Associations between gut microbiota and adverse neurodevelopmental outcomes in preterm infants: a two-sample Mendelian randomization study

Gut microbiota are associated with adverse neurodevelopmental outcomes in preterm infants; however, the precise causal relationship remains unclear. In this study, we conducted a two-sample Mendelian randomization (MR) analysis to comprehensively study the relationship between gut microbiota and adverse neurodevelopmental outcomes in preterm infants and identify specific causal bacteria that may be associated with the occurrence and development of adverse neurodevelopmental outcomes in preterm infants. The genome-wide association analysis (GWAS) of the MiBioGen biogroup was used as the exposure data. The GWAS of six common adverse neurodevelopmental outcomes in premature infants from the FinnGen consortium R9 was used as the outcome data. Genetic variations, namely, single nucleotide polymorphisms (SNPs) below the locus-wide significance level (1 × 10−5) and genome-wide statistical significance threshold (5 × 10−8) were selected as instrumental variables (IVs). MR studies use inverse variance weighting (IVW) as the main method. To supplement this, we also applied three additional MR methods: MR-Egger, weighted median, and weighted mode. In addition, the Cochrane’s Q test, MR-Egger intercept test, Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO), and leave-one-out methods were used for sensitivity analysis. Our study shows a causal relationship between specific gut microbiota and neurodevelopmental outcomes in preterm infants. These findings provide new insights into the mechanism by which gut microbiota may mediate adverse neurodevelopmental outcomes in preterm infants

The Formation Mechanism of Social Identity Based on Knowledge Contribution in Online Knowledge Communities: Empirical Evidence from China

Social identity is a key factor in the sustainable development of online knowledge communities (OKCs). The purpose of this research is to explore the formation mechanism of the respective social identities of lurkers and posters, based on knowledge contribution behavior. To evaluate the research model, an online survey was conducted in the WeChat group and QQ group, which yielded 469 usable questionnaire responses. Structural equation modeling was then adapted to examine the model. We found that knowledge contribution can produce social and psychological outcomes (i.e., cognitive communication, parasocial interaction, a sense of self-worth, social support, and social identity). The posters’ social identity arises through the mediating effects of information support and cognitive communication, while the lurkers’ social identity arises through the mediating effect of parasocial interaction. In addition, this research reveals that personalized behaviors and social identity can coexist in OKCs. Our findings may provide theoretical and practical enlightenment for managers to achieve sustainable and successful operations in OKCs

Application of SRNN-GRU in Photovoltaic power Forecasting

Short-term photovoltaic power forecasting is of great significance for maintaining the security and stability of the power grid and coordinating the utilization of resources. As one of the Deep Learning Methods, Recurrent Neural Network (RNN) is widely used in time series prediction but lacks the ability of parallel computing. With good prediction effect, RNN is faced with the problem of long training time. In this paper, Sliced Recurrent Neural Network (SRNN) is applied to PV power prediction to guarantee the ability of parallel computing. The research result shows that compared to other commonly used models, SRNN can greatly speed up the training of Deep Learning Network with over 4 times higher training speed of the application of PV power prediction than that of ordinary RNN structure like LSTM and GRU. The accuracy of SRNN model is also improved by 0.1102 mae, which is significantly ahead of the others, as its parallel structure causes the more efficient parameter update, thus achieving ideal effect in PV prediction

Research on internal external collaborative optimization strategy for multi microgrids interconnection system

Microgrid is one of the most effective solutions to integrate renewable generation into power system. However, microgrids also have some limitations, such as increasing renewable energy accommodation, promoting multi-energy complementarity, and improving energy efficiency. Through autonomous management and energy interaction among microgrids. The establishment of multi microgrids provides ideas for solving the above problems. Multi microgrids has obvious advantages in promoting renewable energy accommodation, improving the economy and energy utilization of power grid operation, and reducing the impact on the distribution network. Therefore, from the perspective of energy scheduling, this paper proposes a internal external collaborative optimization strategy for multi microgrids. Firstly, a cogeneration microgrid model based on graph theory is established, which can not only represent the energy transmission process inside the microgrid, but also the energy interaction process between microgrids. Secondly, a microgrid interconnection pipeline transmission model based on the loss function is established. This model can not only reflect the energy transmission state in the pipeline, but also facilitate the linear solution of the optimization model. Finally, by setting new decision variables and constraints, the linear solution of the internal external collaborative optimization of the multi microgrids is realized, and its rationality and feasibility are verified by a case

Data_Sheet_1_Associations between gut microbiota and adverse neurodevelopmental outcomes in preterm infants: a two-sample Mendelian randomization study.zip

Gut microbiota are associated with adverse neurodevelopmental outcomes in preterm infants; however, the precise causal relationship remains unclear. In this study, we conducted a two-sample Mendelian randomization (MR) analysis to comprehensively study the relationship between gut microbiota and adverse neurodevelopmental outcomes in preterm infants and identify specific causal bacteria that may be associated with the occurrence and development of adverse neurodevelopmental outcomes in preterm infants. The genome-wide association analysis (GWAS) of the MiBioGen biogroup was used as the exposure data. The GWAS of six common adverse neurodevelopmental outcomes in premature infants from the FinnGen consortium R9 was used as the outcome data. Genetic variations, namely, single nucleotide polymorphisms (SNPs) below the locus-wide significance level (1 × 10−5) and genome-wide statistical significance threshold (5 × 10−8) were selected as instrumental variables (IVs). MR studies use inverse variance weighting (IVW) as the main method. To supplement this, we also applied three additional MR methods: MR-Egger, weighted median, and weighted mode. In addition, the Cochrane’s Q test, MR-Egger intercept test, Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO), and leave-one-out methods were used for sensitivity analysis. Our study shows a causal relationship between specific gut microbiota and neurodevelopmental outcomes in preterm infants. These findings provide new insights into the mechanism by which gut microbiota may mediate adverse neurodevelopmental outcomes in preterm infants.</p

Pyrene-Based Fluorescent Porous Organic Polymers for Recognition and Detection of Pesticides

Eating vegetables with pesticide residues over a long period of time causes serious adverse effects on the human body, such as acute poisoning, chronic poisoning, and endocrine system interference. To achieve the goal of a healthy society, it is an urgent issue to find a simple and effective method to detect organic pesticides. In this work, two fluorescent porous organic polymers, LNU-45 and LNU-47 (abbreviation for Liaoning University), were prepared using π-conjugated dibromopyrene monomer and boronic acid compounds as building units through a Suzuki coupling reaction. Due to the large π-electron delocalization effect, the resulting polymers revealed enhanced fluorescence performance. Significantly, in sharp contrast with the planar π-conjugated polymer framework (LNU-47), the distorted conjugated structure (LNU-45) shows a higher specific surface area and provides a broad interface for analyte interaction, which is helpful to achieve rapid response and detection sensitivity. LNU-45 exhibits strong fluorescence emission at 469 nm after excitation at 365 nm in THF solution, providing strong evidence for its suitability as a luminescent chemosensor for organic pesticides. The fluorescence quenching coefficients of LNU-45 for trifluralin and dicloran were 5710 and 12,000 (LNU-47 sample by ca. 1.98 and 3.38 times), respectively. Therefore, LNU-45 serves as an effective “real-time” sensor for the detection of trifluralin and dicloran with high sensitivity and selectivity

Learning to Transform, Combine, and Reason in Open-domain Question Answering

Users seek direct answers to complex questions from large open-domain knowledge sources like the Web. Open-domain question answering has become a critical task to be solved for building systems that help address users' complex information needs. Most open-domain question answering systems use a search engine to retrieve a set of candidate documents, select one or a few of them as context, and then apply reading comprehension models to extract answers. Some questions, however, require taking a broader context into account, e.g., by considering low-ranked documents that are not immediately relevant, combining information from multiple documents, and reasoning over multiple facts from these documents to infer the answer. In this paper, we propose a model based on the Transformer architecture that is able to efficiently operate over a larger set of candidate documents by effectively combining the evidence from these documents during multiple steps of reasoning, while it is robust against noise from low-ranked non-relevant documents included in the set. We use our proposed model, called TraCRNet, on two public open-domain question answering datasets, SearchQA and Quasar-T, and achieve results that meet or exceed the state-of-the-art