Differentiable Genetic Programming for High-dimensional Symbolic Regression

Symbolic regression (SR) is the process of discovering hidden relationships from data with mathematical expressions, which is considered an effective way to reach interpretable machine learning (ML). Genetic programming (GP) has been the dominator in solving SR problems. However, as the scale of SR problems increases, GP often poorly demonstrates and cannot effectively address the real-world high-dimensional problems. This limitation is mainly caused by the stochastic evolutionary nature of traditional GP in constructing the trees. In this paper, we propose a differentiable approach named DGP to construct GP trees towards high-dimensional SR for the first time. Specifically, a new data structure called differentiable symbolic tree is proposed to relax the discrete structure to be continuous, thus a gradient-based optimizer can be presented for the efficient optimization. In addition, a sampling method is proposed to eliminate the discrepancy caused by the above relaxation for valid symbolic expressions. Furthermore, a diversification mechanism is introduced to promote the optimizer escaping from local optima for globally better solutions. With these designs, the proposed DGP method can efficiently search for the GP trees with higher performance, thus being capable of dealing with high-dimensional SR. To demonstrate the effectiveness of DGP, we conducted various experiments against the state of the arts based on both GP and deep neural networks. The experiment results reveal that DGP can outperform these chosen peer competitors on high-dimensional regression benchmarks with dimensions varying from tens to thousands. In addition, on the synthetic SR problems, the proposed DGP method can also achieve the best recovery rate even with different noisy levels. It is believed this work can facilitate SR being a powerful alternative to interpretable ML for a broader range of real-world problems

Polarization-sensitive optical projection tomography for muscle fiber imaging

Optical projection tomography (OPT) is a tool used for three-dimensional imaging of millimeter-scale biological samples, with the advantage of exhibiting isotropic resolution typically in the micron range. OPT can be divided into two types: transmission OPT (tOPT) and emission OPT (eOPT). Compared with eOPT, tOPT discriminates different tissues based on their absorption coefficient, either intrinsic or after specific staining. However, it fails to distinguish muscle fibers whose absorption coefficients are similar to surrounding tissues. To circumvent this problem, in this article we demonstrate a polarization sensitive OPT system which improves the detection and 3D imaging of muscle fibers by using polarized light. We also developed image acquisition and processing protocols that, together with the system, enable the clear visualization of muscles. Experimental results show that the muscle fibers of diaphragm and stomach, difficult to be distinguished in regular tOPT, were clearly displayed in our system, proving its potential use. Moreover, polarization sensitive OPT was fused with tOPT to investigate the stomach tissue comprehensively. Future applications of polarization sensitive OPT could be imaging other fiberlike structures such as myocardium or other tissues presenting high optical anisotropy.This work is supported by the National Basic Research Program of China (973 Program) under Grant 2011CB707700, the National Natural Science Foundation of China under Grant No. 81227901, 61231004, 81501616, 81301346, 81527805 the Chinese Academy of Sciences Fellowship for Young Foreign Scientists under Grant No. 2010Y2GA03, 2013Y1GA0004, the Chinese Academy of Sciences Visiting Professorship for Senior International Scientists under Grant No. 2012T1G0036, 2013T1G0013, the Instrument Developing Project of the Chinese Academy of Sciences under Grant No. YZ201502, YZ201457 and the Strategic Priority Research Program (B) of Chinese Academy of Sciences (XDB02060010). A. Arranz acknowledges support from the Marie Curie Intra-European Fellowship program IEF-2010-275137. J.R. acknowledges support from EC FP7 IMI project PREDICT-TB, the EC FP7 CIG grant HIGH-THROUGHPUT TOMO, and the Spanish MINECO project grant FIS2013-41802-R MESO-IMAGING

Heterogeneous network embedding enabling accurate disease association predictions.

BackgroundIt is significant to identificate complex biological mechanisms of various diseases in biomedical research. Recently, the growing generation of tremendous amount of data in genomics, epigenomics, metagenomics, proteomics, metabolomics, nutriomics, etc., has resulted in the rise of systematic biological means of exploring complex diseases. However, the disparity between the production of the multiple data and our capability of analyzing data has been broaden gradually. Furthermore, we observe that networks can represent many of the above-mentioned data, and founded on the vector representations learned by network embedding methods, entities which are in close proximity but at present do not actually possess direct links are very likely to be related, therefore they are promising candidate subjects for biological investigation.ResultsWe incorporate six public biological databases to construct a heterogeneous biological network containing three categories of entities (i.e., genes, diseases, miRNAs) and multiple types of edges (i.e., the known relationships). To tackle the inherent heterogeneity, we develop a heterogeneous network embedding model for mapping the network into a low dimensional vector space in which the relationships between entities are preserved well. And in order to assess the effectiveness of our method, we conduct gene-disease as well as miRNA-disease associations predictions, results of which show the superiority of our novel method over several state-of-the-arts. Furthermore, many associations predicted by our method are verified in the latest real-world dataset.ConclusionsWe propose a novel heterogeneous network embedding method which can adequately take advantage of the abundant contextual information and structures of heterogeneous network. Moreover, we illustrate the performance of the proposed method on directing studies in biology, which can assist in identifying new hypotheses in biological investigation

A Hierarchical Sparse Discriminant Autoencoder for Bearing Fault Diagnosis

Although some traditional autoencoders and their extensions have been widely used in the research of intelligent fault diagnosis of rotating parts, their feature extraction capabilities are limited without label information. In response to this problem, this research proposes a hierarchical sparse discriminant autoencoder (HSDAE) method for fault diagnosis of rotating components, which is a new semi-supervised autoencoder structure. By considering the sparsity of autoencoders, a hierarchical sparsity strategy was proposed to improve the stacked sparsity autoencoders, and the particle swarm optimization algorithm was used to obtain the optimal sparsity parameters to improve network performance. In order to enhance the classification of the autoencoder, a class aggregation and class separability strategy was used, which is an additional discriminative distance that was added as a penalty term in the loss function to enhance the feature extraction ability of the network. Finally, the reliability of the proposed method was verified on the bearing data set of Case Western Reserve University and the bearing data set of the laboratory test platform. The results of comparison with other methods show that the HSDAE method can enhance the feature extraction ability of the network and has reliability and stability for different data sets

The use of facial recognition for online business with the perspective of customer adoption

Facial recognition technology is an emerging digital payment method in online business to provide customers with a better shopping experience. Therefore, this study aims at analyzing factors influencing customers\u27 perception of facial recognition in online shopping. The research combines the privacy-trust-behavioral-intention model and innovation resistance theory to create a new model. Based on the new model, the study investigates the influence of hedonic motivation, privacy, and innovation resistance on customers\u27 intention to use and recommend facial recognition. The study shows that privacy and innovation resistance negatively influence customers\u27 acceptance of facial recognition in online business. At the same time, the hedonic motivation reflects positively from the customers\u27 side - this paper benefits e-commerce to improve facial recognition in online shopping and improve customers\u27 online shopping experience

Traditional Chinese medicine Lianhua Qingwen treating corona virus disease 2019(COVID-19): Meta-analysis of randomized controlled trials.

IntroductionAs the global epidemic continues to spread, countries have tapped effective drugs to treat new coronavirus pneumonia. The therapeutic effect of the traditional Chinese medicine Lianhua Qingwen in this new coronary pneumonia epidemic has attracted attention from all walks of life, and relevant research reports continue to appear. Therefore, we conducted a systematic review of the clinical efficacy and safety of the traditional Chinese medicine Lianhua Qingwen in the treatment of new coronavirus pneumonia (COVID-19) (referred to as "new coronary pneumonia"), and evaluated the overall level of research quality.MethodsWe searched seven databases and retrieved the Chinese Journal Full-text Database (CNKI), Vip Database (VIP), China Biomedicine (SinoMed), Wanfang Database and PubMed, Cochrane Central, EMBASE from October 2019 to May 2020 Literature references. We included randomized controlled trials (RCTs) that tested the efficacy of the traditional Chinese medicine lotus clearing plague in the treatment of new coronavirus pneumonia. The authors extracted data and independently assessed quality. We used Stata15.1 software to analyze the data of randomized trials.ResultsA total of 2 articles were identified, including 154 patients. All the participating patients were diagnosed with new coronavirus pneumonia (COVID-19). The meta-analysis results showed that the disappearance rate of the main clinical symptoms of Chinese medicine Lianhua Qingwen in the treatment of new coronavirus pneumonia was significantly higher than that of the control group [OR = 3.34, 95% CI (2.06, 5.44), P ConclusionThe treatment of new pneumonia with traditional Chinese medicine lotus clearing plague can be used as an effective therapy to improve the clinical symptoms of new coronary pneumonia. More rigorous design, multi-center, and prospective RCTs are necessary to further determine the effectiveness and safety of the traditional Chinese medicine lotus decoction in the treatment of new pneumonia

Efficacy and safety of sodium hyaluronate combined with celecoxib for knee osteoarthritis: A systematic review and meta-analysis

Due to the large cost of joint replacement for surgical treatment of knee osteoarthritis, there are many complications in elderly patients, and there are many contraindications to surgery, and conservative treatment is still based on drugs. To further evaluate the efficacy and safety of sodium hyaluronate combined with celecoxib for the treatment of osteoarthritis of the knee. In total, 202 studies were screened, with a final selection of 9 RCTs involving 2339 participants; of these, 9 RCTs were included in the final meta-analysis. Treatment group reduces VAS (SMD = −1.61; 95 % CI [−2.25, −0.98]; I2 = 95 %; P < 0.00001) and adverse reactions (OR = 0.45; 95 % CI [0.22,0.94]; I2 = 0 %; P < 0.33); Meanwhile, improving Lysholm knee scores (SMD = 0.19; 95 % CI [−0.06, −0.44]; I2 = 76 %; P = 0.0004) and Clinical efficiency (OR = 0.31; 95 % CI [0.19,0.50]; I2 = 0 %; P < 0.00001). All indicators were superior to the control group. Our primary findings suggest that KOA treatment with celecoxib combined with sodium hyaluronate reduces VAS, while improving Lysholm scores and Clinical efficiency. In addition, we found that celecoxib combined with sodium hyaluronate treatment had fewer adverse effects than the control group, indicating that the combination is safe and effective in the treatment of KOA

Arrhenius Equation-Based Model to Predict Lithium-Ions Batteries’ Performance

The accuracy of Peukert’s battery capacity equation may decrease under the conditions of variable current and variable temperatures. Some researchers have previously tried to overcome the lack of C-rate change. However, the dependence of battery capacity on temperature is still not included. In this paper, we mainly studied the capacity reduction effect of batteries under variable temperatures. The proposed method can calculate the battery’s available capacity according to the specific discharge conditions. The experimental method proposed in this paper provides a reasonable test method to generate the required coefficients in order to establish a state of charge prediction model with high accuracy. After establishing the method, we can make a real-time prediction of the available energy of battery including the remaining energy of battery. From the result, we can see that the result is of great precision and the method is valuable

Research and Progress on the Mechanism of Iron Transfer and Accumulation in Rice Grains

Iron (Fe) is one of the most important micronutrients for organisms. Currently, Fe deficiency is a growing nutritional problem and is becoming a serious threat to human health worldwide. A method that could help alleviate this &ldquo;hidden hunger&rdquo; is increasing the bioavailable Fe concentrations in edible tissues of major food crops. Therefore, understanding the molecular mechanisms of Fe accumulation in different crop tissues will help to develop crops with higher Fe nutritional values. Biofortification significantly increases the concentration of Fe in crops. This paper considers the important food crop of rice (Oryza sativa L.) as an example and highlights recent research advances on the molecular mechanisms of Fe uptake and allogeneic uptake in different tissues of rice. In addition, different approaches to the biofortification of Fe nutrition in rice and their outcomes are described and discussed. To address the problems that occur during the development and application of improving nutritional Fe in rice, technical strategies and long-term solutions are also proposed as a reference for the future improvement of staple food nutrition with micronutrients