The evaluation of port wine stain based on three-dimensional information can overcome the inaccuracy of twodimensional image evaluation methods commonly used in clinic. In this paper, an end-to-end multitasking method is designed for the application of 3D information acquisition of port wine stain. Based on deep learning and position map regression network, the reconstruction from 2D pictures to face 3D point cloud is realized. the facial information of patients with port wine stain is represented by UV position map recording 3D point information of the face, and the dense relationship between 3D points and points with semantic meaning in UV space is characterized with this method. The deep learning network framework based on Encoder-Decoder structure is used to complete unconstrained end-to-end face alignment and 3D face reconstruction, whose parameters are obtained by training the data set with lightweight CNN structure. In the process of neural network training and end-to-end unconstrained image facial reconstruction, each point on the UV position map can be assigned different weights, which can not only be used to improve the network performance in neural network training, but also be used to assign corresponding weights to the focus areas with different disease course in the three-dimensional information reconstruction of the focus area therefore the accuracy of the reconstruction results can be increased. With the help of this method, the three-dimensional reconstruction results can be quickly obtained from a single patient's face image, which can be used for subsequent accurate lesion information analysis and treatment.

Objective and accurate lesion assessment is one of the key factors for the optimal photodynamic therapy (PDT) of Port Wine Stains (PWS). The evaluation method based on 3D point cloud can effectively solve the problems. Comparing to the traditional point cloud registration algorithms such as ICP which tend to global registration, the feature description method is more suitable for facial point clouds fusion since each clouds is obtained from different angle and just overlap in certain area. In this paper, the method of fusion of multiple point clouds is applied, in which point cloud fusion is achieved by characterizing randomly selected sampling points. The point cloud fusion result is obtained by matching the public area between the two point clouds. Several fusion experiments are designed and conducted using the facial point cloud acquired by Artec Eva three-dimensional scanner. The experimental results show that it can accomplish the point cloud fusion of patient facial point cloud (including all the information on the skin lesions of PWS), which could be an efficient support for effective 3D lesion assessment.

The monochromators, which are able to separate a specific wavelength light from a complex spectrum and a continuous spectrum light source, have been widely used in optical measurements. As one crucial specification, wavelength accuracy has always been a research theme for monochromator users around the world. When the monochromator is utilized for calibrating the wavelength in the ultraviolet, visible, and near-infrared regions, low-pressure discharged lamps, such as mercury lamps, are usually employed to obtain wavelength deviations at certain wavelength points of atomic emission lines of lamps,which are well-known recognized For the requirement of monochromator wavelength calibration, a light path based on the low-pressure discharged lamp method was constructed. Three strong radiation flux lines of the mercury lamps were selected and tested,whose wavelength values were 365.015 nm, 435.833 nm, and 546.075 nm. Meanwhile a new method based on continuous spectrum light source and Fourier transform spectrometer was proposed and light path was also built. Near the wavelength value points of the selected atomic emission lines of mercury lamp, a measurement experiment was performed both on the same monochromator and the same experimental conditions, and the wavelength deviation values of the monochromator was obtained. The consistency of the two methods was good, and the best repeatability error was better than 0.005nm. The work of this article is of positive for those measurement laboratories to select suitable approach to perform their monochromators wavelength calibrations.

针对现有的静脉成像系统的易受到成像环境和受试者个体差异的影响,实际成像效果不理想的问题.从增强静脉图像信息的角度出发,设计了一种可以进行亮度自适应调节的双波长近红外静脉成像系统光源,同时使用双波长融合算法对不同波长下采集到的图像进行融合.实验结果表明:在设计实现的双波长近红外静脉成像系统中,光源能够根据个体皮肤和静脉差异进行自适应光强调节,为静脉采集装置提供均匀良好的照明;融合后的双波长静脉图像信息丰富度提升,能够为静脉辅助穿刺和静脉身份识别奠定良好基础.

drug reactions between chemicals and diseases make the topic of chemical-disease relations (CDRs) become a focus that receives much concern.And automatic extraction of chemical-induced disease (CID) relations from the biomedical literature can be used to support biocuration,new drug discovery and drug safety surveillance.In this paper,we present a CID relation extraction system,called CDRExtractor,to extract CID relations from biomedical literature at both sentence and document levels.To extract the CID relations located in the same sentence,we first manually annotate a sentence-level training set which is used to train the sentence-level classifier.And to improve the performances of the classifier,Co-training algorithm is used to exploit the unlabeled data with the feature kernel and graph kernel as two independent views.Then CDRExtractor uses a document-level classifier to extract the span sentence CID relations.The classifier utilizes the document level information (features) of the chemical and disease pair,and then returns the CID relations at the document level.Finally,the post-processing rules are applied to the union set of two classifiers and generate the final outputs.Experimental results show that CDRExtractor achieves an F-score of 67.72％ on the test set of the BioCreative V CDR CID subtask.

The monochromator has been widely used in the field of optical precision measurement. It can effectively separate the monochromatic light of the specific wavelength required for the experiment from a complex spectrum and a continuous spectrum light source. The wavelength accuracy of a monochromator is an important indicator of its performance, and the research of wavelength accuracy calibration methods to improve measurement accuracy is a hot theme for researchers around the world. When the monochromator is utilized for calibrating the wavelength in the ultraviolet, visible, and near-infrared region, low-pressure discharged lamps, such as mercury lamps and neon lamps, are usually used to calibrate on the well-known limited atomic emission lines of lamp to obtain the wavelength deviation value at the wavelength points of these lines. For some high-precision requirements, for example, when measuring the spectral responsivity of a reference solar cell using a tunable laser as light source, it is difficult for these discrete and finite lines to fully satisfy the demand. To solve this problem, a new method based on combination of continuous spectrum light source and fourier transform spectroradiometer was used. A calibrated experimental optical path was successfully built, and the wavelength deviation values of 322 wavelength points from 400 nm to 2000 nm were obtained. The optimal measurement repeatability reached 0.3 pm, which met the need for high-precision measurement requirement. As a comparison, a low-pressure discharged lamp method was also used. Using a mercury lamp and a neon lamp, a wavelength calibration experiment was performed on the same monochromator in the same wavelength range, and only 20 wavelength deviation values were obtained at its atomic emission lines wavelength point. The number of wavelength deviation values is less than 6.5% of that of the new method. The new method proposed in this paper, which not only can significantly improve the quality of the monochromator calibration wavelength deviation values, but in which the obtained values are able to establish traceability to the international SI unit system, is an ideal wavelength accuracy calibration method.

Adverse drug reactions between chemicals and diseases make chemical-disease relations (CDR) become a research focus. In this paper, we present a chemical-induced disease (CID) relation extraction system, CIDExtractor, to extract CID relations from biomedical literature. CIDExtractor first employs a sentence-level classifier to extract the CID relations located in the same sentence. To construct the classifier, a sentence-level training set is manually annotated and then Co-Training algorithm is used to exploit the unlabeled data with the feature kernel and graph kernel as two independent views. Then CIDExtractor uses a document-level classifier to extract the CID relations spanning multiple sentences. The classifier utilizes the document level information (features) of the chemical and disease pair. Finally, some post-processing rules are applied to the union set of two classifiers and generate the final outputs. Experimental results on the test set of BioCreative V CDR CID subtask show that CIDExtractor can achieve better performance (an F-score of 67.72%) than the state-of-the-art methods. The online CIDExtractor demonstration system is available at http://202.118.75.18:8888/cdr-dut-ir/cid.html.

With the rapid growth of biomedical literature, a large amount of knowledge about diseases, symptoms, and therapeutic substances hidden in the literature can be used for drug discovery and disease therapy. In this paper, we present a method of constructing two models for extracting the relations between the disease and symptom and symptom and therapeutic substance from biomedical texts, respectively. The former judges whether a disease causes a certain physiological phenomenon while the latter determines whether a substance relieves or eliminates a certain physiological phenomenon. These two kinds of relations can be further utilized to extract the relations between disease and therapeutic substance. In our method, first two training sets for extracting the relations between the disease-symptom and symptom-therapeutic substance are manually annotated and then two semisupervised learning algorithms, that is, Co-Training and Tri-Training, are applied to utilize the unlabeled data to boost the relation extraction performance. Experimental results show that exploiting the unlabeled data with both Co-Training and Tri-Training algorithms can enhance the performance effectively.

High-throughput experimental techniques have produced a large amount of human protein-protein interactions, making it possible to construct a large-scale human PPI network and detect human protein complexes from the network with computational approaches. However, most of current complex detection methods are based on graph theory which can't utilize the information of the known complexes. In this paper, we present a supervised learning method to detect protein complexes in a human PPI network. In this method, biological characteristics and properties of the network are taken into consideration to construct a rich feature set to train a regression model for protein complex detection. In addition, the specific disease related PPIs are extracted from biomedical literatures and then integrated into the original PPI network for detecting the disease-specific protein complexes more effectively. Experimental results show that the performance of our method is superior to other existing state-of-the-art methods. Furthermore, through the analysis of the breast cancer specific complexes detected with our method, more biological insights for breast cancer (e.g., some candidate susceptible genes of breast cancer) are provided.

In order to reduce traffic accidents caused by the driver′s unintentional lane deviation, based on the ARM embedded and OpenCV this paper builds a lane departure warning system. It focuses on the design of building systems, platforms, and processing algorithms. The system acquires the image information by the camera, and preprocesses the image using OpenCV. By extracting lane image information, it assesses whether the vehicle running state deviates from the lane center position. According to the driving state of the vehicle, it sounds an alarm to alert drivers of vehicles currently travel deviation in order to achieve the purpose of safe driving aid.