Tianhong Dai

Gitika B Kharkwal

Jie Zhao

Tyler G St

Denis

Qiuhe Wu

Yumin Xia

Liyi Huang

Sulbha K Sharma

Christophe D'enfert

Michael R Hamblin
email: ph.d.hamblin@helix.mgh.harvard.edu

Ultraviolet-C Light for Treatment of Candida albicans Burn Infection in Mice

Burn patients are at high risk of invasive fungal infections, which are a leading cause of morbidity, mortality, and related expense exacerbated by the emergence of drug resistant fungal strains. In this study, we investigated the use of UVC light (254-nm) for the treatment of Candida albicans infection in mouse third degree burns. In-vitro studies demonstrated that UVC could selectively kill the pathogenic yeast C. albicans compared to a normal keratinocyte cell line in a light exposure dependent manner. A mouse model of chronic C. albicans infection in non-lethal 3 rd degree burns was developed. The C. albicans strain was stably transformed with a version of the Gaussia princeps luciferase gene that allowed real-time bioluminescence imaging of the progression of C. albicans infection. UVC treatment with a single exposure carried out on day 0 (30 minutes post-infection) gave an average 2.16-log 10 -unit (99.2%) loss of fungal luminescence when 2.92 J/cm 2 UVC had been delivered, while UVC 24-hours post-infection gave 1.94-log 10unit (95.8%) reduction of fungal luminescence after 6.48 J/cm 2 . Statistical analysis demonstrated that UVC treatment carried out both on both day 0 and day 1 significantly reduced the fungal bioburden of infected burns. UVC was found to be superior to a topical antifungal drug, nystatin cream. UVC was tested on normal mouse skin and no gross damage was observed 24 hours after 6.48 J/cm 2 . DNA lesions (cyclobutane pyrimidine dimers) were observed by immunofluorescence in normal mouse skin immediately after a 6.48 J/cm 2 UVC exposure, but the lesions were extensively repaired at 24-hours after UVC exposure.

Introduction

Despite major advances in burn care and management, infections remain a leading cause of mortality, morbidity, and cost in burn patients [START_REF] Ballard | Positive fungal cultures in burn patients: a multicenter review[END_REF] . While the broad acceptance of topical antibiotics, early excision and grafting, and patient isolation practices have resulted in a significant decline in bacterial wound infections, the incidence of fungal wound infections remains unchanged. Since the most severely burned patients suffer from major suppression of the host immune system, patients with large areas of third degree burns are at the highest risk for fungal infections that are difficult to prevent or eradicate [START_REF] Horvath | Fungal wound infection (not colonization) is independently associated with mortality in burn patients[END_REF][START_REF] Becker | Fungal burn wound infection. A 10-year experience[END_REF] . The problem of fungal infections has become even more urgent today due to the increasing emergence of antifungal resistant pathogens [START_REF] Struck | Infection control in burn patients: are fungal infections underestimated?[END_REF][START_REF] Barker | Recent insights into the mechanisms of antifungal resistance[END_REF] . Candida albicans is the most common fungal pathogen responsible for fungal infection in burn patients and is now the fourth most common organism found in blood cultures in intensive care unit (ICU) patients 1, 6. Although it has been known for the last 100 years that ultraviolet (UV) light (particularly UVC with a wavelength range of 240-280 nm) is highly germicidal [START_REF] Hockberger | A history of ultraviolet photobiology for humans, animals and microorganisms[END_REF] , its use to treat wound or other localized infections remains at an early stage of development. There is a widely held perception that UV light is so damaging to skin and other normal human tissue that any positive benefits of killing microorganisms with UV would be outweighed by known dangers of UV in causing carcinogenesis and other UV photodamage. However most of the studies on the damaging effects of UV on skin have been carried out with UVB light (290-340-nm) and UVA (340-400-nm) because these wavelength ranges are contained in sunlight and humans have been exposed to UVB and UVA throughout human history 8. By contrast there is no natural source of UVC and there has therefore been little impetus to study it 9. It should be noted that mammalian cells have efficient DNA repair systems 10 , 11 and since the use of UVC for infections is likely to involve a single treatment or a limited number of treatments, it is possible that any UVC-induced damage to host tissue may be readily repaired. Most carcinogenesis studies of UV have used a large number of chronic exposures over weeks and months [START_REF] Ichihashi | UVinduced skin damage[END_REF] and moreover, the extremely limited penetration of UVC through the stratum corneum provides another layer of protection [START_REF] Bruls | Transmission of human epidermis and stratum corneum as a function of thickness in the ultraviolet and visible wavelengths[END_REF] .

The mechanism of UVC inactivation of organisms is to damage the genetic material in the cell [START_REF] Chang | UV inactivation of pathogenic and indicator microorganisms[END_REF] . The UVC-induced damage to the DNA and RNA of an organism often results from the dimerization of adjacent pyrimidine molecules in DNA chains. In particular, thymine (which is found only in DNA) produces cyclobutane dimers. When these molecules are dimerized, it becomes very difficult for the nucleic acids to replicate and if replication does occur it often produces a defect that prevents the organism from being viable. There have been two clinical reports from one group to demonstrate UVC treatment of wound infections 14, 15. It was shown that UVC irradiation had the ability to reduce bacterial burden and improve wound status in patients with chronic ulcers that were infected with methicillinresistant S. aureus (MRSA) 14, 15. In addition, UVC has been found to be efficient in sterilizing the inner surface of catheters contaminated with bacterial biofilms and subsequently preventing catheter related infections 16 -18. We recently showed that UVC irradiation could be used to destroy a pathogenic fungus such as Trichophyton rubrum that causes onychomycosis in toenails [START_REF] Dai | Ultraviolet C inactivation of dermatophytes: implications for treatment of onychomycosis[END_REF] , and that UVC irradiation could also be used to destroy pathogens in a UVC-transmissive central venous catheter [START_REF] Dai | Ultraviolet-C irradiation for prevention of central venous catheter related infections: an in-vitro study[END_REF] . In the present study, we investigated the use of UVC light at 254 nm emitted from a low-pressure mercury lamp for the treatment of C. albicans infection in mouse burns. The C. albicans strain used in the study was stably transformed with a synthetic, codon-optimized version of the Gaussia princeps luciferase gene [START_REF] Enjalbert | A multifunctional, synthetic Gaussia princeps luciferase reporter for live imaging of Candida albicans infections[END_REF] , allowing a real-time monitoring of the extent of C. albicans infection in mouse burns by use of bioluminescence imaging. The efficacy of UVC treatment was compared with that of a topical antifungal drug, nystatin cream. The effects of UVC on mammalian cells and host tissues were also evaluated.

Materials and Methods

Cell lines and culture conditions

The bioluminescent C. albicans strain used in this study was CEC 749 [START_REF] Enjalbert | A multifunctional, synthetic Gaussia princeps luciferase reporter for live imaging of Candida albicans infections[END_REF] . The luciferase reporter was constructed by fusing a synthetic, codon-optimized version of the Gaussia princeps luciferase gene to C. albicans PGA59, which encodes a glycosylphosphatidylinositol-linked cell wall protein. Luciferase expressed from this PGA59-gLUC fusion was localized at the C. albicans cell surface [START_REF] Enjalbert | A multifunctional, synthetic Gaussia princeps luciferase reporter for live imaging of Candida albicans infections[END_REF] , allowing the detection of luciferase in intact cells after the addition of the luciferase substrate, coelenterazine, using bioluminescence imaging. C. albicans was routinely grown at 30°C on yeast peptone dextrose (YPD) agar and sub-cultured in YPD medium. PAM212 cells [START_REF] Marthinuss | Apoptosis in Pam212, an epidermal keratinocyte cell line: a possible role for bcl-2 in epidermal differentiation[END_REF] are an immortalized mouse keratinocyte cell line and were grown in RPMI 1640 supplemented with 10% foetal bovine serum (FBS) and 1% Penicillin-Streptomycin (Sigma Chemical Corporation, Saint Louis, MO).

UVC light source

UVC light was delivered using a low-pressure mercury vapor lamp (American Ultraviolet Co., Lebanon, IN). Emission spectrum measurement of this lamp by a spectroradiometer (SPR-01; Luzchem Research Inc. Ottawa, ON, Canada) showed a peak emission at 254±2 nm. By manipulating the distance between the UVC lamp and the target to be irradiated, the maximum irradiance can reach up to 20 mW/cm 2 . The irradiance was measured using a model IL-1700 research radiometer/photometer (International Light Inc., Newburyport, MA) over the wavelength range of 250-400 nm.

UVC irradiation of yeast suspensions or monolayer keratinocyte cultures under comparable conditions

Three mL C. albicans suspension at 10 8 cells/mL in PBS was placed into 35-mm petri dishes without lids that were irradiated with the germicidal lamp at an irradiance of 1.6 mW/ cm 2 . During the irradiation, the C. albicans suspension was stirred by a mini-magnetic bar (Fisher Scientific Co., Norcross, GA,). Aliquots of 40 µL of the suspension were withdrawn at 0, 3, 6, 9, and 12 s, respectively (UVC irradiance ≈ 1.6 mW cm 2 ). Colony forming units (CFU) were then determined by serial dilution on YPD agar plates by the method of Jett et al [START_REF] Jett | Simplified agar plate method for quantifying viable bacteria[END_REF] . Colonies were allowed to grow for 24-48 hours at 30C. Experiments were performed in triplicate. PAM212 cells were seeded into 35 mm petri dishes at a density of approximately 200 cells/ dish. The dishes were then incubated at 37 °C in a humidified atmosphere of 5% CO 2 for 24 hours. Prior to UVC irradiation, the culture medium was replaced with PBS and lids were removed. For each trial, five dishes were illuminated under UVC for 0, 3, 6, 9, and 12 seconds, respectively, at an irradiance of 1.6 mW/cm 2 . After UVC irradiation, PBS was removed from the 35 mm petri dishes. The cell cultures were then detached using 2 mL trypsin-0.25% EDTA solution (Sigma-Aldrich, St. Louis, MO) per dish and recultured into 150 mm petri dishes inoculated with 50 mL fresh culture medium. The 150 mm culture dishes were then incubated at 37 °C for 10 days for growing keratinocyte colonies to a visible size before counting. Culture medium in the 150 mm dishes was refreshed every three days. After 10 days incubation, the culture medium was removed from the 150 mm petri dishes, and keratinocyte colonies were stained with 0.2% crystal violet solution (ATCC, Manassas, VA). The number of stained keratinocyte colonies was counted manually.

Animals

Adult female BALB/c mice (Charles River Laboratories, Wilmington, MA), 6-8 week old and weighing 17-21 g, were used in all experiments. The animals were housed one per cage with access to food and water ad libitum, and were maintained on a 12-hour light/dark cycle under a room temperature of 21 °C. All animal procedures in this study were approved by the Subcommittee on Research Animal Care (IACUC) of the Massachusetts General Hospital and were in accordance with the guidelines of the National Institutes of Health (NIH).

Production of C. albicans infection in mouse burns

Before the creation of burns, the mice were anesthetized by intraperitoneal (I.P.) injection of a ketamine-xylazine cocktail, shaved on the dorsal surfaces, and then depilated with Nair (Carter-Wallace Inc, New York, NY). Burns were created by applying a single preheated (≈95°C) brass block (Small Parts, Inc., Miami, FL) to the dorsal surface of each mouse for 7 seconds, resulting in a nonlethal, full-thickness, third-degree burn. The brass block area was 10 mm × 10 mm, giving an area of 100 mm 2 and corresponding to 2.5% of the total body surface area [START_REF] Gilpin | Calculation of a new Meeh constant and experimental determination of burn size[END_REF] . Immediately after the creation of the burns, the mice were resuscitated with I.P. injections of 0.5 mL sterile saline (Phoenix Scientific Inc., St. Joseph, MO) to prevent dehydration.

Fungal infection took place as described by Ha and Jin [START_REF] Ha | Expression of the soxR gene of Pseudomonas aeruginosa is inducible during infection of burn wounds in mice and is required to cause efficient bacteremia[END_REF] . Five (5) minutes after the creation of the burns (to allow the burns to cool), a suspension (40 µL) of C. albicans in sterile phosphate buffered saline (PBS) containing 10 6 cells was inoculated onto the eschar of each burn with a pipette tip and was then smeared onto the eschar with an inoculating loop. The mice were imaged with the luminescence camera immediately after the application of the C. albicans to ensure that the fungal inoculum applied to each burn was consistent.

Bioluminescence imaging

An ICCD photon-counting camera (Model C2400-30H; Hamamatsu Photonics, Bridgewater, NJ) was used for bioluminescence imaging. The camera was mounted in a light-tight specimen chamber, fitted with a light-emitting diode, a set-up that allowed for a background gray-scale image of the entire mouse to be captured. By accumulating many images containing binary photon information (an integration time of 2 minutes was used), a pseudo-color luminescence image was generated. Superimposition of this image onto the grayscale background image yielded information on the location and intensity in terms of photon number. Argus-50 control program (Hamamatsu Photonics) was used to acquire images and to analyze the image data collected.

Prior to imaging, mice were anesthetized by I.P. injections of ketamine/xylazine cocktail. Twenty µL coelenterazine (Gold Biotechnology, Inc, St. Louis, MO; 500 µg/mL in 1:9 methanol-PBS) was topically applied to the eschar of each infected burn. Mice were then placed on an adjustable stage in the specimen chamber, and the infected burns were positioned directly under the camera. A gray-scale background image of each wound was made, and this was followed by a photon count of the same region. This entire burn photon count was quantified as relative luminescence units (RLUs) and was displayed in a false color scale ranging from pink (most intense) to blue (least intense).

UVC treatment of C. albicans infection in mouse burns

UVC light was delivered either at 30 minutes or at 24 hours after infection to investigate the therapeutic efficacies of UVC for both early-stage and established infections. The irradiance used was 2.7 mW/cm 2 . Mice were given a total UVC exposure of up to 6.48 J/cm 2 in aliquots with bioluminescence imaging taking place after each aliquot of light. During the UVC irradiation, the un-burned areas surrounding the burns of the mouse backs were masked using aluminum foil. To record the time course of the extent of fungal infection, fungal luminescence from mouse burns was captured daily after UVC treatment until the infections were cured (characterized by the disappearance of fungal luminescence) or the burns healed.

Extraction and quantification of C. albican from infected mouse burns

To correlate the fungal luminescence intensity to the viability of C. albicans, we extracted and quantified the colony forming units (CFU) of C. albicans from the infected mouse burns (on day 1 post-infection). Mouse burns exposed to varying doses of UVC were imaged with the luminescence camera immediately after irradiation, and then surgically excised and homogenized in 10 mL PBS with a tissue homogenizer (Omni International, Kennesaw, GA). The suspensions contained a mixture of tissue and C. albicans and were subjected to 5 serial 10-fold dilutions in PBS and then streaked on square YPD agar plates for CFU counting after 24-48 hours incubation at 30 °C.

Nystatin cream treatment of C. albicans infection in mouse burns

Nystatin cream (E. Fougera & Co, Melville, NY) was topically applied on a daily basis by smearing 25 mg/cm 2 -burn/day onto the burn surface with an inoculating loop from day 1 post-infection to day 6. The formation of the scab prevented effective application of Nystatin cream for longer than day 6. Eight mice were used for this experiment as Nystatin cream was supposed to be a positive control (standard therapy) for the study.

Immunohistochemical analyese of DNA lesions in mouse skin after UVC exposure

Adult female BALB/c mice described above were anesthetized, shaved, and depilated. UVC light was delivered to a square area of 2 cm × 2 cm (defined by a template made of aluminum foil) on anesthetized mice at a radiant exposure of 6.48 J/cm 2 . Skin biopsies from the irradiated area were taken before, immediately after, and at 24 hours after UVC exposure, respectively. They were fixed in 10% phosphate-buffered formalin at 4°C for 18-24 h, processed, and then embedded in paraffin. For immunohistochemical analyses, serial tissue sections of skin (4 µm) were prepared, deparaffinized, and rehydrated. Skin tissue sections were subjected to antigen retrieval using DAKO Target Retrieval Solution (Cat# S1699) to unmask the formalin cross linked antigens. After blocking in DAKO Universal Protein Blocking Solution (Cat# X0909) for 20 minutes, the tissue sections were incubated with mouse anti-thymine dimer monoclonal antibody (mAb) (Kaniya Biomedical Co., Seattle, WA) 30 min at room temperature. The anti-thymine dimer mAb reacts specifically with thymine cyclobutane dimers produced by UV irradiation in double-or single-stranded DNA. Antibody dilution was 1:50. Skin sections were then incubated with biotinylated antimouse IgG antibody (2 nd antibody, Vector Labs, Burlingame, CA) for 10 minutes at room temperature. The Vector ® M.O.M biotinylated anti-mouse IgG reagent was used specially to localize mouse primary monoclonal antibodies on mouse tissue according to the kit instructions. Briefly, after specific mouse-on-mouse blocking, the skin tissue sections were incubated in dark for 5 minutes at room temperature with fluorescein avidin DCS (Vector Labs) to attach the fluorescein to the tissue-bound anti-thymine dimer mAb through biotinavidin interaction. Between the incubations, the skin sections were extensively washed in PBS. The skin sections were then mounted with cover slips using mounting medium with DAPI (diamidino-2-phenylindole, Vector Labs), and observed using an Olympus Fluoview ® FV1000 MPE multiphoton microscope (Olympus Co., Tokyo, Japan). DAPI was used as nuclear counter stain. Masson' trichrome stained sections were also performed, and appearances of the irradiated skin area at varying time points after UVC exposure were recorded using photography.

Statistical analysis

In a two-dimensional coordinate system, the area-under-the-plot data, which represent the time courses of fungal luminescence of the burns and also the overall fungal bioburden of the mouse burns, were calculated using numerical integration [START_REF] Davis | Methods of numerical integration[END_REF] . Differences in the areas under the plots between the untreated control and the treatment groups and between different treatments groups were compared for statistical significance using one-way ANOVA. P values of <0.05 were considered significant.

Results

UVC selectively kills C. albicans compared to keratinocytes

Figure 1 compares the in vitro susceptibilities to UVC irradiation between keratinocytes and C. albicans under strictly comparable conditions and using similar colony forming assays for viability. UVC selectively killed of C. albicans compared to keratinocytes in a UVC dose dependent manner. On average, when 19.2 mJ/cm 2 UVC light had been delivered, the viability loss of keratinocytes was approximately 1.22-log 10 , while a 3.02-log 10 inactivation of C. albicans was achieved at the same light dose (p<0.001), resulting in a nearly 2-log 10 selective inactivation of C. albicans over keratinocytes. If we consider a 2-log 10 inactivation of C. albicans as the therapeutically effective fraction, the viability loss of keratinocytes is approximately 0.77-log 10 at the comparable UVC dose.

Quantification of CFU of C. albicans extracted from infected mouse burns correlated well with the fungal bioluminescence

Mice were imaged for bioluminescence and then sacrificed for removal of the infected tissue, followed by homogenization and CFU determination. Some of the mouse burns were exposed to UVC irradiation on day 1 post-infection and some were untreated. Figure 2 correlates the C. albicans CFU extracted from the excised mouse burns to the fungal bioluminescence signal measured in relative light units (RLU) from the same mouse burn. An excellent linear correlation (R 2 =0.9989, P< 0.001) between the CFU and RLU can be observed in Figure 2.

Chronic C. albicans infection was developed in mouse burns as characterized by bioluminescence imaging and histological evaluation

By applying 10 6 CFU of C. albicans in PBS topically onto the 7-second burns made on the shaved backs of adult female BALB/c mice, a stable and chronic infection was developed as characterized by bioluminescence imaging. Figure 3A shows the successive bioluminescence images of a representative mouse burn infected with C. albicans. After infection, the bioluminescence remained strong and stable until day 12 before the scab peeled off from the mouse back. In all the mice examined in the study, almost no remaining fungal bioluminescence was detected from the burns after the scabs peeled-off, but the detached scabs were all detected as bioluminescent immediately after they came off (Figure 3B). Figures 3C and3D show representative periodic acid-Schiff (PAS)-stained histological sections of C. albicans infected mouse burns biopsied on day 1 and day 4 post-infection, respectively. Massive C. albicans invasion was demonstrated on both days by numerous conidia and hyphae branching out in every direction in the burned tissue.

UVC irradiation significantly reduced the fungal bioburden in mouse burns with either early-stage or established C. albicans infections

Figure 4A shows a set of bioluminescence images after increasing doses of UVC delivered to a representative mouse burn infected with C. albicans and treated with UVC on day 1 (24 hours) after infection. As quantified by fungal bioluminescence intensity, approximately 2.03-log 10 -unit (99.1%, data not shown) reduction in fungal bioluminescence from this particular mouse burn was achieved in a light exposure dependent manner when 6.48 J/cm 2 UVC had been delivered.

When UVC was delivered on day 0 (30 minutes after infection) the Candida was even more susceptible to inactivation by UVC. Figure 4B shows the average values of the fungal bioluminescence from the infected mouse burns treated with an exposure to UVC on day 0 (30 minutes post-infection; n =11) or receiving UVC on day 1 (24 hours post-infection; n=12). UVC treatment carried out on day 0 gave an average 2.16-log 10 -unit (99.2%) loss of fungal luminescence when 2.92 J/cm 2 UVC had been delivered, while UVC treatment carried out on day 1 resulted in an approximately 1.94-log 10 -unit (95.8%) reduction when more than twice as much UVC (6.48 J/cm 2 ) had been delivered.

Figure 4C shows the time courses of the mean fungal luminescence values from day 0 to day 6 of the infected burns in mice treated with a single exposure of UVC on day 0 (2.92 J/cm 2 ), a single exposure of UVC on day 1 (6.48 J/cm 2 ), daily application of nystatin cream from day 1 to day 6, and no treatment (the scabs of the burns peeled off as early as on day 8 postinfection, and almost no remaining fungal luminescence was detected from the wounds after the peel-off of scabs). Although some fungal re-growth in the UVC treated burns was observed, especially in the burns treated on day 1, the averaged fungal luminescence intensities of the burns treated on day 0 and day 1 were at least 2-log 10 and 1-log 10 -unit respectively lower than that of the untreated burns at all time points sampled. Statistical comparison of the areas under the plots of bioluminescence time course in the twodimensional coordinate system in Figure 4C demonstrated that UVC treatment significantly decreased fungal bio-burden of the infected burns (Figures 4D and4E). In addition, the fungal bioburden of the mouse burns treated with UVC on day 0 was significant lower than that of the mouse burns treated with UVC on day 1 (Figure 4F).

UVC was superior to nystatin cream in reducing the fungal burden of mouse burns with established infections

Daily application of nystatin cream (25 mg per cm 2 burned skin per day) from day 1 to day 6 hardly had any effect on controlling the infection. The formation of the scab prevented effective application of Nystatin cream for longer than day 6. The fungal bioburden in the mouse burns treated with a single UVC exposure on day 1 was significantly lower than that in mouse burns treated with daily application of nystatin cream (Figure 4G).

Mouse skin could tolerate UVC irradiation at the effective antifungal dose

Figures 5A-5C show representative morphologies of mouse skin before, immediately after, and 24 hours after being exposed to UVC at a radiant exposure of 6.48 J/cm 2 , which is the effective antifungal dose for treating infections on day 1 post-infection. It can be observed that there is mild wrinkling of the skin evident immediately after UVC and this is slightly more pronounced at 24 hours after UVC exposure, but the wrinkling disappeared in succeeding days. A small lesion (top of square in Fig 5C ) was also observed occurred at 24 hours after UVC exposure, but the lesion eventually healed without problem. Cyclobutane pyrimidine dimer (CPD) positive nuclei were observed in the immunofluorescence micrograph of the biopsy taken immediately after UVC exposure (Figure 5E). However, the damage was extensively repaired at 24 hours after UVC exposure where only traces of fluorescence remained (Figure 5F). Mild epidermal shrinkage was seen at 24 hours after UVC exposure (Figure 5L).

Discussion

In this study, we developed a mouse model of non-lethal 3 rd degree burn infected with a bioluminescent strain of C. albicans, and investigated the use of UVC light for treatment of C. albicans infection in mouse burns. Extraction of C. albicans from the infected mouse burns confirmed the fungal luminescence to be accurate at predicting the viability of C. albicans in the infected burns. It was found in this study that UVC significantly reduced the fungal bioburden in mouse burns with both early-stage and established infections. To the best of our knowledge, this study is the first report on both the development of the animal model and the use of UVC to treat C. albicans burn infection.

Fungal cells prefer relatively low temperatures for growth compared to bacteria. While most pathogenic bacteria grow well at body temperature (37°C), this property is rare amongst fungi and yeasts [START_REF] Robert | Vertebrate endothermy restricts most fungi as potential pathogens[END_REF] . The requirement for lower temperatures means that fungal infections on skin, mucosa, toenails and burns tend to be superficial in nature and therefore more susceptible to UVC treatment. UVC does not penetrate to any significant extent in living tissue (less than 1 mm) so its use as an antimicrobial therapy is limited to superficial infections. We previously reported 19 that UVC could be used for treatment of onychomycosis, a fungal infection of nails caused by dermatophytes such as Trichophyton rubrum. A clinical trial was conducted of UVC for toenail onychomycosis with careful masking of the periungual skin [START_REF] Boker | A single-center, prospective, open-label, pilot study of the safety, local tolerability, and efficacy of ultraviolet-C (UVC) phototherapy for the treatment of great toenail onychomycosis[END_REF] .

It is well known that UV radiation is damaging to human tissue and particularly damaging to skin. UVB irradiation of skin has been particularly well studied, and is accepted as the main cause of skin cancer and photoaging. Using appropriate risk-benefit analysis, UVB treatments are still delivered to various areas of the bodies of millions of patients in the United States per year for the treatment of psoriasis, dermatitis, vitiligo, etc [START_REF] Nolan | A review of home phototherapy for psoriasis[END_REF][START_REF] Gambichler | Management of atopic dermatitis using photo(chemo)therapy[END_REF][START_REF] Abdulla | Treatment of vitiligo with narrow-band ultraviolet B: advantages and disadvantages[END_REF] . Although the dangers of UVC exposure to host cells and tissues are well-known, we would argue that the selectivity for UVC inactivation of C. albicans over keratinocytes, combined with the ability of host cells to repair DNA lesions, the likelihood that antimicrobial UVC will not be repeated in a chronic manner, and the ability to mask healthy skin around the burns with UVC-opaque material will combine together to mitigate these dangers. For burn infections, the use of UVC light is especially compelling in that third degree burns are usually composed of dead tissue. As a result, there is minimal concern about the side effects of UVC on host tissues when the healthy skin surrounding the burns is carefully protected. We believe there may be situations where the risk benefit ratio is favorable for the use of UVC for treating wound infections particularly when the microbes responsible are antibiotic resistant. While permissible UVC dose exposure limits for human tissue do not exist, it is expected that there would be an acceptable maximum number of repetition times of UVC irradiation and possibly a total lifetime cumulative exposure in J/cm 2 .

The infections on day 1 were shown to be more resistant to UVC treatment than they were on day 0. According to the results from the present study, to achieve an approximately 2-log10-unit inactivation of C. albicans in vivo, over a double dose of UVC light was required for day 1 treatment in comparison to day 0 treatment. On day 1 post-infection, the infections were fully established as demonstrated by the histological sections (see Figure 3C). In fully established infections, C. albicans exists predominantly as biofilms [START_REF] Douglas | Candida biofilms and their role in infection[END_REF] , which are structured microbial communities attached to the wound surface. It was found in previous mechanistic studies that the biofilm matrix can block UV light [START_REF] Martinez | Cryptococcus neoformans biofilm formation depends on surface support and carbon source and reduces fungal cell susceptibility to heat, cold, and UV light[END_REF] and make fungal cells less susceptible to UV light [START_REF] Douglas | Candida biofilms and their role in infection[END_REF] . In addition, when the infections were fully established, the fungal cells may have proliferated into the deeper layer of the tissue, where the UVC light was attenuated.

Regrowth of C. albicans was sometimes observed especially in the mouse burns treated with a single UVC exposure on day 1 post-infection, although the overall fungal bioburden of the treated mouse burns was still significantly lower than the untreated mouse burns. This problem, if proved to occur predictably, might be overcome by multiple UVC exposures during the following days after the initial treatment. Future studies in this regard are warranted.

The scabs of the mouse burns in this study formed as early as on day 5 post-infection and peeled off from the mouse backs as early as on day 8. When the scabs peeled off, almost no remaining fungal luminescence could be detected from the wounds but the peeled-off scabs were bioluminescent, indicating that C. albicans cells were mostly located within the scabs and isolated from the host when approaching the peel-off of the scabs.

Conclusions

UVC offers a selective inactivation of C. albicans over keratinocytes in a light dose dependent manner. UVC appears to be an effective treatment strategy for mouse burns with both early-stage and established C. albicans infections and is worth further investigation for potential clinical utility. UVC is superior to nystatin cream in reducing the fungal bioburden of mouse burns with established infections. Normal mouse skin could tolerate UVC irradiation at the effective antifungal doses. In vivo linear correlation of C. albicans CFU extracted from tissue at necropsy to fungal bioluminescence from infected mouse burns.

Figure 1 .

 1 Figure 1.In vitro susceptibility to UVC of keratinocytes and C. albicans. Bars: Standard deviation.

Figure 2 .

 2 Figure 2.

Figure 3 .

 3 Figure 3. A) Successive bioluminescence images captured daily for 12 days of a representative mouse burn infected with bioluminescent C. albicans. B) A representative bioluminescence image of a mouse burn immediately after the peel-off of its scab as well as the detached scab from the same mouse. C) -D) Representative periodic acid-Schiff (PAS)-stained histological sections of C. albicans infected mouse burns biopsied on day 1 and day 4 post-infection, respectively. Scale bars: 20 µm.

Figure 4 .

 4 Figure 4.A) Dose-response of fungal luminescence from a representative mouse burn infected with C. albicans and treated with UVC exposure on day 1 (24 hour) post-infection. B) Doseresponses of mean fungal luminescence of the mouse burns infected with C. albicans and treated by use of a single UVC exposure on day 0 (30 min, n=11) and day 1 (24 hour, n=12) post-infection, respectively. C) Time courses of mean fungal luminescence from infected mouse burns treated with a single UVC exposure on day 0 (n=11), day 1 (n=12), daily application of nystatin cream from day 1 to day 6 (n=8), and no treatment (n=12), respectively. D) -G) Mean areas under the bioluminescence-time plots (in the twodimensional coordinate system in panel C) representing the overall fungal bioburden of the mouse burns in different groups (day 0 UVC treatment vs. no treatment, p=0.003; day 1 UVC treatment vs. no treatment, p=0.004; day 0 treatment vs. day 1 treatment, p=0.006; day 1 UVC treatment vs. daily treatment with nystatin cream, p=0.028).

Figure 5 .

 5 Figure 5. A)-C) Morphologies of a representative mouse skin before, immediately after, and 24 hour after being exposed to UVC at a dose of 6.48 J/cm 2 , respectively. D) -F) Representative immuno-fluorescence micrographs of cyclobutane pyrimidine dimers (CPDs) in skin cell nuclei. G) -I) Micrographs of DAPI counterstaining of cell nuclei. J)-L) Micrographs of Masson's trichrome stained sections. Biopsies were taken before (D, G, J), immediately after (E, H, K), and 24 hours after (F, I, L) being exposed to UVC at a dose of 6.48 J/cm 2 , respectively, from the same mouse. Arrow in panel C: UVC-induced lesion on mouse skin. Arrows in panels D-F: mouse skin surface. Arrow in panel 5L shows epidermal shrinkage. Scale bars: 20 µm.

Photochem Photobiol. Author manuscript; available in PMC 2012 March 1.

Acknowledgments

This work was supported by the NIH (grant RO1 AI050875 to MRH) and US Air Force MFEL Program (FA9550-04-1-0079). TD was partially supported by a Bullock-Wellman Fellowship Award and an Airlift Research Foundation grant (grant #109421).


Yanjun Yin

Weiqing Tang
email: tang@fulong.com.cn

Weiqing Li
email: li_weiqing@139.com

Modeling Group Emotion Based on Emotional Contagion

Keywords: Group Emotion, Emotional Contagion, Emotion Model, Personality

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

Introduction

Emotion is an essential part of human life; it not only influences how we think, adapt, learn, behave and how communicate with others, but impacts other people in same situation through emotional contagion as well. Psychologists pointed out that emotional influence on others is completed by emotional contagion. In social interaction, an individual will unconsciously imitate other people's emotional expressions, experience those emotions and then affect his emotions. The process is called as emotional contagion [START_REF] Hatfield | Emotional contagion[END_REF]. The essence of emotional contagion is exchange and transfer of emotion which influences others' emotion and further impact their cognition and affective attitude toward the environment which they are facing.

In order to better embody the emotional experience of individuals in social situation, the paper adopts the emotional contagion as the main mechanism of individuals' emotion interaction, and proposes a new method to model the group emotion based on the 'bottom-up' approach which is discussed by Barsade and Gibson [START_REF] Barsade | Group Emotion: A View from Top and Bottom[END_REF].

Relative Works

Group emotion can be dated back to the study of mass movements in 19th century. Gustave Le Bon [START_REF] Bon | The Crowd: A Study of the Popular Mind[END_REF] pointed out that crowd emotion was induced by collective mind in his book 'The Crowd: A Study of the Popular Mind', and he explained further that collective mind would emerge in a crowd of people and influence group behavior which would not predicted by simply studying an individual. Modern views proposed that crowd emotion is the functional reaction to situations or events related to individuals. Sociological researches showed the relative deprivation is the most important root causing the negative emotion of the individuals in mass events; it believed that the individuals in crowd will felt discontent when they compare their positions to others and realize that they are lower than others. Smith et al [START_REF] Smith | Social identity and social emotions: toward new conceptualizations of prejudice[END_REF] described that members of the group assessed the sense of unfairness as a group events rather than an individual event, and the group emotion would be formed in the evaluation process of individuals who should responsible for the unfair and unequal situation. Yingxin [START_REF] Xinying | Gas Field' and the Occurring Mechanism of Mass Events-the comparison of two cases[END_REF] viewed the crowd emotion as 'gas field' in study of mass event; he pointed out that the 'gas field' is a kind of special emotional atmosphere formed by mass who vented their discontents. Although the concept of 'gas field' has its limitations, from the perspective of Chinese traditional culture, it interpreted the procession of forming and development of group emotion. Sigal G. Barsade and Donald E.Gibson [START_REF] Barsade | Group Emotion: A View from Top and Bottom[END_REF] offered a brief summary of prominent of research on group emotion and suggested that, from a top-down perspective, group emotion has been characterized as powerful forces which dramatically shape individual emotional response, as social norms prescribing feeling and expression, as the interpersonal glue that keeps groups together and as a window to viewing a group's maturity and development. However, from a bottom-up perspective, they proposed that group emotion can be viewed as the sum of its part of individuals' emotion. There are many computational emotion models recently. However, most of these models focus on the individual, no on the group. Rob Duell et al [START_REF] Duell | An ambient agent model for group emotion support[END_REF] provided firstly the computational group emotion based on the emotional contagion and the work of Barsade and Gibson. In their model [START_REF] Duell | An ambient agent model for group emotion support[END_REF][START_REF] Bosse | A Multi-Agent Model for Emotion Contagion Spirals Integrated within a Supporting Ambient Agent Model[END_REF], the emotion level of a special group was defined as the sum of the product of the emotion intensity of group members and an relevance factor respectively, but the relevance factor did not give any explanation on how to calculate it. In the research on the virtual spectators in game, Xiejun [START_REF] Xiejun | Research of Effect Model of Spectators in Virtual Game[END_REF] provided the level of neighboring emotion of a special individual as the mean emotion of all of his neighbors. Zhangxue and cheng'an [START_REF] Zhang Xue | The Monitoring and Measurement of Mood of Anger Based on Network Review of News[END_REF] provided a way to calculate the group angry based on network review of news. In the way, many factors: index of angry, group correlation and the level of event influence, have been involved, but in fact the group emotion is defined as the weight sum of individual s' emotion.

Based on above theories and technologies, we provided a new way to calculate the group emotion level using emotional contagion. In our method, we define individual's personality, emotional expressivity and susceptibility as main factors to influence the intensity of group emotion.

Main Factors of Group Emotion

Personality

Emotional contagion, proposed by Hatfield et al [START_REF] Hatfield | Emotional contagion[END_REF], is defined as the tendency to automatically mimic and synchronize facial expressions, vocalizations, postures, and movements with those of another person and, consequently, to converge emotionally.

In social situation, individuals are easier to be infected by others' emotion. However, there are obviously different in the degree of which individuals transmit their emotions or catch others' emotions [START_REF] Barsade | The Ripple Effect: Emotional Contagion and Its Influence on Group Behaior[END_REF]. From emotional contagion perspective, Verbeke [START_REF] Verbeke | Individual differences in emotional contagion of salespersons: Its effect on performance and burnout[END_REF] developed a classification method of personality. Based on the ability to infect and the capability to be infected, individuals are divided into four different classifications: charismatic, empathetic, expansive and bland. In general, most descriptions of personality emphasize the distinctive quality of individual. Most widely accepted models of personality have three-dimensional personality model (PEN) and Five Factor model (FFM). In the paper, the definition of personality is based on the work of Verbeke and described as two-dimensional vector, where each dimension is represented by a personality factor. The distribution of the personality factors is modeled by a normal distribution function N with mean i μ and standard i σ :

> =< Es Ex y personalit λ λ , (1)
} , { ] 1 , 0 [ , ) , ( 2 Es Ex i for N i i i i ∈ ∈ = μ σ μ λ (2)
Where Ex (Emotional expressivity) represents the ability to infect others' emotion through emotional expression, and Es (Emotional susceptibility) denotes the capability to be infected by others through catching others' emotion clue and understanding others' inner feeling. As an example, an expansive individual that has the high ability to infect and low capability to be infected is represented as personality=<0.8, 0.3>. However, an empathetic individual that has the low ability to infect and high capability to be infected is represented as personality=<0.1, 0.9>, an charismatic individual as personality=<0.7,0.9>, a bland individual as personality=<0.1, 0.2>.

4

Group Emotion Model

Emotion Intensity of Individual in Crowd

In psychology, emotion is often defined as a complex state of feeling that results in physical and psychological changes that influence thoughts and behaviors. However, there is a clear difference in the extent. When an individual is alone, his emotion is elicited by external stimulus. The emotional experience of the individual is only response to his cognition and the attitude toward what happened to him, and the emotional expression is the nature revelation of his inner feelings. So in most of computational emotion model, emotion is defines as a function of the individual's personality and external stimulus. However, sociological researches showed that the emotion of an individual in crowd will be influenced by other people. From the emotional conta-gion perspective, the process of emotional contagion, in which a group member influences the emotion of anther group member and vice versa, through the conscious and unconscious induction of emotion states [START_REF] Schoenewolf | Emotional Contagion: Behavioral induction in individuals and groups[END_REF], is primary mechanism through which individual emotions create a group emotion. And the social comparison theory also gave the interpretations about the difference. Individuals tend to compare their behavior and attitude with others that are most like them. In the process of comparison, individuals coordinate his behaviors, attitudes and emotions with those of others people.

Based on these analyses and our previous work [START_REF] Yin | Spectator's Emotion Modeling Based on Emotional Contagion, The International Conference on Automatic Control and Artificial Intelligence[END_REF], we defined the emotion of individual in crowd as a time variation function in which the effect of emotional contagion is considered. For a given individual A, at time step t+1, the intensity of his/her emotion is

) 1 ( + t I a , which is ) ( ) ( * ) ( ) 1 ( t IS
}} { \ | * ))] ( ( * ) ( [ max{ ) ( a G s t I I s abs t I s a Ex special ∈ - = τ λ (4)

) (t I t groupimpac

is the emotion intensity of individual caused by group emotion at the time t, it will be described in detail in next section.

Emotion does not immediately disappear with external stimulus' end, but it is bound to undergo decay, which is a natural depression or a gradually decline because of mental satisfaction or emotional catharsis. The differences of the decay degree of emotion are showed on individuals' personality. In general, the emotional decay of individual with the high ability to express his emotions is faster than that of whom with low ability. And an individual tends to forget the positive emotions more quickly than the negative emotions. In social situation, the emotional decay is influenced by group size. Researcher pointed out that the emotional delay of individuals in the group with large size is lower than one with smaller size. Based on the researching in psychology, we propose a decay function as follow:

)] * ) ( * exp( 1 [ * ) ( ) 1 ( timeStep a groupSize t I t I Ex a a λ μ - - = + (6)
Where μ is a moderation factor representing different decay speeds for different emotions. For example, its value is smaller when individual experience positive emotion than negative emotion. timeStep represents the number of emotional contagion times. groupSize is the number of individual in a group.

Group Emotion

Emotion is an inherent part of mass events. Individuals bring their unique emotional tendencies to the group. The group emotion has been viewed as moving upward from the compositional effects of individual group member emotions. The dynamic of group emotion will be in line with the individuals' emotion. That is, the intensity of group emotion should be related with the 'expected value' of individuals. In the paper, we defined group emotion as follow.

) ( * ) ( ) ( ) ( t I s a t I a G a G s Ex Ex group ∑ ∑ = ∈ ∈ λ λ (7)
Where Ex λ is the emotional expressivity of individuals. I group (t) is represent the emotion intensity of group at time step t. The emotion intensity of individual caused by group emotion is defined as follow:

) ( * ) ( * ) ( ) ( t I t I groupSize a t I a group G a Ex t groupimpac ρ λ - ∑ = ∈ (8)
Where ρ is the weight coefficient representing different influence on individual from different group characteristics, for example group size and group type.

Experiences and Result Analysis

A group emotion model based on emotional contagion is proposed in the paper. Our aim is to reflect more believable behavior and emotion of individual in social situation. A large number of simulations have been performed to test the model, using simulation software. In the section, some of simulation results are discussed. Firstly, we have fixed the emotion levels and personalities of individuals in crowd to explore the effect of group size. The value of group size is from 3 to 100. The emotion intensities are random numbers following a normal distribution with mean 0.3 and standard deviation 0.1, and group members are charismatic with personal-ity=<0.9,0.9>. As expected, group size plays a significant role in emotional contagion, and it influences the level of group emotion. The group emotion is easier to outburst in large group than in small group. Although the decaying speed of positive emotion is faster than that of negative emotion, emotional explosion of group is likely to occur if the group is large enough (Fig. 1). And in small group, it is difficult to increase the emotion intensity of individuals, especially when the individuals have positive emotions (Fig 2). The results show that the emotion dynamic of group based on the model provided by the paper is in line with the emotional response of human crowd.

Secondly, we have fixed group size at value 20, and studied the effect of the personalities of individuals in group. We set four groups representing four group types: charismatic, empathetic, expansive and bland respectively. The emotion intensities of individuals in these groups were generated randomly following a normal distribute (μ=0.5,θ=0.1). From the result (Fig. 3), we can see the Charismatic group is easier to form emotion climate than others group, especially for negative emotion. And the bland group has low emotion expressivity and susceptibility, so the void of emotional communication and understanding make the bland group difficult to form collective mind.

At last, this model was implemented in the NetLogo [START_REF]NetLogo homepage[END_REF] environment to simulate the panic crowd. We show an example of the fleeing behavior triggered when a danger is encountered. A fire is treated as a danger to individuals' health. When individuals saw the fire, they would sense the danger and fear/panic is elicited. In our model, the emotion intensity of these individuals is generated random following the normal distribution. The fear/panic will be propagated in the crowd very quickly. All infected individuals (in blue) take the group mind of escaping from the source of danger and performing the flee behavior. There are many individuals who did not saw the danger, their flee behaviors were made based on the information from others emotional expression, for example facial expressions, vocalizations, postures and behavior (Fig. 4, Fig. 5).

Conclusions

In the paper, we proposed a computational model of group emotion, based on the emotional contagion provided by Hatfield. A large number of experiences have been done. And the simulation results show that the group emotion model is reasonable and it can make the virtual individuals in crowd more believable. For the crowd simulation, the model has some referential value. In a future work, we are interesting to investigate the relationship between emotion and behavior choice of individuals in crowd. In the left subfigure, these individual (in grey) did not find any danger. In the right subfigure, some individuals (in red) saw the danger and panic/fear emotion was elicited, and they infected others (in blue) emotion through emotional contagion.

  susceptibility of individual A, ω the degree of attainment for special individual. Ispecial (t) is the emotional influence of special individual on the individual A, and it is defined based on the absorption model described by Bosse et al[START_REF] Bosse | A Multi-Agent Model for Emotion Contagion Spirals Integrated within a Supporting Ambient Agent Model[END_REF].

  expressivity of individual S. a I and s I are the level of emotion of individual A and S. τ is a moderation factor representing the strength of the channel from individual A to S.

  expressivity of individual A.

Fig. 1 .

 1 Fig. 1. The maximum emotional intensity of different group sizes. The maximum emotional intensity is the maximum value of group emotion intensity in the whole variable process.

Fig. 2 .Fig. 3 .Fig. 4 .

 234 Fig. 2. The change of emotion intensity of groups with different participants

Fig. 5 .

 5 Fig. 5. Snapshots of a crowd performing the fleeing behavior

  Where I a (t) is the emotion intensity of the individual A in the last time step.βis an adaptable factor and is set in line with emotional types and environments. IS a (t) is the level of individual emotion elicited by external stimulus at the time t. if there is no external stimulus at the time t, IS a (t) is zero. IC a (t) denotes the intensity emotion caused by others' emotional contagion. It is defined as follow.

							I	a	t	+			=		I	a	t		+	β		IC	a	t	+		a				(3)
	IC	a	(	t	+	) 1	=	λ	Es	(	a	)	*	[	ω	*	I	special	(	t	)	+	1 (	-	ω	)	*	I	goupimpact	(	t	)]


Yu Meng

Tianlong Gu

Liang Chang
email: changl@guet.edu.cn

Reasoning about Assembly Sequences Based on Description Logic and Rule

Keywords: assembly sequence, knowledge representation, reasoning, description logic, rule

Reasoning about assembly sequences is useful for identifying the feasibility of assembly sequences according to the assembly knowledge. Technologies used for reasoning about assembly sequences have crucial impacts on the efficiency and automation of assembly sequence planning. Description Logic (DL) is well-known for representing and reasoning about knowledge of static application domains; it offers considerable expressive power going far beyond propositional logic while reasoning is still decidable. In this paper, we bring the power and character of description logic into reasoning about assembly sequences. Assembly knowledge is firstly described by a description logic enhanced with some rules. Then, the feasibility of assembly operations is decided by utilizing the reasoning services provided by description logics and rules. An example has been provided to demonstrate the usefulness and executability of the proposed approach.

Introduction

Related researches show that 40%~50% of manufacturing cost is spent on assembly, and 20%~70% of all the manufacturing work is assembly [START_REF] Wang | Computer aided assembly planning: a survey[END_REF]. Assembly sequence is the most important part of an assembly plan. Reasoning about assembly sequences is useful for identifying the feasibility of assembly sequences according to the assembly knowledge. Technologies used for reasoning about assembly sequences have crucial impacts on the efficiency and automation of assembly sequence planning. Bourjault [START_REF] Bourjault | Contribution à une approche mé thodologique de l'assemblage automatisé : é laboration automatique des sé quences opé ratoires. Thè se d'État[END_REF] and De Fazio and Whitney [START_REF] De Fazio | Simplified generation of all mechanical assembly sequences[END_REF] have developed the structured methodologies in which a series of Yes-or-No questions must be answered to generate the feasible sequences. In both cases, it will become a difficult and error-prone process for all but simplest assemblies. While Homem de Mello et al. [START_REF] Homem De Mello | A correct and complete algorithm for the generation of mechanical assembly sequences[END_REF] presented the cut-set method to finding the feasible assembly operations. This method suffers from the fact that there may be an exponential number of candidates partitioning to test, even though few satisfy physical constraints. Gottipolu and Ghosh [START_REF] Gottipolu | A simplified and efficient representation for evaluation and selection of assembly sequences[END_REF] developed an algo-rithmic procedure to generate all feasible assembly sequences by representing the contact and translation function into truth tables and then applying the Boolean algebra principles. It does not preclude that the amount of effort required also increases dramatically with the number of parts in product.

In recent years, due to the strong expression power and the decidability of reasoning, Description Logic (DL) [START_REF] Baader | The description logic handbook: theory, implementation and applications[END_REF] has been emphasized by more and more researchers on knowledge representation and reasoning. But it is hard for DL to define multiple, concurrent conditions rules. However, the multiple, concurrent conditions rules must be represented to implement reasoning in some application domains. For example, Fiorentini et al. [START_REF] Fiorentini | An Evaluation of Description Logic for the Development of Product Models[END_REF] and Zhu et al. [START_REF] Zhu | Semantic Applications Enabling Reasoning in Product Assembly Ontologies-Moving Past Modeling[END_REF] have proposed the approaches based on DL and rule in the product assembly domain in order to satisfy design tasks such as verifying conditions for design completeness, product qualification and requirements.

In reasoning about assembly sequences, multiple constraints (e.g. connectivity constraints, precedence constraints) must be considered to verify the feasibility of assembly. In other words, the rules for judging the feasibility of assembly are multiple, concurrent conditions rules. In order to improve the level of reasoning automation about assembly sequences, this paper proposes an approach based on DL and rule. In this approach, we bring the power and character of description logic enhanced with some rules into reasoning about assembly sequences.

Description logic and rule representation

Description Logic (DL) [START_REF] Baader | The description logic handbook: theory, implementation and applications[END_REF] is a knowledge representation formalism and it is a decidable subset of first-order logic (FOL). In many of the formal methods for representing knowledge, DL has received particular attention in the recent years. The cause is mainly that it is highly effective for concept hierarchy and providing reasoning service. The knowledge base (KB) based on DL is partitioned into an assertional part (ABox) and a terminological part (TBox). In DL, a distinction is drawn between TBox and ABox. In general, TBox is a set defining concepts, relationships among concepts, and relationships among relationships, which is an axiom set describing domain structure. ABox describes which concept an individual belongs to and what relationship one individual have with another individual.

However, there is the scarcity of DL in reasoning rule representation, that is, it is hard for DL to define multiple, concurrent conditions rule. For example, the follow rule which states that x1 is the father of x2 and x3 is the brother of x1, then there exists that x3 is the uncle of x2, can't be expressed in DL.

hasFather(x2, x1)  hasBrother(x1, x3)  hasUncle(x2, x3) To offer sophisticated representation and reasoning capabilities, the integration of DL knowledge bases and rule expression representation is necessary. It is one of the methods that the rule-based systems use vocabulary specified in DL knowledge bases.

3

The DL representation of assembly knowledge

Assembly sequence planning begins with representing the assembly knowledge that can be extracted directly from the CAD model of assembly. Gottipolu and Ghosh [START_REF] Gottipolu | A simplified and efficient representation for evaluation and selection of assembly sequences[END_REF] represented the assembly knowledge as two types of uni-directional matrices, which are called the contact and the translation functions Given an assembly and its contact and translational functions, we can represent the assembly model by defining concepts, roles and creating assertions in DL. The related concepts and roles in DL are defined as follows:

(1) Each assembly is made up of several parts. The Part is an atomic concept, the concept Part will be defined to represent parts in the assembly.

(2) For the contact functions, we define six atomic roles DC 1 , DC 2 , DC 3 , DC 4 , DC [START_REF] Gottipolu | A simplified and efficient representation for evaluation and selection of assembly sequences[END_REF] and DC 6 , called contact roles, corresponding to C 1 , C 2 , C 3 , C 4 , C 5 and C 6 in C ab .

(3) We represent the translation functions as six atomic roles DT 1 , DT 2 , DT 3 , DT 4 , DT [START_REF] Gottipolu | A simplified and efficient representation for evaluation and selection of assembly sequences[END_REF] and DT 6 , called translation roles.

For example, if a and b are individual names, then the assertion Part(a) means that a is a part, and Part(b) means that b is a part. For the above part a and part b, if b is in contact with a in the direction i (i = 1~6), there exist the assertion DC i (a, b). In the same way, if b has the freedom of translational motion with respect to a in the direction i (i = 1~6), there exist the assertion DT i (a, b).

The representation of reasoning rule of assembly

To verify the feasibility assembly, two types of constraints, connectivity constraints and precedence constraints, must be considered. We verify these constraints by representing the contact and translational relations into DL roles and then applying reasoning rules. These reasoning rules are expressed as multiple, concurrent conditions rules. The related inference will use vocabulary specified in DL knowledge bases.

For representing reasoning rules, some concepts and roles will be defined in DL. These concepts include ComponentSeq, Subassembly, while these roles include FeasibleAssemblyRole, hasLeftComponent and hasRightComponent. Compo-nentSeq and Subassembly denote the sequence of components and subassembly respectively. FeasibleAssemblyRole means that two components can be assembled. stating that the individuals named a, b and c are parts; s1 is a sequence of components and assembled by a and b. According to the above concepts and roles, the reasoning rule of subassembly is presented as follow.

ComponentSeq(x)

 hasLeftComponent(x, xL)  hasRightComponent(x, xR)  FeasibleAssemblyRole(xL, xR)  Subassembly(x)
In the rest of this section, we represent all reasoning rules for determining the feasibility of assembly sequences as multiple, concurrent conditions rules.

Firstly, the feasibility of two part subassemblies can be verified from the roles DC i and DT i (i = 1~6) of that pair. For any pair (a, b) of parts, at least one of DC i (a, b)(i = 1~6) assertions must exist and at least one of DT i (a, b)(i = 1~6) assertions must exist to make that pair (a, b) a feasible subassembly. For describing these conditions, two roles DRC and DRT will be defined in DL knowledge bases, where DC i ⊑ DRC and DT i ⊑ DRT (i = 1~6) hold. The reasoning rule for verifying the feasibility of two component subassemblies is described as DRC(p1, p2)  DRT(p1, p2)  FeasibleAssemblyRole(p1, p2) Secondly, the feasibility of subassemblies with more than two parts, both roles DC i and DT i (i = 1~6) of the involved ordered pairs must be used. For example, the feasibility of assembly of the subassembly (a, b) and the part c is verified as explained as follows.

Step 1 Consider the subassembly (a, b) as a set {a, b} and the part c to be added as another set {c}. The Cartesian product of these two sets ({a, b}×{c}) gives a set of ordered pairs {(a, c), (b, c)}. For the pair (a, c) and (b, c), if the assertion DRC(a, c) or DRC(b, c) hold, then there is a contact between c and (a, b). So we define the role DRTC in DL knowledge bases and describe this condition as rule expressions such as Subassembly(s)  hasLeftComponent(s, sL)  DRC(sL, p)  DRTC(s, p) Subassembly(s)  hasRightComponent(s, sR) DRC(sR, p) DRTC(s, p) Step 2 The presence of contact above provides only the necessary condition, but it does not guarantee the feasibility of assembly operation due to the precedence constraints. To consider the precedence constraints, the translation roles DT i (i = 1~6) of the Cartesian ordered pairs must be used.

Considering the assembly of part c to the subassembly (a, b), there are two pairs (a, c) and (b, c). Firstly, if the DT i (a, c) and DT i (b, c) assertions hold, then the component c has collision-free disassembly in the directions i with respect to the subassembly (a, b). Secondly, if one of the six directions is a collision-free disassembly direction of the component c with respect to the subassembly (a, b), then the subassembly (a, b, c) is feasible. In this regard, the role DRTT is defined in DL knowledge bases and the above precedence conditions is presented as

Subassembly(s)  hasLeftComponent(s, sL)  hasRightComponent(s, sR)  DT i (sL, p)  DT i (sR, p)  DRTT(s, p) , i = 1~6.
The same logic can be applied to the subassembly including multiple components and the single component.

Step 3 If the rules of Step 1and Step 2 hold, then we can obtain the following reasoning rule for judging the feasibility of assembly. DRTC(s, p)  DRTT(s, p)  FeasibleAssemblyRole(s, p)

The reasoning of assembly sequence

To demonstrate the usefulness of the proposed approach, we described the assembly knowledge and reasoning rules of the assembly shown in Fig. 1 by the languages OWL DL and SWRL with the help of the editing tool Proté gé , and the reasoning is carried out by the JESS reasoning engine [START_REF] Dizza | Using OWL and SWRL to represent and reason with situation-based access control policies[END_REF]. This example assembly includes four parts, a, b, c and d are the name of those parts. The process of generating assembly sequences for the example shown in Fig. 1 is explained as follows. According to the representation of assembly knowledge and reasoning rules given in Section 3 and 4, the ABox of the example shown in Fig. 1 is derived as Fig. 2. After using JESS engine to make inference about OWL individuals of Fig. 2 over the total reasoning rules, Subassembly class stores SWRL inference results, that is Subassembly instances are generated accordingly. These Subassembly instances include:

( (3) S_abc_d and S_acb_d, which denote the feasible assembly task is {(a, b, c), (d)}, and the corresponding feasible four part subassembly is (a, b, c, d).

Through analyzing the above results, the two part subassemblies (a, d) and (b, d) are not used in any tasks in the subsequent assembly, that is, no further higher order subassemblies can be generated from these subassemblies, so (a, d) and (b, d) are invalid subassemblies and should be deleted. Similarly, the (a, b, d), (a, c, d) and(b, c, d

Conclusion

Description Logic (DL) is well-known for representing and reasoning about knowledge of static application domains. But it is hard for DL to define multiple, concurrent conditions rules. In order to improve the level of reasoning about assembly sequences, we firstly describe the knowledge on assembly model by DL and establish a rule set of the assembly reasoning rules. Then, the feasibility of assembly operations is decided by utilizing the reasoning services provided by description logics and rules. Finally, an example has been provided to demonstrate the usefulness and executability of the proposed approach.

  . The contact function C ab = (C 1 , C 2 , C 3 , C 4 , C 5 , C 6 ) is a 1×6 binary function representing contacts between the parts a and b. It can be defined as C ab : C i  {0, 1}, i = 1~6, where C i = 1 indicates presence of contact in the direction i, i.e. part b is in contact with part a in the direction i; C i = 0 indicates absence of contact in that direction. The freedom of translation motion between two parts in an assembly can be defined by a 1×6 binary translation function T ab = (T 1 , T 2 , T 3 , T 4 , T 5 , T 6 ) or T ab : T i  {0, 1}, i = 1~6. If the part b has the freedom of translation motion with respect to the part a in the direction i, then T i = 1, else T i = 0. Here, direction 1, 2 and 3 indicate the positive sense of X, Y and Z axes (X+, Y+ and Z+) respectively, whereas direction 4, 5 and 6 correspond to the negative sense of X, Y and Z axes (X, Y and Z) respectively.

  hasLeftComponent and hasRightComponent denote which components compose the sequence of components or subassembly. There are concept assertions such as Part(a), Part(b), Part(c), ComponentSeq(s1), hasLeftComponent (s1, a), hasRightComponent (s1, b).

Fig. 1 . 2 .

 12 Fig. 1. An example assembly Fig. 2. The ABox of the example shown in Fig. 1

1 )

 1 S_ab, S_ac, S_ad, S_bd and S_cd, which denote the feasible assembly tasks are {(a), (b)}, {(a), (c)}, {(a), (d)}, {(b), (d)}, {(c), (d)}, and the corresponding feasible two part subassemblies are (a, b), (a, c), (a, d), (b, d), and (c, d); (2) S_ab_c, S_ac_b, S_ab_d, S_ac_d, S_cd_a and S_cd_b, which denote the feasible assembly tasks are {(a, b), (c)}, {(a, c), (b)}, {(a, b), (d)}, {(a, c), (d)}, {(c, d), (a)} and{(c, d), (b)}, and the corresponding feasible three part subassemblies are (a, b, c), (a, b, d), (a, c, d) and (b, c, d).

  ) should be deleted. After deleting all the invalid subassemblies, the resulting feasible subassemblies are (a, b), (a, c), (c, d), (a, b, c), (a, b, c, d), and the corresponding feasible assembly tasks are {(a), (b)}, {(a), (c)}, {(c), (d)}, {(a, b), (c)}, {(a, c), (b)} and {(a, b, c), (d)}. Then all the feasible assembly sequences are obtained as follows: (1) ( {{a} {b} {c} {d }} {{a, b} {c} {d }} {{a, b, c} {d }} {{a, b, c, d }} ); (2) ( {{a} {b} {c} {d }} {{a, c} {b} {d }} {{a, b, c} {d }} {{a, b, c, d }} ); (3) ( {{a} {b} {c} {d }} {{a, b} {c} {d }} {{a, b} {c, d }} {{a, b, c, d }} ); (4) ( {{a} {b} {c} {d }} {{a} {b} {c, d }} {{a, b} {c, d }} {{a, b, c, d }} ).

Acknowledgments

This work is supported by the National Natural Science Foundation of China (Nos. 60903079, 60963010) and the Guangxi Key Laboratory of Trusted Software.


Bo Li

Fengxian Shi

Enhong Chen
email: cheneh@ustc.edu.cn

Messages Ranking in Social Network

Keywords: ranking, social network, microblog

Nowadays, people engage more and more in social networks, such as Twitter, FaceBook, and Orkut, etc. In these social network sites, users create relationship with familiar or unfamiliar persons and share short messages between each other. One arising problem for these social networks is information is real-time and updates so quickly that users often feel lost in such huge information flow and struggle to find what really interest them. In this paper, we study the problem of personalized ranking in social network and use the SSRankBoost algorithm, a kind of pairwise learning to rank method to solve this problem. We evaluate our approach using a real microblog dataset for experiment and analyze the result empirically. The result shows clear improvement compared to those without ranking criteria. abstract environment.

Introduction

Nowadays, people engage more and more in social network services like Twitter, Facebook, Google+, Orkut, etc. Most of these social networks provide the short messages sharing service. People use this service heavily for staying in touch with friends and colleagues, receiving and publishing all kinds of real time information from all kinds of users with overlapping and disparate interests. On one hand, due to fast transmission rate and low cost of dissemination, the information flow updates quickly. According to recent reports, Twitter currently has over 73 million users, with more than 50,000 tweets being generated per minute. For China microblog Weibo, up to July 2010, the number of messages sent is more than 3 million per day and the average number of messages generated is close to 40 per second. On the other hand, people tend to idolize the information and have psychological dependence on it which also stimulates this information overload. For example, quite a lot of users follow hundreds or even thousands of people on microblogs.

The above two aspects bring in a problem that how users find what really interest them in this huge information flow. Imaging a Twitter lover who follows many people and these people post all kinds of messages from political to sports, from art to entertainment. To some topics the user likes, but to some other topics she/he dislikes. She/he is quite reluctant and feels frustrated if she/he has to read lots of uninteresting messages until what really catches her/his eyes and interests her/him. Currently, most sites exhibit all the messages she/he receives in reverse chronological order when a user login in. This emphasis on time provides no guarantee that the most interesting messages appear on top, especially given that thousands of new messages are generated every minute. We think a more rational rank model is needed and thus we can better present messages to users.

In this paper, we study the message ranking problem in social network. There are some challenges for this problem. First, the text of these messages are short, usually less than 140 characters which is different from the traditional content based relevant ranking scenario. Second, due to fewer links among texts (we will use term text interchangeable to message), traditional link based ranking algorithms such as PageRank, HITS are not suitable to use. Third, the user behavior information we get is usually quite limit. For instance, in microblog, even we know user may have read a lot of messages, but we don't have explicit feedback information, like the specific extent of how user likes each message. What we have is a small portion of user behavior, such as whether she/he has replied the messages or retweeted the messages. At last, for the real-time applications, we require the time complexity of model is small. Considering all these factors, we use SSRankBoost (Semi-Supervised RankBoost) algorithm [START_REF] Amini | A boosting algorithm for learning bipartite ranking functions with partially labeled data[END_REF], a kind of learning to rank method, to solve this problem. Since user relationship is a main feature of social network, it can be mined for our ranking task. We utilize this underlying information in two perspectives: first, we analyze user authority on social network graph and exploit this as a feature for training. Second, according to the philosophy that similar users have similar interests, we measure user similarity and leverage user data to train the model. Using SSRankBoost, we modify the loss function of the model on the basis of user and his friends training sets. To evaluate the proposed approach, we use data from Sina Weibo, the biggest microblog site in China and empirically analyze the result which shows much improvement compared to those without ranking criteria.

In the remainder of the paper, first we briefly introduce some related work in section 2. Then in section 3 we apply SSRankBoost to message ranking and take microblogging as an example. At last, we present the experiment results in section 4 and conclude in section 5.

Related Work

Social network related problem has attracted many researchers' interests recently. Some researchers investigated the topological properties of the network [START_REF] Mislove | Measurement and analysis of online social networks[END_REF] and user behaviors [START_REF] Schneider | Understanding online social network usage from a network perspective[END_REF]. Many efforts have also been made for the influence analysis and information diffusion in social network. For example, [START_REF] Richardson | Mining knowledge-sharing sites for viral marketing[END_REF] studied influence maximization on networks and proposed an algorithm to solve this problem.

Our work is different from the previous work in [START_REF] Nagmoti | Ranking approaches for microblog search[END_REF] and [START_REF] Sarma | Ranking mechanisms in twitter-like forums[END_REF] which also tackle microblog ranking. [START_REF] Nagmoti | Ranking approaches for microblog search[END_REF] described several strategies for ranking microblogs in a real-time search engine, they proposed methods to re-rank the top-k tweets returned by an existing microblog search engine which was a non-personalized ranking while our work focus on personalized ranking. [START_REF] Sarma | Ranking mechanisms in twitter-like forums[END_REF] studied the problem of designing a mechanism to rank items in forums by making use of the user reviews such as "thumb" and "star" ratings. Their ranking algorithm specifically required manual user input and only utilized this feedback information, while our work and [START_REF] Nagmoti | Ranking approaches for microblog search[END_REF] take into account social network properties and properties of the message itself.

Personalized Messages Ranking in Social Network

Before discusssing the detail of our approach, we first describe our setting and some notations here. Generally, for each target user, the system is given a set of some labeled message set M where each sample m i in it is associated with a label y i ∈ {0, 1}, representing different level of relevance or preference. In our experiment, we set y 1 = 1 or y 0 = 0 which means user prefers a sample m i with y i = 1 more compared with a sample m j with label y j = 0. As we have only two labels and there is no order between samples with the same label, the feedback information we used is called bipartite and thus our ranking is bipartite ranking [START_REF] Amini | A boosting algorithm for learning bipartite ranking functions with partially labeled data[END_REF]. We denote M + , M -to represent these two disjoint sample sets, the samples with label 1 and samples with label 0. For ∀m

+ ∈ M + , m -∈ M -, the function Φ(m + , m -) = 1, Φ(m -, m + ) = -1, while for m 1 , m 2 which are in the same set, Φ(m 1 , m 2 ) = 0.
Finally, the goal of learning can be defined as the search of a scoring function H : m → R which assigns higher scores to more relevant instances than to less relevant or irrelevant ones.

SSRankBoost

Assume the target user is u 0 , and his friend set is F ′ (0) = {u k1 , u k2 , . . . , u kn }.

The similarity between u 0 and u ki (i = 1, . . . , n) is calculated through some kind of criteria (to be discussed in following section), and we select the top K most similar users and form set F (0) = {u k1 , u k2 , . . . , u kK } for u 0 , their corresponding similarities are {s k1 , s k2 , . . . , s kK }. For convenience, we define s 0 = 1 and get the user set for u 0 as U (0) = {u 0 } ∪ F (0) . The training samples that we clean from the source data for each user in

U (0) is M ′ (0) i , e.g. the training samples for u 0 in U (0) is M ′ (0) 0
. To utilize friends' information for the target user u 0 , beside M ′ (0) 0 , we also use some of the data in each M ′ (0) i

(i = 1, . . . , K), which is represented as M (0) i , M (0) i ⊂ M ′ (0) i to train the model for u 0 . We denote M (0) 0 = M ′ (0) 0 , then the data used for training for u 0 is M (0) = ∪ K i=0 M (0)
i . Furthermore, M + i , M - i as the corresponding sample set with label 1 and label 0 for each M i which means messages in M + i are all labeled with 1 and messages in M - i are all labeled with 0.

In this paper, we use SSRankBoost algorithm to model user interests. The final ranking function H(m) that we need in SSRankBoost is in the form of H = ∑ T t=1 α t h t . Let r i be the respectively ranking loss function for set M i which defines in equation ( 2), r i minimizes the average numbers of irrelevant examples scored better than relevant ones in each M i separately. Then by summing all the r i , we have the ranking loss r(H) of H defined in equation ( 1). In the equation, s i is the disfactor weight for each

M i , D i (m -, m + ) = 1/(| M + i || M - i |) (we will denote P i =| M + i || M - i | )
is the weight for pair (m -, m + ) (at the t-th iteration, we use D t,i to denote weight D i ), [π] is equal to 1 if the predicate π holds and 0 otherwise in equation [START_REF] Mislove | Measurement and analysis of online social networks[END_REF]. As r i is the respectively ranking loss function for set M i , using the upper bound [x ≥ 0] ≤ e x , we have its bound shown in equation ( 3), so the total cost r(H) also have a bound.

r(H) = r 0 + k ∑ i=1 s i r i = k ∑ i=0 s i r i ( 1
)
r i = ∑ m-,m+∈M - i ×M + i D i (m -, m + )[H(m -) -H(m + )] (2)
r i ≤ ∑ m-,m+∈M - i ×M + i D i (m -, m + )exp(H(m -) -H(m + )) (3) r(H) = k ∑ i=0 s i r i ≤ ε = k ∑ i=0 ∑ m-,m+∈M - i ×M + i s i D i (m -, m + )exp(H(m -) -H(m + )) (4)
In the implement of SSRankBoost, the algorithm get a weak learner h t according to pair weights at each bound. At the beginning of the algorithm, all sample pairs are considered to be uniformly distributed, that is, the pair weight D 0,i (m -, m + ) = 1/P i , ∀(m -, m + ) ∈ M i . In each iteration of SSRankBoost, the weight for each pair is therefore increased or decreased depending on whether h t orders that pair correctly, leading to the following update rules on the t-th iteration:

D t+1,i (m -, m + ) = D t,i (m -, m + )exp(α t (h t (m -) -h t (m + ))) Z t,i (5)
Where Z t,i is normalization factor such that D t,i remains probability distributions. For a fast implement, SSRankBoost replaces the D t,i (m -, m + ) with v t,i (m -) and v t,i (m + ) and the relationship between them is following:

D t,i (m -, m + ) = v t,i (m -)v t,i (m + ) (6)
The corresponding framework of SSRankBoost applied to messages ranking is given in algorithm 1.

In the above framework, the algorithm needs to train a weak learner at each iteration. We also adopt {0,1}-valued weak learner and as for how to find the weak learner h t (m) and choose the weight α t , reader can refer to SSRankBoost algorithm [START_REF] Amini | A boosting algorithm for learning bipartite ranking functions with partially labeled data[END_REF].

Algorithm 1: SSRankBoost algorithm

Input: Mi, i = 0, 1, ..., K,Mi is the data set from ui Output:

ranking function H = ∑ T i=1 αtht for i ← 0 to K do ∀m ∈ M + i , vi(m) = 1 |M + i | ; ∀m ∈ M - i , vi(m) = 1 |M - i |
;

for i ← 0 to K do Q0,i = 1;
for t ← 1 to T do train the weak learner using vt,i(m), i = 0, 1, ..., K ; choose the weight αt ; for i ← 0 to K do ∀m ∈ Mi,update;

vt+1,i(m) =    v t,i exp(-αtht(m)) Z + t,i if m ∈ M + i v t,i exp(α t h t (m)) Z - t,i if m ∈ M - i ; where Z + t,i , Z - t,i normalized vt,i over M + i and M - i ; update Qt,i : Qt,i = Qt-1,iZ + t,i Z - t,i ;

Features

In this article, we use the Sina Weibo(http://weibo.com) data for experiment and evaluate the proposed model. In this section, we introduce all the features we used in this data set. Sina Weibo is the most popular microblog service in China which is much like the Twitter. We use the open API that it provides and crawle the data from December 2011 to February 2012. In this dataset, there are 224977 users and 196499 messages.

To learn a ranking function, it is necessary to design a suitable set of features which will impact performances significantly. In this dataset, we implement the following features for target user u x and the specific message m: 1) Message aspect features: the tf-idf value, length of text, whether the text contains a URL, topic vector extracted by the LDA model, the retweet number of the message, the replied number of message.

2) Author aspect features: the author authority, the author's message number.

3) Combined features: the interactions ratio of u x and u y (u x is the target user, u y is the author of specific message m), the common friend ratio of u x and u y , the similarity of them. Here interaction means the behavior of user u x retwweted/replied messages from u y .

In the above features, we use the topic of the messages which is extracted by using unsupervised learning method LDA [START_REF] Blei | Latent dirichlet allocation[END_REF]. For features in terms of author, we use the author authority. Here, author authority means user's PageRank value in social network graph. The graph vertices represent users and edges representing the follower relationship between them, in another word, w ( i, j) is 1 in the adjacency matrix of the graph if u i follow u j . Then we apply PageRank on this graph and get each user's authority.

User Similarity

To caculate the user similarity, We think there are some rules that can be obeyed: 1) the more common friends u a and u b have, the more similar they are; 2) the more interactions they have, the more similar they are; 3) the more similar their texts are, the more similar they are. Let the common friends number of u x and u y be s, and their friends number be L ux and L uy respectively, the total interaction number that they have are I ux and

I uy respectively, then we define cf sim(u x , u y ) = s Lu x , in sim(u x , u y ) = Iu xuy Iu x
, where I uxuy denotes the number of interactions between u x and u y . At last, we also consider text similarity between users. We treat all the messages one user writes to be an article. Then we extract the topic by using LDA, whose each element represents the weight of the topic. We can also caculate the TF-IDF vector of each article. Assume the topic vector for u x and u y are

- → v 1 , - → v 2 , while TF-IDF vectors are -→ t 1 , -→ t 2 , we define text sim(u x , u y ) = 0.5sim( -→ v 1 , -→ v 2 ) + 0.5sim( -→ t 1 , -→ t 2 )
where sim( -→ x , -→ y ) is two consine correlation between -→ x , -→ y . Combining the three similiarities, we finally get the similarity between u x , u y with following equation:

sim(u x , u y ) = αcf sim(u x , u y ) + βin sim(u x , u y ) + γtext sim(u x , u y ) (7)
Where α > 0, β > 0, γ > 0, α + β + γ = 1 and those three constants are the importance weights of the three kinds of similarities respectively.

Choose Microblog Samples

For a target user u, the behavior information we have is which messages she/he retweeted/replied, and which messages she/he didn't retweet/reply. It's obvious that if user retweets/replies a message, it indicates that user likes it. Assume that the messages user received from friends form a list L = {m 0 , m 1 , . . . , m t } in which messages are arranged in reverse chronological order. If user retweetes/replies m k , then we set y k to be 1 and for messages in set N 5 (m k ) = {m i , m i / ∈ I(u) ∧ m i ∈ L, i = k -5, . . . , k -1, k + 1, . . . , k + 5}, we set their lables to be 0. Here I(u) is set of messages user have interacted with, N 5 (m k ) is the neighbor of m k whose distance to m k is no more than 5 in L. We didn't choose all the messages which u didn't have interactions because we don't know whether user likes it or not. But it's strongly believed that if two messages m x and m y appear at the successive positions and if u retweeted/replied m x while didn't do it for m y , then we can say that m x is more preferred by u, and value Φ(m x , m y ) can bet set to 1.

Experiment Result

We select 1023 users for experiments and use the metric precison and NDCG [START_REF] Järvelin | Cumulated gain-based evaluation of ir techniques[END_REF] to evaluate our results. We implement three schemes to analyze the impact of friends' data for users. The first scheme is to use all user's data for all users, which means we don't distinguish each user's data and it's not a personalized ranking. For instance, if there is 10 users, each has 100 samples, then we train the general model once with the 1000 samples and use this model for every user. This considers that every user has similar taste. The second scheme is to use user's own data for each user which suffers from the data spareness problem. The third scheme is to use k user's friend combined with user's own data to train the model, which is the method we discuss in this paper. Obviously, the second and third schemes are personalized ranking. show the result of precision, NDCG of the three schemes. Comparing the result of scheme 1,2 to 3, we can see the personalized ranking outperforms non-personalized ranking. It shows scheme 3 performs better than scheme 1,2 clearly. By comparing scheme 2 and scheme 3 in each figure, we can see the impact of friends data and the effects of our algorithm. From Figure 1(a) and 1(b), we see incorporating friend data using our algorithm really improves the result. We also study the impact of different K in the algorithm. For a target user and each of his K friends, we select C messages for training. Figure 2(a) and Figure 2(b) show the result of different K. From the figures, we can see when K ≥ 5 and K ≤ 9, the performance is best. When K is small, the performance improves as K grows. However, when K ≥ 9, the benefit of friend data is decreasing, and the results become worse. To explain the results, we analyze the data and find that in the top K(K ≥ 9) friends lists, there are some users who have many followers and generate messages very frequently. These users mostly are celebrities or organizations and most of the common users have followed them for receiving news. In some sense, the data from these users could not characterize the target user's interest very well. For this reason, when too much data is used, the benefit of friend data is decreased.

conclusion

Generally, this paper studies how to filter the real time web to find the most interesting messages for each user in social network. We apply SSRankBoost algorithm to rank the messages that user subscribed in social network. We utilize all kinds of features to evaluate the ranking method in the real data set. We think there is still a lot of room for improvement. One way is to incorporate more features into the ranking model, such as using the replied/retweeted author information of the message, the retweeted speed of the messages, the novelty of the message, etc.

Fig. 1 .

 1 scheme1 scheme2 scheme3

Fig. 2 .

 2 Fig. 2. results of different K