In this study, we examined whether infant temperament predicted study dropout at 3.5 and 7 months and whether dropout was stable across time. Dropout was measured across four experimental tasks (free-play, ERP, still-face, and eye tracking). Temperament was not related to dropout at either timepoint. Dropout was not stable across time, nor was it stable across tasks. These findings suggest that individual differences in temperament are not systematically related to study completion across experimental tasks with varied requirements. These findings additionally suggest that dropout is not consistent across tasks, which may support the utility of multi-study data collection methods.
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http://dx.doi.org/10.1016/j.infbeh.2021.101630 | DOI Listing |
Sci Rep
January 2025
Department of Psychology, Faculty of Behavioural and Social Sciences, University of Groningen, Grote Kruisstraat 2/1, 9712TS, Groningen, The Netherlands.
Recruits are exposed to high levels of psychological and physical stress during the special forces selection period, resulting in dropout rates of up to 80%. To identify who likely drops out, we assessed a group of 249 recruits, every week of the selection program, on their self-efficacy, motivation, experienced psychological and physical stress, and recovery. Using linear regression as well as state-of-the-art machine learning techniques, we aimed to build a model that could meaningfully predict dropout while remaining interpretable.
View Article and Find Full Text PDFCurr Med Imaging
January 2025
Department of Electrical Engineering, Government College University, Lahore, Pakistan.
Background: Colon diseases are major global health issues that often require early detection and correct diagnosis to be effectively treated. Deep learning approaches and recent developments in medical imaging have demonstrated promise in increasing diagnostic accuracy.
Objective: This work suggests that a Convolutional Neural Network (CNN) model paired with other models can detect different gastrointestinal (GI) abnormalities or diseases from endoscopic images via the fusion of residual blocks, including alpha dropouts (αDO) and auxiliary fusing layers.
Methods Mol Biol
January 2025
Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain.
Lineage tracing has significantly advanced our comprehension in many areas of biology, such as development or immunity, by precisely measuring cellular processes like migration, division, or differentiation across labeled cells and their progeny. Traditional recombinase-based prospective lineage tracing is limited by the need for a priori cell type information and is constrained in the numbers of clones it can simultaneously track. In this sense, clonal lineage tracing with integrated random barcodes offers a robust alternative, enabling researchers to label and track a vast array of cells and their progeny over time.
View Article and Find Full Text PDFPurpose: To evaluate the safety and efficacy of sublingual methazolamide in patients with open-angle glaucoma (OAG) and inform future trial design.
Methods: Fourteen participants (28 eyes) aged 50 to 90 years with bilateral OAG and intraocular pressure (IOP) between 18 and 35 mmHg after medication washout were included. Participants were randomized to receive either 25 mg or 50 mg of sublingual methazolamide once daily for one week, followed by twice-daily administration during the second week.
J Appl Clin Med Phys
December 2024
Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan.
Purpose: To quantitatively evaluate the performance of two types of recurrent neural networks (RNNs), long short-term memory (LSTM) and gated recurrent units (GRU), using Monte Carlo dropout (MCD) to predict pharmacokinetic (PK) parameters from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data.
Methods: DCE-MRI data for simulation studies were synthesized using the extended Tofts model and a population-averaged arterial input function (AIF). The ranges of PK parameters for training the RNNs were determined from data of patients with brain tumors.
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