Implicit learning, the type of learning that occurs without intent to learn or awareness of what has been learned, has been thought to be insensitive to the effects of priming, but recent studies suggest this is not the case. One study found that learning in the serial reaction time (SRT) task was improved by nonconscious goal pursuit, primed via a word search task (Eitam et al. in Psychol Sci 19:261-267, 2008). In two studies, we used the goal priming word search task from Eitam et al., but with a different version of the SRT, the alternating serial reaction time task (ASRT). Unlike the SRT, which often results in explicit knowledge and assesses sequence learning at one point in time, the ASRT has been shown to be implicit through sensitive measures of judgment, and it enables sequence learning to be measured continuously. In both studies, we found that implicit learning was superior in the groups that were primed for goal achievement compared to control groups, but the effect was transient. We discuss possible reasons for the observed time course of the positive effects of goal priming, as well as some future areas of investigation to better understand the mechanisms that underlie this effect, which could lead to methods to prolong the positive effects.
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http://dx.doi.org/10.1007/s00221-014-4054-2 | DOI Listing |
Int J Surg
December 2024
Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu 610072, Sichuan Province, China.
Background: Determining the benign or malignant status of indeterminate pulmonary nodules (IPN) with intermediate malignancy risk is a significant clinical challenge. Oral microbiota-lung cancer interactions have qualified oral microbiota as a promising non-invasive predictive biomarker in IPN.
Materials And Methods: Prospectively collected saliva, throat swabs, and tongue coating samples from 1040 IPN patients and 70 healthy controls across three hospitals.
J Imaging
November 2024
2nd Department of Radiology, Medical School, Attikon University Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece.
Central Nervous System (CNS) tumors represent a significant public health concern due to their high morbidity and mortality rates. Magnetic Resonance Imaging (MRI) has emerged as a critical non-invasive modality for the detection, diagnosis, and management of brain tumors, offering high-resolution visualization of anatomical structures. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), have shown potential in augmenting MRI-based diagnostic accuracy for brain tumor detection.
View Article and Find Full Text PDFNucleic Acids Res
December 2024
Department of Pathology & Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.
RNA sequencing (RNA-seq) is widely adopted for transcriptome analysis but has inherent biases that hinder the comprehensive detection and quantification of alternative splicing. To address this, we present an efficient targeted RNA-seq method that greatly enriches for splicing-informative junction-spanning reads. Local splicing variation sequencing (LSV-seq) utilizes multiplexed reverse transcription from highly scalable pools of primers anchored near splicing events of interest.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan 430079, China.
Identifying phage-host interactions (PHIs) is a crucial step in developing phage therapy, which is the promising solution to addressing the issue of antibiotic resistance in superbugs. However, the lifestyle of phages, which strongly depends on their host for life activities, limits their cultivability, making the study of predicting PHIs time-consuming and labor-intensive for traditional wet lab experiments. Although many deep learning (DL) approaches have been applied to PHIs prediction, most DL methods are predominantly based on sequence information, failing to comprehensively model the intricate relationships within PHIs.
View Article and Find Full Text PDFFront Immunol
December 2024
Department of Oncology, Binzhou Medical University Hospital, Binzhou, Shandong, China.
Background: The heterogeneity of cancer makes it challenging to predict its response to immunotherapy, highlighting the need to find reliable biomarkers for assessment. The sophisticated role of cancer stemness in mediating resistance to immune checkpoint inhibitors (ICIs) is still inadequately comprehended.
Methods: Genome-scale CRISPR screening of RNA sequencing data from Project Achilles was utilized to pinpoint crucial genes unique to Ovarian Cancer (OV).
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