Out of sight but not out of mind:The role of the embryo in hormone mediated maternal effects

In the last few decades, the study of maternal effects, as being strongly suggested by its name, has been substantially investigated from the perspective of mothers in terms of mechanisms and its evolutionary consequences. In addition, when it comes to the maternal effects on the offspring, the studies have been and still are more often than not focusing on the post-natal stage. However, recently, several studies have suggested that embryos are also playing an active and important role that may potentially induce a mother-offspring conflict by heavily converting maternal androgens to other hormones. Yet, this also has led to many unknowns. Therefore, in this thesis, I focused on the role of the embryo in determining its own development under the influence of the mother. In particular, I explored 3 important unknowns about the embryonic metabolism of maternal androgens which are: 1) to what extent do embryos can affect the hormonal environment they are exposed to during early development, 2) how does such a hormonal environment affect their prenatal development, and 3) are embryos able to control their early hormonal environment depending on the context in which they develop

Research and Application of Trajectory Stop Point Detection Algorithm for Time Series Clustering

In order to solve the problem of low accuracy of sampling irregular tracks, a time series clustering algorithm for detecting stops is proposed. Firstly, based on the data field theory, a hybrid feature density detection method considering temporal and spatial characteristics is designed. Secondly, according to the characteristic that the center density of the stop point is greater than the inlet density, the filtering and refining strategy is used to extract the stop point. In the filtration stage, the time duration and the minimum density threshold are selected as the candidate residence points. The maximum threshold is used to identify the actual residence point in the refining stage. The experimental results show that the proposed method can effectively detect the residence points on the irregular trajectories with higher accuracy and less time consumption than the existing methods

Industry-specific prevalence and gender disparity of common mental health problems in the UK: A national repetitive cross-sectional study

Aims: The aim of the study was to evaluate the prevalence and temporal trend of common mental health problems (CMHPs) in the UK by industrial classification from 2012–2014 to 2016–2018 while evaluating the corresponding gender disparities./ Methods: We used data from the Health Survey for England. CMPH was judged by a 12-item General Health Questionnaire. Industrial classifications were defined using the UK Standard Industrial Classification of Economic Activities. Data were fitted by the logistic models./ Results: In this study, 19,581 participants covering 20 industries were included. In total, 18.8% of participants screened positive for CMHP in 2016–2018, which significantly increased from 16.0% in 2012–2014 [adjusted OR (AOR) = 1.17, 95% CI 1.08–1.27]. In 2016–2018, the prevalence of CMHP ranged from 6.2% in the industry of mining and quarrying to 23.8% in the industry of accommodation and food service activities. From 2012–2014 to 2016–2018, none of the 20 industries studied experienced significant decreases in the above prevalence; conversely, three industries saw significant increases, including wholesale and retail trade, repair of motor vehicles and motorcycles (AOR for trend = 1.32, 95% CI 1.04–1.67), construction (AOR for trend = 1.66, 95% CI 1.23–2.24), and other service activities, which cannot be classified (AOR for trend = 1.94, 95% CI 1.06–3.55). In total, 11 of the 20 industries studied had significant gender disparities against women, with the smallest gap being in the industry of transport and storage (AOR = 1.47, 95% CI 1.09–2.0) and the highest in the industry of arts, entertainment, and recreation (AOR = 6.19, 95% CI 2.94–13.03). From 2012–2014 to 2016–2018, gender disparities were narrowed only in two industries, including human health and social work activities (AOR for trend = 0.45, 95% CI 0.27–0.74), and transport and storage (AOR for trend = 0.5, 95% CI 0.27–0.91)./ Conclusion: The prevalence of CMHPs has increased and had a wide variation across industries in the UK. There were disparities against women, and the gender disparities have been keeping almost no improvement from 2012–2014 to 2016–2018

Examination of Putative Histone Acetyltransferases in Cryptococcus Neoformans

Undergraduate Basi

4D Unsupervised Object Discovery

Object discovery is a core task in computer vision. While fast progresses have been made in supervised object detection, its unsupervised counterpart remains largely unexplored. With the growth of data volume, the expensive cost of annotations is the major limitation hindering further study. Therefore, discovering objects without annotations has great significance. However, this task seems impractical on still-image or point cloud alone due to the lack of discriminative information. Previous studies underlook the crucial temporal information and constraints naturally behind multi-modal inputs. In this paper, we propose 4D unsupervised object discovery, jointly discovering objects from 4D data -- 3D point clouds and 2D RGB images with temporal information. We present the first practical approach for this task by proposing a ClusterNet on 3D point clouds, which is jointly iteratively optimized with a 2D localization network. Extensive experiments on the large-scale Waymo Open Dataset suggest that the localization network and ClusterNet achieve competitive performance on both class-agnostic 2D object detection and 3D instance segmentation, bridging the gap between unsupervised methods and full supervised ones. Codes and models will be made available at https://github.com/Robertwyq/LSMOL.Comment: Accepted by NeurIPS 2022. 17 pages, 6 figure