Threat priming diminishes the gaze cueing effect.

Gaze cueing effect (GCE) refers to attention orienting towards the gazed-at location, characterised by faster responses to gazed-at than non-gazed-at stimuli. A previous study investigated the effects of affective priming on GCE and reported that threatening primes enhanced GCE. However, it remains unknown whether the threat or heightened arousal potentiated GCE. We investigated how highly arousing threatening and positive primes, compared to low arousing neutral primes modulate GCE. After a brief exposure to an affective prime (pictures of threat or erotica) or a neutral prime, participants detected an asterisk validly or invalidly cued by the gaze direction of a neutral face. The results showed that the threatening primes diminished the magnitude of GCE. The highly arousing positive primes did not have an effect on GCE. Further analyses showed that, as compared to neutral priming, the reaction times after threatening primes were shortened on invalid trials. This finding was interpreted to suggest that the threatening primes enhanced goal-directed target detection and attenuated attention orienting by irrelevant gaze cues via improving executive control. In sum, the present findings indicate that threat priming modulates GCE, not because of heightened arousal but because of the threat.

Prevalence of developmental dyslexia in primary school children: a protocol for systematic review and meta-analysis.

BACKGROUND: Developmental dyslexia (DD) is a specific impairment during the acquisition of reading skills and may have a lifelong negative impact on individuals. Reliable estimates of the prevalence of DD serve as the basis for evidence-based health resource allocation and policy making. However, the prevalence of DD in primary school children varies largely across studies. Moreover, it is unclear whether there are differences in prevalence in different genders and writing systems. Hence, the present study aims to conduct a systematic review and meta-analysis to assess the global prevalence of DD and to explore related factors. METHODS: We will undertake a comprehensive literature search in 14 databases, including EMBASE, PubMed, Web of Science, China National Knowledge Infrastructure and Cochrane, from their inception to June 2021. Cross-sectional and longitudinal studies that describe the prevalence of DD will be eligible. The quality of the included observational studies will be assessed using the Strengthening the Reporting of Observational Studies in Epidemiology statement. The risk of bias will be determined by sensitivity analysis to identify publication bias. RESULTS: One meta-analysis will be conducted to estimate the prevalence of DD in primary school children. Heterogeneity will be assessed in terms of the properties of subjects (e.g., gender, grade and writing system) and method of diagnosis in the included primary studies. Subgroup analyses will also be performed for population and secondary outcomes. CONCLUSION: The results will synthesize the prevalence of DD and provide information for policy-makers and public health specialists.

Prevalence of Developmental Dyslexia in Primary School Children: A Systematic Review and Meta-Analysis.

BACKGROUND: Developmental dyslexia (DD) is a specific learning disorder concerning reading acquisition that may has a lifelong negative impact on individuals. A reliable estimate of the prevalence of DD serves as the basis for diagnosis, intervention, and evidence-based health resource allocation and policy-making. Hence, the present meta-analysis aims to generate a reliable prevalence estimate of DD worldwide in primary school children and explore the potential variables related to that prevalence. METHODS: Studies from the 1950s to June 2021 were collated using a combination of search terms related to DD and prevalence. Study quality was assessed using the STROBE guidelines according to the study design, with study heterogeneity assessed using the I2 statistic, and random-effects meta-analyses were conducted. Variations in the prevalence of DD in different subgroups were assessed via subgroup meta-analysis and meta-regression. RESULTS: The pooled prevalence of DD was 7.10% (95% CI: 6.27-7.97%). The prevalence in boys was significantly higher than that in girls (boys: 9.22%, 95%CI, 8.07-10.44%; girls: 4.66%, 95% CI, 3.84-5.54%; p < 0.001), but no significant difference was found in the prevalence across different writing systems (alphabetic scripts: 7.26%, 95%CI, 5.94-8.71%; logographic scripts: 6.97%, 95%CI, 5.86-8.16%; p > 0.05) or across different orthographic depths (shallow: 7.13%, 95% CI, 5.23-9.30%; deep: 7.55%, 95% CI, 4.66-11.04%; p > 0.05). It is worth noting that most articles had small sample sizes with diverse operational definitions, making comparisons challenging. CONCLUSIONS: This study provides an estimation of worldwide DD prevalence in primary school children. The prevalence was higher in boys than in girls but was not significantly different across different writing systems.

DeepHBV: a deep learning model to predict hepatitis B virus (HBV) integration sites.

BACKGROUND: The hepatitis B virus (HBV) is one of the main causes of viral hepatitis and liver cancer. HBV integration is one of the key steps in the virus-promoted malignant transformation. RESULTS: An attention-based deep learning model, DeepHBV, was developed to predict HBV integration sites. By learning local genomic features automatically, DeepHBV was trained and tested using HBV integration site data from the dsVIS database. Initially, DeepHBV showed an AUROC of 0.6363 and an AUPR of 0.5471 for the dataset. The integration of genomic features of repeat peaks and TCGA Pan-Cancer peaks significantly improved model performance, with AUROCs of 0.8378 and 0.9430 and AUPRs of 0.7535 and 0.9310, respectively. The transcription factor binding sites (TFBS) were significantly enriched near the genomic positions that were considered. The binding sites of the AR-halfsite, Arnt, Atf1, bHLHE40, bHLHE41, BMAL1, CLOCK, c-Myc, COUP-TFII, E2A, EBF1, Erra, and Foxo3 were highlighted by DeepHBV in both the dsVIS and VISDB datasets, revealing a novel integration preference for HBV. CONCLUSIONS: DeepHBV is a useful tool for predicting HBV integration sites, revealing novel insights into HBV integration-related carcinogenesis.