Mélodie Duval

Pascale Cossart

Alice Lebreton

Mammalian microRNAs and long noncoding RNAs in the host-bacterial pathogen crosstalk

Keywords: Host-pathogen, Bacteria, microRNA, lncRNA, Regulatory network

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

During infection, both bacteria and host undergo significant changes of environmental or physiological conditions, to which they can adapt or react. Extra-and intra-cellular pathogenic bacteria can adjust their metabolism and trigger the expression of virulence genes, which allow them to benefit from their host resources. Likewise, infected organisms are capable of sensing the intrusion by bacterial pathogens, and react by triggering innate or adaptive immune defences. The complex interplay of actions/counter-actions occurring between a bacterial invader and an infected organism is referred to as host-pathogen crosstalk. This crosstalk, on the bacterial or on the host side, involves the coordinated regulation of gene expression programmes with the phase of infection, controlled at the transcriptional or post-transcriptional level. For instance, the transcriptome of cells infected by intracellular bacteria is completely reshaped, as a result of pathogen detection by specific cellular sensors (reviewed in [START_REF] Mogensen | Pathogen recognition and inflammatory signaling in innate immune defenses[END_REF]), and of specific modulation by bacterial effectors (reviewed in [START_REF] Niller | Patho-epigenetics of Infectious Diseases Caused by Intracellular Bacteria[END_REF]).

RNA itself represents a key regulatory molecule. Indeed, its physicochemical properties make non-coding RNAs (ncRNA) versatile tools for interfering with gene expression, independently of the coding function played by messenger RNAs (mRNA). Recognition by base-pairing allows one single ncRNA to bind multiple targets, and thereby to regulate several pathways simultaneously [START_REF] Peterson | Common features of microRNA target prediction tools[END_REF]. Conversely, a single gene can be regulated by a variety of ncRNAs, allowing its control in a broad range of conditions.

Over the past 10 years, mechanisms and effects of RNA-mediated regulation on gene expression have been broadly investigated in prokaryotes and eukaryotes. Various examples of RNA-mediated regulation in pathogenic bacteria have been recently reviewed [START_REF] Bouloc | Fresh layers of RNA-mediated regulation in Gram-positive bacteria[END_REF][START_REF] Caldelari | RNA-mediated regulation in pathogenic bacteria[END_REF][START_REF] Sesto | Bacterial and cellular RNAs at work during Listeria infection[END_REF][START_REF] Wagner | Small RNAs in bacteria and archaea : who they are , what they do , and how they do it[END_REF]. Here, we focus on regulation involving mammalian ncRNAs during host-pathogen interaction, and more precisely on two classes of ncRNAs: microRNAs (miRNA) and long non-coding RNAs (lncRNA). These ncRNAs act at the transcriptional and post-transcriptional levels in the control of host gene expression, and are themselves the object of regulations.

The name lncRNA is given to a class of eukaryotic ncRNAs of more than 200 nucleotides transcribed by RNA polymerase II (PolII). Like messenger RNAs, most of these transcripts are poly-adenylated and spliced [START_REF] Ding | Relationship of long noncoding RNA and viruses[END_REF]. Even though the first mammalian lncRNA, XIST (X-inactive specific transcript), was discovered 25 years ago [START_REF] Brown | A gene from the region of the human X inactivation centre is expressed exclusively from the inactive X chromosome[END_REF][START_REF] Brockdorff | The product of the mouse Xist gene is a 15 kb inactive X-specific transcript containing no conserved ORF and located in the nucleus[END_REF], the identification and functional characterization of these noncoding transcripts remain an emerging field. Several lncRNAs have been shown to regulate gene expression at the level of transcription or translation (for reviews see [START_REF] Quan | Exploring the secrets of long noncoding RNAs[END_REF][START_REF] Geisler | RNA in unexpected places: long non-coding RNA functions in diverse cellular contexts[END_REF][START_REF] Guttman | Modular regulatory principles of large non-coding RNAs[END_REF]). The expression of most lncRNAs is tissue-specific, or restricted to precise developmental stages; in addition, some lncRNAs are involved in host response against viral infection (reviewed in [START_REF] Ding | Relationship of long noncoding RNA and viruses[END_REF]). Very recent studies show that lncRNAs are also involved in the response against pathogenic bacteria, which we will describe below. miRNAs are a class of PolII-dependent transcripts that are processed into a short mature form (21-24 nucleotides). To date, more than 35,000 distinct microRNAs have been described in 223 organisms (miRBase, release 21), and are predicted to regulate 60% of human protein-coding genes [START_REF] Friedman | Most mammalian mRNAs are conserved targets of microRNAs[END_REF]. They are involved in multiple processes including cell proliferation, differentiation, inflammation, etc. [START_REF] O'connell | Physiological and pathological roles for microRNAs in the immune system[END_REF][START_REF] Bueno | Control of cell proliferation pathways by microRNAs[END_REF][START_REF] Kedde | Interplay between microRNAs and RNAbinding proteins determines developmental processes[END_REF]. After being transcribed, the canonical biogenesis of miRNAs involves the processing of a hairpin precursor into a double stranded duplex by two major nucleases, Drosha and Dicer (reviewed in [START_REF] O'connell | Physiological and pathological roles for microRNAs in the immune system[END_REF]). After export to the cytoplasm, one strand of the duplex is loaded into an effector complex called RISC (RNA-induced silencing complex), which drives the microRNA to its target mRNA and mediates its function, usually by promoting RNA degradation or impeding translation, even though a few activation mechanisms have also been described [START_REF] Orang | Mechanisms of miRNA-mediated gene regulation from common downregulation to mRNA-specific upregulation[END_REF]. In addition to transcription, the amount of available functional miRNAs in the cytoplasm can be regulated via their sequestration by binding proteins, or by a class of ncRNAs called miRNA sponges. Their deregulation was shown in some instances to promote diseases, such as cancer, auto-immunity or cardiovascular disorders [START_REF] Parkes | The role of microRNAs in the idiopathic inflammatory myopathies[END_REF][START_REF] Gurha | MicroRNAs in cardiovascular disease[END_REF][START_REF] Huang | miR-126: A novel regulator in colon cancer (Review)[END_REF].

To counteract bacterial infection, host cells adjust their gene expression programme, among other pathways by using miRNA and lncRNA as regulatory molecules. Reciprocally, pathogens can escape host defence mechanisms using a variety of strategies, and in particular by targeting miRNA-mediated regulation. Here, we review the role and subversion of mammalian ncRNAs during host pathogen crosstalk.

Host cell microRNA response to bacterial infection

Discovery of microRNA response against pathogens

In 2006, the first studies describing a host miRNA response to challenges with bacterial components were published. Recognition of Pseudomonas syringae flagellin by Arabidopsis thaliana was shown to induce the expression of miR-393a and the subsequent inhibition of the expression of three F-box auxin receptors. This, in turn, decreases signalling by auxin, a plant hormone that modulates immune response, and limits bacterial spread within tissue [START_REF] Navarro | A Plant miRNA Contributes to Antibacterial Resistance by Repressing Auxin Signaling[END_REF]. In human monocytes, the involvement of miRNAs in innate immune response to lipopolysaccharide (LPS, the major component of the outer membrane of Gram negative bacteria) stimulation was investigated. This work has led to the characterization of miR-146 as an anti-inflammatory miRNA [START_REF] Taganov | NF-kappaB-dependent induction of microRNA miR-146, an inhibitor targeted to signaling proteins of innate immune responses[END_REF].

Following these two studies, the miRNA response upon bacterial infection in mammals has been widely investigated. Infection by different pathogens leads to the expression of specific microRNA sets that are regulated in a time-dependent manner [START_REF] Jin | Transcriptome microRNA profiling of bovine mammary epithelial cells challenged with Escherichia coli or Staphylococcus aureus bacteria reveals pathogen directed microRNA expression profiles[END_REF]. Among them, miR-155, miR-146, let-7 and miR-29 play important roles in the host cells response to bacteria [START_REF] Zeng | An update on the role of miRNA-155 in pathogenic microbial infections[END_REF][START_REF] Staedel | MicroRNAs and bacterial infection[END_REF][START_REF] Eulalio | The mammalian microRNA response to bacterial infections[END_REF][START_REF] Maudet | MicroRNAs in the interaction between host and bacterial pathogens[END_REF]. These miRNAs regulate the immune responses in order to clear bacterial infection, while preserving the organism from deleterious effects of inflammation. They will be described below.

Figure 1. Host microRNA variation upon infection

Upon pathogen exposure, miRNA variability occurs at different levels. (a) The abundance of miRNAs is modified to face infection, and miRNAs are up--or down--regulated early or late in response to pathogens, leading to the expression of a core temporal response common to all pathogens, as well as a specific response; (b) The choice of the arm of pre--miRNA loaded into the RISC complex may be changed by infection, in a process called "arm--switching"; (c) Generation of isomiRs in addition to canonical miRNAs is observed (inspired from [START_REF] Siddle | Bacterial Infection Drives the Expression Dynamics of microRNAs and Their isomiRs[END_REF]).

A recent analysis of the miRNAs expressed in dendritic cells in response to six different bacteria has revealed a core temporal response to infection comprising 49 miRNAs that may play an essential role in response to infection, in addition to subtle variability for each bacterium, that can be considered as a specific signature [START_REF] Siddle | Bacterial Infection Drives the Expression Dynamics of microRNAs and Their isomiRs[END_REF] (Fig. 1a). Moreover, infection can induce a switch in the relative abundance of mature miRNAs deriving from the 5' and 3' arms of the precursor (a process described as "arm-switching", [START_REF] Griffiths-Jones | MicroRNA evolution by arm switching[END_REF], Fig. 1b), and changes the proportion of miRNA variants (isomiRs, Fig. 1c). Such variability in miRNA identity is thought to affect their regulatory potential, but this aspect deserves more investigations [START_REF] Siddle | Bacterial Infection Drives the Expression Dynamics of microRNAs and Their isomiRs[END_REF].

Key microRNAs involved in immunity

Upon infection, miR-155 and miR-146 are two miRNAs induced by the NF-κB pathway through pattern recognition receptors (PRR) sensing of pathogen motifs, in particular LPS (Fig. 2a). These miRNAs regulate distinct sets of genes [START_REF] Taganov | NF-kappaB-dependent induction of microRNA miR-146, an inhibitor targeted to signaling proteins of innate immune responses[END_REF][START_REF] Schulte | Differential activation and functional specialization of miR-146 and miR-155 in innate immune sensing[END_REF].

Expression of miR-146 is induced by sub-inflammatory levels of NF-κB activity. It acts as an anti-inflammatory regulator, by targeting TRAF6 (TNF Receptor-associated factor 6) and IRAK1 (IL-1R-associated kinase 1), which are involved in the NF-κB pathway (Fig. 2b), thus promoting tolerance to low doses of LPS [START_REF] Taganov | NF-kappaB-dependent induction of microRNA miR-146, an inhibitor targeted to signaling proteins of innate immune responses[END_REF][START_REF] Schulte | Differential activation and functional specialization of miR-146 and miR-155 in innate immune sensing[END_REF]. This desensitized state is necessary to protect the organism against septic shock. A tolerance state is also crucial during the postnatal establishment of the intestinal microbiota in the newborn gut, where miR-146 prevents inappropriate inflammation (for a review, [START_REF] Duerr | The mammalian intestinal epithelium as integral player in the establishment and maintenance of hostmicrobial homeostasis[END_REF]).

In contrast, miR-155 is induced by higher doses of LPS, at levels which result in proinflammatory NF-κB activity, as well as by TNF-α and interferon β, via TAB2 [START_REF] Schulte | Differential activation and functional specialization of miR-146 and miR-155 in innate immune sensing[END_REF][START_REF] O'connell | MicroRNA-155 is induced during the macrophage inflammatory response[END_REF][START_REF] Tili | Modulation of miR-155 and miR-125b levels following lipopolysaccharide/TNF-alpha stimulation and their possible roles in regulating the response to endotoxin shock[END_REF]. miR-155 is known to amplify the expression of pro-inflammatory factors, thereby acting in defence against pathogens, and also to exert negative feedback on the immune system, thus protecting the host from potentially damaging overreaction [START_REF] Schulte | Differential activation and functional specialization of miR-146 and miR-155 in innate immune sensing[END_REF]. The involvement of this miRNA in the pro-inflammatory response has been thoroughly investigated; among others, miR-155 targets SHIP1 (Fig. 2c), a negative regulator of the NF-κB pathway [START_REF] O'connell | Inositol phosphatase SHIP1 is a primary target of miR-155[END_REF][START_REF] Cremer | MiR-155 induction by F. novicida but not the virulent F. tularensis results in SHIP down-regulation and enhanced pro-inflammatory cytokine response[END_REF], and SOCS1 (suppressor of cytokine signalling 1), an effector involved in the homeostasis of Treg cells [START_REF] Lu | Foxp3-Dependent MicroRNA155 Confers Competitive Fitness to Regulatory T Cells by Targeting SOCS1 Protein[END_REF]. This in turn, stimulates the expression of the pro-inflammatory cytokines TNF-α, IL-6, IL-1β, IL-8 and IL-12, while it reduces the expression of the anti-inflammatory cytokine IL-10 [START_REF] Billeter | MicroRNA-155 potentiates the inflammatory response in hypothermia by suppressing IL-10 production[END_REF][START_REF] Kurowska-Stolarska | MicroRNA-155 as a proinflammatory regulator in clinical and experimental arthritis[END_REF]. A complex interplay occurs upon infection, as IL-10 itself can reduce miR-155 expression in response to LPS [START_REF] Mccoy | IL-10 inhibits miR-155 induction by toll-like receptors[END_REF]. miR-155 also increases TNF-α production upon LPS stimulation (Fig. 2d), probably by stabilizing the transcript or by promoting its translation when targeting the expression of proteins that binds the transcript 3'-UTR [START_REF] Tili | Modulation of miR-155 and miR-125b levels following lipopolysaccharide/TNF-alpha stimulation and their possible roles in regulating the response to endotoxin shock[END_REF]. Multiple miR-155 targets are also involved in T helper cell development, or promote autophagy by inhibiting the mTOR pathway [START_REF] Hu | MiR-155 Promotes T Follicular Helper Cell Accumulation during Chronic, Low-Grade Inflammation[END_REF][START_REF] Wan | Hypoxia-induced MIR155 is a potent autophagy inducer by targeting multiple players in the MTOR pathway[END_REF][START_REF] Wang | MicroRNA-155 Promotes Autophagy to Eliminate Intracellular Mycobacteria by Targeting Rheb[END_REF]. In line with these data, miR-155 is essential for an efficient immune response to several bacterial pathogens. Indeed, miR-155 null mice show a slower clearance upon Citrobacter rodentium infection [START_REF] Clare | Enhanced susceptibility to citrobacter rodentium infection in microRNA-155-Deficient mice[END_REF], as well as an impaired CD8 + T-cell response to Listeria monocytogenes [START_REF] Lind | Micro-RNA 155 is required for optimal CD8+ T cell responses to acute viral and intracellular bacterial challenges[END_REF]. Moreover, miR-155 is essential in the vaccination process against Salmonella typhimurium [START_REF] Rodriguez | Requirement of bic/microRNA-155 for Normal Immune Function[END_REF]. However, this miRNA has also been shown to repress genes such as NIK, IKKε and TAB2, which encode proteins involved in the inflammatory pathway (Fig. 2e); accordingly, it is proposed that miR-155 could act as a limiter of inflammation [START_REF] Schulte | Differential activation and functional specialization of miR-146 and miR-155 in innate immune sensing[END_REF][START_REF] Tili | Modulation of miR-155 and miR-125b levels following lipopolysaccharide/TNF-alpha stimulation and their possible roles in regulating the response to endotoxin shock[END_REF][START_REF] Imaizumi | IFN-g and TNF-a synergistically induce microRNA-155 which regulates TAB2/IP-10 expression in human mesangial cells[END_REF]. Interestingly, miR-155 expression is also stimulated by the intracellular NOD2 receptor (Fig. 2f). In tolerant macrophages, where miR-146 prevents inflammation, stimulation of NOD2 can restore the NF-κB pathway activation, as well as the feedback control by miR-155 [START_REF] Schulte | Differential activation and functional specialization of miR-146 and miR-155 in innate immune sensing[END_REF].

Another well-described miRNA family is the let-7 family, which targets different genes involved in immunity. This family is repressed upon infection by various pathogens as well as by exposure to LPS [START_REF] Chen | A cellular micro-RNA, let-7i, regulates toll-like receptor 4 expression and contributes to cholangiocyte immune responses against Cryptosporidium parvum infection[END_REF][START_REF] Izar | MicroRNA response to listeria monocytogenes infection in epithelial cells[END_REF][START_REF] Schulte | Analysis of the host microRNA response to Salmonella uncovers the control of major cytokines by the let-7 family[END_REF][START_REF] Teng | Let-7b Is Involved in the Inflammation and Immune Responses Associated with Helicobacter pylori Infection by Targeting Toll-Like Receptor 4[END_REF][START_REF] Hu | Release of Luminal Exosomes Contributes to TLR4-Mediated Epithelial Antimicrobial Defense[END_REF]. Indeed, NF-κB activation induces Lin-28B expression, a protein that blocks let-7 maturation [START_REF] Iliopoulos | An Epigenetic Switch Involving NF-κB, Lin28, Let-7 MicroRNA, and IL6 Links Inflammation to Cell Transformation[END_REF]. In addition, various studies have highlighted an active repression of these miRNAs mediated by the bacteria themselves [START_REF] Matsushima | MicroRNA signatures in Helicobacter pylori-infected gastric mucosa[END_REF][START_REF] Hayashi | CagA mediates epigenetic regulation to attenuate let-7 expression in Helicobacter pylori-related carcinogenesis[END_REF][START_REF] Kumar | MicroRNA let-7 modulates the immune response to mycobacterium tuberculosis infection via control of A20, an inhibitor of the NF-kB pathway[END_REF]. Several targets have been described for let-7 family miRNAs. let-7b targets the TLR4 transcript and as a consequence, bacterial infection-mediated let-7b repression promotes TLR sensing and subsequent NF-κB activation [START_REF] Teng | Let-7b Is Involved in the Inflammation and Immune Responses Associated with Helicobacter pylori Infection by Targeting Toll-Like Receptor 4[END_REF]. let-7a and let-7d target IL-6 and IL-10 transcripts; their down-regulation could contribute to maintaining a balanced immune response upon bacterial exposure [START_REF] Schulte | Analysis of the host microRNA response to Salmonella uncovers the control of major cytokines by the let-7 family[END_REF]. let-7c targets the mTOR pathway, thus modulating T-cell activity [START_REF] Marcais | microRNA-mediated regulation of mTOR complex components facilitates discrimination between activation and anergy in CD4 T cells[END_REF]. let-7i reduces expression of SNAP23 protein, which is involved in exosome release, a process that is part of the antimicrobial response [START_REF] Hu | Release of Luminal Exosomes Contributes to TLR4-Mediated Epithelial Antimicrobial Defense[END_REF]. The abundant let-7f targets the deubiquinating enzyme A20, an inhibitor of the NF-κB pathway [START_REF] Kumar | MicroRNA let-7 modulates the immune response to mycobacterium tuberculosis infection via control of A20, an inhibitor of the NF-kB pathway[END_REF]; its downregulation by Mycobacterium tuberculosis favours bacterial survival in macrophages [START_REF] Kumar | MicroRNA let-7 modulates the immune response to mycobacterium tuberculosis infection via control of A20, an inhibitor of the NF-kB pathway[END_REF]. ,c,d,e) thus promoting or dampening inflammation. miR--155 is also induced by NOD2 cytoplasmic receptors (f). Exosomal transfer of miRNAs allows cell--to--cell communication (g) and extracellular miRNAs might regulate bacterial gene expression (h). To subvert host defences, bacteria can perturb inflammatory response (i,j). Bacteria might also generate miR--like molecules, and thereby interfere with host gene expression (k). lncRNAs are key players in inflammatory response, as they affect chromatin structure (l), thus leading to expression of antimicrobial molecules. They can also hamper inflammation, by sequestering RelA/p65 and thus preventing its DNA binding activity (m).

In murine leucocytes, miR-29 was shown to repress interferon (IFN)-γ expression, and thereby control immune responses to intracellular bacteria [START_REF] Ma | The microRNA miR-29 controls innate and adaptive immune responses to intracellular bacterial infection by targeting interferon-gamma[END_REF]. During infection of natural killer cells, CD4 + and CD8 + T-cells, by Listeria monocytogenes or Mycobacterium bovis bacillus Calmette-Guérin (BCG), miR-29 expression is down-regulated, thereby facilitating IFN-γ production and subsequent bacterial clearance. Strikingly, miR-29 is one of the microRNAs triggered during infection of macrophages by L. monocytogenes [START_REF] Schnitger | Listeria monocytogenes infection in macrophages induces vacuolar-dependent host miRNA response[END_REF]. Even though the effect of this up-regulation is unknown, it suggests that infection can differentially control the expression of miRNAs, and as a consequence, that of their targets, depending on the affected cell type.

Altogether, these examples provide evidence that miRNAs act as major components of regulatory networks in immune pathways. Their production is temporally regulated in a dosedependent manner, resulting in the induction or dampening of immunity, and allowing a rapid and potent response against pathogens.

Circulating microRNAs during infection

Aside from their activity inside the cell, miRNAs are powerful molecules for cell-to-cell communication. Indeed, miRNAs are found in many body fluids, including plasma, saliva, tears, urine, amniotic fluid, colostrum, breast milk, stool, etc. [START_REF] Liu | The Host Shapes the Gut Microbiota via Fecal MicroRNA[END_REF][START_REF] Weber | The microRNA spectrum in 12 body fluids[END_REF]. Exosomes, which are circulating small lipid vesicles formed from cell plasma membranes, can transport molecules. Exosomal transfer of miRNAs is a powerful mechanism to expand the host response to bacterial infection. Recently, it has been shown that upon LPS exposure, miR-155 and miR-146a are released from bone marrow-derived dendritic cells (BMDCs) within exosomes and are successfully transferred to recipient cells [START_REF] Alexander | Exosome-delivered microRNAs modulate the inflammatory response to endotoxin[END_REF] (Fig. 2g). Upon uptake, miRNAs regulate gene expression and reprogram the response of BMDCs to LPS. The authors propose several hypotheses; one of them is that miRNA release in exosomes could be a dynamically regulated process where the ratio of miR-155/miR-146a changes over time, thus enhancing the inflammation upon infection, and then dampening it during recovery.

Exosomal miRNAs could give rise to therapeutic perspectives: miR-146-containing exosomes could be used to treat inflammatory diseases, while miR-155-containing exosomes could be used as an adjuvant to improve vaccine efficacy, or to fight infections as it has been proposed for Helicobacter pylori [START_REF] Wu | MicroRNA-155-enhanced autophagy in human gastric epithelial cell in response to Helicobacter pylori[END_REF]. In addition, these circulating miRNAs are now used or proposed as biomarkers in diagnostic tools for diseases, including sepsis and tuberculosis [64][START_REF] Chakraborty | Profiling cell-free and circulating miRNA: a clinical diagnostic tool for different cancers[END_REF][START_REF] Guay | Circulating microRNAs as novel biomarkers for diabetes mellitus[END_REF][START_REF] Ueberberg | Are microRNAs suitable biomarkers of immunity to tuberculosis?[END_REF]. For instance, the signature pattern of circulating miRNAs in the blood of mice shows a differential response to either LPS [START_REF] Hsieh | Whole blood-derived microRNA signatures in mice exposed to lipopolysaccharides[END_REF] or lipoteichoic acids (LTA, a major constituent of the cell wall of Gram positive bacteria) [START_REF] Hsieh | Circulating microRNA signatures in mice exposed to lipoteichoic acid[END_REF]. Nine specific circulating miRNAs were also identified in the serum of mice infected by Staphylococcus aureus, but not by Escherichia coli [START_REF] Rau | Profiling the circulating miRNAs in mice exposed to grampositive and gram-negative bacteria by Illumina small RNA deep sequencing[END_REF].

Finally, a recent article relates the in vitro ability of miRNAs isolated from faeces to penetrate inside E. coli and Fusobacterium nucleatum and to regulate the expression of bacterial genes in a sequence-specific manner (Fig. 2h). Whether this phenomenon occurs in vivo remains to be determined, nevertheless this discovery opens new perspectives about the possible roles of miRNAs in host-pathogen interaction [START_REF] Liu | The Host Shapes the Gut Microbiota via Fecal MicroRNA[END_REF].

Influence of commensals on the microRNA response to a food-borne pathogen

The resident intestinal microbiota has been long known to play a major role in intestinal homeostasis by shaping the gut transcriptome. An increasing number of studies indicate that miRNAs are participating in this host/commensal crosstalk, and largely contribute to regulatory networks controlling tissue integrity, immunity and metabolism (reviewed in [START_REF] Runtsch | MicroRNAs and the regulation of intestinal homeostasis[END_REF]). For instance, miRNA expression influences the microbiota composition as well as host resistance to dextran sulphate sodium (DSS)-induced colitis [START_REF] Liu | The Host Shapes the Gut Microbiota via Fecal MicroRNA[END_REF]. The presence and nature of the intestinal flora can also tune infection by a bacterial pathogen such as L. monocytogenes, as well as the host mRNA and miRNA responses [START_REF] Archambaud | The Intestinal Microbiota Interferes with the microRNA Response upon Oral Listeria Infection[END_REF][START_REF] Archambaud | Impact of lactobacilli on orally acquired listeriosis[END_REF]. Oral pre-treatment with lactobacilli of otherwise germ-free mice significantly reduces their sensitivity to orally-acquired listeriosis and reshapes host gene expression, notably of genes coding for proteins in the interferon pathway. Furthermore, lactobacilli oral pre-treatment can counteract the down-regulation of miR-192, miR-200b, and miR-215 induced by L. monocytogenes infection [START_REF] Archambaud | Impact of lactobacilli on orally acquired listeriosis[END_REF]. A study comparing the intestinal response to Listeria infection in conventional versus germ-free mice has additionally highlighted five microRNAs down-regulated by Listeria infection only in the presence of the microbiota, and not affected in germ-free mice (miR-143, miR-148a, miR-200b, miR-200c, and miR-378), suggesting that commensals can prime the host microRNA response to infection [START_REF] Archambaud | The Intestinal Microbiota Interferes with the microRNA Response upon Oral Listeria Infection[END_REF]. This work has also outlined a network of putative regulatory interactions between host microRNAs and their target mRNAs impacted by infection, paving the way to future mechanistic studies aiming at understanding how these microRNAs modulate the intestinal response to infection.

Subversion of microRNA pathways by bacterial pathogens

Manipulation of cell physiology

During infection, bacteria can interfere with many cellular processes using effector molecules [START_REF] Reddick | Bacteria fighting back: How pathogens target and subvert the host innate immune system[END_REF]. In particular, by modulating the production of miRNAs, they manipulate their host cell physiology and defences, with favourable consequences for pathogen survival (Table 1). For example, Helicobacter pylori induces the increase of miR-1289, which represses HKα, a component of the gastric H + /K + ATPase. This results in a transient decrease of gastric acidity that promotes H. pylori colonization. The mechanism is dependent on CagA, a bacterial effector known to activate the NF-κB pathway [START_REF] Zhang | Helicobacter pylori-induced posttranscriptional regulation of H-K-ATPase -subunit gene expression by miRNA[END_REF]. H. pylori also interferes with autophagy by inducing the expression of miR-30b, which targets BECN1 and ATG12 transcripts that encode proteins involved in the formation and maturation of autophagosomes [START_REF] Tang | Compromised autophagy by MIR30B benefits the intracellular survival of Helicobacter pylori[END_REF].

Another example of this subversion is the reduction of host SUMOylation by Salmonella, via the stimulation of two miR-30 family miRNAs, miR-30c and miR-30e, and subsequent downregulation of their target Ubc9, the only cellular E2 SUMO-conjugating enzyme [START_REF] Verma | Salmonella engages host microRNAs to modulate SUMOylation: a new arsenal for intracellular survival[END_REF]. The mechanism used by Salmonella to up-regulate these microRNAs is unknown. The cell cycle is an important target for pathogens, as it influences their ability to survive and/or proliferate. The perturbation of specific miRNAs is used to this end by Salmonella. Indeed, by inhibiting the production of the transcriptional factor E2F1, Salmonella reduces the expression of the miR-15 family [START_REF] Maudet | Functional high-throughput screening identifies the miR-15 microRNA family as cellular restriction factors for Salmonella infection[END_REF]. This induces the de-repression of cyclin D1 and thus promotes the G1/S cell cycle transition, which is favouring bacterial intracellular replication (Fig. 3a). A similar mechanism has been described for H. pylori, which down-regulates miR-372 and miR-373 expression in a CagA-dependent manner. This induces a cell cycle arrest at the G1/S transition and inhibits renewal of the gastric epithelium (Fig. 3b), a major host defence mechanism against bacterial infection [START_REF] Belair | Helicobacter pylori interferes with an embryonic stem cell micro RNA cluster to block cell cycle progression[END_REF]. CagA also promotes the expression of miR-584 and miR-1290, which target FOXA1, a negative regulator of the epithelial-mesenchymal transition [START_REF] Zhu | MicroRNAs up-regulated by CagA of helicobacter pylori induce intestinal metaplasia of gastric epithelial cells[END_REF]. In addition to short-term, acute bacterial infections, pathogens also induce long-term effects, as it is exemplified by H. pylori in carcinogenesis. The investigation of the roles of miRNAs in this process has revealed that this bacterium induces the expression of miR-21 and miR-222, two miRNAs that target the tumor suppressor RECK [START_REF] Li | Increased miR-222 in H. pylori-associated gastric cancer correlated with tumor progression by promoting cancer cell proliferation and targeting RECK[END_REF][START_REF] Zhang | miR-21 plays a pivotal role in gastric cancer pathogenesis and progression[END_REF]. miR--125b, miR--99b, miR--155 up, up, down production of TNF--α hMDM, mDC [START_REF] Singh | Mycobacterium tuberculosis controls MicroRNA-99b (miR-99b) expression in infected murine dendritic cells to modulate host immunity[END_REF][START_REF] Kumar | Identification of a novel role of ESAT-6-dependent miR-155 induction during infection of macrophages with mycobacterium tuberculosis[END_REF] let--7f down NF--κB pathway RAW264.7, BMDM, and hMDM [START_REF] Marcais | microRNA-mediated regulation of mTOR complex components facilitates discrimination between activation and anergy in CD4 T cells[END_REF] miR--155 up NF--κB pathway RAW264.7 and BMDM [START_REF] Yang | Early secreted antigen ESAT-6 of mycobacterium tuberculosis promotes apoptosis of macrophages via targeting the microrna155-SOCS1 interaction[END_REF] Table 1. Bacteria subvert host defences by modulating microRNA expression.

Bacteria manipulate the expression of various miRNAs in order to modulate cellular processes, favouring their survival and/or proliferation. AGS, SGC7901, MKN28, MKN45, HGC--27, BGC--823, GES--1 and 293T: human gastric cell lines; HCT--8 and HT--29: human colonic cell lines; HeLa: human cervix cell line; J774A.2 and RAW 264.7: murine macrophage cell lines; hMDM: human monocyte--derived macrophage cell line; BMDM: murine bone marrow--derived macrophage cell line; mDC: mice dendritic cell line.

Effect on immune response

Bacteria deploy diverse mechanisms to counteract macrophage recruitment and/or activation, as well as induction of pro-inflammatory factors. For instance, secreted effectors from Salmonella stimulate, via the p53 signalling pathway, the expression of miR-128, which targets the macrophage colony-stimulating factor (M-CSF) and thereby leads to impaired M-CSF-mediated macrophage recruitment [START_REF] Zhang | Salmonella enterica serovar enteritidis modulates intestinal epithelial mir-128 levels to decrease macrophage recruitment via macrophage colony-stimulating factor[END_REF]. Likewise, M. tuberculosis can limit macrophage response to IFN-γ by upregulating miR-132 and miR-26a [START_REF] Ni | Mycobacterium tuberculosis decreases human macrophage IFN-γ responsiveness through miR-132 and miR-26a[END_REF]. These two miRNAs down-regulate the transcriptional coactivator p300, a component of the IFN-γ signalling cascade. Mycobacterium also reduces the production of TNF-α, a potent mediator of macrophage activation and bacterial clearance, by regulating the abundance of miR-125b, miR-155 and miR-99b [START_REF] Rajaram | Mycobacterium tuberculosis lipomannan blocks TNF biosynthesis by regulating macrophage MAPKactivated protein kinase 2 (MK2) and microRNA miR-125b[END_REF][START_REF] Singh | Mycobacterium tuberculosis controls MicroRNA-99b (miR-99b) expression in infected murine dendritic cells to modulate host immunity[END_REF] (Fig. 2i). This leads to the destabilization of TNF-α mRNA transcripts and an increase of SHIP1 production, subsequently reducing the production of TNF-α. In addition, the secreted effector from M. tuberculosis ESAT-6 down-regulates the expression of let-7f in macrophages, which leads to increased expression of the deubiquitinating enzyme A20, a negative regulator of the NF-κB pathway [START_REF] Kumar | MicroRNA let-7 modulates the immune response to mycobacterium tuberculosis infection via control of A20, an inhibitor of the NF-kB pathway[END_REF]. M. tuberculosis ESAT-6 also stimulates miR-155 expression in macrophages, thus repressing BACH1 and SHIP1. This, in turn, induces the expression of the heme oxygenase 1, and activates the serine/threonine kinase AKT, which respectively promote bacterial dormancy and survival [START_REF] Kumar | Identification of a novel role of ESAT-6-dependent miR-155 induction during infection of macrophages with mycobacterium tuberculosis[END_REF] (Fig. 2j). However, the ESAT-6-dependent induction of miR-155 also induces macrophage apoptosis by down regulating SOCS1 protein [START_REF] Yang | Early secreted antigen ESAT-6 of mycobacterium tuberculosis promotes apoptosis of macrophages via targeting the microrna155-SOCS1 interaction[END_REF] and targeting Rheb [START_REF] Wang | MicroRNA-155 Promotes Autophagy to Eliminate Intracellular Mycobacteria by Targeting Rheb[END_REF] which is deleterious for the pathogen.

Taken together, these studies indicate that bacteria can control miRNA expression in order to subvert host defences.

Regulation of host gene expression by bacterial non-coding RNAs

In addition to deregulation of endogenous microRNAs, emerging data suggest that bacteria could also produce regulatory RNAs that would modulate the host gene expression, as previously shown for viruses (reviewed in [START_REF] Grundhoff | Virus-encoded microRNAs[END_REF]). A typical example of this system is the E. coli OxyS and DsrA ncRNAs which, after ingestion by Caenorhabditis elegans, modulate che-2 and F42G9.6 gene expression, probably by promoting the degradation of host transcripts [START_REF] Liu | Escherichia coli noncoding RNAs can affect gene expression and physiology of Caenorhabditis elegans[END_REF]. Recently, two studies have investigated bacterial microRNA-like molecules. First, an in silico search for bacterial ncRNAs harbouring a secondary structure that might generate miRNA if processed by the host has identified 68 candidate bacterial RNAs from 28 bacterial genomes [START_REF] Shmaryahu | Prediction of Bacterial microRNAs and possible targets in human cell transcriptome[END_REF]. These putative bacterial miRNAs are predicted to target mRNAs involved in 47 different human diseases, including cancer and diabetes, thus providing a new perspective for bacterial influence on health and disease. In an independent study, a pre-microRNA was identified in Mycobacterium marinum, for which a mature 23 nucleotide-long form was found associated with the host RISC complex upon infection (Fig. 2k) [START_REF] Furuse | Search for MicroRNAs expressed by intracellular bacterial pathogens in infected mammalian cells[END_REF]. While the abundance of the endogenous pre-microRNA was too low to induce any detectable effect, their overexpression efficiently decreased mRNA target expression. This type of bacterialderived miRNA molecules may thus represent a novel class of regulatory factors in the host pathogen crosstalk.

Long noncoding RNAs at play in response to bacterial infection

The involvement of lncRNAs in defence against viruses has been well described (reviewed in [START_REF] Ding | Relationship of long noncoding RNA and viruses[END_REF]), and recent publications show that several lncRNAs are involved in inflammatory response to LPS and live bacteria. Among them, LINoCR (for LPS Inducible NonCoding RNA) activates the expression of lysozyme in a chicken macrophage cell line challenged with LPS, by remodelling the chromatin (Fig. 2l) [START_REF] Lefevre | The LPS-Induced Transcriptional Upregulation of the Chicken Lysozyme Locus Involves CTCF Eviction and Noncoding RNA Transcription[END_REF]. More recently, lncRNA profiles in response to infection or LPS stimulation have been analysed in several mammalian cell lines, and the mechanism of action for some of these lncRNAs has been elucidated.

In human monocytes, 76 enhancer RNAs (eRNA), 40 canonical lncRNAs, 65 antisense lncRNAs and 35 regions of bidirectional transcription are differently expressed upon LPS stimulation, [START_REF] Ilott | Long non-coding RNAs and enhancer RNAs regulate the lipopolysaccharide-induced inflammatory response in human monocytes[END_REF]. Knockdowns of IL1β-eRNA and of a region of bidirectional transcription (IL1β-RBT46) attenuate LPS-induced mRNA transcription and release of proinflammatory mediators, highlighting their implication in the inflammatory response. One lncRNA, lnc-IL7R, has been well characterized: this lncRNA is a negative regulator of proinflammatory mediators such as IL-8, IL-6, E-selectin and VCAM-1, by maintaining a repressive chromatin mark, trimethylation of histone H3 at lysine 27 (H3K27me3) their gene promoters [START_REF] Cui | The human long noncoding RNA, lnc-IL7R, regulates inflammatory response[END_REF]. In contrast, NeST lncRNA expressed in mice CD4 + /CD8 + T cells promotes IFN-γ production by methylating lysine 4 of histone H3 -a mark of active transcription -at the IFN-γ locus [START_REF] Gomez | The NeST Long ncRNA Controls Microbial Susceptibility and Epigenetic Activation of the Interferon-g Locus[END_REF]. In mouse bone-marrow derived macrophages (BMDMs), 27 lncRNAs are differentially expressed upon LPS stimulation, and their de-regulation is associated with histone trimethylation or acetylation of neighbouring genes, suggesting a possible regulatory role in innate-immune response [START_REF] Mao | Expression and regulation of long noncoding RNAs in TLR4 signaling in mouse macrophages[END_REF]. In HeLa cells infected by Salmonella, a variation of 44% of total lncRNAs has been monitored, leading the authors to propose that lncRNAs could be used as sensitive markers for pathogen activity in the early infection phase [START_REF] Westermann | Dual RNA-seq unveils noncoding RNA functions in host-pathogen interactions[END_REF]. Last, HOTAIR, a lncRNA first shown to participate in the transcriptional repression of HOX genes [START_REF] Rinn | Functional Demarcation of Active and Silent Chromatin Domains in Human HOX Loci by Noncoding RNAs[END_REF], is also a positive regulator of inflammation [START_REF] Wu | LncRNA-HOTAIR promotes TNF-α production in cardiomyocytes of LPS-induced sepsis mice by activating NF-κB pathway[END_REF]. Indeed, HOTAIR up-regulation in mice cardiomyocytes after LPS-induced sepsis induces TNF-α production by promoting phosphorylation of p65 protein and NF-κB activation.

A subtype of lncRNAs named lincRNAs (long intergenic non-coding RNAs), which are expressed from intergenic regions, have also been studied. In bone marrow dendritic cells, LPS stimulation induces the expression of about 20 lincRNAs, most of them being dependent of NF-κB. Among them, lincRNA-Cox2 is the most expressed [START_REF] Guttman | Chromatin signature reveals over a thousand highly conserved large non-coding RNAs in mammals[END_REF]. This lincRNA is also up-regulated in mouse bone-marrow derived macrophages upon stimulation by TLR ligands, as well as by Listeria monocytogenes [START_REF] Carpenter | A Long Noncoding RNA Mediates Both Activation and Repression of Immune Response[END_REF], and modulates the expression of many genes involved in inflammation. More precisely, the authors could identify that lincRNA-Cox2 forms a complex with hnRNP-A/B and -A2/B1, two nuclear RNA binding proteins, and thereby repress the transcription of immune genes [START_REF] Carpenter | A Long Noncoding RNA Mediates Both Activation and Repression of Immune Response[END_REF].

LncRNAs can also arise from the expression of pseudogenes. Among them Lethe, which is induced by NF-κB upon TNF-α stimulation, was recently found to contribute to a negative feedback regulation of this pathway, by sequestering the RelA/p65 subunit and thus inactivating its DNA binding activity [START_REF] Rapicavoli | A mammalian pseudogene lncRNA at the interface of inflammation and antiinflammatory therapeutics[END_REF] (Fig. 2m). The active regulation of pseudogene lncRNA expression can thus constitute a means to control inflammatory signalling.

The above-reported studies highlight the involvement of lncRNAs in inflammatory response. Even though some of them were performed using live bacteria, whether bacterial pathogens can subvert lncRNA-mediated regulation remains to be further explored. A first hint of bacterial subversion of lncRNA was obtained in macrophages infected by BCG, showing that 11 lncRNAs were less expressed during a challenge with live bacteria compared to heat inactivated bacteria [START_REF] Pawar | Down regulated lncRNA MEG3 eliminates mycobacteria in macrophages via autophagy[END_REF].

Conclusions

The major property of ncRNAs is certainly their ability to regulate several targets by specific base-pairing; thereby, they constitute a potent tool allowing cells to tune the expression of a whole regulon with one simple mechanism. As described above, ncRNAs can affect all steps in the gene expression process. When affecting the translation and stability of mature mRNAs, their action takes place at a downstream step in the pathway; as a consequence, their effects are more immediate and flexible than those of transcriptional regulators. Regulation by ncRNAs thus provide a diversity of responses by the host cell during the early stages of bacterial infection, when cells readjust promptly their gene expression programme to face damage and organize immune defences. Given the various microRNAs and lncRNAs that are differentially regulated in response to bacterial pathogens, in diverse cell types and tissues, they constitute a reservoir of gene expression that can fine-tune an impressive number of putative targets.

In addition to their direct regulatory activity on target gene expression, lncRNA and miRNA may also regulate each other; lncRNAs can act as miRNA sponges, in a process described as "competitive endogenous RNA" (ceRNA, reviewed in [START_REF] Salmena | A ceRNA hypothesis: The rosetta stone of a hidden RNA language?[END_REF]). Reciprocally, miRNAs may indirectly influence gene expression by targeting lncRNAs [START_REF] Pawar | Down regulated lncRNA MEG3 eliminates mycobacteria in macrophages via autophagy[END_REF]. Thus, a complex regulatory network is at play in the host cell to tightly modulate gene expression. This type of interplay might be involved in host response against bacterial infection, as recently shown for cancer [START_REF] Du | Integrative analyses reveal a long noncoding RNA-mediated sponge regulatory network in prostate cancer[END_REF]. Given the arising data on the ability of regulatory RNAs produced by pathogens to regulate host gene expression and also the possible regulatory function of cellular RNAs on the pathogen, the role of RNA in the host-pathogen crosstalk might be even more sophisticated than previously anticipated (Fig. 4). During host--pathogen interaction, RNA is a key regulatory molecule allowing bacterial adaptation, subversion and survival, as well as host response and immunity. Eukaryotic ncRNAs are able to cross--sregulate themselves, and could even regulate bacterial genes. In addition, bacterial regulatory RNAs as well as secreted effectors are able to modulate the host transcriptome. Thus, a complex RNA network is at play in the host/pathogen crosstalk.

Figure 2 .

 2 Figure 2. Mammalian non--coding RNAs at work during host--pathogen crosstalkA complex interplay of microRNA and lncRNA occurs during bacterial infection. miRNAs miR--155 and --146 are induced by PRRs in a NF--κB--dependent pathway (a), and regulate different sets of transcripts (b,c,d,e) thus promoting or dampening inflammation. miR--155 is also induced by NOD2 cytoplasmic receptors (f). Exosomal transfer of miRNAs allows cell--to--cell communication (g) and extracellular miRNAs might regulate bacterial gene expression (h). To subvert host defences, bacteria can perturb inflammatory response (i,j). Bacteria might also generate miR--like molecules, and thereby interfere with host gene expression (k). lncRNAs are key players in inflammatory response, as they affect chromatin structure (l), thus leading to expression of antimicrobial molecules. They can also hamper inflammation, by sequestering RelA/p65 and thus preventing its DNA binding activity (m).

Figure 3 .

 3 Figure 3. Modulation of microRNA by pathogens can perturb the cell cycle. By inhibiting different sets of miRNAs, Salmonella (a) and Helicobacter pylori (b) perturb the host cell cycle, thus promoting their survival and proliferation.

Figure 4 .

 4 Figure 4. The coding/non--coding RNA interplay in host--pathogen crosstalk

Acknowledgements

We would like to thank Olivier Dussurget for his careful reading and helpful comments on the manuscript. Work in the laboratory of P.C. received financial support from European Research 12 Council advanced grant BacCellEpi-670823, ERA-NET Infect-ERA ProAntiLis, Investissements d'Avenir BACNET (ANR-10-BINF-0201) and IBEID (ANR-10-LABX-62-01), Human Frontier Science Program (RGP0011/2013), and Fondation le Roch Les Mousquetaires. The laboratory of A.L. is supported by Fondation pour la Recherche Médicale (FRM-AJE20131128944), Inserm ATIP-Avenir, Mairie de Paris (Programme Émergences -Recherche médicale) and Investissements d'Avenir MemoLife (ANR-10-LABX-54). P.C. is a Senior International Research Scholar of the Howard Hughes Medical Institute.


Wenqin Cao

Haiyan Zhu

Bin Li

IMPACT OF REGIONAL INDUSTRIES ON LOGISTICS DEMAND WITH IMPROVED GREY ANALYSIS

Keywords: Logistics Demand, Grey System, Grey Rational Analysis (GRA), Industry

An improved Grey Relational Analysis (GRA) is used to quantitatively investigate the impact of Jiangxi Province regional economy on local logistics demand. The grey characteristics of data from regional economy and logistics is studied, which shows GRA is applicable; then a multi-sequence GRA is proposed, which generates total 4 groups of grey relational sequence from three indices of industrial added value and logistics demand based on GRA. Finally statement and explanation are given based on the Jiangxi Provincial industrial situation.

Introduction

The planning of regional logistics industry is influenced by multiple complex factors. However, the main factor is always depended on the development of local industries, so an analytical model for relationship between these factors is hard to setup [START_REF] Wen-Qin | Logistics demand forecast for urban logistics planning[END_REF]. Hence a study to the development of local economy and its impact on logistics industry will lay the milestone to the logistics planning.

The level of regional economical development, industrial structure, industrial distribution and industrial upgrading has direct impact on the demand and the level of regional logistics [START_REF] Deng | Control problems of grey systems[END_REF]. So in this paper, the gray relational theory is used to analyze the impact of Jiangxi provincial regional economy on the local logistics demand from a quantitative respective [START_REF]Statistic bureau of Jiangxi, Jiangxi statistical yearbook[END_REF].

Applicability of Grey Relational Analysis and Its Improvement

Applicability of grey relational analysis

The Grey System theory proposed by [START_REF] Deng | Control problems of grey systems[END_REF] has been proven to be useful for dealing with poor, incomplete and uncertain information system called grey system. The Grey System theory is aimed at problems with no experience and insufficient, uncertainty data, which fuzzy mathematics, statistics and probability theory can not solve [4] . Grey Relational Analysis (GRA) is part of Grey System theory, which is suitable for solving problems with complicated interrelationships between multiple factors and variables [START_REF] Hibiki | DEA sensitivity analysis by changing a reference set: regional contribution to Japanese industrial development[END_REF][START_REF] Wu | A comparative study of using grey relational analysis in multiple attribute decision making problems[END_REF][START_REF] Deng | Introduction to grey system theory[END_REF]. GRA is to establish gray correlation model to make grey relationship whose operating mechanism and the physical prototype is not clear or non-existent quantification, sequence and obvious. GRA can define system or factor boundary, analyze influence of system and behavior, distinguish primary and secondary factors, identify patterns and so on [START_REF] Yeh | Application of grey correlation analysis for evaluating the artificial lake site in Pingtung Plain,Taiwan[END_REF]. Technical connotation of GRA is to obtain difference from variable sequences; establish space of difference; establish and calculate comparison measure of differences, called gray relational grade; establish sequence relationship between factors.

Data of logistics demand is regarded as grey because the statistical data relating with regional logistics demand is not complete for historical reasons, at the same time these statistical data have its flaws ( such as inaccurate, incomplete, estimation, etc.) [START_REF] Wen | The cardinal form of grey relational grade[END_REF]. Furthermore, socio-economic system is an open system influenced by complexity factors, which is stochastic interacting with after-effect processes and uncertain relationship. The mathematical model for relationship between various economic factors and logistics demand has not been established. Above all, it proves that the relationship between industries and logistics demand shows significant grey characteristic.

Variable definition

Variable definition is the first step of grey theory application. In the logistics field, almost all goods need transportation, and transportation is first one of the seven logistics activities. So the freight traffic data from the transportation field can be an indicator as logistics demand when logistics demand has no systematic statistics, and be regarded as a reference sequence of GRA model [START_REF] Wu | An alternative form for grey relational grades[END_REF]. The scale and level of regional economy directly impact on logistics demand. The statistical Industrial Added Value (IAV) of primary industry, secondary industry and tertiary industry can indicate industrial structure, as compared sequence of GRA model. In our study, the time span of variable data is eight years from 2003 to 2010.

Improved GRA based on multi-group grey relational grade

Past research just used only one group of grey relational grade to analyze the degree of influence, because the operational mechanism of grey system was assumed certain and unchanged [START_REF] Lin | Use of the Taguchi Method and Grey Relational Analysis to Optimize Turning Operations with Multiple Performance Characteristics[END_REF]. However, the economy of Jiangxi Province is in the rapid development stage; structure of three industries is adjusted in wider margin variation by years [12]. So model of economic operation is uncertain and variable, which cause relative degree of three factors impact on freight traffic changing. If using only one group of gray relational grade, these changes can not be expressed. The paper introduces multi-group GRA which establishes eight grey relational matrixes, but not just traditional one.

Every sequence of matrix is made of variable data of five consecutive years of 2003 to 2010 by consecutively rolling, that is to say data of year 2003~2007 as group 1, data of year 2004~2008 as group 2,…, and data of year 2006~2010 as group 4 [13]. Finally four groups of grey relational sequence can be gained based on GRA. By horizontal comparison, changes can be grasped dynamically, so analysis is more complete and reliable.

Grey Relational Model and Data Analysis

Source data and eight groups of matrix

Variable data is from Jiangxi Statistical Yearbooks. Considering inflation, Industrial Added Value (IAV) of three industries is calculated at 2003 constant prices. All source data is showed in table 1, eight groups of matrix is showed in table 2, which reference sequence is y 0 and the comparison sequence is y i .

Calculation of grey relational grade

Data dimensionless

Source data should be made dimensionless in order to ensure the accuracy of gray correlation calculation, because of their unified units and large difference in value. The three methods commonly used for dimensionless are interval-based method, mean method and initial value method. The initial value method is used as follows:

) 1 ( ) ( i i ik y k y x  n k , , 2 , 1   m i , , 2 , 1 , 0   (1)

Consideration of grey relational grade Absolute difference is expressed by Eq (2)

 

m i n k k i ,... 2 , 1 , 0 , ,... 2 , 1 | ) ( 0      (2)
Environmental parameters are expressed by Eq ( 3)

) ( min min ), ( max max 0 0 k m k M i k i i k i     (3)
The grey rational coefficient at point k is defined by Eq (4)

; ,... 2 , 1 ; ,... 2 , 1 , ) ( ) , ( 0 0 m i n k M k M m x x i ik k          (4)
In Deng's study (1989), the general grey relational grade is expressed by Eq (5)

) , ( 1) , (

1 0 0    n k ik k i x x n x x   (5)
The ζ is a distinguishing factor, which is set to be 0.5 generally.

Here we adopt the grade computation, and finally the eight groups of grey relational sequence of three industries impact on freight traffic is showed in table 3, which is retained three decimal.

Grey relational sequence analysis

The higher value of the grey relational grade represents the stronger relational degree between the reference sequence and the given sequence. Table 3 shows that descending order of grey relational coefficient is the primary industry, tertiary industry and secondary industry.

Jiangxi is a granary province, whose Primary industry is developed with a rural population of 32 million, accounting for about 77% of the total population. Jaingxi allocates to the state 8 to 10 billion kilograms of grain annually, is one of the few provinces with grains output surplus [14].

Jiangxi's future policy and strategies on the Primary industry is to reinforce ecoagricultural construction, whose essential content is to vigorously develop harmless agriculture, green food and organic food. So freight traffic of agricultural produce will still increase [START_REF] Deng | Introduction to Grey System Theory[END_REF]. In generally, the agricultural produce's price is low, so its contribution to IAV is limited in the past few years. At the same, outsourcing logistics of Primary industry accounted for a large proportion. From 1998 to 2008, although the IAV from the Primary industries accounted for small proportion of GDP (about 20%), The Primary industry has the greatest impact on the freight transportation.

The relationship between the tertiary industry and freight traffic is becoming closer. Rapid development of modern logistics industry raises the level and quality of logistics services effectively, which facilitates the logistics outsourcing in turn [START_REF] Kuo | The Use of Grey Relational Analysis in Solving Facilities Layout Design Problem[END_REF]. Such virtuous circle makes industrial transport, post and telecommunication industries increase year by year, accounting about 40% of the tertiary industry.

Although IAV of the secondary industry accounts large proportion of GDP between year 1998 and 2006, about 35 ~ 45%, its gray relational grade is the smallest of the three factors.

With the level of technology improvement, IAV of the secondary industry accounts for a large proportion of production value, and IAV per unit is high. The proportion of logistics outsourcing is low in industrial enterprises.

The grey rational grades of economic indicators and freight traffic in different province are different because of the different types of provinces and its different economics stages of evolution.

The improved GRA can be referred to analyze the main factors influencing logistics demand, which will be used to improve logistics industry and coordinate the development of regional economics. It is difficult to establish the physical prototype because of less scientific theory and practice for regional logistics development and planning guidance and more conditions with complex relationship. In the past, regional economic factors and their impact on logistics are limited to qualitative analysis, which is subjectively appraisal, lack of data support. GRA can rule out subjectivity and arbitrariness at a certain extent, which make traditional approach quantitative, scientific and artificial intelligence. Conclusion based on GRA is more comprehensive, objective and impartial.

Further study is considered to the amendment of statistical data. Some Statistical data has its own shortcomings, data of freight traffic has big jump in some years. So further study need revising statistical data to include the strange data and vacancy data to make them more realistic. Then it needs more qualitative method. Many other factors which are eliminated in the model should be analyzed with more qualitative method.

Group1(

  Year 2003~2007) Y0(k)=(27709,31924,33996,37517,40046); Y1(k) =(560,605,644,686,720) Y2(k)=(1227,1455,1704,1982,2325; Y3(k) =(1043,1143,1267,1392,1541) Group2 (Year 2004~2008) Y0(k)=(31924,33996,37517,40046,43011); Y1(k) = (605,644,686,720,755) Y2(k)=(1455,1704,1982,2325,2711; Y3(k) = (1143,1267,1392,1541,1697) Group3 (Year 2005~2009) Y0(k)=(33996,37517,40046,43011,46237); Y1(k) = (644,686,720,755,787) Y2(k)=(1704,1982,2325,2711,2968; Y3(k)= (1267,1392,1541,1697,1848) Group4 (Year 2006~2010) Y0(k)=(37517,40046,43011,46237,54190); Y1(k) = (686,720,755,787,818) Y2(k)=(1982,2325,2711,2968,3511; Y3(k) = (1392,1541,1697,1848,2047)

Table 1 .

 1 Added value of three industries and freight traffic from year 2003 to 2010 in jiangxi province

	Year	Freight (10000 tons), y 0 Traffic	IAV of primary industry (RMB 100 million), y 1	IAV of Secondary industry (RMB 100 million) y 2	IAV of Tertiary industry (RMB 100 million) y 3
	2003	27709	560	1227	1043
	2004	31924	605	1455	1143
	2005	33996	644	1704	1267
	2006	37517	686	1982	1392
	2007	40046	720	2325	1541
	2008	43011	755	2711	1697
	2009	46237	787	2968	1848
	2010	54190	818	3511	2047

Table 2 .

 2 Four groups of matrix

Table 3 .

 3 Eight groups of grey relational sequence of three industries impact on freight traffic

	Group Year	Group 1 (2003~2007)	Group 2 (2004~2008)	Group 3 (2005~2009)	Group 4 (2006~2010)
	Primary industry	0.745	0.877	0.769	0.749
	Secondary industry	0.649	0.610	0.594	0.550
	Tertiary industry	0.910	0.820	0.837	0.799

Please note that the LNCS Editorial assumes that all authors have used the western naming convention, with given names preceding surnames. This determines the structure of the names in the running heads and the author index.

Acknowledgments. The paper is one of the results of project, Reverse Logistics Network Design for Jiangxi Province Based on Circular Economy (GL1114) supported by the provincial education department of Jiangxi, and assisted by key laboratory of conveyance and equipment, ministry of education.


Wei Xiao

Qinghai Li

Longzhe Quan
email: 3.quanlongzhe@163.com

Research on the Shape of Wheat Kernels Based on Fourier Describer

Keywords: machine vision, wheat kernel, Fourier describer, pattern recognition, BPnetwork

The shape of wheat kernels is one of the most important criterions for quality inspection and grading. This paper has used Fourier describer to describe the three views of wheat shape accurately and has had a counter-construction operation.Also it verified this method for describing the shape, small but complex. Finally, the appropriate characteristic parameters were selected, and the BP-network was used to classify the four varieties wheat kernels. This method deserves a recognition rate of 98%~99%.

Introduction

Seed identification, which can keep economic order, increase farmers' income and promote economic growth, is always one important issue in the area of agriculture.They can also help us understand kinds of seeds better. There have been more studies on appearance characteristics of wheat kernels with machine vision at home and abroad, which have gained approving effect [START_REF] Jin-Zhong | Discrimination of Numerous Maize Cultivars Based on Seed Image Process[END_REF][START_REF] Jianwen | Recognition System for Corn Species by Exterior Parameters[END_REF][START_REF] Cheng | .Machine vision analysis of characteristics and image information base construction for hybrid rice seed.Rice[END_REF][START_REF] Dubey | .Potential of artificial neural networks in varietals identification using morphometry of wheat grains.Biosyst[END_REF][START_REF] Yang | .Primary valuations in morphologic traits of foreign outstanding germplasmsn[END_REF][START_REF] Hao Jian-Ping | A Study on Basic Morphologic Information and Classification of Maize Cultivars Based on Seed Image Process[END_REF]. However, it was difficult to describe the wheat kernel shape accurately with traditional description methods because the views of wheat shape are too complex. On the other side, traditional description methods need more parameters, which would go against the pattern classification, while the Fourier describer can describe the shape of wheat kernel more precisely with fewer parameters, and set a reliable foundation for the pattern classification.So this paper will classify the four varieties of wheat kernels with the method of Fourier describer.

Acquisition of kernel marginal coordinates

At First, before doing Fourier describer of kernel shape, the coordinates on marginal images should be ensured, namely,determining each pixel's position in the images.

Fig.4 The relation of every pixel coordinatesin eight connected domains

According to Fig. 4, any relational pixel point coordinate can be determined if center pixel coordinates (x, y) were known in eight connected domains. Similarly, nearby pixels coordinates can be identified by the edge of a known pixel coordinate. And so, each pixel coordinates can be obtained.

Determination of edge spread vector and phase angle

Spread vector is an indispensable condition for Fourier describer, and phase angle is the essential condition of the counter-construction of kernel shape.

(x , y) (x , y+1) (x , y-1) (x-1 , y) (x-1 , y+1) (x-1 , y-1) (x+1 , y) (x-1 , y+1) (x+1 , y-1)
Fig. 5 The edge curve of wheat kernel image We need to calculate the distance from every edge pixel to the centroid and get a length sequence base on the centroid of wheat kernel. That is the unfolded of edge vector (as shown in Fig. 5).

2 * 2 * ) ( ) ( y y x x l k k k     (1)
Where: k l --The distance from the kth edge pixel to the centroid;

The discrete Fourier descriptor to the vector of wheat shape

Fourier transformation is a familiar kind of linear transformation, which can simplify the dimensions of 2-D boundary data easily [START_REF] Yibin | Application of Machine Vision Technique to Quality Automatic Identification of Agricultural Products[END_REF].

The Fourier transformation and countertransformation on the vector of wheat shape

N-D vector of wheat shape can be acquired with its edge, which consists of N pixels, and it is transformed in the way of discrete Fourier transformation:

1 , , 1 , 0 ] / 2 exp[ 1 ) ( 1 0        N N k j l N L N k k    

) ( L

is the Fourier transformation sequence of marginal vector. From the Fig. 6, we can conclude that major information about the wheat shape was well reflected by the first few larger numbers, rather than the last smaller numbers, which were supposed to the secondary information or noise.

Counter-transformation about

) ( L : 1 , , 1 , 0 ] / 2 exp[ ) ( 1 0       N k N k j L l N k     (4)

Table 1 Data errors

The marginal vector can be reciprocal converted between length field and frequency domain with the expression (4) and [START_REF] Yang | .Primary valuations in morphologic traits of foreign outstanding germplasmsn[END_REF]. Discrete Fourier transformation is a reversible linear transformation, so there is no information wastage in this process. However, it is unprofitable to describe wheat shape with entire Fourier transformation sequences for pattern classifying. Thus, the wheat shape can be described precisely; characteristic parameters can be simplified by the first few components including larger information. A vector k l ˆ, similar with k l , can be obtained with the help of counter-transformation on parts of Fourier transformation coefficients.

1 , , 1 , 0 ] / 2 exp[ ) ( ˆ1 0       N k N k j L l M k     (5)
Where: M--the number of Fourier transformation values.

4.2Counter-construction of kernel shape

A group of proximate edge pixel coordinates vector can be counter -constructed through approximate vector k l ˆ and phase angle vector could underlie the determination of characteristic model.That can be seen from the Fig. 7,Fig. 8, the larger the value of M, the better the counter-construction. However, if the value of M is too large, description model will become confused(as shown in Table 1). Thus,according to the specific circumstance, it is necessary to choose the appropriate M value.

k  [7~9].          k k k k k k l y y l x x   sin ˆcos * * (6) Where: k k y x , ˆ--

4.3Error analysis of counter-construction of kernel shape

The relationship between error of counter-construction of each M can be figured out through the Fourier describer of 300 wheat kernels' 3-D views of four varieties. According to the research, the average error between similar kernel shape and original one were less than 2 pixels that do not affect the pattern discrimination. Thus, choosing the first four of the Fourier transformation, can describe the front view of kernel shape and serve as the judgment characters. Likewise, the first three were chosen for the side view and so were the top view.

Evaluation mode of 3-D wheat kernel consists of following parts:(1)The first four

( 4 3 2 1 , , , f f f f ) in

, , t t t

) in top view image.

Examples of application

In this paper, four varieties of 300 wheat kernels (Dong nong99-6501(variety A), Dong97-3821(variety B), Dong97-4056(variety C), Long94-4083(variety D)) have been made pattern classification. Specifically, all varieties of wheat have been classified with the help of BP network method, and characteristic parameters of 3-D shape of wheat kernels have respectively extracted.

M=1 M=2 M=3 M=4 M=5 M=6 M=7 M=8 M=1 M=2 M=3 M=4 M=1 M=2 M=3
Iteration number of times

The errors value

Data normalization

As Table 2 shown, normalized treatment should be taken among these datas, as some orders of magnitude exit in characteristic modes.

m in m ax m in o o o o n i i    (7)
Where:

Network design

With the neural network toolbox in MATLAB, a 3-layer neural network was designed [START_REF] Wolfnann | Video monitorinn of pulverized coalinjection in blast furnace[END_REF]. The input includes 10 neurons and the transfer function is "pureline()". The middle layer contains 15 neurons and the transfer function is tansig(). The output consists 1 neuron and the transfer function is pureline(). The input datas was normalized which belong to each characteristic pattern, and the number 1, 2, 3, 4 are labled as the varieties of wheat kernels in the output datas.

Network simulation

Take 250 wheat kernels as the training samples, and put them in the course of neural network training, a good effect was achieved (Fig. 9). The other 50 wheat kernels serve as test samples that used to test the constructed network. The recognition correct rate of nerve network is presented in the Table 3.

1 f 2 f 3 f 4 f 1 s 2 s 3 s 1 t 2 t

Conclusion

In this paper, the Fourier descriptor was used to further describe the 3-D shape of wheat kernels only with several appropriate characteristic parameters and a satisfactory result was obtained. In particular, the counter-construction of wheat shape was achieved by means of the transformation coefficients of the vector of the wheat.

A better description pattern was determined to discribe the shape of wheat kernels by use of statistical error analysis, simultaneously, the classification of four varieties of wheat shape was come true by means of BP nerve network.

It is thus clear that, irregular shape of wheat kernels can described precisely by the Fourier describer; meanwhile, it can distinguish the delicate different shape between wheat kernels. What is more, by the use of the counter-construction method, the capacity to describe the wheat shape got a good validation. In conclusion, this method has laid the wide foundation for application in detecting and classifying of grain kinds of agricultural products.

Fig. 1 Fig. 2 Fig. 3

 123 Fig.1 The front view of wheat kernel

  , --Every edge pixel's coordinate; centroid of contour curve of wheat kernel. The edge pixel coordinates cannot be determined with only the value of k l when making the counter-construction. The phase angle( k  ) of every pixel's should also be identified.

Fig. 6 the

 6 Fig.6 the Fourier transformation sequence of wheat edge curve

Fig. 7 Fig. 8

 78 Fig.7 the counter-construction image of wheat in front view image

)

  Fourier transformation of front view image;(2)The first three ( in side view image;(3)The first three (

io-

  feature data; i n -normalized data; Table2 the mean judgment pattern values of four varieties of wheat kernel image

  proximate edge pixel coordinates

	Error	M=1	M=2	M=3	M=4	M=5			M=6
	Front	4.04	2.43	2.34	1.23	0.88			0.73
	Size	8.96	8.92	1.89	1.65	0.83			0.73
	Top	8.68	8.67	0.95	0.74	8.68			8.67
	By means of analyzing the level of similarity between	(	x	, k y	k	)	and	( x	, k y	k	)	,
	the describing ability of each component for kernel shape can be represented which

correct rate of nerve network

	3 4.881 0.003 0.01 t 5.144 0.014 0.93 5.980 0.016 1.12 5.224 0.016 0.91 Fig.9 the error curve of BP neural network learning A 2.255 0.063 0.091 0.118 0.337 0.061 0.679 B 2.070 0.047 0.078 0.092 4.387 0.047 0.806 C 1.998 0.049 0.127 0.076 4.954 0.036 1.079 D 2.174 0.055 0.044 0.093 4.693 0.075 0.809 Table3 The Recognition	out 1 2 3


Rong Dai

Xiangmin Duan

Research on Knowledge Acquisition of Motorcycle Intelligent Design System Based on Rough Set

Keywords: Motorcycle, intelligent design, rough set, attribute reduction, value reduction

In the intelligent design of motorcycle, a large number of data in the simulation or physical experiment are almost not utilized to guide our design and make decision for the design, so rough set theory is introduced to the intelligent design of motorcycle. Then, aim at experimental data of the engine piston performance, rough set theory is used. An attribute reduction algorithm of decision table based on discernibility matrix and heuristic value reduction algorithm are adopted. Knowledge is extracted from the data of performance experiment of the engine piston, in order to enrich knowledge base in motorcycle intelligent design system.

Introduction

Simulation analysis and physical testing are adopted to carry out research on the machine performance, but most of these tools are only as a verification of design result and are the lack of guidance and decision-making for design; At the same time, the results of test and analysis are mostly shown in the charts, graphs, etc., and interpretation and evaluation of results are lacked, so that the guiding role of the test and analysis for design has not been fully realized. With the simulation analysis and experimental research carried out, and a large amount of raw data about performance are accumulated, how to quickly extract valuable knowledge of motorcycle performance evaluation from a mass of data for guiding designer is a very urgent and meaningful questions.

Based on the above requirements, for the motorcycle engine piston test data and simulation features, rough set method is introduced. Knowledge acquisition based rough set is proposed. Experts level domain knowledge can be accessed to through processing and extraction of results on test and simulation analysis.

There are two main ways in the knowledge acquisition: First is obtained from experts in the field of expertise, and the second is directly accessed from the text or database. Expert domain knowledge for knowledge acquisition is got by artificial methods, and it will be inputted directly into the knowledge base. To acquire knowledge from text has two ways: One is to extract directly concepts and relationships from the text automatically. However, a fully automated approach is not always effective, because the text is often ambiguous, irregular knowledge. Therefore, if the machine does not have a certain amount of "background knowledge", the realization of fully automated access is not realistic. The second way is semiautomatic method, which requires the necessary knowledge engineer intervention [START_REF] Joo | An integrated knowledge base development tool for knowledge acquisition and verification for NPP dynamic alarm processing systems[END_REF][START_REF] Shao | Application of ID3 algorithm in knowledge acquisition for tolerance design[END_REF][START_REF] Wang | Attribute reduction algorithm for decision table based on relative discernibility matrix[END_REF][START_REF] Hullermeier | Fuzzy sets in machine learning and data mining[END_REF][START_REF] Feng | Knowledge acquisition in vague objective information systems based on rough sets[END_REF] .

Rough Set Theory

S i =<U,R i ,V i ,f i >, U is the domain, R i =C∪{d i }
is a set of properties, subset C and subset {d i }are respectively called condition attributes set and the result attributes set,

V i =  i R r r V 
is the set of attribute values , V r indicates attribute values range of the property rR i , that is the range of attribute r,

f i :U  R i  V i is an information function.
Definition 2 On the knowledge representation system S= (U, A, V, f), Definition 5 The decision table system S=<U,R,V,f>, R=P∪D is a set of attributes, the subset P = {ai | i = 1, ..., m} and D = {d}, respectively, is called the condition attribute set and decision attribute set, U={x 1 ,x 2 ,…,x n } is the domain, a i (x j ) is the value the property a i in the sample x j . C D (i,j) is the element of column i row j in the identification matrix, the identification matrix can be defined as follows:

A P  , the indiscernibility relationship of attribute sets P is )} , ( ) , ( , ) ( ) , {( ) ( a y f a x f P a U U Y X P ind      
         ) ( ) ( , 0 ) ( ) ( )}, ( ) ( | { ) , ( j i j i j k i k k k D x d x d x d x d x a x a P a a j i C (1)
Where i, j = 1, ..., n.

Attribute Reduction Algorithm

Based on the concept of discernibility matrix of decision table , we can get the following attribute reduction algorithm through discernibility matrix and logical operation [START_REF] Feng | Knowledge acquisition in vague objective information systems based on rough sets[END_REF] .

1) The discernibility matrix of decision table is calculated C D ;

2) The corresponding logic expressions L ij is established by using

C ij (C ij ≠0 , C ij ≠) in the discernibility matrix. i C a ij a V L ij i   (2)
3) When all the disjunction logical expression L ij are conjunctive, CNF L can be got:

ij C C L L ij ij      , 0 (3)
4) L is converted to disjunctive normal form:

i i L L    (4)
5) Attribute reduction results output. Each conjunction entry of disjunctive normal form corresponds to attribute reduction result. The attributes that each conjunction item contains compose a set of condition attributes after being reduced.

Process can be seen from the above, disjunction logical expression L ij created in step 2 of the algorithm is a lot, which will result in increasing the computational time when logical formulation is reduced. Therefore, certain measures need to be taken to further streamline the process of attribute reduction. It can be found through the discernibility matrix that, if there is a matrix element, its value is a collection of element containing a single attribute, it indicates that the necessary attribute is to distinguish between the matrix elements corresponding to two samples, and is the only distinction between the two sample properties. The collection of the attributes included these elements in discernibility matrix is actually the relative attribute nuclear of deciding-table system. So first of all, these attributes can be removed, but the value of the matrix element containing the nuclear attributes will be change to 0 to get a new matrix, and then in the new basis matrix, the algorithm 2,3,4 step can be implemented. Disjunctive logic expressions can be given. Finally the result of attribute reduction can be got through adding all the nuclear attributes to each conjunction item in disjunctive normal formal.

Value Reduction Algorithm of Decision Table

Through attribute reduction, the unnecessary attributes for decision-making in the decision tables can be omitted, then a simplified decision table can be achieved. This is beneficial to found the attributes that play role in decision-making classification. However, the attribute reduction is, to some extent, to remove the redundant attribute in the decision table, but not fully remove redundant information in the decision table. Therefore, the decision table needs to be further processed to be more streamlined decision-making table, i.e. reduction of decision table values.

Heuristic value reduction algorithm: Input: Information system T (assuming the system has only one decision attribute) Output: a value reduction T' of T.

1) The condition attributes of information table are inspected by column. If you remove the column, conflict of the records occurs, then keep the value of the property of conflict records; Otherwise, if there are duplicate records, the property value of duplicate records will be recorded as "*"; For other records, the property value is marked "?";

2) Possible duplicate records are deleted, and each record containing the mark "?" is examined. If the property value that is not marked can make decision, it will mark from"?" To "*"; Otherwise, it will mark from"?" to the original property value; if all the condition attributes of a record are mark, the mark "?" was changed to the original property value;

3) Remove record that all condition attributes are marked "*" , and possible duplication records after removing;

4) If only one condition attribute values between the two records are different, and the property of a record is marked as "*", then if the property value that is not mark can determine the decision-making for the record, then delete the other one record; Otherwise, delete the record.

After a new information table is obtained through the reduction, all the property values are the core of table, every record corresponds to a decision rule respectively.

Knowledge Acquisition of Engine Piston Performance Based on Rough Set

In the motorcycle intelligent design system, the knowledge acquisition will have access to expert experience, book knowledge processed and abstracted into knowledge base. By operation of the system ,the knowledge is constantly improved and modified. While the potential knowledge is identified and extracted from the large amount of simulation analysis and experimental test results, revealing the inherent law that the implicate in the data, so as to provide decision support for development and design, CAE technology and physical test are to achieve position from design verification to the design guidance and design decisions.

From the reference [6] section 6.3.1, study of orthogonal testing on piston performance shows that, orthogonal design method to ensure that the various levels of each factor mix respectively once in the test, so orthogonal table used is completely the indiscernibility relationship collection. Since rough set theory emphasize the indiscernibility relation between objects of the collection, in order to use rough set theory to acquire knowledge, combined with reference [6], Chapter 6, the forecasting method of neural network, the results of orthogonal test in the table 6.2 are extended analyze. Through random combination of multiple experimental conditions and the BP network [START_REF] Dai | Performance Forcasting of Piston Element in Motorcycle Engine Based on BP Neural Network[END_REF] , the prediction results are obtained in Table 1. At the same time equidistant partitioning algorithm is used, the test conditions and test results are processed, the results are shown in Table 2. Because there are five conditions property, set up materials piston skirt and cylinder liner clearance A, the domain is X = [10,140], the X is divided into 5 grades, ie X = {-2, -1,0,1 , 2}. Also set up the domain B of piston head and cylinder liner clearance, the domain is Y = [40,340], is divided into five grades, namely, Y = {-2, -1, 0, 1, 2}. Also set up the piston pin offset C , the domain Z = [0.05, 1.55], divided into 5 grades, ie Z = {-2, -1,0,1,2}. Set up the piston skirt length D, the domain W = [40, 61], is divided into five grades, namely, W = {-2, -1,0,1,2}. the domain of E liner surface roughness RMS is V = [0.1,9.10], divided into 5 grades, ie V = {-2, -1,0,1,2}, the domain of friction power F is U = [1.41,3.04], divided into five grades, namely U = {-2, -1,0,1,2}.

As can be seen from Table 1, there are four decision attribute in the table, belong to more decision-making. Therefore, according to the method described by definition 1, Table 1 is divided into four equivalent of a single decision table by the decision attribute, that is, the condition attributes of the four tables are identical, decisionmaking is not same. Namely, condition attributes such as the friction power, power, fuel consumption, noise, and the decision attribute for the friction power, this single decision table is shown in Table 2. Analysis shows that, when Table 1 is converted to four single decision tables, the table has not duplicate records in the condition attributes, so objects of a single decision-making table are still 23. The single decision table aiming to friction power is as an example that all data are analyzed and processed, and extracted the rule.

Attribute Reduction

When the decision attribute is "f = friction power", so that Q = decision attribute set = {f}, P = condition attribute set = {a, b, c, d, e}, then IND(P)={{1},{2},{3},{4},{5},{6},{7},{8},{9},{10},{11},{12},{13},{14},{15}, {16},{17},{18},{19},{20},{21},{22},{23}}, IND(Q)={{1,5,6,17},{2,3,7,9},{4,8,10,11,18,21,23},{12,13,19},{14,15,16,20,22}}, POS P (Q)=U.

The domain U is consistent with P relative to Q, which shows that the decision table decision table is completely determined, the table does not contain inconsistent information.

Table 2. Decision Table System of Piston Performance

friction power f -2 -2 -2 -2 -2 2 -2 -1 -1 -1 -1 1 -2 1 1 1 1 1 -2 2 2 2 2 0 -1 -2 -1 1 2 2 -1 -1 -2 2 1 2 -1 1 2 -2 -1 1 -1 2 1 -1 -2 0 1 -2 1 2 -1 1 10 1 -1 2 1 -2 0 11 1 1 -2 -1 2 0 12 1 2 -1 -2 1 -1 13 2 -2 2 -1 1 -1 14 2 -1 1 -2 2 -2 15 2 1 -1 2 -2 -2 16 2 2 -2 1 -1 -2 17 -2 -1 1 1 -1 2 18 -1 1 1 -1 2 0 19 1 -1 -1 -1 2 -1 20 2 -1 2 -1 1 -2 21 -2 1 -1 -1 -1 0 22 1 2 2 2 2 -2 23 -1 2 1 1 -2 0 IND(P\{a})={{1},
(Q)=U=POS P (Q) POS (P\{d}) (Q)=U=POS P (Q) POS (P\{e}) (Q)=U=POS P (Q)
It can be seen that, attributes c, d, e may be omitted relatively to the decision attribute f, but it can not be omitted at same time. The properties a and b that are relative to the decision attribute f can not be deleted, so CORE Q (P)={a,b} The property c, d and e are the relative properties nuclear of the decision-making table. According to 2.1 section, attribute reduction algorithm based on discernibility matrix and logical operation, when discernibility matrix of decision-making table is calculated, these two properties can be removed first, while at the time the value of the elements that contains the nuclear attribute in the matrix will rewritten to 0 , then obtained a new matrix. New matrix of which elements are modified is showed in Table 3.

According to Table 3, CNF L can be got.

L=(cde)(cd)

After simplification, disjunctive CNF L ' was obtained.

L′=cd (6)

After the nuclear properties are added to conjunctive items, reduction results (abc)( abd) can be obtained, i.e. producing two new decision table, attributes

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 cde cde cde cde cd
in the table are respectively as a, b, c and a, b, d. Reduction results obtained (abc) as new decision-making table is treated.

friction power (f) -2 -2 -2 2 12 1 2 -1 -1 -2 -1 -1 1 13 2 -2 2 -1 -2 1 1 1 14 2 -1 1 -2 -2 2 2 0 15 2 1 -1 -2 -1 -2 -1 2 16 2 2 -2 -2 -1 -1 -2 2 17 -2 -1 1 2 -1 1 2 1 18 -1 1 1 0 -1 2 1 0 19 1 -1 -1 -1 1 -2 1 1 20 2 -1 2 -2 10 1 -1 2 0 21 -2 1 -1 0 11 1 1 -2 0 22 1 2 2 - 2

Value reduction

The new decision Table 4 is got from the reduction result (abc) . By analyzing the data in the table, the table does not contain inconsistent information. However, when value reduction algorithm is carried out, there will be inconsistencies between samples. Therefore decision-making rules are treated as follows. That is part of the decision rules for inconsistency, assuming it can not be simplified, the value of these attributes is fully retained. For the consistent part of the decision-making rule, and inconsistent part of it together, and then only to examine whether the property value of the same part of the decision rule may be eliminated. If you eliminate the value of some properties, its positive field changes, or data table becomes inconsistent, then the property can not be omitted. Thus, data tables that may be consistent and inconsistent are handled by a unified approach. According to the above approach, the value reduction resulting are shown in Table 5, each row of which represents a decision rule.

Decision attribute value f is discretized into five zones, namely the information system has five concepts. Based on the reduction results of Table 5, some of rules are analyzed as follows.

friction power (f) 1 -2 -2 -2 2 10 1 - -1 -1 2 -2 -1 -1 1 11 2 -2 - -1 3 -2 1 1 1 12 2 -1 - -2 4 -2 2 2 0 13 2 1 - -2 5 -1 - 2 1 14 2 2 - -2 6 -1 - 1 0 15 -2 -1 1 2 7 1 -2 - 1 16 -2 1 -1 0 8 1 -1 2 0 17 1 2 2 -2 9 1 1 - 0 A rule of the concept R=2 is (114<a≤140 )∧(100<b≤160)->(r=-2) |1
This rule covers 40% samples of the concepts r=-2 in Table 1. Its meaning is: If the clearance value between piston skirt and cylinder material is among 114~140μm, and the clearance value between piston head and cylinder is among 100~160μm, the friction power value is among 1.41 ~ 1.736kw, its credibility is equal to 1.

A rule of the concept R=-1 is (88 <a ≤ 114) ∧ (0.35 <c≤0.65)-> (r =-1) | 1 This rule covers 66.7% samples of the concepts r=-1 in Table 1. Its meaning is: If the clearance value between piston skirt and cylinder material is among 88~140μm, and the eccentricity of piston pin is among 0.35~0.65μm, the friction power value is among 1.736 ~ 2.062kw, its credibility is equal to 1.

A rule of the concept R=0 is (10<a≤36)∧(280<b≤340) ∧(1.25<c≤1.55)->(r=0) |1 This rule covers 16.7% samples of the concepts r=0 in Table 1. Its meaning is: If the clearance value between piston skirt and cylinder material is among 10~36μm, and the clearance value between piston head and cylinder is among 280~340μm, and the eccentricity of piston pin is among 1.25~1.55μm, the friction power value is among 2.062 ~ 2.388kw, its credibility is equal to 1.

A rule of the concept R=1 is (10<a≤36)∧(100<b≤160) ∧(0.35<b≤0.65)->(r=1) |1 This rule covers 25% samples of the concepts r=1 in Table 1. Its meaning is: If the clearance value between piston skirt and cylinder material is among 10~36μm, and the clearance value between piston head and cylinder is among 100~160μm, and the eccentricity of piston pin is among 0.35~0.65μm, the friction power value is among 2.388~2.714kw, its credibility is equal to 1.

A rule of the concept R=2 is (10<a≤36)∧(40<b≤100) ∧(0.05<b≤0.35)->(r=2) |1 This rule covers 25% samples of the concepts r=2 in Table 1. Its meaning is: If the clearance value between piston skirt and cylinder material is among 10~36μm, and the clearance value between piston head and cylinder is among 40~100μm, and the eccentricity of piston pin is among 0.05~0.35μm, the friction power value is among 2.714~3.04kw, its credibility is equal to 1.

Conclusion

The rough set method is adopted to identify and extract the potential knowledge from the experiment result, and the inherent laws implication behind these data are revealed. The results show that rough set theory is adopt to acquire knowledge, not only the attributes that have important influence on decision-making information can be found, but also redundant information of information table may be deleted. Thus the final decision table has not only the simplified information, but also not affect the original decision table information. Using the extracted knowledge, reasoning process of neural network can be explained, and it can provide decision support for the designer, improving the intelligence level of intelligent design system. ACKNOWLEDGEMENT This work is supported by "the Fundamental Research Funds for the Central Universities."(N0.XDJK2009C005) and supported by "the Doctoral Fund of Southwest University"(N0.SWU109043).

.Definition 3

 3 Indiscernibility relation ind (P) is the equivalence relation on U, all the equivalence classes is educed by ind (P), denoted by U / P, which constitutes a partition of the domain U. Knowledge representation system S=(U,CUD,V,f) , C a   , if POS ind(C) (ind(D)) = Pos ind(c-{a}) (ind(D)), a is called unnecessary in C on D, otherwise, and is known as necessary in C on D. POS ind(C) (ind(D)) is the set of all objects that are correctly classified to the every equivalence class U / D, namely equivalence class on the positive region of ind(C) is derived from ind(D). If each attribute of C is necessary, C is independent to D. Definition 4 knowledge representation system S= (U, C U D,V, f), C B  , if pos ind(B) (ind(D)) = Pos ind(C) (ind(D)) , and B is independent to D, B is called reduction with C relative to D, and is denoted red D (C). It should be noted that reduction of C is not the only. the intersection of all D reduction in C is D core of C, which is denoted by core D (C) = ∩redD (C) .

Definition 1

 1 A decision table is an information system S=<U,R,V,f>, R=C∪D is a set of properties, subset C and D are respectively called condition attributes set and the result attribute set, D ≠. If the result attribute set D={d 1 ,d 2 ,…,d n }, the decision table can be decomposed into n different single decision-making table{S 1 ,S 2 ,…,S n }, where

Table 1 .

 1 Results of Piston Orthogonal Experimental Analysis & Results of BP Neural Network

							friction		fuel	Nois
	No.	A	B	C	D	E	power /kw	power /kw	consumption (g/kw.h)	e dB(A)
		1	1	1	1	1	2.93	64.04	259.3	108. 1
		1	2	2	2	2	2.68	64.17	259.2	108. 2
		1	3	3	3	3	2.4	64.1	259.5	106. 8
		1	4	4	4	4	2.33	63.02	259.5	108. 2
		2	1	2	3	4	3.04	64.06	259.6	108. 1
		2	2	1	4	3	2.93	64.07	259.7	108. 5
		2	3	4	1	2	2.43	65.02	258.5	108
		2	4	3	2	1	2.17	6.3.85	259.1	108. 2
		3	1	3	4	2	2.52	64.02	259.6	108. 3

Table 3 .

 3 Identification Matrix Revised by Core Attributes

Table 4 .

 4 Attribute Reduction Result of Friction Power

	U	condition attribute	decision attribute		condition attribute	decision attribute
		Materia
		ls piston skirt and cylinder liner clearan	piston head and cylinder liner clearance (b)	eccentri city of piston pin (c)	friction power(f)	U	Materials piston skirt and cylinder liner (a) clearance	piston head and cylinder liner (b) clearance	eccentricity of piston pin (c)
		ce (a)

Table 5 .

 5 Value Reduction Results of Vriction Power

					decisio					deci
		condition attribute	n attribut		condition attribute	sion attri
					e					bute
		Materia
	R	ls piston skirt and cylinder liner clearan	piston head and cylinder liner clearance (b)	eccentri city of piston pin (c)	friction power (f)	R	Materials piston skirt and cylinder liner clearance (a)	piston head and cylinder liner clearance (b)	eccent ricity of piston pin (c)
		ce (a)