Gender Differences in Vitamin D Status in China.

BACKGROUND Vitamin D insufficiency is widespread in China. Various factors influence vitamin D level in the body. The present study investigated vitamin D status of residents in Jinzhong city, China, and analyzed the influence of gender on vitamin D status. MATERIAL AND METHODS In this cross-sectional study, 302 participants (176 men and 126 women) were recruited. Anthropometric data (body circumferences and height, weight) were collected, and serum vitamin D concentration was tested. RESULTS Inadequate levels of vitamin D were found in 69% of men and 75% of women. Women's 25(OH)D level (38.40±12.37 nmol/l) was substantially lower than that of the men (43.49±14.78 nmol/l) (p<0.01). The young women group had the lowest vitamin D level, which was even significantly below that of the elderly women group. Multiple linear regression analysis showed gender was significantly associated with vitamin D status (p<0.01). CONCLUSIONS Vitamin D deficiency is common in residents of Jinzhong during the winter. Compared to men, women are more prone to have inadequate vitamin D levels.

DRCmpVis: Visual Comparison of Physical Targets in Mobile Diminished and Mixed Reality.

Numerous physical objects in our daily lives are grouped or ranked according to a stereotyped presentation style. For example, in a library, books are typically grouped and ranked based on classification numbers. However, for better comparison, we often need to re-group or re-rank the books using additional attributes such as ratings, publishers, comments, publication years, keywords, prices, etc., or a combination of these factors. In this paper, we propose a novel mobile DR/MR-based application framework named DRCmpVis to achieve in-context multi-attribute comparisons of physical objects with text labels or textual information. The physical objects are scanned in the real world using mobile cameras. All scanned objects are then segmented and labeled by a convolutional neural network and replaced (diminished) by their virtual avatars in a DR environment. We formulate three visual comparison strategies, including filtering, re-grouping, and re-ranking, which can be intuitively, flexibly, and seamlessly performed on their avatars. This approach avoids breaking the original layouts of the physical objects. The computation resources in virtual space can be fully utilized to support efficient object searching and multi-attribute visual comparisons. We demonstrate the usability, expressiveness, and efficiency of DRCmpVis through a user study, NASA TLX assessment, quantitative evaluation, and case studies involving different scenarios.

Atom-ProteinQA: Atom-level protein model quality assessment through fine-grained joint learning.

MOTIVATION: Protein model quality assessment (ProteinQA) is a fundamental task that is essential for biologically relevant applications, i.e., protein structure refinement, protein design, etc. Previous works aimed to conduct ProteinQA only on the global structure or per-residue level, ignoring potentially usable and precise cues from a fine-grained per-atom perspective. In this study, we propose an atom-level ProteinQA model, named Atom-ProteinQA, in which two innovative modules are designed to extract geometric and topological atom-level relationships respectively. Specifically, on the one hand, a geometric perception module exploits 3D sparse convolution to capture the geometric features of the input protein, generating fine-grained atom-level predictions. On the other hand, natural chemical bonds are utilized to construct an atom-level graph, then message passing from a topological perception module is applied to output residue-level predictions in parallel. Eventually, through a cross-model aggregation module, features from different modules mutually interact, enhancing performance on both the atom and residue levels. RESULTS: Extensive experiments show that our proposed Atom-ProteinQA outperforms previous methods by a large margin, regardless of residue-level or atom-level assessment. Concretely, we achieved state-of-the-art performance on CATH-2084, Decoy-8000, public benchmarks CASP13 & CASP14, and the CAMEO. AVAILABILITY: The repository of this project is released on: https://github.com/luyfcandy/Atom_ProteinQA.

Graphical Enhancements for Effective Exemplar Identification in Contextual Data Visualizations.

An exemplar is an entity that represents a desirable instance in a multi-attribute configuration space. It offers certain strengths in some of its attributes without unduly compromising the strengths in other attributes. Exemplars are frequently sought after in real life applications, such as systems engineering, investment banking, drug advisory, product marketing and many others. We study a specific method for the visualization of multi-attribute configuration spaces, the Data Context Map (DCM), for its capacity in enabling users to identify proper exemplars. The DCM produces a 2D embedding where users can view the data objects in the context of the data attributes. We ask whether certain graphical enhancements can aid users to gain a better understanding of the attribute-wise tradeoffs and so select better exemplar sets. We conducted several user studies for three different graphical designs, namely iso-contour, value-shaded topographic rendering and terrain topographic rendering, and compare these with a baseline DCM display. As a benchmark we use an exemplar set generated via Pareto optimization which has similar goals but unlike humans can operate in the native high-dimensional data space. Our study finds that the two topographic maps are statistically superior to both the iso-contour and the DCM baseline display.

NG-NAS: Node growth neural architecture search for 3D medical image segmentation.

Neural architecture search (NAS) has been applied to design proper 3D networks for medical image segmentation. In order to reduce the computation cost in NAS, researchers tend to adopt weight sharing mechanism to search architectures in a supernet. However, recent studies state that the searched architecture rankings may not be accurate with weight sharing mechanism because the training situations are inconsistent between the searching and training phases. In addition, some NAS algorithms design inflexible supernets that only search operators in a pre-defined backbone and ignore the importance of network topology, which limits the performance of searched architecture. To avoid weight sharing mechanism which may lead to inaccurate results and to comprehensively search network topology and operators, we propose a novel NAS algorithm called NG-NAS. Following the previous studies, we consider the segmentation network as a U-shape structure composed of a set of nodes. Instead of searching from the supernet with a limited search space, our NG-NAS starts from a simple architecture with only 5 nodes, and greedily grows the best candidate node until meeting the constraint. We design 2 kinds of node generations to form various network topological structures and prepare 4 candidate operators for each node. To efficiently evaluate candidate node generations, we use NAS without training strategies. We evaluate our method on several public 3D medical image segmentation benchmarks and achieve state-of-the-art performance, demonstrating the effectiveness of the searched architecture and our NG-NAS. Concretely, our method achieves an average Dice score of 85.11 on MSD liver, 65.70 on MSD brain, and 87.59 in BTCV, which performs much better than the previous SOTA methods.

EVNet: An Explainable Deep Network for Dimension Reduction.

Dimension reduction (DR) is commonly utilized to capture the intrinsic structure and transform high-dimensional data into low-dimensional space while retaining meaningful properties of the original data. It is used in various applications, such as image recognition, single-cell sequencing analysis, and biomarker discovery. However, contemporary parametric-free and parametric DR techniques suffer from several significant shortcomings, such as the inability to preserve global and local features and the pool generalization performance. On the other hand, regarding explainability, it is crucial to comprehend the embedding process, especially the contribution of each part to the embedding process, while understanding how each feature affects the embedding results that identify critical components and help diagnose the embedding process. To address these problems, we have developed a deep neural network method called EVNet, which provides not only excellent performance in structural maintainability but also explainability to the DR therein. EVNet starts with data augmentation and a manifold-based loss function to improve embedding performance. The explanation is based on saliency maps and aims to examine the trained EVNet parameters and contributions of components during the embedding process. The proposed techniques are integrated with a visual interface to help the user to adjust EVNet to achieve better DR performance and explainability. The interactive visual interface makes it easier to illustrate the data features, compare different DR techniques, and investigate DR. An in-depth experimental comparison shows that EVNet consistently outperforms the state-of-the-art methods in both performance measures and explainability.

ColorMapND: A Data-Driven Approach and Tool for Mapping Multivariate Data to Color.

A wide variety of color schemes have been devised for mapping scalar data to color. We address the challenge of color-mapping multivariate data. While a number of methods can map low-dimensional data to color, for example, using bilinear or barycentric interpolation for two or three variables, these methods do not scale to higher data dimensions. Likewise, schemes that take a more artistic approach through color mixing and the like also face limits when it comes to the number of variables they can encode. Our approach does not have these limitations. It is data driven in that it determines a proper and consistent color map from first embedding the data samples into a circular interactive multivariate color mapping display (ICD) and then fusing this display with a convex (CIE HCL) color space. The variables (data attributes) are arranged in terms of their similarity and mapped to the ICD's boundary to control the embedding. Using this layout, the color of a multivariate data sample is then obtained via modified generalized barycentric coordinate interpolation of the map. The system we devised has facilities for contrast and feature enhancement, supports both regular and irregular grids, can deal with multi-field as well as multispectral data, and can produce heat maps, choropleth maps, and diagrams such as scatterplots.

Epidemiological Characteristics and Spatial-Temporal Clusters of Mumps in Shandong Province, China, 2005-2014.

Mumps presents a serious threat to public health in China. We conducted a descriptive analysis to identify the epidemiological characteristics of mumps in Shandong Province. Spatial autocorrelation and space-time scan analyses were utilized to detect spatial-temporal clusters. From 2005 to 2014, 115745 mumps cases were reported in Shandong, with an average male-to-female ratio of 1.94. Mumps occurred mostly in spring (32.17% of all reported cases) and in children aged 5 to 9 (40.79% of all reported cases). The Moran's I test was significant and local indicators of spatial autocorrelation (LISA) analysis revealed significant spatial clusters with high incidence. The results showed that the mid-west of Shandong Province and some coastal regions (Qingdao City and Weihai City) were high-risk areas, particularly in the center of the Jining City and the junction of Dongying City, Binzhou City and Zibo City. The results could assist local and national public health agencies in formulating better public health strategic planning and resource allocation.

The Data Context Map: Fusing Data and Attributes into a Unified Display.

Numerous methods have been described that allow the visualization of the data matrix. But all suffer from a common problem - observing the data points in the context of the attributes is either impossible or inaccurate. We describe a method that allows these types of comprehensive layouts. We achieve it by combining two similarity matrices typically used in isolation - the matrix encoding the similarity of the attributes and the matrix encoding the similarity of the data points. This combined matrix yields two of the four submatrices needed for a full multi-dimensional scaling type layout. The remaining two submatrices are obtained by creating a fused similarity matrix - one that measures the similarity of the data points with respect to the attributes, and vice versa. The resulting layout places the data objects in direct context of the attributes and hence we call it the data context map. It allows users to simultaneously appreciate (1) the similarity of data objects, (2) the similarity of attributes in the specific scope of the collection of data objects, and (3) the relationships of data objects with attributes and vice versa. The contextual layout also allows data regions to be segmented and labeled based on the locations of the attributes. This enables, for example, the map's application in selection tasks where users seek to identify one or more data objects that best fit a certain configuration of factors, using the map to visually balance the tradeoffs.