Choroid plexus and its relations with age-related diseases.

The choroid plexus is composed of epithelial cells situated on the basal layer. The tight junctions between adjacent choroid plexus epithelial cells establish the blood-cerebrospinal fluid barrier. This barrier, in conjunction with the blood-brain barrier, is crucial for the homeostasis of the brain microenvironment. The choroid plexus epithelium secretes cerebrospinal fluid, growth factors, neuropeptides, and lipids into the ventricles and also serves as a gateway for immune cells to enter the brain. The pathophysiology of aging and neurodegenerative diseases remains largely enigmatic, with an increasing body of research linking the choroid plexus to the etiology of these age-related disorders. In this review, we summarize the known relationship between the choroid plexus epithelium and age-related diseases, aiming to provide new therapeutic clues for these disorders.

Efficient Token-Guided Image-Text Retrieval With Consistent Multimodal Contrastive Training.

Image-text retrieval is a central problem for understanding the semantic relationship between vision and language, and serves as the basis for various visual and language tasks. Most previous works either simply learn coarse-grained representations of the overall image and text, or elaborately establish the correspondence between image regions or pixels and text words. However, the close relations between coarse- and fine-grained representations for each modality are important for image-text retrieval but almost neglected. As a result, such previous works inevitably suffer from low retrieval accuracy or heavy computational cost. In this work, we address image-text retrieval from a novel perspective by combining coarse- and fine-grained representation learning into a unified framework. This framework is consistent with human cognition, as humans simultaneously pay attention to the entire sample and regional elements to understand the semantic content. To this end, a Token-Guided Dual Transformer (TGDT) architecture which consists of two homogeneous branches for image and text modalities, respectively, is proposed for image-text retrieval. The TGDT incorporates both coarse- and fine-grained retrievals into a unified framework and beneficially leverages the advantages of both retrieval approaches. A novel training objective called Consistent Multimodal Contrastive (CMC) loss is proposed accordingly to ensure the intra- and inter-modal semantic consistencies between images and texts in the common embedding space. Equipped with a two-stage inference method based on the mixed global and local cross-modal similarity, the proposed method achieves state-of-the-art retrieval performances with extremely low inference time when compared with representative recent approaches. Code is publicly available: github.com/LCFractal/TGDT.

Self-Paced Clustering Ensemble.

The clustering ensemble has emerged as an important extension of the classical clustering problem. It provides an elegant framework to integrate multiple weak base clusterings to generate a strong consensus result. Most existing clustering ensemble methods usually exploit all data to learn a consensus clustering result, which does not sufficiently consider the adverse effects caused by some difficult instances. To handle this problem, we propose a novel self-paced clustering ensemble (SPCE) method, which gradually involves instances from easy to difficult ones into the ensemble learning. In our method, we integrate the evaluation of the difficulty of instances and ensemble learning into a unified framework, which can automatically estimate the difficulty of instances and ensemble the base clusterings. To optimize the corresponding objective function, we propose a joint learning algorithm to obtain the final consensus clustering result. Experimental results on benchmark data sets demonstrate the effectiveness of our method.

Person Reidentification via Multi-Feature Fusion With Adaptive Graph Learning.

The goal of person reidentification (Re-ID) is to identify a given pedestrian from a network of nonoverlapping surveillance cameras. Most existing works follow the supervised learning paradigm which requires pairwise labeled training data for each pair of cameras. However, this limits their scalability to real-world applications where abundant unlabeled data are available. To address this issue, we propose a multi-feature fusion with adaptive graph learning model for unsupervised Re-ID. Our model aims to negotiate comprehensive assessment on the consistent graph structure of pedestrians with the help of special information of feature descriptors. Specifically, we incorporate multi-feature dictionary learning and adaptive multi-feature graph learning into a unified learning model such that the learned dictionaries are discriminative and the subsequent graph structure learning is accurate. An alternating optimization algorithm with proved convergence is developed to solve the final optimization objective. Extensive experiments on four benchmark data sets demonstrate the superiority and effectiveness of the proposed method.

Maternal dietary patterns, supplements intake and autism spectrum disorders: A preliminary case-control study.

The aim of this study was first to investigate associations between maternal dietary patterns and autism spectrum disorders (ASDs) and second to investigate association between maternal supplement intake and ASD.We used a case-control study design to enroll typically developing (TD) children and children with ASD, and data were derived from the Autism Clinical and Environmental Database (ACED).Three seventy four children with AUTISM and 354 age matched TD children were included. The multivariate logistic regression model revealed that maternal unbalanced dietary patterns before conception had a significant increased risk of ASD in offspring (mostly meat: adjusted OR, 4.010 [95% CI, 1.080, 14.887]; mostly vegetable: adjusted OR, 2.234 [95% CI, 1.009, 4.946]); maternal supplementation of calcium during pregnancy preparation was associated with decreased ASD risk (adjusted OR, 0.480 [95% CI, 0.276, 0.836]).This study provided preliminary evidence that maternal unbalanced dietary patterns may be a risk factor for ASD and supplementation of calcium during pregnancy preparation may be inversely associated with ASD in offspring.

Social impairment of children with autism spectrum disorder affects parental quality of life in different ways.

This study evaluated the life quality of Chinese parents of preschool children with autism spectrum disorder (ASD) and their association with child social impairment and childcare burden. The participants included 406 families of children with ASD and 513 families with typically developing (TD) children. The findings indicated that parents in the ASD group had a lower quality of life than parents in the TD group, whereas only mother of children with ASD experienced a greater childcare burden than mother with TD children. Lower parental quality of life were associated with higher social impairment of children. To further clarify the correlativity of child social impairment, parental quality of life and childcare burden, the mediation analyses were conducted. The results showed that childcare burden mediated the influence of child social impairment on maternal quality of life, while it has no mediating effect on fathers. It implies that social impairment of children affects parental quality of life in different ways.

Aggressive behaviors and treatable risk factors of preschool children with autism spectrum disorder.

Aggressive behaviors of children with autism spectrum disorder (ASD) are common. We conducted this study to describe the aggressive mode of preschool children with ASD and examine the associations between specific aggressive behaviors and two treatable factors: sleep problems and attention deficit hyperactivity disorder (ADHD) symptoms. In total, 577 typically developing (TD) children and 490 children with ASD were investigated in this study. The Institute for Basic Research - Modified Overt Aggression Scale (IBR-MOAS) was used to assess aggressive behaviors. Children's social impairments, sleep problems and ADHD symptoms were also measured with specific scales. The total IBR-MOAS score was significantly higher (worse) in the TD group [4.47 (5.36)] than in the ASD group [3.47 (5.63), P = 0.004]. The aggressive modes differed between groups: when compared with each other, the TD group received higher scores on Verbal and Physical Aggression Toward Others (all P < 0.01), while the ASD group had higher scores on Physical Aggression Against Self (P = 0.006). The linear regression model demonstrated that the aggressive behaviors of children with ASD were significantly associated with two treatable factors: sleep problems and ADHD symptoms. These findings have substantial clinical implications: treatment of these two risk factors may be helpful in managing aggressive behavior in children with ASD. Autism Res 2017. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. Autism Res 2017, 10: 1155-1162. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.