and parental negative con... by Judith S Brand, Romy Gaillard, Jane West, Rosemary R C McEachan, John Wright, Ellis Voerman, Janine F Felix, Kate Tilling, Deborah A Lawlor

“…<h4>Methods and findings</h4>We analysed data from 8,621 European liveborn singletons in 2 population-based pregnancy cohorts (the Generation R Study, the Netherlands 2002-2006 [n = 4,682]) and the Born in Bradford study, United Kingdom 2007-2010 [n = 3,939]) with fetal ultrasound and birth anthropometric measures, parental smoking during pregnancy, and maternal genetic data. …”
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Effect of Ticagrelor on Glycaemic Parameters and Inflammatory Mediators in High Fat Diet and Streptozotocin Induced Diabetic Rats: An Experimental Study by Sachin Patel, Nayana K Hashilkar, Vidya Mahalmani

“…Inflammatory markers Interleukin (IL)-1β, Tumour Necrosis Factor (TNF)-α, and IL-6 were assessed at the end of the study. Data were presented as Mean±Standard Error of the Mean (SEM). …”
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Interleukin-4 and specific IgE to oranges levels study in persons with allergic anamnesis by A. S. Prilutskiy, N. B. Abylgazinova, K. Y. Tkachenko

“…The median, the median error, the left and right limits of 95% confidence interval were calculated for all of three samples (IL-4, total IgE-Ab, sIgE-Ab). …”
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Quantitative scale validation of the Dimensional Anhedonia Rating Scale in the treatment of Chinese patients with major depressive disorder by XiaoJing Gu, Xiaowei Chen, Yun-Ai Su, Tianmei Si, Jingyu Lin, Donald M Bushnell, Dongjing Fu, Carol Jamieson, Heather Rozjabek

“…Confirmatory factor analysis showed adequate fit statistics (comparative fit index 0.976, goodness-of-fit index 0.935 and root mean square error of approximation 0.055). …”
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Forensic medical evaluation of fatal cases of gastrointestinal diseases as a marker of the quality of surgical care for children in Ukraine by А.М. Biliakov, B.V. Mikhailychenko, Ye.O. Rudenko

“…In addition, the representativeness error (mP) was determined with a confidence interval (CI) at the level of 95%. …”
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Optimization, Design, and Implementation of Biodiversity-Focused Fisheries Survey Stations: A Case Study of the Coastal Waters of Maoming by Yuexiu DENG, Yongheng HUANG, Jianyu DONG, Ning CHEN, Jing ZHANG, Xuefeng WANG

“…According to relative error analysis, when stratified by environmental factors and water depth, the mean relative errors for H′, D, and J′ are 2.38, 2.02, 2.85, and 0.43, 3.14, and 1.74, respectively, with stratification by water depth resulting in smaller mean relative errors for all indices except J′.Through stratified sampling and appropriate sample allocation, reducing the number of required sampling stations while maintaining data accuracy, thus minimizing adverse impacts on marine ecosystems, is possible. …”
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Healthcare workers' priorities of WHO snakebite strategic objectives for the control and prevention of snakebite envenoming in Ghana: A machine learning statistical design of exper... by Eric Nyarko, Iddrisu Abugbil Atubiga, Emmanuel Tetteh Siame, José María Gutiérrez, Eduardo Alberto Fernandez

“…We divided the final dataset using a hold-back validation method, maintaining a training-to-validation ratio of 70:30. For data analysis, we utilized a diverse range of five machine learning models: Ridge Regression, Elastic Net, LASSO, a Generalized Regression Model with Pruned Forward Selection, and Forward Selection. …”
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Antihypertensive Drug Recommendations for Reducing Arterial Stiffness in Patients With Hypertension: Machine Learning–Based Multicohort (RIGIPREV) Study by Iván Cavero-Redondo, Arturo Martinez-Rodrigo, Alicia Saz-Lara, Nerea Moreno-Herraiz, Veronica Casado-Vicente, Leticia Gomez-Sanchez, Luis Garcia-Ortiz, Manuel A Gomez-Marcos

“…MethodsThis study, known as the RIGIPREV study, used data from the EVA, LOD-DIABETES, and EVIDENT studies involving individuals with hypertension with baseline and follow-up measurements. …”
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Modeling of Ultrasonic Flaw Detection Processes in the Task of Searching and Visualizing Internal Defects in Assemblies and Structures by B. V. Sobol, A. N. Soloviev, P. V. Vasiliev, A. A. Lyapin

“…This approach identifies nonlinear relationships between the geometry of defects and the amplitude-frequency and amplitude-time data obtained during signal analysis. Artificial neural networks are a model that can be trained on examples, which provides for an effective solution to problems that are difficult to express in traditional forms. …”
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Modeling active layer thickness in permafrost rock walls based on an analytical solution of the heat transport equation, Kitzsteinhorn, Hohe Tauern Range, Austria by W. Aumer, W. Aumer, I. Hartmeyer, C.-M. Görres, D. Uteau, M. Offer, M. Offer, S. Peth

“…<p>The active layer thickness (ALT) refers to the seasonal thaw depth of a permafrost body and in high alpine environments represents an essential parameter for natural hazard analysis. The aim of this study is to model ALT based on bedrock temperature data measured in four shallow boreholes (SBs, 0.1 m deep) in the summit region of the Kitzsteinhorn (Hohe Tauern Range, Austria, Europe). …”
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VGGBM-Net: A Novel Pixel-Based Transfer Features Engineering for Automated Coffee Bean Diseases Classification by Muhammad Shadab Alam Hashmi, Azam Mehmood Qadri, Ali Raza, Saleem Ullah, Aseel Smerat, Changgyun Kim, Muhammad Syafrudin, Norma Latif Fitriyani

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Volumetric evolution of supraglacial lakes in southwestern Greenland using ICESat-2 and Sentinel-2 by T. Feng, T. Feng, X. Ma, X. Ma, X. Liu, X. Liu, X. Liu

“…Second, the depth of SGLs along the ICESat-2 profile is detected using the kernel density analysis method. Finally, a multi-layer perceptron (MLP) model constructs the nonlinear relationship between the reflectance ratio from Sentinel-2 imagery and the depth of SGLs detected by ICESat-2 data. …”
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Enhancing water balance simulations in SWAT for paddy-dominated catchments through refined soil moisture dynamics by Meng Xiang, Di Wu, Qianan Yu, Haitao Wu, Tianchi Cai, Yuanlai Cui

“…While the results are demonstrated in the YSD basin, SWAT-PW’s modular design enables its application to global paddy-dominated catchments with similar irrigation practices, provided local soil, crop, and climate data are available. While the accuracy of ET depends on region-specific crop coefficients, future integration with remote sensing data could enhance its scalability. …”
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Machine Learning-Based Alfalfa Height Estimation Using Sentinel-2 Multispectral Imagery by Hazhir Bahrami, Karem Chokmani, Saeid Homayouni, Viacheslav I. Adamchuk, Rami Albasha, Md Saifuzzaman, Maxime Leduc

“…We demonstrated that RF and XGB surpassed SVR when assessing test data performance. Our findings showed that XGB and RF could predict alfalfa crop height with an R² of 0.79 and a mean absolute error (MAE) of around 4 cm Our findings indicated that SVR exhibited the lowest accuracy among the three algorithms tested, with R² of 0.69 and an MAE of 4.63 cm. …”
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Graph-Based 3-Dimensional Spatial Gene Neighborhood Networks of Single Cells in Gels and Tissues by Zhou Fang, Kelsey Krusen, Hannah Priest, Mingshuang Wang, Sungwoong Kim, Anirudh Sriram, Ashritha Yellanki, Ankur Singh, Edwin Horwitz, Ahmet F. Coskun

“…Lastly, we compared astrocyte–neuron CCC in mouse hypothalamus and cortex by analyzing 3D multiplexed-error-robust fluorescence in situ hybridization (MERFISH) data and identified regional gene proximity differences. …”
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Projections of single-level indirect lumbar interbody fusion volume and associated costs for Medicare patients to 2050 by Kyle A. Mani, BS, Samuel N. Goldman, BS, Noel Akioyamen, MD, Emily Kleinbart, BS, Yaroslav Gelfand, MD, Saikiran Murthy, DO, Jonathan Krystal, MD, Ananth Eleswarapu, MD, Reza Yassari, MD, Mitchell S. Fourman, MD, MPhil

“…Predictive performance was evaluated by splitting the data into training (75%) and validation (25%) sets, and calculating normalized root mean square error (NRMSE). …”
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Mortality in Frankfurt am Main, Germany, 2020–2023: higher excess mortality during an influenza wave in 2022 than during all COVID-19 waves altogether by Heudorf, Ursel, Kowall, Bernd

“…Method: We obtained population data for Frankfurt am Main for 2016–2023 from the Municipal Office of Statistics, City of Frankfurt/Main, mortality data from 2016 to 2023 from the Hessian State Office for Health and Care, data on SARS-CoV-2 and influenza notifications from the homepage of the Robert Koch-Institute and weather data from the homepage of the German Meteorological Office. …”
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Construction of a sugar and acid content estimation model for Miliang-1 kiwifruit during storage by LIU Li, YANG Tianyi, DONG Congying, SHI Caiyun, SI Peng, WEI Zhifeng, GAO Dengtao

“…These methods help correct baseline variations, scatter effects and noise, improving the quality of the spectral data. Each preprocessing technique addresses specific issues within the spectral data, such as correcting for light scattering, baseline shifts and other interferences, thereby optimizing the data for further analysis. …”
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Quantitative determination of blended proportions in tobacco formulations using near-infrared spectroscopy and transfer learning by Qinlin Xiao, Qinlin Xiao, Ruifang Gu, Li Li, Jing Wen, Xixiang Zhang, Yi Shen, Yang Liu, Lan Xiao, Qinqin Tang, Jun Yang, Yong He, Juan Yang

“…The results show that TCA-PLSR achieved substantial reductions in prediction error in most transfer tasks involving large discrepancies in feature distributions. …”
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Chasing (macro)elements in microinvertebrates: A new methodological approach for quantitative and qualitative analyses of phosphorus by PIXE (Particle‐Induced X‐Ray Emission) appli... by Tereza Novotná Jaroměřská, Vladimír Havránek, David Novotný, Krzysztof Zawierucha, Oleksandr Romanenko, Jakub Štenc, Lenka Vondrovicová, Marie Fayadová, Jakub Trubač

“…We identified specimens to the lowest taxonomic resolution by light microscopy prior to analysis. The error in the estimation of body P % by the PIXE–PESA approach was approximately 10%. …”
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