Shadow Datasets, New challenging datasets for Causal Representation Learning

Discovering causal relations among semantic factors is an emergent topic in representation learning. Most causal representation learning (CRL) methods are fully supervised, which is impractical due to costly labeling. To resolve this restriction, weakly supervised CRL methods were introduced. To evaluate CRL performance, four existing datasets, Pendulum, Flow, CelebA(BEARD) and CelebA(SMILE), are utilized. However, existing CRL datasets are limited to simple graphs with few generative factors. Thus we propose two new datasets with a larger number of diverse generative factors and more sophisticated causal graphs. In addition, current real datasets, CelebA(BEARD) and CelebA(SMILE), the originally proposed causal graphs are not aligned with the dataset distributions. Thus, we propose modifications to them

Contraceptive practices and induced abortions status among internal migrant women in Guangzhou, China: a cross-sectional study

Background: China is facing the unprecedented challenges of internal migration. Migrants tend to have poorer utilization of health and family planning services as compared to the local residents. Migrant women are at greater risk of induced abortions due to their poor contraceptive knowledge and attitude. This study aims to understand the contraceptive practices and history of induced abortions, explore the potential factors influencing induced abortions, and evaluate the utilization of family planning services among migrant women in Guangzhou, China. Methods: An anonymous, self-administered questionnaire survey was conducted with 1003 migrant women aged 18-49 in Guangzhou, China in 2013. A multi-stage sampling method was employed. Binary logistic regression model was used for analyzing risk factors of induced abortions. Results: Among the 1003 participants, 810 (80.8 %) reported having sex in the past 6 months, including 715 (88.3 %) married and 95 (11.7 %) unmarried. The most reported contraceptive method was male condom (44.9 %), while 8.1 % never used any contraceptive methods. Only 10.4 % reported having attained free condoms from family planning service stations (FPSSs) and 39.3 % reported having acquired contraceptive knowledge from family planning workers. Of all the participants, 417 (41.6 %) had a history of induced abortion. Of married and unmarried women, 389 (49.1 %) and 28 (14.0 %) had induced abortion respectively. Of these, 152 (36.5 %) had repeated abortions. The most reported reason for having induced abortion was failure of contraception (31.9 %), followed by nonuse of any contraceptives (21.1 %). Migrants who had induced abortion tended to be older, have household registration outside Guangdong province, receive no annual health checkup, have lower education, have urban household registration, have lived longer in Guangzhou and have children (P < 0.05). Conclusions: The prevalence rate of induced abortion, especially repeated abortions among migrant women was high in Guangzhou, China. There is an urgent need to improve the awareness of regular and appropriate use of contraceptives. The utilization of FPSSs among migrant women was reportedly low. Family planning system should be improved to provide better access for migrants and better integrated with the general health services. 2015 Zeng et al.This paper outlines some of the findings from a QAA (Scotland) funded project exploring first year curriculum design (Bovill et al. 2008). Whilst many examples exist of curricula being designed in ways to engage first year students, there are fewer published examples of active student participation in curriculum design processes. In the current higher education context where student engagement in learning is emphasised (Carini et al,2006), this paper asks more generally whether students should be actively participating in curriculum design.In order to answer this question, several elements of the project findings are explored: student views gathered in focus groups; staff views collected in workshops; and the case studies where students were actively involved in curriculum design. The data are examined for lessons that inform the debate about whether students should be participating in curriculum design, in first year and at other levels. Alongside these findings, relevant literature is critiqued in order to ascertain the desirability and feasibility of adopting curriculum design approaches that offer opportunities for active student participation.sch_iih15pub3989pub55

A Finite Queue Model Analysis of PMRC-based Wireless Sensor networks

In our previous work, a highly scalable and fault- tolerant network architecture, the Progressive Multi-hop Rotational Clustered (PMRC) structure, is proposed for constructing large-scale wireless sensor networks. Further, the overlapped scheme is proposed to solve the bottleneck problem in PMRC-based sensor networks. As buffer space is often scarce in sensor nodes, in this paper, we focus on studying the queuing performance of cluster heads in PMRC-based sensor networks. We develop a finite queuing model to analyze the queuing performance of cluster heads for both non-overlapped and overlapped PMRC-based sensor network. The average queue length and average queue delay of cluster head in different layers are derived. To validate the analysis results, simulations have been conducted with different loads for both non- overlapped and overlapped PMRC-based sensor networks. Simulation results match with the analysis results in general and confirm the advantage of selecting two cluster heads over selecting single cluster head in terms of the improved queuing performance

Load-Similar Node Distribution for Prolonging Network Lifetime in PMRC-Based Wireless Sensor Networks

In this paper, the energy hole problem in Progressive Multi-hop Rotational Clustered (PMRC)-based wireless sensor networks (WSNs) is studied. We first analyze the traffic load distribution in PMRC-based WSNs. Based on the analysis, we propose a novel load-similar node distribution strategy combined with the Minimum Overlapping Layers (MOL) scheme to solve the energy hole problem in PMRC-based WSNs. Simulation results demonstrate that the load-similar node distribution strategy significantly prolongs network lifetime than uniform node distribution and an existing nonuniform node distribution strategies. The analysis model and the proposed load-similar node distribution strategy have the potential to be applied to other multi-hop WSN structures

Varying performance of eight evapotranspiration products with aridity and vegetation greenness across the globe

The wide application of the evapotranspiration (ET) products has deepened our understanding of the water, energy and carbon cycles, driving increased interest in regional and global assessments of their performance. However, evaluating ET products at a global scale with varying levels of dryness and vegetation greenness poses challenges due to a relative lack of reference data and potential water imbalance. Here, we evaluated the performance of eight state-of-the-art ET products derived from remote sensing, Land Surface Models, and machine learning methods. Specifically, we assessed their ability to capture ET magnitude, variability, and trend, using 1,381 global watershed water balance ET as a baseline. Furthermore, we created aridity and vegetation categories to investigate performance differences among products under varying environmental conditions. Our results demonstrate that the spatial and temporal performances of the ET products were strongly affected by aridity and vegetation greenness. The poorer performances, such as underestimation of interannual variability and misjudged trend, tend to occur in abundant humidity and vegetation. Our findings emphasize the significance of considering aridity and vegetation greenness into ET product generation, especially in the context of ongoing global warming and greening. Which hopefully will contribute to the directional optimizations and effective applications of ET simulations

Multi-functional Frequency Selective Absorber Enabling FR1 and FR2 5G OTA Tests in a Hybrid Reverberation Chamber

Due to the special advantages of the reverberation chamber (RC), it has been recognized as a standardized over-the-air (OTA) testing methodology by the cellular telecommunications industry association (CTIA). Currently, OTA tests of the fifth-generation (5G) wireless terminals are usually performed in different testing environments, e.g., in a reverberation chamber for fast isotropic tests in the sub-6 GHz frequency band, and in an anechoic chamber for directional tests in the millimeter-wave (mm-wave) frequency band, which are inconvenient and costly. In this work, two multi-functional frequency selective absorbers (FSAs) are designed to realize absorption in the 5G mm-wave band and reflection/transmission in the sub-6 GHz band. By loading the FSAs to the RC, an integrated testing environment for 5G terminals can be established. Namely, a quasi-anechoic environment in the mm-wave band and a reverberation environment in the sub-6 GHz band can be simultaneously achieved in the RC loaded with the proposed FSAs. Experimental and simulated results are presented to demonstrate the effectiveness of the proposed scheme

Novel tumor necrosis factor-related long non-coding RNAs signature for risk stratification and prognosis in glioblastoma

BackgroundTumor necrosis factor (TNF) is an inflammatory cytokine that can coordinate tissue homeostasis by co-regulating the production of cytokines, cell survival, or death. It widely expresses in various tumor tissues and correlates with the malignant clinical features of patients. As an important inflammatory factor, the role of TNFα is involved in all steps of tumorigenesis and development, including cell transformation, survival, proliferation, invasion and metastasis. Recent research has showed that long non-coding RNAs (lncRNAs), defined as RNA transcripts >200 nucleotides that do not encode a protein, influence numerous cellular processes. However, little is known about the genomic profile of TNF pathway related-lncRNAs in GBM. This study investigated the molecular mechanism of TNF related-lncRNAs and their immune characteristics in glioblastoma multiforme (GBM) patients.MethodsTo identify TNF associations in GBM patients, we performed bioinformatics analysis of public databases - The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). The ConsensusClusterPlus, CIBERSORT, Estimate, GSVA and TIDE and first-order bias correlation and so on approaches were conducted to comprehensively characterize and compare differences among TNF-related subtypes.ResultsBased on the comprehensive analysis of TNF-related lncRNAs expression profiles, we constructed six TNF-related lncRNAs (C1RL-AS1, LINC00968, MIR155HG, CPB2-AS1, LINC00906, and WDR11-AS1) risk signature to determine the role of TNF-related lncRNAs in GBM. This signature could divide GBM patients into subtypes with distinct clinical and immune characteristics and prognoses. We identified three molecular subtypes (C1, C2, and C3), with C2 showing the best prognosis; otherwise, C3 showing the worst prognosis. Moreover, we assessed the prognostic value, immune infiltration, immune checkpoints, chemokines cytokines and enrichment analysis of this signature in GBM. The TNF-related lncRNA signature was tightly associated with the regulation of tumor immune therapy and could serve as an independent prognostic biomarker in GBM.ConclusionThis analysis provides a comprehensive understanding of the role of TNF-related characters, which may improve the clinical outcome of GBM patients

Development and validation of a three-dimensional deep learning-based system for assessing bowel preparation on colonoscopy video

BackgroundThe performance of existing image-based training models in evaluating bowel preparation on colonoscopy videos was relatively low, and only a few models used external data to prove their generalization. Therefore, this study attempted to develop a more precise and stable AI system for assessing bowel preparation of colonoscopy video.MethodsWe proposed a system named ViENDO to assess the bowel preparation quality, including two CNNs. First, Information-Net was used to identify and filter out colonoscopy video frames unsuitable for Boston bowel preparation scale (BBPS) scoring. Second, BBPS-Net was trained and tested with 5,566 suitable short video clips through three-dimensional (3D) convolutional neural network (CNN) technology to detect BBPS-based insufficient bowel preparation. Then, ViENDO was applied to complete withdrawal colonoscopy videos from multiple centers to predict BBPS segment scores in clinical settings. We also conducted a human-machine contest to compare its performance with endoscopists.ResultsIn video clips, BBPS-Net for determining inadequate bowel preparation generated an area under the curve of up to 0.98 and accuracy of 95.2%. When applied to full-length withdrawal colonoscopy videos, ViENDO assessed bowel cleanliness with an accuracy of 93.8% in the internal test set and 91.7% in the external dataset. The human-machine contest demonstrated that the accuracy of ViENDO was slightly superior compared to most endoscopists, though no statistical significance was found.ConclusionThe 3D-CNN-based AI model showed good performance in evaluating full-length bowel preparation on colonoscopy video. It has the potential as a substitute for endoscopists to provide BBPS-based assessments during daily clinical practice