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Particle Swarm Optimization with Migration (MPSO) is proposed to solve the issue that PSO will come across unbearable time cost problem when dealing with High-dimension, Expensive and Black-box objective function tasks. Migration operator is inspired by the migration of Salmon. Salmon will start a dangerous journey from the ocean to the home rivers for reproduction. The process of the entire behavior is similar with the reduction and recovery of dimension. Therefore, we design the Migration operator where a pre-trained Wasserstein Auto-encoders (WAE) is applied to simulates the migration behavior to accelerate the process of evolution in PSO, and we use Least-Squares Regression in lower space to product better generation. In comparison with famous baselines methods in some benchmark functions, MPSO converges more faster and more accurate which show the great potential of migration operation.

Genetically modified pigs have shown considerable application potential in the fields of life science research and livestock breeding. Nevertheless, a barrier impedes the production of genetically modified pigs. There are too few safe harbor loci for the insertion of foreign genes into the pig genome. Only a few loci (

On-site quantitative analysis of pesticide is of significant importance for addressing serious public health issues in clinical, food, and environmental settings. Herein, we designed a novel smartphone-assisted sensing platform for on-site monitoring of 2,4-dichlorophenoxyacetic acid (2,4-D) based on carbon dots/cobalt oxyhydroxide nanosheet (CDs/CoOOH) composite. In this work, a red emissive CDs/CoOOH composite was proposed as a signal indicator for shielding background interference, enhancing anti-interference capability. 2,4-D as an inhibitor of alkaline phosphatase could specifically suppress the production of ascorbic acid, which restrained in situ etching of the CDs/CoOOH composite and further triggered the fluorescence response of the biosensor. By employing a lab-on-smartphone based device and self-designed application software, the fluorescence image was directly captured and analyzed with a sensitive detection limit of 100 μg L

Although large-scale and accurate identification of cis-regulatory elements on pig protein-coding and long non-coding genes has been reported, similar study on pig miRNAs is still lacking. Here, we systematically characterized the cis-regulatory elements of pig miRNAs in muscle and fat by adopting miRNAomes, ChIP-seq, ATAC-seq, RNA-seq and Hi-C data. In total, the cis-regulatory elements of 257 (85.95%) expressed miRNAs including 226 known and 31 novel miRNAs were identified. Especially, the miRNAs associated with super-enhancers, active promoters, and "A" compartment were significantly higher than those associated by typical enhancers, prompters without H3K27ac, and "B" compartment, respectively. The tissue specific transcription factors were the primary determination of core miRNA expression pattern in muscle and fat. Moreover, the miRNA promoters are more evolutionarily conserved than miRNA enhancers, like other type genes. Our study adds additional important information to existing pig epigenetic data and provides essential resource for future in-depth investigation of pig epigenetics.

On-site monitoring of carbamate pesticide in complex matrix remians as a challenge in terms of the real-time control of food safety and supervision of environmental quality. Herein, we fabricated robust upconversion nanoparticles (UCNPS)/polydopamine (PDA)-based hydrogel portable suit that precisely quantified carbaryl in complex tea samples with smartphone detector. UCNPS/PDA nanoprobe was developed by polymerization of dopamine monomers on the surface of NaErF4: 0.5% Tm3+@NaYF4 through electrostatic interaction, leading to efficient red luminescence quenching of UCNPS under near-infrared excitation, which circumvented autofluorescence and background interference in complicated environment. Such a luminescence quenching could be suppressed by thiocholine that was produced by acetylcholinesterase-mediated catalytic reaction, thus enabling carbaryl bioassay by inhibiting the activity of enzyme. Bestowed with the feasibility analysis of fluorescent output, portable platform was designed by integrating UCNPS-embedded sodium alginate hydrogel with 3D-printed smartphone device for quantitatively on-site monitoring of carbaryl in the range of 0.5–200 ng mL-1 in tea sample, accompanied by a detection limit of 0.5 ng mL-1. Owing to specific UCNPS signatures and hydrogel immobilization, this modular platform displayed sensitive response, portability and anti-interference capability in complex matrix analysis, thus holding great potential in point-of-care application.

In this paper, we build a deep learning network to predict the trends of natural gas prices. Given a time series, for each day, the gas price trend is classified as “up” and “down” according to the price compared to the last day. Meanwhile, we collect news articles as experimental materials from some natural gas related websites. Every article was then embedded into vectors by word2vec, weighted with its sentiment score, and labeled with corresponding day’s price trend. A CNN and LSTM fused network was then trained to predict price trend by these news vectors. Finally, the model’s predictive accuracy reached 62.3%, which outperformed most of other traditional classifiers.

Objective To study the process and significance of intercellular mRNA transfer by using a Cre-loxP system. Methods Cre-expressing donor cells and loxP-DsRed-loxP-eGFP-expressing recipient cells were established. Co-culture and Transwell

Additional file 1: Table S1. Primer pairs used in this study. Table S2. Summary of embryo transfer for the generation of genetically modified pigs. Figure S1. The porcine reconstructed embryos were cultured in vitro. Figure S2. Detection of predicted off-target sites mutation by the T7ENI cleavage assay. Figure S3. Genotyping of cell clones and genetically modified pigs by PCR.

Although deep neural networks (DNNs) play important roles in many fields, the architecture design of DNNs can be challenging due to the difficulty of input data representation, the huge number of parameters and the complex layer relationships. To overcome the obstacles of architecture design, we developed a new method to generate the optimal structure of DNNs, named Evolutionary Strategy-based Architecture Evolution (ESAE), consisting of a bi-level representation and a probability distribution learning approach. The bi-level representation encodes architectures in the gene and parameter levels. The probability distribution learning approach ensures the efficient convergence of the architecture searching process. By using Fashion-MNIST and CIFAR-10, the effectiveness of the proposed ESAS is verified. The evolved DNNs, starting from a trivial initial architecture with one single convolutional layer, achieved the accuracies of 94.48% and 93.49% on Fashion-MNIST and CIFAR-10, respectively, and require remarkably less hardware costs in terms of GPUs and running time, compared with the existing state-of-the-art manual screwed architectures.

Traditional Chinese Medicine (TCM) started considering the medicinal and health effects of food thousands of years ago. TCM labels are placed on foods based on cold, neutral, and hot properties similar to Chinese herbal medicine. However, it is unclear whether such a classification has any molecular or biochemical basis, and what the relationship is between this TCM classification and the nutrient composition of food. To answer these questions, we collected a large dataset, in which each type of food has both TCM labels and molecular composition records for statistical analyses and machine-learning predictions. We applied machine-learning methods by using food molecular composition to predict the hot, neutral or cold label of food, and achieved more than 80% accuracy, which clearly indicated that TCM labels have a significant molecular basis. We also applied ANOVA to analyze the main factors contributing to the TCM labels. The ANOVA analysis shows that some molecular/biochemical compositions and categories, such as Energy, Fat, Protein, Water and Selenium (Se), have the strongest correlations with the TCM labels of food. To the best of our knowledge, this study represents the first effort to quantitatively explore the relationship between TCM labels and the molecular composition of food.

It is well known that the classical particle swarm optimization (PSO) is time-consuming when used to solve complex fitness optimization problems. In this study, we perform in-depth research on fitness estimation based on the distance between particles and affinity propagation clustering. In addition, support vector regression is employed as a surrogate model for estimating fitness values instead of using the objective function. The particle swarm optimization algorithm based on affinity propagation clustering, the efficient particle swarm optimization algorithm, and the particle swarm optimization algorithm based on support vector regression machine are then proposed. The experimental results show that the new algorithms significantly reduce the computational counts of the objective function. Compared with the classical PSO, the optimization results exhibit no loss of accuracy or stability.

Particle swarm optimizations (PSOs) are drawing extensive attention from both research and engineering fields due to their simplicity and powerful global search ability. However, there are two issues needing to be improved: one is that the classical PSO converges slowly; the other is that classical PSO tends to result in premature convergence, especially for multi-modal problems. This paper attempts to address these two issues. Firstly, to improve the convergent efficiency, this paper proposes an asymptotic predicting model of the globally-optimal solution, which is used to predict the global optimum based on extracting the features reflecting the evolutionary trend. The predicted global optimum is then taken as the third exemplar, in a way similar to the individual historical best solution and the swarm historical best solution in guiding the evolutionary process of other particles. To reduce the probability that the population is trapped into a local optimum due to the premature phenomenon, this paper proposes an adaptive mutation strategy, which is used to help the trapped particles to escape away from the local optimum by using the extended non-uniform mutation operator. Finally, we combine the two entities to develop a globally-optimal prediction-based adaptive mutation particle swarm optimization (GPAM-PSO). In numerical experimental parts, we compare the proposed GPAM-PSO with 11 existing PSO variants by using 22 benchmark problems of 30-dimensions and 100-dimensions, respectively. Numerical experiments demonstrate that the proposed GPAM-PSO could improve the accuracy and efficiency remarkably, which means that the combination of the globally-optimal prediction-based search and the adaptive mutation strategy could accelerate the convergence and reduce premature phenomenon effectively. Generally speaking, GPAM-PSO performs most efficiently and robustly. Moreover, the performance on an engineering problem demonstrates the practical application of the proposed GPAM-PSO algorithm.

In recent years, the Pearl River Delta has experienced rapid economic growth which may create a substantial burden to its ecology. In this study, the wetlands of the Pearl River Delta are investigated. Through the use of remote sensing methods, we analyze spatial and temporal variations of wetlands in this area over the past twenty years. The support vector machine (SVM) method is proven to be an effective approach for classifying the wetlands of the Pearl River Delta, and the total classification resolution reaches 94.94% with a Kappa coefficient exceeding 0.94, higher than other comparable analysis methods. Our results show that wetland areas were reduced by 36.9% during the past twenty years. The change detection analysis method shows that there was a 95.58% intertidal zone change to other land-use types, most of which (57.12%) was converted to construction land. In addition, farmland was reduced by 54.89% during the past twenty years, 47.19% of which was changed to construction land use. The inland water area increased 19.02%, but most of this growth (18.77%) was converted from the intertidal zone.

Divide and ruling is used to handle classification on complex distributed dataset.AP clustering is better than other clustering methods to divide dataset.An improved PSO and LHS make SA_SVDD parameters self-adaption.Average density is proposed to describe the dataset distribution. The Support Vector Data Description (SVDD) is one of the best-known one-class-classification methods used to solve problems where the sample data are of high dimension but limited amount. The results of SVDD can be greatly affected when the target data are poorly distributed and their density varies extremely. To deal with situations like these, we propose an improved SVDD algorithm, termed as Self-Adaptive SVDD (SA_SVDD), which combines Affinity Propagation (AP) clustering algorithm and SVDD. Firstly, SA_SVDD applies AP clustering to the input data set to obtain a set of compact subclasses, and then the boundary for each subclass is identified by SVDD, and these boundaries are exploited as final classification criteria. AP clustering is tried with different input preferences generated with Latin Hypercube Sampling, and an improved Particle Swarm Optimization (PSO) algorithm evolves the parameters of SVDD. The proposed algorithm is evaluated on a conventional benchmark set and shows significant improvement with respect to other one-class-classification methods. Display Omitted

This paper presents a Chinese topic crawler focused on customer development, in order to meet the needs of users for more accurate and particular Internet information. The concept of meta-search engine is introduced, and the keywords are expanded by the ontology of HowNet. Through the web crawler, preprocessing and classification, the information on customer relations can be divided into three categories: company, platform and meaningless. Numerical experiments show that satisfactory results can be obtained in some particular information-seeking areas. The average accuracy for classification is more than 80%, which can meet customer needs in most cases.

In this work, we develop a novel and sensitive sensor for the detection of organophosphorus pesticides based on the inner-filter effect (IFE) between gold nanoparticles (AuNPs) and ratiometric fluorescent quantum dots (RF-QDs). The RF-QDs has been designed by hybridizing two differently colored CdTe QDs, in which the red emissive QDs entrapped in the silica sphere acting as the reference signal, and the green emissive QDs covalently attached on the silica surface serving as the response signal.The fluorescence of RF-QDs could be quenched by AuNPs based on IFE. Protamine could effectively turn on the fluorescence due to the electrostatic attraction between protamine and AuNPs. Trypsin can easily hydrolyze protamine, leading to the quench of the fluorescence. Then, the fluorescence could be recovered again by the addition of parathion-methyl (PM) which could inhibit the activity of trypsin. By measuring the fluorescence of RF-QDs, the inhibition efficiency of PM to trypsin activity was evaluated. Under the optimized conditions, the inhibition efficiency was proportional to the logarithm of PM concentration in the range of 0.04–400 ng mL −1 , with a detection limit of 0.018 ng mL −1 . Furthermore, the simple and convenient method had been used for PM detection in environmental and agricultural samples with satisfactory results.

Oxalate is commonly employed as adjuvant of pesticide agent, causing renal injury of human even in trace residues. Despite the great achievements of the existing point-of-care testing (POCT) technology, accurate on-site screening of oxalate remains a tricky issue. To this aim, we proposed a “lab in a tube” platform which integrated portable hydrogel kit with smartphone for real-time monitoring of oxalate to achieve quantitatively precise analysis. In this work, a stimuli-responsive hydrogel-based kit was constructed via embedding manganese dioxide (MnO2) nanosheets into sodium alginate hydrogel system. Based on the intrinsic oxidase-like activity, MnO2 nanosheets-based nanozyme triggered color reaction by introducing a common sensing probe 3,3′,5,5′-tetramethylbenzidine. Meanwhile, the presence of oxalate would decompose MnO2 nanosheets, inducing the decrease of nanozyme activity, which resulted in the color response of portable kit. Coupling with ImageJ software, the image information of kit captured via smartphone could be transduced into the hue intensity, which provided a directly quantitative tool to detect oxalate with a detection limit of 8.0 μmol L−1. This portable smartphone biosensor was successfully applied for screening urine sample within 10 min for high-throughput analysis (twelve samples) without the need for any advanced analytical instruments. Based on the merits of simple operation, cost-efficiency, and good selectivity, the availability of the miniaturized biosensor platform for POCT will achieve the requirements of routine screening and disease prevention.

It is well known that when the fitness function is relatively complex, the optimization time cost of the genetic algorithm will be extremely huge. To address this issue, the surrogate model was employed to predict the fitness value of the optimization problem, to reduce the number of actual calculated fitness values. In this paper, BP neural network, the least square method and support vector machine were fused in the genetic algorithm to evaluate partial individuals’ fitness. Sufficient benchmark numerical experiments were conducted, and the results proved that the strategy could reduce the calculating counts of fitness function on similar accuracy basis compared with simple genetic algorithm.

ADP-ribosylhydrolases (ARH1, ARH2 and ARH3) are a family of enzymes to catalyze ADP-ribosylation, a reversible and covalent post-translational modification (PTM). There are four phosphorylated sites (Tyr-4, Tyr-19, Tyr-20, and Tyr-205) in ARH1. To explore the structural changes and functional impact induced by phosphorylation, molecular dynamics (MD) simulations and steered molecular dynamics (SMD) simulations were performed for the phosphorylated and non-phosphorylated ARH1 with the ligands. MD simulations results indicate that: (1) Glu-25 is more frequently in the α helix group in the phosphorylated state with the adenosine-5-diphosphate-ribosylarginine (ADP-RA) complex (51.56%) than that of the non-phosphorylated state(2.12%); (2) Ser-124 and Ser-264 become less flexible in the phosphorylated state with ADP-RA complex, which helps two residues form hydrogen bonds with ADP-RA; and (3) Tyr-211 is also less flexible in the phosphorylated state with ADP-RA complex, which helps stabilize the cation-π interaction of Y211-R119. All these changes facilitate ADP-RA to bind ARH1. In addition, according to the crystal structure of adenosine-5-diphosphate-ribose (ADP-ribose) in complex with non-phosphorylated and phosphorylated ARH1, the possible unbinding pathways of ADP-ribose from non-phosphorylated and phosphorylated ARH1 were explored respectively using SMD simulations. Our results show that phosphorylated ARH1 has more ordered structures than the non-phosphorylated type.

With the development of Internet of Things technology, radio-frequency identification localization methods have been widely applied due to their low cost and ease of deployment. The indoor radio-frequency identification localization algorithm based on received signal strength indication technology is a currently hot topic. Because the received signal strength is highly dependent on environments, the classic algorithms may result in large errors in localization accuracy. This article proposed a new radio-frequency identification localization algorithm, named BP_LANDMARC, by utilizing the back propagation neural network, which is designed to address nonlinear changes in radio-frequency signals. A strategy for selecting different working parameters in variable environments is presented. The evaluation methods of root mean square error and cumulative distribution function are used to compare the proposed algorithm with some existing algorithms. Experimental results show that the proposed algorithm remarkably improves the localization accuracy of both absolute distance and cumulative probability. Moreover, the proposed algorithm performs effectively and efficiently when it is applied to a logistics warehouse management system., The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Natural Science Foundation of China (61472158, 61503150, and 61572228), the Zhuhai Premier Discipline Enhancement Scheme (2015YXXK02), and the Guangdong Premier Key-Discipline Enhancement Scheme (2016GDYSZDXK036) and Science & Technology Development Foundation of Jilin Province (20180201045GX and 20160520012JH).

In order to distinguish and extract the topic information from other interferential information on the BBC news website for the study in social computing, the BBC News Hunter was proposed in this paper. The whole system consists of 6 subsystems, respectively named: UI, Control, Download, Analysis, Storage and Log. Numerical experiments show that satisfactory results can be obtained from the BBC news website, whose average accuracy as well as efficiency are acceptable.