Factors Influencing the Answerability and Popularity of a Health-Related Post in the Question-and-Answer Community: Infodemiology Study of Metafilter.

BACKGROUND: The web-based health question-and-answer (Q&A) community has become the primary and handy way for people to access health information and knowledge directly. OBJECTIVE: The objective of our study is to investigate how content-related, context-related, and user-related variables influence the answerability and popularity of health-related posts based on a user-dynamic, social network, and topic-dynamic semantic network, respectively. METHODS: Full-scale data on health consultations were acquired from the Metafilter Q&A community. These variables were designed in terms of context, content, and contributors. Negative binomial regression models were used to examine the influence of these variables on the favorite and comment counts of a health-related post. RESULTS: A total of 18,099 post records were collected from a well-known Q&A community. The findings of this study include the following. Content-related variables have a strong impact on both the answerability and popularity of posts. Notably, sentiment values were positively related to favorite counts and negatively associated with comment counts. User-related variables significantly affected the answerability and popularity of posts. Specifically, participation intensity was positively related to comment count and negatively associated with favorite count. Sociability breadth only had a significant impact on comment count. Context-related variables have a more substantial influence on the popularity of posts than on their answerability. The topic diversity variable exhibits an inverse correlation with the comment count while manifesting a positive correlation with the favorite count. Nevertheless, topic intensity has a significant effect only on favorite count. CONCLUSIONS: The research results not only reveal the factors influencing the answerability and popularity of health-related posts, which can help them obtain high-quality answers more efficiently, but also provide a theoretical basis for platform operators to enhance user engagement within health Q&A communities.

Self-Supervised 3D Behavior Representation Learning Based on Homotopic Hyperbolic Embedding.

Behavior sequences are generated by a series of spatio-temporal interactions and have a high-dimensional nonlinear manifold structure. Therefore, it is difficult to learn 3D behavior representations without relying on supervised signals. To this end, self-supervised learning methods can be used to explore the rich information contained in the data itself. Context-context contrastive self-supervised methods construct the manifold embedded in Euclidean space by learning the distance relationship between data, and find the geometric distribution of data. However, traditional Euclidean space is difficult to express context joint features. In order to obtain an effective global representation from the relationship between data under unlabeled conditions, this paper adopts contrastive learning to compare global feature, and proposes a self-supervised learning method based on hyperbolic embedding to mine the nonlinear relationship of behavior trajectories. This method adopts the framework of discarding negative samples, which overcomes the shortcomings of the paradigm based on positive and negative samples that pull similar data away in the feature space. Meanwhile, the output of the network is embedded in a hyperbolic space, and a multi-layer perceptron is added to convert the entire module into a homotopic mapping by using the geometric properties of operations in the hyperbolic space, so as to obtain homotopy invariant knowledge. The proposed method combines the geometric properties of hyperbolic manifolds and the equivariance of homotopy groups to promote better supervised signals for the network, which improves the performance of unsupervised learning.

Graph-Based Self-Training for Semi-Supervised Deep Similarity Learning.

Semi-supervised learning is a learning pattern that can utilize labeled data and unlabeled data to train deep neural networks. In semi-supervised learning methods, self-training-based methods do not depend on a data augmentation strategy and have better generalization ability. However, their performance is limited by the accuracy of predicted pseudo-labels. In this paper, we propose to reduce the noise in the pseudo-labels from two aspects: the accuracy of predictions and the confidence of the predictions. For the first aspect, we propose a similarity graph structure learning (SGSL) model that considers the correlation between unlabeled and labeled samples, which facilitates the learning of more discriminative features and, thus, obtains more accurate predictions. For the second aspect, we propose an uncertainty-based graph convolutional network (UGCN), which can aggregate similar features based on the learned graph structure in the training phase, making the features more discriminative. It can also output the uncertainty of predictions in the pseudo-label generation phase, generating pseudo-labels only for unlabeled samples with low uncertainty; thus, reducing the noise in the pseudo-labels. Further, a positive and negative self-training framework is proposed, which combines the proposed SGSL model and UGCN into the self-training framework for end-to-end training. In addition, in order to introduce more supervised signals in the self-training process, negative pseudo-labels are generated for unlabeled samples with low prediction confidence, and then the positive and negative pseudo-labeled samples are trained together with a small number of labeled samples to improve the performance of semi-supervised learning. The code is available upon request.

In Situ Observation of Thermoelastic Martensitic Transformation of Cu-Al-Mn Cryogenic Shape Memory Alloy with Compressive Stress.

The thermoelastic martensitic transformation and its reverse transformation of the Cu-Al-Mn cryogenic shape memory alloy, both with and without compressive stress, has been dynamically in situ observed. During the process of thermoelastic martensitic transformation, martensite nucleates and gradually grow up as they cool, and shrink to disappearance as they heat. The order of martensite disappearance is just opposite to that of their formation. Observations of the self-accommodation of martensite variants, which were carried out by using a low temperature metallographic in situ observation apparatus, showed that the variants could interact with each other. The results of in situ synchrotron radiation X-ray and metallographic observation also suggested there were some residual austenites, even if the temperature was below Mf, which means the martensitic transformation could not be 100% accomplished. The external compressive stress would promote the preferential formation of martensite with some orientation, and also hinder the formation of martensite with other nonequivalent directions. The possible mechanism of the martensitic reverse transformation is discussed.

Therapeutic Effects of Melatonin on Ocular Diseases: Knowledge Map and Perspective.

Melatonin plays a critical role in the pathophysiological process including circadian rhythm, apoptosis, and oxidative stress. It can be synthesized in ocular tissues, and its receptors are also found in the eye, triggering more investigations concentrated on the role of melatonin in the eye. In the past decades, the protective and therapeutic potentials of melatonin for ocular diseases have been widely revealed in animal models. Herein, we construct a knowledge map of melatonin in treating ocular diseases through bibliometric analysis and review its current understanding and clinical evidence. The overall field could be divided into twelve topics through keywords co-occurrence analysis, in which the glaucoma, myopia, and retinal diseases were of greatest research interests according to the keywords burst detection. The existing clinical trials of melatonin in ocular diseases mainly focused on the glaucoma, and more research should be promoted, especially for various diseases and drug administration. We also discuss its bioavailability and further research topics including developing melatonin sensors for personalized medication, acting as stem cell therapy assistant drug, and consuming food-derived melatonin for facilitating its clinical transformation.

Enhanced interaction in TiO2/BiVO4 heterostructures via MXene Ti3C2-derived 2D-carbon for highly efficient visible-light photocatalysis.

Heterostructured photocatalysts play a significant role in the removal of contaminants by decreasing the recombination of the photo-induced charges. Herein, we presented novel TiO2/C/BiVO4 ternary hybrids employing a 2D layered Ti3C2 MXene precursor, overcoming the lattice mismatching of TiO2/BiVO4 binary heterostructures simultaneously. Raman and XPS analyses proved the strong coupling effects of TiO2, carbon and BiVO4 components, and the heterostructures were identified from high-resolution transmission electron microscopy results. Moreover, the ternary TiO2/C/BiVO4 composites exhibit excellent photocatalytic performance of Rhodamine B degradation, which is about four times higher than pure BiVO4 and twice that of binary TiO2/BiVO4 heterostructures, reaching a reaction constant of 13.7 × 10-3 min-1 under visible-light irradiation (λ > 420 nm). In addition, for the possible mechanism for dye elimination it was proposed that RhB molecule be directly oxidized by photo-induced holes (h+) on the BiVO4 components and superoxide radical ([Formula: see text]) generated from conduction band electrons of the heterostructures. This work will provide possibilities for developing visible-light responsive nanomaterials for efficient solar utilization.

Range clusters based time-of-flight 3D imaging obstacle detection in manifold space.

A new obstacle detection method using time-of-flight 3D imaging sensor based on range clusters is proposed. To effectively reduce the influence of outlier and noise in range images, we utilize intensity images to estimate noise deviation of the range images and a weighted local linear smoothing is used to project the data into a new manifold surface. The proposed method divides the 3D imaging data into range clusters with different shapes and sizes according to the distance ambient relation between the pixels, and some regulation criterions are set to adjust the range clusters into optimal shape and size. Experiments on the SwissRanger sensor data show that, compared to the traditional obstacle detection methods based on regular data patches, the proposed method can give more precious detection results.

Briefing in family characteristics of microRNAs and their applications in cancer research.

MicroRNAs (miRNAs) are endogenous, short, non-coding RNA molecules that are directly involved in the post-transcriptional regulation of gene expression. Dysregulation of miRNAs is usually associated with diseases. Since miRNAs in a family intend to have common functional characteristics, proper assignment of miRNA family becomes heuristic for better understanding of miRNA nature and their potentials in clinic. In this review, we will briefly discuss the recent progress in miRNA research, particularly its impact on protein and its clinical application in cancer research in a view of miRNA family. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications. Guest Editor: Yudong Cai.

Supervised preserving projection for learning scene information based on time-of-flight imaging sensor.

In this paper, we propose a new supervised manifold learning approach, supervised preserving projection (SPP), for the depth images of a 3D imaging sensor based on the time-of-flight (TOF) principle. We present a novel manifold sense to learn scene information produced by the TOF camera along with depth images. First, we use a local surface patch to approximate the underlying manifold structures represented by the scene information. The fundamental idea is that, because TOF data have nonstatic noise and distance ambiguity problems, the surface patches can more efficiently approximate the local neighborhood structures of the underlying manifold than TOF data points, and they are robust to the nonstatic noise of TOF data. Second, we propose SPP to preserve the pairwise similarity between the local neighboring patches in TOF depth images. Moreover, SPP accomplishes the low-dimensional embedding by adding the scene region class label information accompanying the training samples and obtains the predictive mapping by incorporating the local geometrical properties of the dataset. The proposed approach has advantages of both classical linear and nonlinear manifold learning, and real-time estimation results of the test samples are obtained by the low-dimensional embedding and the predictive mapping. Experiments show that our approach obtains information effectively from three scenes and is robust to the nonstatic noise of 3D imaging sensor data.