Anorectal malformation associated with a mutation in the P63 gene in a family with split hand–foot malformation

PURPOSE: The aims of this study were to identify the mutation gene of a Chinese family with anorectal malformation (ARM) associated with split hand–foot malformation and to determine the spatiotemporal expression of the mutated gene during hindgut and anorectum development in human embryos. METHOD: A Chinese family with intrafamilial clinically variable manifestation was analyzed and primers were designed for exons 3–14 of P63, DLX5, DLX6, DAC, and HOXD13 as candidate genes and direct sequence analysis of the exons was performed. Immunohistochemical study of mutated gene in the hindgut and anorectum of human embryos of 4th–10th weeks was performed. RESULT: Affected individuals were found to have an Arg227Gln P63 gene mutation. From the 4th–10th weeks of gestation of the human embryo, the P63-positive cells were mainly located on the epithelium of the apical urorectal septum, hindgut, and cloacal membrane. After the anorectum ruptured during the 8th week, the P63 remained strongly immunoreactive on the epithelium of the anal canal and urethra, but the mucous membrane of the rectum exhibited no reaction. CONCLUSIONS: The mutation identified strongly suggests a causal relationship between the ARM phenotype and P63. The expression of P63 was persistently active during the dynamic and incessant septation of the cloaca and hindgut, suggesting that P63 may play a pivotal role in the morphogenesis of the hindgut and anorectum

Data for the gene expression profiling and alternative splicing events during the chondrogenic differentiation of human cartilage endplate-derived stem cells under hypoxia

AbstractThis article contains relevant data of the research article titled Global profiling of the gene expression and alternative splicing events during the hypoxia-regulated chondrogenic differentiation in human cartilage endplate-derived stem cells (Yao et al., 2016) [1]. The data show global profiling of the DEGs (Differentially expressed genes) and AS (Alternative splicing) events during the hypoxia-regulated chondrogenesis of CESCs (human cartilage endplate-derived stem cells) by using Affymetrix Human Transcriptome Array 2.0 (HTA 2.0) system. In addition, the enriched GO (Gene Ontology) functions and signaling pathways are listed. The information presented here includes the information of patients from which the clinical samples are obtained, the list of primers used for validation, the identification, GO and KEGG analysis of DEG and AS events

AdaTT: Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations

Multi-task learning (MTL) aims at enhancing the performance and efficiency of machine learning models by training them on multiple tasks simultaneously. However, MTL research faces two challenges: 1) modeling the relationships between tasks to effectively share knowledge between them, and 2) jointly learning task-specific and shared knowledge. In this paper, we present a novel model Adaptive Task-to-Task Fusion Network (AdaTT) to address both challenges. AdaTT is a deep fusion network built with task specific and optional shared fusion units at multiple levels. By leveraging a residual mechanism and gating mechanism for task-to-task fusion, these units adaptively learn shared knowledge and task specific knowledge. To evaluate the performance of AdaTT, we conduct experiments on a public benchmark and an industrial recommendation dataset using various task groups. Results demonstrate AdaTT can significantly outperform existing state-of-the-art baselines

The iMaterialist Fashion Attribute Dataset

Large-scale image databases such as ImageNet have significantly advanced image classification and other visual recognition tasks. However much of these datasets are constructed only for single-label and coarse object-level classification. For real-world applications, multiple labels and fine-grained categories are often needed, yet very few such datasets exist publicly, especially those of large-scale and high quality. In this work, we contribute to the community a new dataset called iMaterialist Fashion Attribute (iFashion-Attribute) to address this problem in the fashion domain. The dataset is constructed from over one million fashion images with a label space that includes 8 groups of 228 fine-grained attributes in total. Each image is annotated by experts with multiple, high-quality fashion attributes. The result is the first known million-scale multi-label and fine-grained image dataset. We conduct extensive experiments and provide baseline results with modern deep Convolutional Neural Networks (CNNs). Additionally, we demonstrate models pre-trained on iFashion-Attribute achieve superior transfer learning performance on fashion related tasks compared with pre-training from ImageNet or other fashion datasets. Data is available at: https://github.com/visipedia/imat_fashion_com

A Moderated Mediation Model of Academic Supervisor Developmental Feedback and Postgraduate Student Creativity: Evidence from China

Academic supervisors plays a significant role in the cultivation of postgraduate students, but little is known about how academic supervisor feedback affects their creativity. This study hypothesizes and tests a moderated mediation model to explore how and when academic supervisor developmental feedback (ASDF) affects postgraduate student creativity (PSC), including the mediating effect of intrinsic motivation and the moderating effect of creative self-efficacy. After collecting three-wave time-lagged data from 374 postgraduate students and their academic supervisors, SPSS and Amos software were used to test the research hypotheses and the whole model. The results show that ASDF is positively related to intrinsic motivation and PSC. Intrinsic motivation not only has a positive effect on PSC, but it also plays a mediating role in the relationship between ASDF and PSC. Creative self-efficacy plays a moderating role in the relationships between ASDF, intrinsic motivation, and PSC, that is, ASDF can cause postgraduate students with high creative self-efficacy to develop higher levels of intrinsic motivation than those with low creative self-efficacy, which ultimately leads to more PSC. These findings not only enrich the literature on feedback, motivation, and creativity research in the field of education, but also provide some suggestions for promoting PSC from the perspective of universities, academic supervisors, and postgraduate students

Incorporating External Knowledge Reasoning for Vision-and-Language Navigation with Assistant’s Help

Vision-and-Language Navigation (VLN) is a task designed to enable embodied agents carry out natural language instructions in realistic environments. Most VLN tasks, however, are guided by an elaborate set of instructions that is depicted step-by-step. This approach deviates from real-world problems in which humans only describe the object and its surroundings and allow the robot to ask for help when required. Vision-based Navigation with Language-based Assistance (VNLA) is a recently proposed task that requires an agent to navigate and find a target object according to a high-level language instruction. Due to the lack of step-by-step navigation guidance, the key to VNLA is to conduct goal-oriented exploration. In this paper, we design an Attention-based Knowledge-enabled Cross-modality Reasoning with Assistant’s Help (AKCR-AH) model to address the unique challenges of this task. AKCR-AH learns a generalized navigation strategy from three new perspectives: (1) external commonsense knowledge is incorporated into visual relational reasoning, so as to take proper action at each viewpoint by learning the internal–external correlations among object- and room-entities; (2) a simulated human assistant is introduced in the environment, who provides direct intervention assistance when required; (3) a memory-based Transformer architecture is adopted as the policy framework to make full use of the history clues stored in memory tokens for exploration. Extensive experiments demonstrate the effectiveness of our method compared with other baselines

A Case Study on Observed and Simulated CO

Observations of atmospheric CO2 concentration profiles provide significative constraints on the global/regional inversions of carbon sources and sinks. Anhui Institute of Optics and Fine Mechanics of Chinese Academy of Sciences developed a Raman Lidar system to detect the vertical distribution of atmospheric CO2. This paper compared the observations with the modeled results from a three-dimensional global chemistry transport model-GEOS-Chem, which showed a good agreement in the trend of change with lidar measurements. The case study indicated a potential for better simulating vertical distribution of atmospheric CO2 by combining with lidar measurements