fMRI was used to examine lexical processing in native adult Chinese speakers. A 2 task (semantics and phonology) × 2 modality (visual and auditory) within-subject design was adopted. The semantic task involved a meaning association judgment and the phonological task involved a rhyming judgment to two sequentially presented words. The overall effect across tasks and modalities was used to identify seven ROIs, including the left fusiform gyrus (FG), the left superior temporal gyrus (STG), the left ventral inferior frontal gyrus (VIFG), the left middle temporal gyrus (MTG), the left dorsal inferior frontal gyrus (DIFG), the left inferior parietal lobule (IPL), and the left middle frontal gyrus (MFG). ROI analyses revealed two modality-specific areas, FG for visual and STG for auditory, and three task-specific areas, IPL and DIFG for phonology and VIFG for semantics. Greater DIFG activation was associated with conflicting tonal information between words for the auditory rhyming task, suggesting this region’s role in strategic phonological processing, and greater VIFG activation was correlated with lower association between words for both the auditory and the visual meaning task, suggesting this region’s role in retrieval and selection of semantic representations. The modality- and task-specific effects in Chinese revealed by this study are similar to those found in alphabetical languages. Unlike English, we found that MFG was both modality- and task-specific, suggesting that MFG may be responsible for the visuospatial analysis of Chinese characters and orthography-to-phonology integration at a syllabic level.

Micro-expression recognition (MER) has attracted much attention with various practical applications, particularly in clinical diagnosis and interrogations. In this paper, we propose a three-stream convolutional neural network (TSCNN) to recognize MEs by learning ME-discriminative features in three key frames of ME videos. We design a dynamic-temporal stream, static-spatial stream, and local-spatial stream module for the TSCNN that respectively attempt to learn and integrate temporal, entire facial region, and facial local region cues in ME videos with the goal of recognizing MEs. In addition, to allow the TSCNN to recognize MEs without using the index values of apex frames, we design a reliable apex frame detection algorithm. Extensive experiments are conducted with five public ME databases: CASME II, SMIC-HS, SAMM, CAS(ME) 2 , and CASME. Our proposed TSCNN is shown to achieve more promising recognition results when compared with many other methods. INDEX TERMS Micro-expression recognition, convolutional neural networks, apex frame location, spatiotemporal information.

The results suggest that the AID and the MID tasks have unique activation patterns. Our findings also suggest that the AID task may be more sensitive in detecting anhedonia in people with trait anhedonia.

These findings suggest that the bilateral occipital-parietal conjunction cortex is responsible for matching information input from multiple modalities during audiovisual sensory integration. (PsycINFO Database Record

Non-homologous end joining (NHEJ) is one of the pathways used to repair the DNA double-strand breaks. A number of genes involved in NHEJ have been implicated as lung cancer susceptibility genes such as the LIG1. However, some studies have generated conflicting results. The aim of this review and meta-analysis was to investigate the association between the LIG1 gene polymorphism and lung cancer risk. Studies focusing on the relationship between the LIG1 gene polymorphisms and susceptibility to lung cancer were selected from several electronic databases, with the last search up to October 25, 2014. Data were extracted by two independent reviewers, and the meta-analysis was performed with STATA version 12.0 software, calculating odds ratios (ORs) with 95 % confidence intervals (95 % CIs). According to the inclusion criteria, we included ten studies with a total of 4012 lung cancer cases and 5629 healthy controls in the meta-analysis. The results showed that the rs156641 polymorphism was significantly associated with lung cancer risk (dominant model: OR 0.694, 95 % CI 0.549-0.878; homozygote model: OR 0.677, 95 % CI 0.526-0.871; heterozygote model: OR 0.712, 95 % CI 0.556-0.913; additive model: OR 0.859, 95 % CI 0.767-0.962), whereas no association was found between rs3730931/rs439132/rs20579 polymorphisms and lung cancer. Our meta-analysis suggested that the rs156641 polymorphism in the LIG1 gene might be associated with an increased risk of lung cancer.

PurposeThe massive amount of available information and functionality of the Internet makes selective information seeking effortless. This paper aims to understand the selective exposure to information during a health decision-making task.Design/methodology/approachThis study conducted an experiment with a sample of 36 students to examine the influence of prior attitude, perceived threat level and information limit on users’ selective exposure to and recall of coronavirus disease 2019 (COVID-19) vaccination information. Participants were assigned to two conditions with or without an upper limit of the number of articles to be examined, and this study collected the number of articles read, the number of articles included in the report and recall score of the articles after one day of the experiment.FindingsThis study found that (1) participants with a negative attitude were more inclined to view attitude-consistent information and recalled attitude-consistent information more accurately, while participants with a positive attitude viewed more balanced information; (2) participants perceiving higher health threat level recalled attitude-consistent information more accurately; and (3) an upper limit on the number of articles to be viewed does not have any impact on selective exposure.Research limitations/implicationsThe findings of this paper pinpoint the disparity of influence of positive and negative attitudes on selective exposure to and selective recall of health information, which was not previously recognized.Practical implicationsVaccination campaigns should focus on reaching people with negative attitudes who are more prone to selective exposure to encourage them to seek more balanced information.Originality/valueThis is the first paper to explore selective exposure to COVID-19 vaccination information. This study found that people with a negative attitude and a higher level of perceived health threat are more prone to selective exposure, which was not found in previous research.

Existing emotion-aware conversational models usually focus on controlling the response contents to align with a specific emotion class, whereas empathy is the ability to understand and concern the feelings and experience of others. Hence, it is critical to learn the causes that evoke the users' emotion for empathetic responding, a.k.a. emotion causes. To gather emotion causes in online environments, we leverage counseling strategies and develop an empathetic chatbot to utilize the causal emotion information. On a real-world online dataset, we verify the effectiveness of the proposed approach by comparing our chatbot with several SOTA methods using automatic metrics, expert-based human judgements as well as userbased online evaluation. CCS CONCEPTS• Computing methodologies → Discourse, dialogue and pragmatics; Natural language generation.

Cross-database micro-expression recognition (CDMER) under semi supervised conditions is a difficult task, where the target (testing) and source (training) samples come from different microexpression (ME) databases, resulting in the inconsistency of the feature distributions between each other, and hence affecting the performance of many existing MER methods. To address this problem, we propose a dual-stream convolutional neural network (DSCNN) for dealing with CDMER tasks. In the DSCNN, two stream branches are designed to study temporal and facial region cues in ME samples with the goal of recognizing MEs. In addition, in the training process, the domain discrepancy loss is used to enforce the target and source samples to have similar feature distributions in some layers of the DSCNN. Extensive CDMER experiments are conducted to evaluate the DSCNN. The results show that our proposed DSCNN model achieves a higher recognition accuracy when compared with some representative CDMER methods.