Search

Background: with the development of medical science, lncRNA, originally considered as a noise gene, has been found to participate in a variety of biological activities. Nowadays, more and more studies show that lncRNA is involved in various human diseases, such as gastric cancer, prostate cancer, lung cancer, etc. However, obtaining lncRNA-disease association only through biological experiments not only costs manpower and material resources, but also gains little. Therefore, it is very important to develop effective computational models for predicting lncRNA-disease association. Results: In this paper, a new lncRNA-disease association prediction model LDAP-WMPS based on weight distribution and projection score is proposed. Based on the existing research results of disease semantic similarity, the integrated lncRNA similarity matrix and the integrated disease similarity matrix are calculated according to the disease semantic similarity and the association information between data. On this basis, the weight algorithm is combined with the improved projection algorithm to predict the lncRNA-disease association through the known lncRNA-miRNA association and miRNA-disease association. The simulation results show that under the loocv framework, the AUC of LDAP-WMPS can reach 0.8822. Better than the latest results. Through the case study of adenocarcinoma and colorectal cancer, it is proved that LDAP-WMPS can effectively infer lncRNA-disease association. Conclusions: The simulation results show that LDAP-WMPS has good prediction performance, which is an important supplement to the research of lncRNA-disease association prediction without lncRNA-disease association data. Keywords: lncRNA-miRNA association, miRNA-disease association, disease semantic similarity, Integrated lncRNA similarity, integrated disease similarity, Weight allocation algorithm, Projection score.

Survival classification on time-to-event clinical data is useful in clinical research. Survival analysis techniques are widely used to predict the survival probability over time that a particular event occurs. It is crucial to identify the risk of experiencing the event for patients, which can be achieved by classifying risk groups of patients. However, the existing classification methods, such as logistic regression, support vector machines and artificial neural network, cannot be applied to clinical data directly, due to the context of censoring. To address this problem, we propose a new two-tied labeling approach for reformulating the clinical data and make classifiers available to process such data. We perform such labeling on real-life clinical data and the good performances achieved by the classification methods reveal the great promise of our approach for risk group classification.

In this work, we designed a MEMS device which allows simultaneous direct measurement of mechanical properties during deformation under external stress and characterization of the evolution of microstructure of nanomaterials within a transmission electron microscope. This MEMS device makes it easy to establish the correlation between microstructure and mechanical properties of nanomaterials. The device uses piezoresistive sensors to qualitatively measure the force and displacement of nanomaterials, e.g., in wire and thin plate forms. The device has a theoretical displacement resolution of 0.19 nm and a force resolution of 2.1 μN. The device has a theoretical displacement range limit of 2.74 μm and a load range limit of 27.75 mN.

This article proposes a type of proportional navigation law design of plane-symmetrical vehicle with terminal attack angle constraint for over target flight. Firstly, the line of sight rotating rate and the velocity rotating rate model of the vehicle are expressed. Then, the attitude of the vehicle is constructed by the acceleration vector requirement of proportional navigation law. Accordingly, the guidance command uncertain issue can be avoided for plane-symmetrical vehicle over target flight. It guarantees high precision to hit the target. The effect and efficiency of the guidance law are shown by simulations of characteristic trajectories.

In this work, we designed a micro-electromechanical systems (MEMS) device that allows simultaneous direct measurement of mechanical properties during deformation under external stress and characterization of the evolution of nanomaterial microstructure within a transmission electron microscope. This MEMS device makes it easy to establish the correlation between microstructure and mechanical properties of nanomaterials. The device uses piezoresistive sensors to measure the force and displacement of nanomaterials qualitatively, e.g., in wire and thin plate forms. The device has a theoretical displacement resolution of 0.19 nm and a force resolution of 2.1 μN. The device has a theoretical displacement range limit of 5.47 μm and a load range limit of 55.0 mN.

A pulsed method for impedance spectroscopy measurement, different from the traditional swept frequency methods, is presented. A string of square-wave pulses is digitally generated as the excitation signal to the sensing electrodes. A pulsed measurement system is described. A sample-collecting method for sampling the instantaneous response signals is implemented based on a field programmable gate arrays chip. The sampled data is further processed to obtain the impedance spectrum. Experiments are carried out for the measurement of electrical impedance spectroscopy of the demo electric circuits in order to inspect the feasibility of the pulsed measurement system. The preliminary experimental results indicate that the pulsed measurement system can effectively extract the electrical impedance spectroscopy information of the object detected. The proposed approach is feasible for evaluating the electrical impedance spectroscopy of the object detected.

The edge detection for images based on traditional wavelet analysis has the disadvantage of not being sensitive to direction properties, so aimed at the problem the article makes some improvement and puts forward a new kind of wavelet transform used for image edge detection. Compared with the detection based on Canny operator, it can be concluded that the new kind of edge detection based on wavelet transform can obtain complete image edge as well as accurate positioning and can reserve better detail information.