Search

Attention can be focused on specific locations in our visual field (space-based attention), but also spreads along objects (object-based attention). However, object-based effects are prone to large individual differences and seem not as prevalent as previously assumed. In a previous study, we investigated individual differences in the temporal dynamics of attentional selection. Our results showed that individual differences in the temporal aspects of attentional selection are not the underlying cause for the low-prevalence of object-based effects. We further assessed the rhythmic sampling of visual space underlying object- and space-based effects. Our data suggest that the rhythmic sampling depends on the target frequency and is particularly enhanced for targets with a low frequency (invalid targets: 10% validity for invalid same and different targets) and robust for those with a high frequency (valid trials: 80% cue validity). In three experiments, we further assess the effects of target frequency on attentional selection and the rhythmic sampling of visual space. Experiment 1 is a direct replication of our previous study. However, we will collect data for all three experiments online. In Experiment 2, we change the target frequency to be 50% (50% valid, 25% invalid same- and different-object target locations). In Experiment 3, target frequency was balanced across valid, invalid same- and different-object target locations.

Emerging industries are the key to China’s efforts to transform itself from one of the world’s major manufacturers into a global manufacturing powerhouse. Chinese government departments at all levels have always implemented support measures, including financial subsidies, preferential land prices, tax relief, and exemptions to cultivate and expand emerging industries, with some success. Furthermore, issues such as adverse selection and moral hazards have meant that traditional government support models have suffered from weak guidance of private capital and inadequate support for emerging industries. Reform and innovation of the government equity support model, leveraging the participation of private capital, and supporting the high-quality development of emerging industries are practical issues worthy of attention. Shenzhen is at the forefront of China’s efforts to reform and open up. It is also at the forefront of the implementation of the Chinese government’s equity support model. Based on a review of the policies of the Chinese government that support emerging industries and of the development status of government equity investment funds, this study summarizes the functions, features of the model, and decision-making process of government equity support. Using a government equity investment institution in Shenzhen as a case study, this study also analyzes in-depth the 85 firms the fund has invested in. A comprehensive analysis and evaluation of the effect of the government equity support model was carried out from multiple dimensions, including the degree of fit with industrial policy. Following an evaluation of the efficacy of government equity support, this study proposes methods of optimizing the model and the support given to emerging industries, including improving the competence of government equity investment institutions, deepening cooperation with market-based venture capitalists, leveraging incentives such as fiscal rewards and subsidies, and strengthening management of the investment process. As indústrias emergentes são a chave para os esforços da China na transformação de um dos maiores fabricantes do mundo numa potência global de fabricação. Os departamentos do governo chinês, em todos os níveis, sempre implementaram medidas de apoio, incluindo subsídios financeiros, preços preferenciais de solos e isenção de impostos para criar e expandir indústrias emergentes, com algum sucesso. Além disso, questões como seleção adversa e riscos morais significaram que os modelos tradicionais de apoio do governo sofreram com a insuficiente participação do capital privado e o apoio inadequado às indústrias emergentes. A reforma e inovação do modelo de apoio ao património por parte do governo, para alavancar a participação do capital privado e apoiar o desenvolvimento de alta qualidade de indústrias emergentes, são questões práticas que merecem atenção. Shenzhen está na vanguarda dos esforços da China para reformar e se abrir. Também está na vanguarda da implementação do modelo de suporte ao capital próprio por parte do governo chinês. Com base numa revisão das políticas do governo chinês que apoiam as indústrias emergentes e do estado de desenvolvimento dos fundos de investimento em ações do governo, este estudo resume as funções, características do modelo e processo de tomada de decisão de suporte de ações do governo. Usando uma instituição governamental de investimento em ações em Shenzhen como estudo de caso, esta investigação também analisa em profundidade as 85 empresas nas quais o fundo investiu. incluindo o grau de adequação com a política industrial. Após uma avaliação da eficácia do apoio de capital do governo, este estudo propõe métodos para otimizar o modelo e o apoio dado às indústrias emergentes, incluindo melhorar a competência das instituições governamentais de investimento em capital, aprofundar a cooperação com capitais de risco baseadas no mercado, alavancar incentivos como recompensas e subsídios fiscais e fortalecimento da gestão do processo de investimento.

Additional file 1: Figure S1. Summary of meteorological data in Wuhan from July 7, 08:00 to July 9, 08:00, 2017. Figure S2. Rhythmic occupancy of RNA polymerase II (RNAPII). Figure S3. Whole-transcriptome RNA-seq analysis of rhythmic gene expression in paddy field rice leaves. Figure S4. Reproducibility and loop span of RNAPII-mediated ChIA-PET data. Figure S5. Global patterns of chromatin interactions in rice. Figure S6. Characterization of RNAPII-mediated chromatin interactions. Figure S7. Percentage of overlap or dynamic anchors throughout the day and the correlation between peak intensity and transcription. Figure S8. Properties of RNAPII-organized chromatin spatial clusters (CSCs). Figure S9. Interaction frequencies around AM- and PM- specific node genes in the RNAPII-arranged networks. Figure S10. Features of node genes in the RNAPII-mediated networks. Figure S11. Core circadian clock genes-associated chromatin interaction networks.

Additional file 2: Table S1. Sensitivity analysis. Table S2. The distribution of the score of the Self-Rating of Biological Rhythm Disorder for Adolescents (SBRDA). Table S3. Sample characteristics stratified by sleep rhythm disorder (N = 3693). Table S4. Sample characteristics stratified by activity rhythm disorder (N = 3693). Table S5. Sample characteristics stratified by eating habits rhythm disorder (N = 3693). Table S6. Sample characteristics stratified by digital media use (N = 3693). Table S7. Partial coefficients between self-rating biological rhythm disorders and Depression and anxiety symptoms. Table S8. Adjusted associations between self-rating biological rhythm disorders and depression symptoms. Table S9. Adjusted associations between self-rating biological rhythm disorders and anxiety symptoms.