End-to-End Pareto Set Prediction with Graph Neural Networks for Multi-objective Facility Location

The facility location problems (FLPs) are a typical class of NP-hard combinatorial optimization problems, which are widely seen in the supply chain and logistics. Many mathematical and heuristic algorithms have been developed for optimizing the FLP. In addition to the transportation cost, there are usually multiple conflicting objectives in realistic applications. It is therefore desirable to design algorithms that find a set of Pareto solutions efficiently without enormous search cost. In this paper, we consider the multi-objective facility location problem (MO-FLP) that simultaneously minimizes the overall cost and maximizes the system reliability. We develop a learning-based approach to predicting the distribution probability of the entire Pareto set for a given problem. To this end, the MO-FLP is modeled as a bipartite graph optimization problem and two graph neural networks are constructed to learn the implicit graph representation on nodes and edges. The network outputs are then converted into the probability distribution of the Pareto set, from which a set of non-dominated solutions can be sampled non-autoregressively. Experimental results on MO-FLP instances of different scales show that the proposed approach achieves a comparable performance to a widely used multi-objective evolutionary algorithm in terms of the solution quality while significantly reducing the computational cost for search.Comment: 14 pages, 3 figure

An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Travelling Salesman Problems

Recent years have witnessed a surge in research on machine learning for combinatorial optimization since learning-based approaches can outperform traditional heuristics and approximate exact solvers at a lower computation cost. However, most existing work on supervised neural combinatorial optimization focuses on TSP instances with a fixed number of cities and requires large amounts of training samples to achieve a good performance, making them less practical to be applied to realistic optimization scenarios. This work aims to develop a data-driven graph representation learning method for solving travelling salesman problems (TSPs) with various numbers of cities. To this end, we propose an edge-aware graph autoencoder (EdgeGAE) model that can learn to solve TSPs after being trained on solution data of various sizes with an imbalanced distribution. We formulate the TSP as a link prediction task on sparse connected graphs. A residual gated encoder is trained to learn latent edge embeddings, followed by an edge-centered decoder to output link predictions in an end-to-end manner. To improve the model's generalization capability of solving large-scale problems, we introduce an active sampling strategy into the training process. In addition, we generate a benchmark dataset containing 50,000 TSP instances with a size from 50 to 500 cities, following an extremely scale-imbalanced distribution, making it ideal for investigating the model's performance for practical applications. We conduct experiments using different amounts of training data with various scales, and the experimental results demonstrate that the proposed data-driven approach achieves a highly competitive performance among state-of-the-art learning-based methods for solving TSPs.Comment: 35 pages, 7 figure

Specific, simple and rapid detection of porcine circovirus type 2 using the loop-mediated isothermal amplification method

<p>Abstract</p> <p>Background</p> <p>Porcine circovirus type 2 (PCV2) is the causative agent of postweaning multisystemic wasting syndrome (PMWS), and porcine dermatitis and nephropathy syndrome (PDNS). It has caused heavy losses in global agriculture in recent decades. Rapid detection of PCV2 is very important for the effective prophylaxis and treatment of PMWS.</p> <p>Results</p> <p>A loop-mediated isothermal amplification (LAMP) assay was used to detect PCV2 in this study. Three pairs of primers were specially designed for recognizing eight distinct sequences of the ORF2 gene. This gene lies in the PCV2 virus genome sequence, and encodes the Rep protein that is involved in virus replication. Time and temperature conditions for amplification of PCV2 genes were optimized to be 55 min at 59°C. The analysis of clinical samples indicated that the LAMP method was highly sensitive. The detection limit for PCV2 by the LAMP assay was 10 copies, whereas the limit by conventional PCR was 1000 copies. The assay did not cross-react with PCV1, porcine reproductive and respiratory syndrome virus, porcine epidemic diarrhea virus, transmissible gastroenteritis of pigs virus or rotavirus. When 110 samples were tested using the established LAMP system, 95 were detected as positive.</p> <p>Conclusion</p> <p>The newly developed LAMP detection method for PCV2 was more specific, sensitive, rapid and simple than before. It complements and extends previous methods for PCV2 detection and provides an alternative approach for detection of PCV2.</p

The Exocyst Component Sec3 Controls Egg Chamber Development Through Notch During Drosophila Oogenesis

The exocyst complex plays multiple roles via tethering secretory or recycling vesicles to the plasma membrane. Previous studies have demonstrated that the exocyst contains eight components, which possibly have some redundant but distinct functions. It is therefore interesting to investigate the biological function of each component. Here, we found that Sec3, one component of exocyst complex, is involved in Drosophila egg chamber development. Loss of sec3 results in egg chamber fusion through the abolishment of cell differentiation. In addition, loss of sec3 increases cell numbers but decreases cell size. These defects phenocopy Notch pathway inactivation. In line with this, loss of sec3 indeed leads to Notch protein accumulation, suggesting that the loss of Sec3 inhibits the delivery of Notch onto the plasma membrane and accumulates inactive Notch in the cytoplasm. Loss of sec3 also leads to the ectopic expression of two Notch pathway target genes, Cut and FasciclinIII, which should normally be downregulated by Notch. Altogether, our study revealed that Sec3 governs egg chamber development through the regulation of Notch, and provides fresh insights into the regulation of oogenesis

Rapid detection of porcine circovirus type 2 using a TaqMan-based real-time PCR

Porcine circovirus type 2 (PCV2) and the associated disease postweaning multisystemic wasting syndrome (PMWS) have caused heavy losses in global agriculture in recent decades. Rapid detection of PCV2 is very important for the effective prophylaxis and treatment of PMWS. To establish a sensitive, specific assay for the detection and quantitation of PCV2, we designed and synthesized specific primers and a probe in the open reading frame 2. The assay had a wide dynamic range with excellent linearity and reliable reproducibility, and detected between 102 and 1010 copies of the genomic DNA per reaction. The coefficient of variation for Ct values varied from 0.59% to 1.05% in the same assay and from 1.9% to 4.2% in 10 different assays. The assay did not cross-react with porcine circovirus type 1, porcine reproductive and respiratory, porcine epidemic diarrhea, transmissible gastroenteritis of pigs and rotavirus. The limits of detection and quantitation were 10 and 100 copies, respectively. Using the established real-time PCR system, 39 of the 40 samples we tested were detected as positive

Microbial diversity and community composition of fecal microbiota in dual-purpose and egg type ducks

IntroductionDucks are important agricultural animals, which can be divided into egg and dual-purpose type ducks according to economic use. The gut microbiota of ducks plays an important role in their metabolism, immune regulation, and health maintenance.MethodsHere, we use 16S rDNA V4 hypervariable amplicon sequencing to investigate the compositions and community structures of fecal microbiota between egg (five breeds, 96 individuals) and dual-purpose type ducks (four breeds, 73 individuals) that were reared under the same conditions.ResultsThe alpha diversity of fecal microflora in egg type ducks was significantly higher than that in dual-type ducks. In contrast, there is no significant difference in the fecal microbial community richness between the two groups. MetaStat analysis showed that the abundance of Peptostreptococcaceae, Streptococcaceae, Lactobacillus, Romboutsia, and Campylobacter were significantly different between the two groups. The biomarkers associated with the egg and dual-purpose type ducks were identified using LEfSe analysis and IndVal index. Function prediction of the gut microbiota indicated significant differences between the two groups. The functions of environmental information processing, carbohydrate metabolism, lipid metabolism, xenobiotic biodegradation and metabolism, and metabolism of terpenoids and polyketides were more abundant in egg type ducks. Conversely, the genetic information processing, nucleotide metabolism, biosynthesis of amino acids and secondary metabolites, glycan biosynthesis and metabolism, fatty acid elongation, and insulin resistance were significantly enriched in dual-purpose type ducks.DiscussionThis study explored the structure and diversity of the gut microbiota of ducks from different economic-use groups, and provides a reference for improving duck performance by using related probiotics in production

A TRPV4-dependent neuroimmune axis in the spinal cord promotes neuropathic pain

Microglia, resident macrophages of the CNS, are essential to brain development, homeostasis, and disease. Microglial activation and proliferation are hallmarks of many CNS diseases, including neuropathic pain. However, molecular mechanisms that govern the spinal neuroimmune axis in the setting of neuropathic pain remain incompletely understood. Here, we show that genetic ablation or pharmacological blockade of transient receptor potential vanilloid type 4 (TRPV4) markedly attenuated neuropathic pain-like behaviors in a mouse model of spared nerve injury. Mechanistically, microglia-expressed TRPV4 mediated microglial activation and proliferation and promoted functional and structural plasticity of excitatory spinal neurons through release of lipocalin-2. Our results suggest that microglial TRPV4 channels reside at the center of the neuroimmune axis in the spinal cord, which transforms peripheral nerve injury into central sensitization and neuropathic pain, thereby identifying TRPV4 as a potential new target for the treatment of chronic pain

Identification of Target Genes at Juvenile Idiopathic Arthritis GWAS Loci in Human Neutrophils

Juvenile idiopathic arthritis (JIA) is the most common chronic rheumatic disease among children which could cause severe disability. Genomic studies have discovered substantial number of risk loci for JIA, however, the mechanism of how these loci affect JIA development is not fully understood. Neutrophil is an important cell type involved in autoimmune diseases. To better understand the biological function of genetic loci in neutrophils during JIA development, we took an integrated multi-omics approach to identify target genes at JIA risk loci in neutrophils and constructed a protein-protein interaction network via a machine learning approach. We identified genes likely to be JIA risk loci targeted genes in neutrophils which could contribute to JIA development