Although cognitive theories of psychopathology suggest that attention bias toward threat plays a role in the etiology and maintenance of anxiety, there is relatively little evidence regarding individual differences in the earliest development of attention bias toward threat. The current study examines attention bias toward threat during its potential first emergence by evaluating the relations between attention bias and known risk factors of anxiety (i.e., temperamental negative affect and maternal anxiety). We measured attention bias to emotional faces in infants (N = 98; 57 male) ages 4 to 24 months during an attention disengagement eye-tracking paradigm. We hypothesized that (a) there would be an attentional bias toward threat in the full sample of infants, replicating previous studies; (b) attentional bias toward threat would be positively related to maternal anxiety; and (c) attention bias toward threat would be positively related to temperamental negative affect. Finally, (d) we explored the potential interaction between temperament and maternal anxiety in predicting attention bias toward threat. We found that attention bias to the affective faces did not change with age, and that bias was not related to temperament. However, attention bias to threat, but not attention bias to happy faces, was positively related to maternal anxiety, such that higher maternal anxiety predicted a larger attention bias for all infants. These findings provide support for attention bias as a putative early mechanism by which early markers of risk are associated with socioemotional development. (PsycINFO Database Record
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http://dx.doi.org/10.1037/emo0000275 | DOI Listing |
The genus boasts abundant germplasm resources and comprises numerous species. Among these, medicinal plants of this genus, which have a long history, have garnered attention of scholars. This study sequenced and analyzed the chloroplast genomes of six species of medicinal plants (, , , , , and , respectively) to explore their interspecific relationships.
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January 2025
Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK.
While numerous reviews have assessed the association between traumatic brain injury (TBI) and various mental and physical health outcomes, a comprehensive evaluation of the scope, validity, and quality of evidence is lacking. Here we present an umbrella review of a wide range of health outcomes following TBI and outline outcome risks across subpopulations. On 17 May 2023, we searched Embase, Medline, Global Health, PsycINFO, and Cochrane Database of Systematic Reviews for systematic reviews and meta-analyses.
View Article and Find Full Text PDFPsychol Belg
January 2025
Department of Data-Analysis, Ghent University, Belgium.
Performing hypothesis tests with adequate statistical power is indispensable for psychological research. In response to several large-scale replication projects following the replication crisis, concerns about the root causes of this crisis - such as questionable research practices (QRPs) - have grown. While initial efforts primarily addressed the inflation of the type I error rate of research due to QRPs, recent attention has shifted to the adverse consequences of low statistical power.
View Article and Find Full Text PDFEClinicalMedicine
January 2025
College of Competitive Sports, Beijing Sport University, Beijing, China.
Background: Given the distinctive physiological characteristics of pregnant women, non-pharmacological therapies are increasingly being used to improve depressive and anxiety symptoms. Our objective was to explore and compare the impact of various non-pharmacological interventions in improving depressive and anxiety symptoms, and to identify the most effective strategies for pregnant women with depressive and/or anxiety symptoms.
Methods: We conducted a systematic search of PubMed, Embase, the Cochrane Library, and Web of Science for randomized controlled trials (RCTs) that compared non-pharmacological interventions to usual care, from the inception of each database up to October 5, 2024.
Neural Netw
January 2025
School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China; Ministry of Education Key Laboratory for Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an, 710049, China. Electronic address:
Graph Neural Networks (GNNs) have received extensive research attention due to their powerful information aggregation capabilities. Despite the success of GNNs, most of them suffer from the popularity bias issue in a graph caused by a small number of popular categories. Additionally, real graph datasets always contain incorrect node labels, which hinders GNNs from learning effective node representations.
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