Draft Genome Sequence of Streptomyces sp. Strain CT34, Isolated from a Ghanaian Soil Sample

Copyright © 2015 Zhai et al. This work was supported by the China “973” program (2012CB721001), the “863” Program (2012AA092201), the National Natural Science Foundation of China (31170467), and the EU FP7 project PharmaSea (312184). K.K., M.J., and H.D. thank the Royal Society–Leverhulme Trust Africa for the financial support (award AA090088) that enabled the sampling of sediments and subsequent isolation of this unique Ghanaian strain.Non peer reviewedPublisher PD

Machine learning-based prediction models for patients no-show in online outpatient appointments

With the development of information and communication technologies, all public tertiary hospitals in China began to use online outpatient appointment systems. However, the phenomenon of patient no-shows in online outpatient appointments is becoming more serious. The objective of this study is to design a prediction model for patient no-shows, thereby assisting hospitals in making relevant decisions, and reducing the probability of patient no-show behavior. We used 382,004 original online outpatient appointment records, and divided the data set into a training set (N1 = 286,503), and a validation set (N2 = 95,501). We used machine learning algorithms such as logistic regression, k-nearest neighbor (KNN), boosting, decision tree (DT), random forest (RF) and bagging to design prediction models for patient no-show in online outpatient appointments. The patient no-show rate of online outpatient appointment was 11.1% (N = 42,224). From the validation set, bagging had the highest area under the ROC curve and AUC value, which was 0.990, followed by random forest and boosting models, which were 0.987 and 0.976, respectively. In contrast, compared with the previous prediction models, the area under ROC and AUC values of the logistic regression, decision tree, and k-nearest neighbors were lower at 0.597, 0.499 and 0.843, respectively. This study demonstrates the possibility of using data from multiple sources to predict patient no-shows. The prediction model results can provide decision basis for hospitals to reduce medical resource waste, develop effective outpatient appointment policies, and optimize operations

Efficient Multi-View Inverse Rendering Using a Hybrid Differentiable Rendering Method

Recovering the shape and appearance of real-world objects from natural 2D images is a long-standing and challenging inverse rendering problem. In this paper, we introduce a novel hybrid differentiable rendering method to efficiently reconstruct the 3D geometry and reflectance of a scene from multi-view images captured by conventional hand-held cameras. Our method follows an analysis-by-synthesis approach and consists of two phases. In the initialization phase, we use traditional SfM and MVS methods to reconstruct a virtual scene roughly matching the real scene. Then in the optimization phase, we adopt a hybrid approach to refine the geometry and reflectance, where the geometry is first optimized using an approximate differentiable rendering method, and the reflectance is optimized afterward using a physically-based differentiable rendering method. Our hybrid approach combines the efficiency of approximate methods with the high-quality results of physically-based methods. Extensive experiments on synthetic and real data demonstrate that our method can produce reconstructions with similar or higher quality than state-of-the-art methods while being more efficient.Comment: IJCAI202

Point Normal Orientation and Surface Reconstruction by Incorporating Isovalue Constraints to Poisson Equation

Oriented normals are common pre-requisites for many geometric algorithms based on point clouds, such as Poisson surface reconstruction. However, it is not trivial to obtain a consistent orientation. In this work, we bridge orientation and reconstruction in implicit space and propose a novel approach to orient point clouds by incorporating isovalue constraints to the Poisson equation. Feeding a well-oriented point cloud into a reconstruction approach, the indicator function values of the sample points should be close to the isovalue. Based on this observation and the Poisson equation, we propose an optimization formulation that combines isovalue constraints with local consistency requirements for normals. We optimize normals and implicit functions simultaneously and solve for a globally consistent orientation. Owing to the sparsity of the linear system, an average laptop can be used to run our method within reasonable time. Experiments show that our method can achieve high performance in non-uniform and noisy data and manage varying sampling densities, artifacts, multiple connected components, and nested surfaces

Emission Inventory for PFOS in China: Review of Past Methodologies and Suggestions

Perfluorooctane sulfonate (PFOS) is a persistent, bioaccumulative, and toxic chemical that has the potential for long-range transport in the environment. Its use in a wide variety of consumer products and industrial processes makes a detailed characterization of its emissions sources very challenging. These varied emissions sources all contribute to PFOS' existence within nearly all environmental media. Currently, China is the only country documented to still be producing PFOS, though there is no China PFOS emission inventory available. This study reviews the inventory methodologies for PFOS in other countries to suggest a China-specific methodology framework for a PFOS emission inventory. The suggested framework combines unknowns for PFOS-containing product penetration into the Chinese market with product lifecycle assumptions, centralizing these diverse sources into municipal sewage treatment plants. Releases from industrial sources can be quantified separately using another set of emission factors. Industrial sources likely to be relevant to the Chinese environment are identified

Thermal Stability and Rheological Properties of Polyethylene (PE)/Polyvinylchloride (PVC)/Wood Composites

This paper investigated the thermorheological properties, thermal properties and flame retardant properties of wood-plastic composites (WPCs). With the addition of wood flour (WF), the rheological behavior became complexity. The critical frequency of shear-thinning phenomenon of the melt viscosity was shifted toward lower value. The temperature dependence of elastic modulus, loss modulus became more serious with the addition of WF. The Cole-Cole plot indicated the existence of complex multi-phase structure in the WPC melt. The CONE calorimetry results showed that ammonium polyphosphate (APP) had good flame retardancy through promoting the formation of the intumescent carbon layer. The present study will supply good insight into the optimization of WPC formulation