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The important factor for the supply chain management of fresh products is partner selection. Environment protection is also an important factor, however the factor is not taken into account by the traditional supplier selection. Therefore, the supplier selection standard includes the green standard in this paper. Our work is to investigate an optimal mathematical modeling for green partner selection. The four targets of the proposed model are cost, product quality, green appraisal score and time. The proposed genetic algorithm with multi-targets are to search the set of optimum solutions using by weighted sum method. The proposed model introduces a supply chain network structure to analysis average number Pareto-optimal solutions with genetic algorithm for the four problems. It is pointed out that the variation of f2 and f3 with f1 and f4 is kept within obvious ranges. This practical result highlights the fact that the effects of the fact that effects of f2 and f3 are important factors affecting the performance supply chain network of fresh product.

Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease and is associated with high morbidity and mortality. Pogostemon cablin (Blanco) Benth/Huo Xiang (HX) is a perennial herb with unique anti-oxidant and anti-inflammatory properties, and thus, can positively affect liver function. In this study, we used network pharmacology to predict the potential mechanism of HX on NAFLD. Pharmacological experiments were used to verify the effect of HX on the functions of NAFLD. Network pharmacology identified nine components that interacted with 82 NAFLD-related targets, revealing four target genes: TNF, IL6, TP53, and AKT1. HX prevents the development and progression of NAFLD through different pathways and targets with quercetin-regulated lipid metabolism, anti-inflammatory, and anti-oxidant pathways playing an essential role in the treatment of NAFLD. Compared with feeding HFD, HX significantly attenuated lipid accumulation in vivo with mice and also in vitro with mouse liver cells. A high dose of HX decreased hepatocyte lipid accumulation and the abundance of SREBF1 and FASN. Validation experiments revealed that HX inhibited the activation of NF-κB/IκB signaling and decreased the release and levels of pro-inflammatory factors (TNF-α and IL-6). These data suggest that HX can attenuate abnormal lipid metabolic responses and enhance antioxidant mechanisms. Thus, the pharmacological effects from plants used in traditional Chinese medicine are achievde through a multi-level response.

Distributed radar array brings several new forthcoming advantages in aerospace target detection and imaging. The two-dimensional distributed array avoids the imperfect motion compensation in coherent processing along slow time and can achieve single snapshot 3D imaging. Some difficulties exist in the 3D imaging processing. The first one is that the distributed array may be only in small amount. This means that the sampling does not meet the Nyquist sample theorem. The second one refers to echoes of objects in the same beam that will be mixed together, which makes sparse optimization dictionary too long for it to bring the huge computation burden in the imaging process. In this paper, we propose an innovative method on 3D imaging of the aerospace targets in the wide airspace with sparse radar array. Firstly, the case of multiple targets is not suitable to be processed uniformly in the imaging process. A 3D Hough transform is proposed based on the range profiles plane difference, which can detect and separate the echoes of different targets. Secondly, in the subsequent imaging process, considering the non-uniform sparse sampling of the distributed array in space, the migration through range cell (MTRC)-tolerated imaging method is proposed to process the signal of the two-dimensional sparse array. The uniformized method combining compressed sensing (CS) imaging in the azimuth direction and matched filtering in the range direction can realize the 3D imaging effectively. Before imaging in the azimuth direction, interpolation in the range direction is carried out. The main contributions of the proposed method are: (1) echo separation based on 3D transform avoids the huge amount of computation of direct sparse optimization imaging of three-dimensional data, and ensures the realizability of the algorithm; and (2) uniformized sparse solving imaging is proposed, which can remove the difficulty cause by MTRC. Simulation experiments verified the effectiveness and feasibility of the proposed method.

Although medicinal plants have played an important role in the development of therapeutics as natural compound sources, the synergistic effects of the multiple chemicals in the plants on multiple targets have not been studied widely. Recently, network-based methods for the analysis of drug-target interactions have gained attention and relies on the paradigm that a single drug can act on multiple targets rather than a single target. In this study, we have presented a novel approach to analyze the interactions between the chemicals in the medicinal plants and multiple targets based on the complex multipartite network of the medicinal plants, multi-chemicals, and multiple targets. The multipartite network was constructed via the conjunction of two relationships: chemicals in plants and the biological actions of those chemicals on the targets. By analyzing the structural characteristics of the network using the network analysis techniques, we investigated the similarities among the plants via their chemical profiles obtained from the hierarchical clustering analysis. In doing so, we introduced an index of the efficacy of chemicals in a plant on a protein target of interest, called target potency score (TPS). We showed that the analysis can identify specific chemical profiles from each group of plants, which can then be employed for discovering new alternative therapeutic agents. Furthermore, specific clusters of plants and chemicals acting on specific targets were retrieved using TPS that suggested potential drug candidates with high probability of clinical success. We expect that this approach may open a way to predict the biological functions of multi-chemicals and multi-plants on the targets of interest and enable repositioning of the plants and chemicals.

Considerable pharmacological studies have demonstrated that the extracts and ingredients from different parts (seeds, peels, pulps, and flowers) of Litchi exhibited anticancer effects by affecting the proliferation, apoptosis, autophagy, metastasis, chemotherapy and radiotherapy sensitivity, stemness, metabolism, angiogenesis, and immunity via multiple targeting. However, there is no systematical analysis on the interaction network of “multiple ingredients-multiple targets-multiple pathways” anticancer effects of Litchi. In this study, we summarized the confirmed anticancer ingredients and molecular targets of Litchi based on published articles and applied network pharmacology approach to explore the complex mechanisms underlying these effects from a perspective of system biology. The top ingredients, top targets, and top pathways of each anticancer function were identified using network pharmacology approach. Further intersecting analyses showed that Epigallocatechin gallate (EGCG), Gallic acid, Kaempferol, Luteolin, and Betulinic acid were the top ingredients which might be the key ingredients exerting anticancer function of Litchi, while BAX, BCL2, CASP3, and AKT1 were the top targets which might be the main targets underling the anticancer mechanisms of these top ingredients. These results provided references for further understanding and exploration of Litchi as therapeutics in cancer as well as the application of “Component Formula” based on Litchi’s effective ingredients.

In practical device-free localization (DFL) applications, for enlarging the monitoring area and improving localization accuracy, too many nodes need to be deployed, which results in a large volume of DFL data with high dimensions. This arises a key problem of seeking an accurate and efficient approach for DFL. In order to address this problem, this paper regards DFL as a problem of sparse-representation-based classification; builds a sparse model; and then proposes two sparse-coding-based algorithms. The first algorithm, sparse coding via the iterative shrinkage-thresholding algorithm (SC-ISTA), is efficient for handling high-dimensional data. And then, subspace techniques are further utilized, followed by performing sparse coding in the low-dimensional signal subspace, which leads to the second algorithm termed subspace-based SC-ISTA (SSC-ISTA). Experiments with the real-world data set are conducted for single-target and multi-target localization, and three typical machine learning algorithms, deep learning based on auto encoder, K-nearest neighbor, and orthogonal matching pursuit, are compared. Experimental results show that both SC-ISTA and SSC-ISTA can achieve high localization accuracies of 100% and are robust to noisy data when SNR is greater than 10 dB, and the time costs for sparse coding of SC-ISTA and SSC-ISTA are 2.1 × 10-3 s and 2.1 × 10-4 s respectively, which indicates that the proposed algorithms outperform the other three ones.

The literature focuses on drug promiscuity, which is a drug&rsquo, s ability to bind to several targets, because it plays an essential role in polypharmacology. However, little work has been completed regarding binding site promiscuity, even though its properties are now recognized among the key factors that impact drug promiscuity. Here, we quantified and characterized the promiscuity of druggable binding sites from protein-ligand complexes in the high quality Mother Of All Databases while using statistical methods. Most of the sites (80%) exhibited promiscuity, irrespective of the protein class. Nearly half were highly promiscuous and able to interact with various types of ligands. The corresponding pockets were rather large and hydrophobic, with high sulfur atom and aliphatic residue frequencies, but few side chain atoms. Consequently, their interacting ligands can be large, rigid, and weakly hydrophilic. The selective sites that interacted with one ligand type presented less favorable pocket properties for establishing ligand contacts. Thus, their ligands were highly adaptable, small, and hydrophilic. In the dataset, the promiscuity of the site rather than the drug mainly explains the multiple interactions between the drug and target, as most ligand types are dedicated to one site. This underlines the essential contribution of binding site promiscuity to drug promiscuity between different protein classes.

A key focus in the field of drug discovery has been motivated by the neuroprotection of natural compounds. Cerebral ischemia is a multifaceted pathological process with a series of mechanisms, and a perspective for the development of neuroprotectants from traditional herbal medicine or natural products is a promising treatment for this disease. Natural compounds with the effects of anti-oxidation, anti-inflammation, anti-apoptosis, and neurofunctional regulation exhibit therapeutic effects on experimental ischemic brain injury. Conferring to the pharmacological mechanisms underlying neuroprotection, a study found that androgapholide, a diterpene lactone compound, exhibits varying degrees of neuroprotective activities in both in vitro and in vivo experimental models of stroke. The neuroprotective mechanisms of andrographolide are suggested as: (I) increasing nuclear factor E2-related factor 2-heme oxygenase (Nrf2-HO-1) expression through p38-mitogen activated protein kinase (MAPK) regulation, (II) inducing cerebral endothelial cells (CEC) apoptosis and caspase-3 activation, (III) down regulating Bax, inducible nitric oxide synthase (iNOS), and (IV) inhibiting hydroxyl radical (OH-) formation, and activating transcription factor NF-κB signaling pathways. Recently, several researchers have also been trying to unveil the principal mechanisms involved in the neuroprotective effects of andrographolide. Therefore, this review aims to summarize an overview on the neuroprotective effects of andrographolide and exemplifies the essential mechanisms involved. This paper can provide information that andrographolide drug discovery may be a promising strategy for the development of a novel class of neuroprotective drug.

Current models for stance classification often treat each target independently, but in many applications, there exist natural dependencies among targets, e.g., stance towards two or more politicians in an election or towards several brands of the same product. In this paper, we focus on the problem of multi-target stance detection. We present a new dataset that we built for this task. Furthermore, We experiment with several neural models on the dataset and show that they are more effective in jointly modeling the overall position towards two related targets compared to independent predictions and other models of joint learning, such as cascading classification. We make the new dataset publicly available, in order to facilitate further research in multi-target stance classification. © 2017 Association for Computational Linguistics., 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017, April 3-7, 2017, Valencia, Spain

For solving the problem of the multi-targets threat from the air, this paper analyzes the threat degree of many kinds of different moving stages in naval gun system. The authors analyze the drawbacks of pre- vious methods and provide a new analytical model which is based on the theory of grey correlation and correla- tion coefficient. Using the new mathematical threat degree analysis model, the authors verify the new model by practical experiment and practical applications. In addition, this paper compares the merit and demerit of tradi- tional method and the new method of analytical model. It turned out that the new model is better than previous ones and it can be used effectively and easily, and its consumption of system recourse is in theoretical range.

This study proposed a multi-target hierarchical detection (MTHD) method to simultaneously and automatically detect multiple directional land cover changes. MTHD used a hierarchical strategy to detect both abrupt and trend land cover changes successively. First, Grubbs’ test eliminated short-lived changes by considering them outliers. Then, the Brown-Forsythe test and the combination of Tomé’s method and the Chow test were applied to determine abrupt changes. Finally, Sen’s slope estimation coordinated with the Mann-Kendall test detection method was used to detect trend changes. Results demonstrated that both abrupt and trend land cover changes could be detected accurately and automatically. The overall accuracy of abrupt land cover changes was 87.0% and the kappa index was 0.74. Detected trends of land cover change indicated high consistency between NDVI (Normalized Difference Vegetation Index), change trends from LTS (Landsat Thematic Mapper and Enhanced Thematic Mapper Plus time series dataset), and MODIS (Moderate Resolution Imaging Spectroradiometer) time series datasets with the percentage of samples indicating consistency of 100%. For cropland, trends of millet yield per unit and average NDVI of cropland indicated high consistency with a linear regression determination coefficient of 0.94 (p < 0.01). Compared with other multi-target change detection methods, the changes detected by the MTHD could be related closely with specific ecosystem changes, reducing the risk of false changes in the area with frequent and strong interannual fluctuations.