349 research outputs found

    Perinatal risk factors for mental disorders in the offspring and in their mothers

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    Mental disorders are common in the population, but their etiology remains largely unknown. Early-life factors have been hypothesized to be potential risk factors; however, current evidence is conflicting and incomplete. This thesis aimed to evaluate cesarean delivery and infections as early-life risk factors for mental disorders, as well as the impact of cesarean delivery on maternal suicidality. In Study I, we systematically synthesized the literature on the association between cesarean delivery and the risk of neurodevelopmental and psychiatric disorders in the offspring, and then quantified the extent of this association in a meta-analysis. Of the 6,953 identified articles, 61 studies comprising over 20 million deliveries were included and meta-analyzed using randomeffect models. We found that, compared to birth by vaginal delivery, cesarean delivery births were associated with significantly increased odds of attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorders (ASD). We found that the estimates were less precise for intellectual disabilities, obsessive-compulsive disorder (OCD), and eating disorders. No significant associations were observed for depression/affective psychoses or non-affective psychoses. Estimates were comparable for emergency and elective cesarean delivery. This study highlighted the need to explore potential mechanisms underlying the described associations and showed that current literature overall suffered from unmeasured confounding. Study II, which was prompted by Study I, further explored the association between cesarean delivery and the risk of neurodevelopmental and psychiatric disorders in the offspring using data from the Swedish national registers, while taking previously identified knowledge gaps into consideration. This population-based birth cohort study identified 1,179,341 individuals born between 1990 and 2003 and followed up through 2013. We found that, compared with vaginal delivery, cesarean delivery was associated with 10-30% increased risk of ADHD and intellectual disability, irrespective of cesarean delivery modality (i.e., planned or intrapartum). Planned cesarean delivery was associated with 10-20% higher risk of ASD, communication disorders, and learning disorders. Cesarean delivery was not associated with psychiatric disorders. However, when the analyses were repeated in clusters of relatives, where shared genetic and environmental factors were adjusted for, cesarean delivery was no longer associated with any outcome. Overall, the findings suggested that the association between cesarean delivery and neurodevelopmental disorders was likely not causal but could be explained by unmeasured familial factors. Study III estimated the incidence and the risk of suicide attempts and deaths during the first postpartum year in mothers who delivered via cesarean vs. vaginally. All deliveries in Sweden between 1973 and 2012 were identified (n=4,016,789). The mothers were followed since delivery for 12 months or until the date of one of the outcomes, death by other causes or emigration. During the 12-month follow-up, 504 (0.098%) suicide attempts were observed in the cesarean delivery group and 2,240 (0.064%) in the vaginal delivery group, while 11 (0.0037%) deaths by suicide were registered in the cesarean delivery group and 109 (0.0029%) in the vaginal delivery group. Compared to vaginal delivery, cesarean delivery was associated with a 46% increased risk of suicide attempts, but not of deaths by suicide. The subgroup analyses suggested that mothers’ country of birth likely modified the association between cesarean delivery and the risk of suicide attempts and deaths by suicide, which may have clinical implications. In summary, the findings indicated that postpartum maternal suicidal behaviors were uncommon in Sweden. Improved understanding of the association between cesarean delivery and maternal suicidal behaviors may promote more interventions to improve maternal mental health and further reduce suicidal risks. Study IV examined the association between prenatal and early childhood infections and the subsequent risk of OCD and tic disorders in a Swedish population-based cohort. The cohort identified 2,949,080 singletons born in Sweden between 1973 and 2003, and followed through 2013. The exposures were prenatal maternal (and paternal, as an internal control) infections and early childhood infections in the offspring (i.e., during the first three years of life). Results showed that, at the population level, and after adjusting for parental psychiatric history and autoimmunity, both prenatal maternal (but not paternal) and early childhood infections were associated with 19-33% increased risk of OCD and tic disorders. However, all associations fully attenuated to the null in the sibling models. Overall, the results do not support a causal association between prenatal maternal or early-life infections and the development of OCD or tic disorders. Instead, familial factors (e.g., genetic pleiotropy) may explain both the propensity to infections and the liability to OCD and tic disorders. Large scale genome-wide association studies are motivated to provide novel insights into genetic correlations between immunerelated phenotypes and OCD and tic disorders. Overall, this thesis expanded our knowledge on the role of two perinatal risk factors, cesarean delivery and early-life infections, in the development of child and maternal mental health outcomes. The thesis outlines a series of research and clinical implications, as well as directions for future studies

    Structure controllability of complex network based on preferential matching

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    Minimum driver node sets (MDSs) play an important role in studying the structural controllability of complex networks. Recent research has shown that MDSs tend to avoid high-degree nodes. However, this observation is based on the analysis of a small number of MDSs, because enumerating all of the MDSs of a network is a #P problem. Therefore, past research has not been sufficient to arrive at a convincing conclusion. In this paper, first, we propose a preferential matching algorithm to find MDSs that have a specific degree property. Then, we show that the MDSs obtained by preferential matching can be composed of high- and medium-degree nodes. Moreover, the experimental results also show that the average degree of the MDSs of some networks tends to be greater than that of the overall network, even when the MDSs are obtained using previous research method. Further analysis shows that whether the driver nodes tend to be high-degree nodes or not is closely related to the edge direction of the network

    Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory

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    Perception and expression of emotion are key factors to the success of dialogue systems or conversational agents. However, this problem has not been studied in large-scale conversation generation so far. In this paper, we propose Emotional Chatting Machine (ECM) that can generate appropriate responses not only in content (relevant and grammatical) but also in emotion (emotionally consistent). To the best of our knowledge, this is the first work that addresses the emotion factor in large-scale conversation generation. ECM addresses the factor using three new mechanisms that respectively (1) models the high-level abstraction of emotion expressions by embedding emotion categories, (2) captures the change of implicit internal emotion states, and (3) uses explicit emotion expressions with an external emotion vocabulary. Experiments show that the proposed model can generate responses appropriate not only in content but also in emotion.Comment: Accepted in AAAI 201

    Three essays on financial markets

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    This dissertation focuses on the financial markets including stock markets, commodity futures and options markets. Chapter 2 studies the trading activity in commodity futures and options markets. Little is known about trading activity in commodity options market. We study the information content of commodity futures and options trading volume. Time-series tests indicate that futures contracts in a portfolio with the lowest options-to-futures volume ratio (O/F) outperform those in a portfolio with the highest ratio by 0.3% per week. Cross-sectional tests show that O/F has higher predictive power for futures returns than such traditional risk factors as the carry, momentum, and liquidity factors. O/F has longer predictive horizon for post-announcement returns than the information contained in the monthly World Agricultural Supply and Demand Estimates (WASDE) reports. The analysis of the weekly Commitments of Traders (COT) reports indicates that commercials (hedgers) provide liquidity to non-commercials (speculators) in short-term in commodity options market. Chapter 3 explores what kinds of information can explain the USDA forecast errors in crop ending stocks. In the empirical analysis using Markov Chain Monte Carlo (MCMC) method, we find that the futures basis, level of monthly ending stocks, and level of planted area are significant to explain the forecast errors. The out of sample test is employed and the adjusted forecasts improve the forecast accuracy of crop ending stocks. Chapter 4 investigates the liquidity effect in Chinese stock market using an asset pricing model. The empirical results show that liquidity has a significant effect on stock returns and the liquidity premium exists in Chinese stock market. However, neither CAPM nor Fama French three-factor model can explain the liquidity premium. We propose a new two-factor (market and liquidity) model in which the liquidity factor captures two dimensions of liquidity. The two-factor model performs well in explaining the liquidity premium. Furthermore, unlike CAPM and Fama-French three-factor model, the two-factor model is able to explain the size effect in Chinese stock market

    Efficient target control of complex networks based on preferential matching

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    Controlling a complex network towards a desire state is of great importance in many applications. Existing works present an approximate algorithm to find the driver nodes used to control partial nodes of the network. However, the driver nodes obtained by this algorithm depend on the matching order of nodes and cannot get the optimum results. Here we present a novel algorithm to find the driver nodes for target control based on preferential matching. The algorithm elaborately arrange the matching order of nodes in order to minimize the size of the driver nodes set. The results on both synthetic and real networks indicate that the performance of proposed algorithm are better than the previous one. The algorithm may have various application in controlling complex networks
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