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Elemental models of associative learning typically employ a common prediction-error term. Following a conditioning trial, they predict that the change in the strength of an association between a cue and an outcome is dependent upon how well the outcome was predicted. When multiple cues are present, they each contribute to that prediction. The same rule applies both to increases in associative strength during excitatory conditioning and the loss of associative strength during extinction. In five experiments using an allergy prediction task, we tested the involvement of a common error term in the extinction of causal learning. Two target cues were each paired with an outcome prior to undergoing extinction in compound either with a second excitatory cue or with a cue that had previously undergone extinction in isolation. At test, there was no difference in the causal ratings of the two target cues. Manipulations designed to bias participants toward elemental processing of cue compounds, to promote the acquisition of inhibitory associations, or to reduce generalization decrement between training and test were each without effect. These results are not consistent with common error term models of associative learning. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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Hum Brain Mapp
September 2025
Department of Neurosurgery, Heidelberg University Hospital, Heidelberg, Germany.
Postoperative aphasia (POA) is a common complication in patients undergoing surgery for language-eloquent lesions. This study aimed to enhance the prediction of POA by leveraging preoperative navigated transcranial magnetic stimulation (nTMS) language mapping and diffusion tensor imaging (DTI)-based tractography, incorporating deep learning (DL) algorithms. One hundred patients with left-hemispheric lesions were retrospectively enrolled (43 developed postoperative aphasia, as the POA group; 57 did not, as the non-aphasia (NA) group).
View Article and Find Full Text PDFImmunol Res
September 2025
Department of Immunology and Allergy, Faculty of Medicine, Necmettin Erbakan University, Konya, Türkiye.
Background: Variants of uncertain significance (VUS) represent a major diagnostic challenge in the interpretation of genetic testing results, particularly in the context of inborn errors of immunity such as severe combined immunodeficiency (SCID). The inconsistency among computational prediction tools often necessitates expensive and time-consuming wet-lab analyses.
Objective: This study aimed to develop disease-specific, multi-class machine learning models using in silico scores to classify SCID-associated genetic variants and improve the interpretation of VUS.
Sci Rep
September 2025
Grupo de investigación en Biología Matemática y Computacional (BIOMAC), Departamento de Ingeniería Biomédica, Universidad de los Andes, Bogotá, Colombia.
Snakebite envenoming is a neglected tropical disease that affects mainly rural populations, where antivenom is scarce. Understanding environmental drivers of snakebite incidence is critical for public health preparedness. This study employs causal inference to assess the impact of rainfall on snakebite surges in Colombia, with broader implications for tropical regions.
View Article and Find Full Text PDFRen Fail
December 2025
Department of Critical Care Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, China.
This study aimed to develop a predictive model and construct a graded nomogram to estimate the risk of severe acute kidney injury (AKI) in patients without preexisting kidney dysfunction undergoing liver transplantation (LT). Patients undergoing LT between January 2022 and June 2023 were prospectively screened. Severe AKI was defined as Kidney Disease: Improving Global Outcomes stage 3.
View Article and Find Full Text PDFLife Sci Alliance
November 2025
Graduate School of Science, Technology and Innovation, Kobe University, Kobe, Japan
Mass-based fingerprinting can characterize microorganisms; however, expansion of these methods to predict specific gene functions is lacking. Therefore, mass fingerprinting was developed to functionally profile a yeast knockout library. Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) fingerprints of 3,238 knockouts were digitized for correlation with gene ontology (GO).
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