Fernanda Lima

Torres De Aquino

Juliane Pereira

Da Silva

Jamylle Nunes

Souza Ferro

Vincent Lagente

Emiliano Barreto
email: emilianobarreto@icbs.ufal.br

Fernanda Lima Torres De Aquino

Juliane Pereira Da Silva

trans-Cinnamic acid, but not p-coumaric acid or methyl cinnamate, induces fibroblast migration through PKA-and p38-MAPK signalling pathways

Keywords: fibroblast, trans-cinnamic acid, methyl cinnamate, p-coumaric acid, wound healing

come

Introduction

Wound healing is a dynamic process involving complex biological and molecular events, which requires the participation of many types of cells, including macrophages, keratinocytes, endothelial cells, and fibroblasts [START_REF] Guo | Factors affecting wound healing[END_REF]. During this process, fibroblasts play an important role by depositing extracellular matrix (ECM) [START_REF] Eming | Wound repair and regeneration: mechanisms, signaling, and translation[END_REF]. which guides angiogenesis [START_REF] Pastar | Epithelialization in Wound Healing: A Comprehensive Review[END_REF] and supports the migration and proliferation of other cells that eventually form the scar [START_REF] Barchitta | Nutrition and Wound Healing: An Overview Focusing on the Beneficial Effects of Curcumin[END_REF]. Thus, drugs targeting fibroblast function may contribute to skin wound healing.

The increasing popularity of natural therapies has stimulated research into the use of plant-derived compounds for wound management [START_REF] Chen | Conservation and sustainable use of medicinal plants: problems, progress, and prospects[END_REF][START_REF] Agra | Triterpenes with healing activity: A systematic review[END_REF]. It is worth pointing out that medicinal plants containing phenolic compounds, such as the species Achyrocline alata, Strychnos pseudoquina, and Grammatophyllum speciosum have been used in traditional medicinal practices worldwide to treat skin wounds [START_REF] Sarandy | Hydroethanolic Extract of Strychnos pseudoquina Accelerates Skin Wound Healing by Modulating the Oxidative Status and Microstructural Reorganization of Scar Tissue in Experimental Type I Diabetes[END_REF][START_REF] Harikarnpakdee | Grammatophyllum speciosum Ethanolic Extract Promotes Wound Healing in Human Primary Fibroblast Cells[END_REF][START_REF] Pereira | Achyrocline alata potentiates repair of skin full thickness excision in mice[END_REF]. Indeed, phytochemicals have been used to treat wounds for years. In particular, hydroxycinnamic acid derivatives are an important group of compounds present in fruits and foods, including apples, pears, peas, beans, and tomatoes, that exhibit a wide spectrum of biological activities [START_REF] Herrmann | Occurrence and content of hydroxycinnamic and hydroxybenzoic acid compounds in foods[END_REF]. Recent studies have reported that hydroxycinnamic acids exert health-promoting effects such as reduction of blood glucose levels [START_REF] Kasprzak | Phenolic Acid Content and Antioxidant Properties of Extruded Corn Snacks Enriched with Kale[END_REF] and possess anti-microbial, anti-tumour, anti-inflammatory, and wound healing properties [START_REF] Nagasaka | Anti-inflammatory effects of hydroxycinnamic acid derivatives[END_REF][START_REF] El-Seedi | Biosynthesis, natural sources, dietary intake, pharmacokinetic properties, and biological activities of hydroxycinnamic acids[END_REF].

Hydroxycinnamic acids derivatives such as trans-cinnamic acid, p-coumaric acid, and methyl cinnamate have been reported to possess antioxidant effects, an important characteristic for wound healing [START_REF] Agra | Triterpenes with healing activity: A systematic review[END_REF]. Moreover, it has been reported that trans-cinnamic acid ameliorates the UVA-induced dermatotoxic effects on human dermal fibroblasts and mouse skin [START_REF] Hseu | Trans-cinnamic acid attenuates UVAinduced photoaging through inhibition of AP-1 activation and induction of Nrf2-mediated antioxidant genes in human skin fibroblasts[END_REF]. In addition, trans-cinnamic acid also increases J o u r n a l P r e -p r o o f the capability of phagocytic innate immune cells, a phenomenon important for the healing process [START_REF] Yilmaz | Trans-cinnamic acid application for rainbow trout (Oncorhynchus mykiss): I. Effects on haematological, serum biochemical, non-specific immune and head kidney gene expression responses[END_REF]. Studies have also found that p-coumaric acid has free radical scavenging ability, anti-ulcer activity [START_REF] Pragasam | Immunomodulatory and anti-inflammatory effect of p-coumaric acid, a common dietary polyphenol on experimental inflammation in rats[END_REF], and stimulates bone formation [START_REF] Yamaguchi | The bone anabolic carotenoid p-hydroxycinnamic acid promotes osteoblast mineralization and suppresses osteoclast differentiation by antagonizing NF-kappaB activation[END_REF]. Previous studies have demonstrated the effect of methyl cinnamate on inducing nitric oxide, an important inducer of collagen deposition and stimulator of wound healing [START_REF] Murakami | Cytotoxicity and Pro-/Anti-inflammatory Properties of Cinnamates, Acrylates and Methacrylates Against RAW264.7 Cells[END_REF]. In addition, hydroxycinnamic acid derivatives also stimulates distinct intracellular signaling pathways such as protein kinase A (PKA), c-Jun N-terminal kinase (JNK) and mitogen-activated protein kinases (MAPK) [START_REF] Park | 4-Hydroxycinnamic acid protects mice from cigarette smoke-induced pulmonary inflammation via MAPK pathways[END_REF], which are associated with multiple biological functions, including changes in the actin cytoskeleton, migration and extracellular matrix (ECM) production [START_REF] Nikoloudaki | JNK Signaling as a Key Modulator of Soft Connective Tissue Physiology, Pathology, and Healing[END_REF]. All properties listed above indicate the potential of hydroxycinnamic acid derivatives in promoting wound healing. However, the effects of trans-cinnamic acid, methyl cinnamate, and p-coumaric acid on fibroblast functions related to healing, such as migration, have not been demonstrated previously.

Thus, the aim of this study was to evaluate the effect of the three hydroxycinnamic acid derivatives, trans-cinnamic acid, methyl cinnamate, and pcoumaric acid, on fibroblast migration and suggest the underlying mechanism.

Moreover, their effect on the synthesis of ECM and the cell cycle was also investigated.

Methods

Chemical and reagents

The following reagents were purchased from Sigma Chemical Co. (St. Louis, MO, USA): trans-cinnamic acid (PubChem CID: 444539, Figure 1A), p-coumaric acid (PubChem CID: 637542, Figure 1B), methyl cinnamate (PubChem CID: 637520, Figure

Cell culture

NIH 3T3 mouse embryonic fibroblasts or L929 fibroblast cell line were cultured in DMEM, supplemented with 10% FBS, 2 mM L-glutamine, and 0.02% penicillin/streptomycin, at 37°C in a humidified atmosphere containing 5% CO 2 . The J o u r n a l P r e -p r o o f assay was performed using cells between 3 and 5 passages. Hydroxycinnamic acid derivatives were dissolved in DMSO and then diluted with DMEM. The final concentration of DMSO during treatments did not exceed 0.1% (v/v) in cell cultures. DMEM containing of dimethyl sulfoxide (0.1% DMSO) was used as the vehicle.

Cell viability assay

The effect of trans-cinnamic acid, p-coumaric acid, and methyl cinnamate on cell viability was evaluated by the MTT assay [START_REF] Cardoso | Synthesis of newly functionalized 1,4-naphthoquinone derivatives and their effects on wound healing in alloxan-induced diabetic mice[END_REF]. NIH 3T3 fibroblasts were plated in 96-well plates (7×10 3 /well) and treated with vehicle (negative control) or hydroxycinnamic acid derivatives at concentrations of 3, 30, and 300 μM for 24 h. Cells lysed by 1% Triton-X-100 were used as the positive control. Next, the medium was replaced with fresh DMEM containing 5 mg/mL MTT and the plates were incubated for a further for 4 h in a humidified CO 2 incubator at 37 °C and 5% CO 2 . The resulting culture supernatant was removed and DMSO (150 µL/well) was added to each culture plate. After incubation at room temperature for 15 min, the absorbance of the solubilized MTT formazan product was spectrophotometrically measured at 540 nm. Three individual wells were assayed for each treatment, and the percentage viability relative to the control sample was determined as (absorbance of treated cells/absorbance of untreated cells)×100%.

Scratch wound healing assay

The scratch wound-healing assay was applied as previously described [START_REF] Suvarna | Identification of a small-molecule ligand of beta-arrestin1 as an inhibitor of stromal fibroblast cell migration accelerated by cancer cells[END_REF]. NIH 3T3 fibroblast cells (2×10 5 ) were seeded into each well of a 24-well plate and maintained for 24 h until they reached 90% confluency. Next, a 200 μl sterile pipette tip was used to create a straight cell-free scratch. The wells were washed with PBS to J o u r n a l P r e -p r o o f remove dead cells and debris, and then subjected to treatment with 30 μM of transcinnamic acid, p-coumaric acid, or methyl cinnamate in serum-free medium. As a control, the cells were treated with vehicle. After 8, 12 or 24 h of treatment with hydroxycinnamic acid derivatives, the cells that migrated onto the scratched region were photographed using an inverted microscope with a digital camera (Nikon Eclipse TS100, Nikon Corporation, Tokyo, Japan). In another set of experiments, the scratched monolayer was treated with 10 µM SB203580, 10 µM JNK-IN-8, or 10 µM PKI-(6-22)amide, followed by the addition of 30 µM trans-cinnamic acid for 24 h.

The same procedure described above was used to evaluate the effect of transcinnamic acid, p-coumaric acid, or methyl cinnamate on L929 dermal fibroblast cell migration. After reaching confluence, the monolayer of L929 fibroblasts was scratched.

Cells were then treated with 30 μM of trans-cinnamic acid, p-coumaric acid, or methyl cinnamate. As a control, the cells were treated with vehicle (DMSO 0.1% in DMEM). After 24h of treatment, cells were photographed using an inverted microscope with a digital camera to measure wound closure. To prevent cell proliferation, mitomycin C (4 μg/ml) was added to each well, including vehicle and hydroxycinnamic acid derivatives-treated cells; this ensured that only migration contributed to wound closure.

Cell migration was analysed using ImageJ software, and was expressed as the percentage of closure of the initial wound (scratch width at time zero) using the control group (vehicle) as 100% and the experimental group compared with the control group to ascertain the difference between the groups. Hydroxycinnamic acid derivatives-exposed and vehicle control cultures were compared. All the experiments were done in triplicate and repeated 3 times.

Transwell migration assay

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Migration assays were performed using a method described by Suvarna et al.

(2018) [START_REF] Suvarna | Identification of a small-molecule ligand of beta-arrestin1 as an inhibitor of stromal fibroblast cell migration accelerated by cancer cells[END_REF] using 24-well culture chambers and transwell inserts with polycarbonate membranes (diameter, 10 mm; pore size, 8.0 μm; Corning Costar, Cambridge, USA). NIH 3T3 fibroblasts (5×10 4 ) were seeded into the upper chamber in 200 μL DMEM medium devoid of serum and containing 30 μM trans-cinnamic acid. As a control, the cells were treated with vehicle (DMSO 0.1% in DMEM). Cells were allowed to migrate for 8 hours at 37°C in a 5% CO 2 incubator. At the end of the incubation period, the membrane was fixed with 3.7% formaldehyde, and stained with 1% crystal violet and subjected to inverted microscope image under 10× objective. Cells that penetrated the filter were counted in three predetermined fields. Three independent experiments were performed, each performed in triplicate, and a representative image is shown.

Cell-cycle analysis

The cell cycle analysis using flow cytometry was carried out as described previously by [START_REF] Pozarowski | Analysis of cell cycle by flow cytometry[END_REF] [START_REF] Pozarowski | Analysis of cell cycle by flow cytometry[END_REF]. In brief, cells were seeded on 24-well plates (6×10 4 cells/well), and 24 h later, these cells were treated with vehicle (DMSO 0.1% in DMEM) or hydroxycinnamic acid derivatives (30 µM). Subsequently, cells were trypsinized, washed with PBS, and then fixed in 70% ice-cold ethanol for 1 h. Later, the ethanol was removed by centrifugation (460 ×g for 10 min), and the cell pellets were resuspended in 500 μL of DNA-staining buffer containing 5 μg/mL PI, 1% Triton X-100, and 0.05 mg/mL RNase. After 30 min of incubation at room temperature in the dark, cells were examined using the FACSCanto II flow cytometer system (BD Biosciences, San Jose, CA, USA).

Immunofluorescence staining

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Fibroblasts (6×10 4 ) were cultured in 8-well Lab-tek chamber glass slides (Nunc, NY, USA) with complete medium (DMEM) for 24 h. The medium was then replaced and the cells were treated with 30 μM of trans-cinnamic acid for 24 h. Cells maintained in vehicle (DMSO 0.1% in DMEM) under the same conditions were used as controls.

After treatment, the cultures were washed with PBS, fixed with 100% methanol for 10 min, and subjected to an indirect immunofluorescence assay, as previously described [START_REF] Smaniotto | Growth hormone modulates thymocyte development in vivo through a combined action of laminin and CXC chemokine ligand 12[END_REF] [START_REF] Smaniotto | Growth hormone modulates thymocyte development in vivo through a combined action of laminin and CXC chemokine ligand 12[END_REF]. Briefly, cells were rehydrated in PBS and incubated for 1 h with PBS containing 1% BSA to block non-specific binding. Next, samples were incubated with primary specific anti-laminin, anti-fibronectin, or anti-collagen type I antibodies (1:50) for 1 h in a humidified chamber, washed with PBS, and incubated with appropriate FITC-conjugated secondary antibody (1:200) for 45 min at room temperature. DAPI staining was used to visualize the nuclei. Immunostained samples were analysed by fluorescence microscopy. Controls comprised specimens in which primary antibodies were replaced by unrelated Igs and generated no significant labelling. The staining of each section was analysed to obtain a mean optical density value (MOD), which represented the strength of the staining signal per positive pixel/µm 2 [START_REF] Dong | IL-27 attenuates the TGF-beta1-induced proliferation, differentiation and collagen synthesis in lung fibroblasts[END_REF].

Statistical analysis

Data are reported as mean ± standard deviation (SD) and were analysed using GraphPad Prism software, version 5.0 (San Diego, CA, USA). Comparisons between the experimental groups were performed either by one-way ANOVA followed by Tukey's test, or by two-way ANOVA followed by the Bonferroni post hoc test. P values less than 0.05 were considered statistically significant.

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RESULTS

Effect of trans-cinnamic acid, p-coumaric acid, or methyl cinnamate on cell viability

The MTT assay was used to test the potential cytotoxic effect of the hydroxycinnamic acid derivatives on fibroblasts. As presented in Figure 2, we identified that the fibroblast viability was not significantly affected by treatment for 24 h with trans-cinnamic acid, p-coumaric acid, or methyl cinnamate at doses 3, 30, or 300 µM.

Effects of hydroxycinnamic acid derivatives treatment on fibroblast motility

The effect of trans-cinnamic acid, p-coumaric acid, or methyl cinnamate on fibroblast migratory activity was examined using a scratch assay. Scratches were made on the monolayer of confluent cells and then treated with vehicle or hydroxycinnamic J o u r n a l P r e -p r o o f acid derivatives at concentration of 30 μM. Cell migration into the scratch area was evaluated at 8, 12 and 24 h. As shown in Figure 3A, fibroblasts treated with transcinnamic acid, p-coumaric acid, or methyl cinnamate exhibited a faster scratch closure rate compared to vehicle (control). In Figure 3B, after 8, 12, and 24 h of culture, basal levels of scratch closure in vehicle-treated cells were 10%, 16% and 29% respectively, while fibroblasts cultured in the presence of 30 µM of trans-cinnamic acid exhibited respectively, an increase in migration to 19% at 8 hours, 26% at 12 hours and 52% at 24 hours. A similar profile of migration occurred in cells cultured with 30 µM p-coumaric acid (Figure 3B), where cells had their migratory capacity increased to 15% at 8 hours, 20% at 12 hours and 40% at 24 hours (Figure 3B). As observed in the two preceding treatments, exposure of the cells to 30 µM of methyl cinnamate increased the migration rate to 13% at 8 hours, 23% at 12 hours and 37% at 24 hours (Figure 3B).

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We also used the established L929 fibroblast cell line in the scratch assay to assess the effects of hydroxycinnamic acid derivative on fibroblast migration. Figure 4A shows representative images captured under the inverted microscope of cells treated with vehicle (DMSO 0.1% in DMEM), trans-cinnamic acid, p-coumaric acid, or methyl cinnamate. As indicated in Figure 4B, quantitative analysis of the migratory response of L929 fibroblasts treated with mitomycin C (4 μg/ml) and exposed to trans-cinnamic acid (30 µM) showed a significant increase in total migration at 24 hours post-scratch.

However, the migratory response of L929 fibroblasts was only slightly increased by treatment with p-coumaric acid or methyl cinnamate, and these increases, compared with vehicle-treated cells, were not significant. vehicle, trans-cinnamic acid, p-coumaric acid, or methyl cinnamate, and cell migration into the cell-free area after 24 hours. Scale bars, 50 µm. B) The percentage of scratch covered was measured by quantifying the total distance the cells moved from the edges of each scratch towards the center, using ImageJ software, followed by conversion to a percentage of the wound covered. The bars represent mean ± SD of three independent experiments, each performed in triplicate. (***) P < 0.001 compared with respective vehicle-treated groups at time 0.

The effect of hydroxycinnamic acid derivatives on cell cycle progression and migration

To determine the effect of each hydroxycinnamic acid derivative on cell cycle progression, flow cytometry was performed. As shown in Figure 5, cells cultured in vehicle for 24 h (control group) exhibited a cell cycle distribution at G0/G1, S, and G2/M phases of 77.4%, 5.9%, and 16.7%, respectively. Compared with the control group, the group treated with 30 µM of trans-cinnamic acid showed a reduction in the G0/G1 phase population to 66.8% and a concomitant 16.7% increase in the S phase population without any change in the G2/M phase population (16.5%) (Figure 5).

Similarly, the treatment with p-coumaric acid or methyl cinnamate decreased, respectively, the cells in G0/G1 phase to 66.9% and 68% and, simultaneously, increased the cell population in S phase to 13.7% and 14.7%, while maintaining the numbers of cells in G2/M phase at 19.4% and 17.3%, respectively (Figure 5). These results indicate that a proliferative state of the fibroblasts was induced by treatment with the hydroxycinnamic acid derivatives.

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To eliminate the possibility that the scratch wound closure is dependent on proliferation, we treated the fibroblasts with mitomycin C, a potent inhibitor of cell proliferation. As shown in Figure 6A, cells were pre-treated with 4 μg/ml of mitomycin C for 1 h and then exposed to 30 µM hydroxycinnamic acid derivatives for 24 h. It was observed that the cell proliferation was halted by mitomycin C, while the migratory response was maintained at approximately 54% when compared to vehicle-treated cells (white bars). However, the migratory response of fibroblasts after treatment with pcoumaric acid or methyl cinnamate was reduced to the basal levels after exposure to mitomycin C (Figure 6A). This situation was different from that where fibroblasts were pre-treated with trans-cinnamic acid, wherein mitomycin C treatment did not cause a significant reduction in migratory response of fibroblasts (Figure 6A).

To confirm the effect of trans-cinnamic acid on migration, we performed transwell migration assays (Figure 6B). NIH 3T3 fibroblasts were seeded into the upper chamber with the filters, and were allowed to migrate for 8 hours. trans-Cinnamic acidtreatment resulted in a significant (close to 50%) increase in fibroblast migration (Figure 6B), thereby reinforcing the suggestion that trans-cinnamic acid plays a direct role in fibroblast migration in vitro. J o u r n a l P r e -p r o o f

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The role of the p38-MAPK, JNK, and PKA signalling pathways in transcinnamic acid-induced fibroblast migration

To identify the possible molecular mechanisms of trans-cinnamic acid-induced migration, specific inhibitors of intracellular signalling were used. As shown in Figure 7, the PKA inhibitor (PKI-(6-22)-amide, 10 µM) and p38-MAPK inhibitor (SB203580, 10 µM) inhibited the trans-cinnamic acid-induced migration of fibroblasts by 27% and 29%, respectively. Treatment with the JNK inhibitor (JNK-IN-8, 10 µM) did not cause a significant alteration in trans-cinnamic acid-induced fibroblast migration (Figure 7).

These results indicate that trans-cinnamic acid exerted a positive effect on fibroblast migration via specific mechanisms. J o u r n a l P r e -p r o o f

Effect of trans-cinnamic acid on ECM deposition by fibroblasts

Considering the importance of the ECM for the various functions of fibroblasts, including migration, we evaluated whether trans-cinnamic acid treatment could affect ECM protein synthesis. To this end, fibroblasts were exposed for 30 h to trans-cinnamic acid and the laminin, fibronectin, and collagen type I production was assessed by immunofluorescence analysis. As shown in Figure 8A, cells treated with vehicle showed a basal level of laminin protein production organized around the cell nucleus.

The treatment with trans-cinnamic acid (30 µM) intensified this immunofluorescence staining of cytoplasmic laminin. Image analysis showed a 30% increase in the intracytoplasmic fluorescence reflecting laminin levels after treatment (Figure 8B). The intracytoplasmic level of fibronectin in fibroblasts treated with vehicle was relatively low and appeared to be organized in small clusters around the cell nucleus (Figure 8A).

The treatment with trans-cinnamic acid caused a significant reduction in the amount of fibronectin produced by fibroblasts. This reduction was confirmed after image analysis showed a 27% decrease in the cytoplasmic fluorescence for fibronectin after treatment with trans-cinnamic acid (Figure 8B). When the collagen type I expression was evaluated, fibroblasts treated with vehicle exhibited a weak immunofluorescence staining that was diffused in the cytoplasm (Figure 8A). Treatment with trans-cinnamic acid (30 µM) increased the immunofluorescence staining of collagen type I in cells.

When the image was analysed, it showed an increase in collagen type I content by 45% in the intracytoplasmic fluorescence (Figure 8B). This immuno-labelling of collagen type I in the fibroblasts was increased after trans-cinnamic acid treatment (30 µM).

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Analysis of this image showed an increase by 45% in the intracytoplasmic fluorescence for collagen type I content (Figure 8B). Figure 8. Effect of trans-cinnamic acid on the levels of laminin, fibronectin, and collagen type I in fibroblasts using immunofluorescence analysis. A) Fibroblasts were cultured with or without 30 µM trans-cinnamic acid, and after 24 h, the cells were fixed and extracellular matrix was immunostained using antibodies against laminin, fibronectin, and collagen type I. Nuclei were stained with DAPI. Each panel shows an image of one representative field from three independent experiments. B) Graph showing the results of the quantification of extracellular matrix synthesis of images from panel A. Bars represent mean ± SD of three independent experiments, each performed in triplicate. Statistical significance between groups was determined by ANOVA followed by Bonferroni's test. (+) P < 0.05 and (++) P < 0.01 compared with respective vehicle-treated group.

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DISCUSSION

Hydroxycinnamic acid derivatives constitute a class of compounds originating from plants with a wide range of pharmacological effects that may be useful for treating various pathological conditions, including cutaneous wounds [START_REF] Taofiq | Hydroxycinnamic Acids and Their Derivatives: Cosmeceutical Significance, Challenges and Future Perspectives, a Review[END_REF][START_REF] Amigo-Benavent | Antiproliferative and cytotoxic effects of green coffee and yerba mate extracts, their main hydroxycinnamic acids, methylxanthine and metabolites in different human cell lines[END_REF]. In the healing process, the migration of fibroblast towards the injury area is a vital mechanism behind natural wound healing [START_REF] Tracy | Extracellular Matrix and Dermal Fibroblast Function in the Healing Wound[END_REF]. Thus, identification of compounds that can improve fibroblast migration can lead to the discovery of new and cost-effective drugs for treating wounds that do not heal. In the present study, we demonstrated that transcinnamic acid, but not its derivatives p-coumaric acid and methyl cinnamate, enhanced the migration of fibroblasts in vitro by a signalling mechanism involving PKA and p38-MAPK pathways. Additionally, we also identified a modulatory effect of transcinnamic acid on the production of ECM, which resulted in an increase of synthesis of laminin and collagen type I and reduction in fibronectin synthesis by fibroblasts.

Fibroblast functions during the wound healing process are essential for restoring tissue structural integrity and maintaining tissue functionality. The initial responses of fibroblasts adjacent to the wound site include proliferation, migration, and ECM deposition [START_REF] Tracy | Extracellular Matrix and Dermal Fibroblast Function in the Healing Wound[END_REF]. It is important to mention that the lack of these functions can compromise healing, and fibroblast dysfunction is a key factor that leads to the nonhealing of chronic venous leg ulcers [START_REF] Wall | Fibroblast dysfunction is a key factor in the non-healing of chronic venous leg ulcers[END_REF], as well as the impairment of wound healing observed in diabetic patients [START_REF] Jhamb | Genetic and molecular basis of diabetic foot ulcers: Clinical review[END_REF]. Thus, these observations clearly demonstrate that compounds with the ability to improve fibroblast functions can have a positive role in wound healing.

Previous studies have shown that hydroxycinnamic acids have preferential cytotoxicity for cancer cell lines [START_REF] Matsuno | Preferential cytotoxicity to tumor cells of 3,5-diprenyl-4-hydroxycinnamic acid (artepillin C) isolated from propolis[END_REF][START_REF] Menezes | Long Chain Alkyl Esters of Hydroxycinnamic Acids as Promising Anticancer Agents: Selective Induction of Apoptosis in Cancer Cells[END_REF] and that changes in the structure of these molecules can change their biological activity or even impart new activities [START_REF] Taofiq | Hydroxycinnamic Acids and Their Derivatives: Cosmeceutical Significance, Challenges and Future Perspectives, a Review[END_REF]. Here, J o u r n a l P r e -p r o o f we used MTT assay to estimate the cytotoxic potential of the hydroxycinnamic acid derivatives. The MTT assay has been widely used in experimental research to assess cell viability [START_REF] Kumar | Analysis of Cell Viability by the MTT Assay[END_REF]. In addition, the advantages of the MTT assay include ease and rapidity of performance, reproducibility of the results, and observed clinical correlation between in vitro and in vivo testing [START_REF] Sasaki | Evaluation of cell viability and metabolic activity of a 3D cultured human epidermal model using a dynamic autoradiographic technique with a PET radiopharmaceutical[END_REF]. In the present study, we evaluated the in vitro cytotoxicity of trans-cinnamic acid, p-coumaric acid, and methyl cinnamate, and we noted no toxic effects on fibroblasts at dose ranges from 3-300 µM and 24 h exposure. This result confirms previous reports that demonstrated the absence of cytotoxicity of these hydroxycinnamic acid derivatives in fibroblasts [START_REF] Hseu | Trans-cinnamic acid attenuates UVAinduced photoaging through inhibition of AP-1 activation and induction of Nrf2-mediated antioxidant genes in human skin fibroblasts[END_REF][START_REF] Seok | p-Coumaric Acid Attenuates UVB-Induced Release of Stratifin from Keratinocytes and Indirectly Regulates Matrix Metalloproteinase 1 Release from Fibroblasts[END_REF][START_REF] Panel | A toxicologic and dermatologic assessment of related esters and alcohols of cinnamic acid and cinnamyl alcohol when used as fragrance ingredients[END_REF].

Whereas that mouse fibroblastic lines are comparable to human cell lines in responsiveness to migration pattern [START_REF] Lan | Integrating transient cellular and nuclear motions to comprehensively describe cell migration patterns[END_REF], we decided to evaluated the effect of hydroxycinnamic acid derivatives on mouse fibroblast migration via the scratch assay. This assay is a standard in vitro methodology, which enables screening of substances that are able to regulate cellular migration [START_REF] Jonkman | An introduction to the wound healing assay using live-cell microscopy[END_REF]. For this, a cell-free area is created in a confluent monolayer through mechanical damage, and the cells that remain move towards the artificial gap, which allows for measuring the rate of gap closure (wound closure) [START_REF] Liang | In vitro scratch assay: a convenient and inexpensive method for analysis of cell migration in vitro[END_REF].

In this assay, the mouse fibroblast cell lines are commonly used in screening drugs that can improve wound healing [START_REF] Pusnik | The agar diffusion scratch assay--A novel method to assess the bioactive and cytotoxic potential of new materials and compounds[END_REF]. In addition, the importance of NIH/3T3 and L929 cells as model for translational study have been recognized by many biomedical and pharmaceutical companies [START_REF] Sarandy | Hydroethanolic Extract of Strychnos pseudoquina Accelerates Skin Wound Healing by Modulating the Oxidative Status and Microstructural Reorganization of Scar Tissue in Experimental Type I Diabetes[END_REF]. In our study, trans-cinnamic acid, p-coumaric acid, or methyl cinnamate were used as a treatment during the scratch assay, and all these compounds were found to accelerate the closure of the scratched area in a timedependent manner, suggesting that these compounds have a direct effect on fibroblasts.

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These results are consistent with a previous study in which an hydroxycinnamic acid, ferulic acid, significantly improved fibroblast motility [START_REF] Miguel | Antioxidants counteract nicotine and promote migration via RacGTP in oral fibroblast cells[END_REF].

It is well-known that during a scratch assay, cells not only migrate into the gap, but also proliferate. Thus, we decided to characterize the effect of these hydroxycinnamic acid derivatives on cell cycle phases. Our results revealed that transcinnamic acid, p-coumaric acid, or methyl cinnamate promoted proliferation of fibroblasts, accelerating the entry of cells into the S phase of the cell cycle. It is wellestablished that serum starving is the most common non-pharmaceutical method for minimizing proliferation in wound healing assays [START_REF] Jonkman | An introduction to the wound healing assay using live-cell microscopy[END_REF]. Even so, we decided to carry out the scratch assay with hydroxycinnamic acid derivatives using mitomycin C, a drug used to arrest mitosis at various stages. To our surprise, we found that the use of mitomycin C prevented the migration of fibroblasts treated with p-coumaric acid or methyl cinnamate, but not trans-cinnamic acid, in the scratch assay. Such an effect of trans-cinnamic acid in accelerating closure of the scratched area was also observed when L929 fibroblasts were used in the scratch assay. To further confirm this effect on fibroblast motility, trans-cinnamic acid-treated cells showed markedly increased migratory activity compared with untreated cells when evaluated using transwell migration assays. These observation supports the notion that trans-cinnamic acid directly affect fibroblast migration. This selective influence on cell migration is not unique to trans-cinnamic acid, the gallic acid, a hydroxybenzoic acid occurring mostly in red fruits, also exerts migration-stimulating effects on human dermal fibroblasts without inducing proliferation [START_REF] Liang | In vitro scratch assay: a convenient and inexpensive method for analysis of cell migration in vitro[END_REF]. Furthermore, this finding clearly shows that the chemical modifications in the structure of these compounds have a direct impact on the profile of their biological activities, indicating the need for further studies on the structure-activity relationship of these hydroxycinnamic acid derivatives. A probable, J o u r n a l P r e -p r o o f albeit speculative, explanation may be that the chemical structure of p-coumaric acid has one -OH moiety in its aromatic ring, while methyl cinnamate has one -CH3 moiety in its carboxyl group; these modifications may affect electronic charge distribution across the whole structure, bringing changes in these molecules' polarity, and causing loss of activity. Prior chemical studies have demonstrated that simple chemical modifications may result in unexpected effects in hydroxycinnamic acids, including new modes of action [START_REF] Calabriso | Multiple antiinflammatory and anti-atherosclerotic properties of red wine polyphenolic extracts: differential role of hydroxycinnamic acids, flavonols and stilbenes on endothelial inflammatory gene expression[END_REF][START_REF] Razzaghi-Asl | Antioxidant properties of hydroxycinnamic acids: a review of structure-activity relationships[END_REF]. Further studies are required to investigate this concept.

Fibroblast migration is an event mediated by multiple intracellular signalling pathways, including protein kinase A (PKA), CREB, and p38 and JNK MAPK pathways [START_REF] Cargnello | Activation and function of the MAPKs and their substrates, the MAPK-activated protein kinases[END_REF]. However, despite scientific advancements in recent years, knowledge about the fibroblast signalling pathways that are activated by trans-cinnamic acid remains scarce. Thus, to evaluate whether the pro-migratory effect of trans-cinnamic acid is exerted via any intracellular signalling pathways, we evaluated fibroblast migration using the scratch assay in the presence of inhibitors of distinct signalling pathways. Our results demonstrated that inhibitors PKI-(6-22)-amide and SB203580, but not the inhibitor of JNK-IN-8, attenuated the trans-cinnamic acid-induced fibroblast migration, indicating the involvement of both signalling pathway PKA and p38-MAPK in fibroblast migration. These data are in agreement with previous studies showing that trans-cinnamic acid induces an increase in PKA and p38-MAPK phosphorylation in other cell types, such as adipocytes [START_REF] Razzaghi-Asl | Antioxidant properties of hydroxycinnamic acids: a review of structure-activity relationships[END_REF] and epithelial cells [START_REF] Chen | Novel Nrf2/ARE activator, trans-Coniferylaldehyde, induces a HO-1-mediated defense mechanism through a dual p38alpha/MAPKAPK-2 and PK-N3 signaling pathway[END_REF]. Therefore, the involvement of these intracellular signalling pathways in the positive regulation of fibroblast migration induced by trans-cinnamic acid supports the proposition of this compound as a new tool for improving fibroblast migration. However, further studies are required to determine how trans-cinnamic acid leads to both PKA and p38-MAPK activation.

J o u r n a l P r e -p r o o f

It is also worth highlighting that previous studies demonstrated that activation of PKA and p38-MAPK intracellular signalling pathways induces the upregulation of ECM production by fibroblasts [START_REF] Lv | Proliferation in cardiac fibroblasts induced by beta1-adrenoceptor autoantibody and the underlying mechanisms[END_REF][START_REF] Schiller | Increased cAMP levels modulate transforming growth factor-beta/Smad-induced expression of extracellular matrix components and other key fibroblast effector functions[END_REF]. Considering that the deposition of laminin, collagen type I and fibronectin plays a crucial role in fibroblast migration, adhesion, and wound healing, we evaluated whether trans-cinnamic acid also affected the production of these ECM proteins. Indeed, we verified that trans-cinnamic acid increased the synthesis of laminin and collagen type I and inhibited the synthesis of fibronectin. In line with our study, previous findings have also shown this dual effect of cinnamic acid in stimulating laminin and collagen synthesis while simultaneously inhibiting fibronectin production in epidermal cells [START_REF] Taofiq | Hydroxycinnamic Acids and Their Derivatives: Cosmeceutical Significance, Challenges and Future Perspectives, a Review[END_REF][START_REF] Gunia-Krzyzak | Cinnamic acid derivatives in cosmetics: current use and future prospects[END_REF]. Considering that ECM can induce intracellular signals or interact with transduction pathways triggered by specific receptors, thereby promoting cell migration, we can infer that trans-cinnamic acid might represent a new chemical that can induce fibroblast migration.

CONCLUSION

In conclusion, we have presented for the first time that in addition to stimulating laminin and collagen type I synthesis, trans-cinnamic acid significantly stimulates fibroblast function to promote their migration via a mechanism that seems to involve both the PKA and p38-MAPK signalling pathways. These effects might have an important role in the wound healing process and are relevant for wound therapies.

J o u r n a l P r e -p r o o f

  J o u r n a l P r e -p r o o f 1C), Tween-20, dimethyl sulfoxide (DMSO), phosphate-buffered saline (PBS), Dulbecco's modified Eagle medium (DMEM), mitomycin C, propidium iodide (PI), methylthiazolyldiphenyl-tetrazolium bromide (MTT), 4′,6-diamidino-2′-phenylindole dihydrochloride (DAPI), SB203580 (p38-MAPK inhibitor), JNK-IN-8 (inhibitor of JNK1/2/3), and PKI-(6-22)-amide (inhibitor of cAMP-dependent protein kinase, CREB). Foetal bovine serum (FBS), trypsin, penicillin, streptomycin, and L-glutamine were purchased from Invitrogen (Carlsbad, CA, USA). Antibodies against laminin, fibronectin, and collagen type I were purchased from Novotec (St. Martin-LaGarenne, France), while anti-goat IgG-fluorescein isothiocyanate (FITC) was from Santa Cruz Biotechnology (CA, USA).

Figure 1 .

 1 Figure 1. The chemical structure of the trans-cinnamic acid (A), p-coumaric acid (B), and methyl cinnamate (C) used in the present study.

Figure 2 .

 2 Figure 2. Effect of trans-cinnamic acid, p-coumaric acid, or methyl cinnamate on the viability of NIH 3T3 fibroblasts. Cells were plated at 7×10 3 cells/well and treated with hydroxycinnamic acid derivatives (3-300 µM) for 24 h. Cell viability was measured by MTT assay. The dashed line represents cells maintained only with vehicle (DMSO 0.1% in DMEM), while the solid line represents the cells lysed by 1% Triton-X-100 (positive control). The bars represent mean ± SD of three independent experiments, each performed in triplicate.

Figure 3 .

 3 Figure 3. The effect of trans-cinnamic acid, p-coumaric acid, and methyl cinnamate on the cell migration of NHI 3T3 fibroblasts assayed by the scratch assay. Fibroblasts were treated with 30 µM of each hydroxycinnamic acid derivative, and images were captured to calculate the scratch closure. A) Representative photomicrography images showing the scratch area of cells treated with vehicle (DMSO 0.1% in DMEM), trans-cinnamic acid, p-coumaric acid, or methyl cinnamate and the cell migration towards the cell-free area after 8, 12 and 24 h. Scale bars, 50 µm. B) The percentage of scratch covered was measured by quantifying the total distance the cells moved from the edge of the scratch towards the center of the scratch, using ImageJ software, followed by conversion to a percentage of the wound covered. The bars represent mean ± SD of three independent experiments, each performed in triplicate. (*) P < 0.05, (**) P < 0.01 and (***) P < 0.001 compared with vehicle-treated cells at the respective time points.

Figure 4 .

 4 Figure 4. The effect of trans-cinnamic acid, p-coumaric acid, and methyl cinnamate on cell migration by L929 fibroblasts, assayed by the scratch assay. Fibroblasts were treated with 30 µM of each hydroxycinnamic acid derivative or vehicle (DMSO 0.1% in DMEM), and photomicrographs were captured to calculate scratch closure. A) Representative photomicrographic images showing the scratch area of cells treated with

Figure 5 .

 5 Figure 5. Effect of trans-cinnamic acid, p-coumaric acid, and methyl cinnamate on cell cycle progression in fibroblasts. Cells were exposed to the treatment for 24 h. Cell cycle progression in fibroblasts was assessed by flow cytometry by analysing propidium iodide fluorescence in the slow acquisition and with doublet discrimination; 10,000 events were recorded. A) Histogram showing cell cycle of cells treated with 30 µM hydroxycinnamic acid derivatives. B) Quantification of G0/G1-, S-, and G2/M-stage in fibroblasts. The bars represent mean ± SD of three independent experiments, each performed in triplicate. Statistical significance between groups was determined by ANOVA followed by Bonferroni's test. (+) P < 0.05 compared with vehicle-treated cell in the respective phase.

Figure 6 .

 6 Figure 6. trans-Cinnamic acid promotes fibroblast migration. A) NIH 3T3 fibroblasts were treated with mitomycin C before adding 30 µM of each hydroxycinnamic acid derivative. Cell migration in the scratch assay was determined by photomicrography at 0-and 24-hours post scratch with the aid of an inverted microscope. B) NIH 3T3 Fibroblast migration analyses via transwell cell migration assay. Cells were seeded into transwell chambers in serum-free medium containing 30 μM trans-cinnamic acid or vehicle (DMSO 0.1% in DMEM), and allowed to migrate across the membranes for 8 hours. Membranes were stained with crystal violet, and images were captured with an inverted microscope. Bars represent mean ± SD of three independent experiments, each performed in triplicate. Statistical significance between groups was determined by ANOVA followed by Bonferroni's test. (+) P < 0.05 and (++) P < 0.01 compared with medium-treated cells, (**) P < 0.01 and (***) P < 0.001 compared with vehicle-treated cells. (# # P < 0.01).

Figure 7 .

 7 Figure 7. Inhibition of PKA and p38-MAPK but not JNK signalling pathways attenuates fibroblast migration induced by trans-cinnamic acid. Scratch wounds were created in cell monolayers of fibroblasts using a sterile pipette tip. Then, cells were cultured only with vehicle (DMSO 0.1% in DMEM) or PKI-(6-22)-amide (a PKA inhibitor, 10 µM), SB203580 (a p38-MAPK inhibitor, 10 µM), or JNK-IN-8 (a JNK inhibitor, 10 µM) in the presence of trans-cinnamic acid (30 µM) for 24 h. The bars represent mean ± SD of three independent experiments, each performed in triplicate. Statistical differences were detected with two-way ANOVA followed by the Bonferroni test. (+++) P < 0.001 compared to vehicle-treated cells, (**) P < 0.01 compared to trans-cinnamic acid-treated cells.

This study was supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, 308898/2018-4). This work was conducted during a scholarship supported by the International Cooperation Program CAPES/COFECUB Foundation at the University of Rennes-FR. Financed by CAPES -Brazilian Federal Agency for Support and Evaluation of Graduate Education within the Ministry of Education of Brazil. Declarations of interest: none J o u r n a l P r e -p r o o f 1 trans-Cinnamic acid, but not p-coumaric acid or methyl cinnamate, induces fibroblast migration through PKA-and p38-

Highlights

• Treatment with trans-cinnamic acid, but not methyl cinnamate or p-coumaric acid, caused an increase in fibroblasts migration.

• trans-Cinnamic acid-induced fibroblast migration was dependent of PKA and p38-MAPK signaling pathways.

• trans-Cinnamic acid provoked in fibroblasts the synthesis of laminin and collagen type 1, but not of fibronectin.

J o u r n a l P r e -p r o o f

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

J o u r n a l P r e -p r o o f


Bhusan Chettri
email: b.chettri@qmul.ac.uk

Rosa González Hautamäki

Md Sahidullah
email: md.sahidullah@inria.fr

Tomi Kinnunen

Data Quality as Predictor of Voice Anti-Spoofing Generalization

Keywords: anti-spoofing, data quality, interpretative models

Voice anti-spoofing aims at classifying a given utterance either as a bonafide human sample, or a spoofing attack (e.g. synthetic or replayed sample). Many anti-spoofing methods have been proposed but most of them fail to generalize across domains (corpora) -and we do not know why. We outline a novel interpretative framework for gauging the impact of data quality upon anti-spoofing performance. Our within-and betweendomain experiments pool data from seven public corpora and three anti-spoofing methods based on Gaussian mixture and convolutive neural network models. We assess the impacts of long-term spectral information, speaker population (through xvector speaker embeddings), signal-to-noise ratio, and selected voice quality features.

Introduction

In the context of biometrics, presentation attack detection (PAD) or anti-spoofing aims at classifying a given signal either as a bonafide (human) sample or a spoofing attack. Replay, text-to-speech, and voice conversion attacks degrade the performance of automatic speaker verification (ASV) systems. Driven by fraud prevention in call-centers and securing our identities in other applications, a new research community working on voice anti-spoofing has emerged during the past few years. In part, research has been enabled by increased number of corpora containing both bonafide and spoofed data, such as ASVspoof [START_REF] Kinnunen | ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech[END_REF]. There are also other public (and proprietary) data such as BTAS 2016 [START_REF]Overview of BTAS 2016 speaker anti-spoofing competition[END_REF], SAS [START_REF]SAS: A speaker verification spoofing database containing diverse attacks[END_REF], ReMASC [START_REF] Gong | ReMASC: Realistic replay attack corpus for voice controlled systems[END_REF], and PhoneSpoof [START_REF]PHONESPOOF: A new dataset for spoofing attack detection in telephone channel[END_REF].

Numerous speaker-independent voice anti-spoofing methods have been proposed. Many focus on designing new acoustic features [START_REF] Todisco | Constant Q cepstral coefficients: A spoofing countermeasure for automatic speaker verification[END_REF][START_REF] Suthokumar | Modulation dynamic features for the detection of replay attacks[END_REF], deep neural network (DNN) architectures [START_REF] Lavrentyeva | Audio replay attack detection with deep learning frameworks[END_REF][START_REF] Gomez-Alanis | A gated recurrent convolutional neural network for robust spoofing detection[END_REF] or combining different models [START_REF]Ensemble models for spoofing detection in automatic speaker verification[END_REF][START_REF] Chen | ResNet and model fusion for automatic spoofing detection[END_REF] through classifier fusion. Many studies report low spoofing attack detection error rates (even 0 %) though the methods are usually tested using a single corpus only. With sufficiently many architectural modifications, control parameter optimizations and experiments it may be feasible to push error rates down on a given corpus. Performance on a single corpus, however, should not be viewed as a measure of generality or to suggest a solved task. Real-world operation demands reliable operation across many test conditions, most of which are never encountered during system development.

Lack of generality has been noted in (limited number of) cross-corpus studies [START_REF] Korshunov | A Cross-Database Study of Voice Presentation Attack Detection[END_REF][START_REF] Paul | Generalization of spoofing countermeasures: A case study with asvspoof 2015 and btas 2016 corpora[END_REF][START_REF] Das | Assessing the scope of generalized countermeasures for anti-spoofing[END_REF][START_REF] Parasu | Investigating light-ResNet architecture for spoofing detection under mismatched conditions[END_REF] where the training and test data originate from disjoint collections (often compiled by different research teams). The reported error rates, sometimes close to chance level, are disturbing as they suggest overfitting Figure 1: Voice anti-spoofing corpora visualized with tstochastic neighborhood embedding (t-SNE). Each point corresponds to long-term average spectrum (LTAS) of one utterance.

on the existing corpora. With a spoiler alert, the reader is encouraged to peek our cross-corpus results reported in Table 1.

But why voice anti-spoofing, especially across corpora, is so difficult? As an intuitive motivation, Fig. 1 visualizes spectral differences in seven different voice anti-spoofing corpora. Even if each corpus contains different spoofing attacks of varied difficulty, at the corpus level the audio files can be homogenous. This is due to shared acoustic properties that may depend on speaker population, original recording environment, choice of microphones, data processing pipelines -and perhaps even on signal scale and audio file format. Similarly, there are systematic differences across corpora due to differences in such characteristics. When a voice anti-spoofing system is trained and tested using data in a single corpus only, one conveniently sidesteps the issue of feature or representation compatibility across domains; it may not be needed as the training and test data are already homogenous in their qualities.

Our work aims at quantifying the impact of corpus-level acoustic mismatch factors upon voice anti-spoofing performance. Our work is differentiated from majority of prior work in anti-spoofing by an explanatory perspective. As a community, we lack understanding of the role of training and test data in anti-spoofing. Given the central role of data in any machine learning task (including anti-spoofing), we argue that it is useful to uncover data-related factors that contribute negatively (or positively) to performance. We approach this problem by focusing on a few carefully selected corpus level attributes, such as distribution of signal-to-noise ratio and speakers. These potentially confounding variables are then used as predictors of anti-spoofing performance in a regression analysis setting.

Our work is not the first to address the impact of factors that may influence anti-spoofing performance or bias evalua-tion results. Prior work has addressed, for instance, the impact of waveform sample distributions [START_REF] Lapidot | Effects of waveform PMF on anti-spoofing detection for replay data -ASVspoof 2019[END_REF] and biases due to presence of silence regions [START_REF]Ensemble models for spoofing detection in automatic speaker verification[END_REF][START_REF] Chettri | Dataset artefacts in antispoofing systems: a case study on the ASVspoof 2017 benchmark[END_REF]. Other work, such as [START_REF] Tak | An explainability study of the constant Q cepstral coefficient spoofing countermeasure for automatic speaker verification[END_REF], have provided interpretations beyond error rates for specific anti-spoofing methods. Our work is differentiated from these studies in that we propose a unified framework for assessing data-related quality factors, treated as predictors in a regression model setting. What follows is description of our framework and preliminary experiments that pool data from seven different anti-spoofing corpora.

Methodology

Re-thinking training and test sets as random data

Assume that we have a total of M distinct, labeled anti-spoofing collections {Di} M i=1 available (here, M = 7). The i th collection contains, respectively, N (i) bona and N (i) spoof bonafide (human) and spoof audio files. Each file is labeled as either one of these two classes. Each collection (e.g. particular ASVspoof edition) is assumed to consist of somewhat homogenous audio material, while different collections -possibly compiled by different researchers -are assumed to be more heterogenous. Each collection can be understood as a cluster or group of audio files that share some commonalities. The reported performance gap of within-corpus vs. cross-corpus results [START_REF] Korshunov | A Cross-Database Study of Voice Presentation Attack Detection[END_REF], along with Table 1 and the visualization in Fig. 1 on long-term spectral characteristics provide support for these assumptions.

Typically, a voice anti-spoofing corpus contains an evaluation protocol that defines partitioning of the speech files into training and test portions 1 . Even if standard evaluation protocols are necessary for commensurable performance comparisons, a protocol defines only one possible data partitioning of all the available data. As a result, reported anti-spoofing results on a given corpus may be specific to that random partitioning. In stark contrast to fixed train-test protocol division, we consider the training/test corpora as random observations. Whenever the anti-spoofing system (and its parameters) are frozen, one obtains one performance number (such as equal error rate, EER) for a fixed evaluation protocol. We, instead, gather several repeated measurements of the selected performance measure (here, the EER) within and across data collections.

In practice, for each of the M collections we designate a single training set D (i) train and multiple test sets,

D (i,j) test , j = 1, . . . , N (i)
test . In principle, this choice is arbitrary and we could have also fixed the test sets and sample random training sets instead. The choice is primarily dictated by computational reasons elaborated shortly. We sample equal number of test portions within each collection:

N (1) test = • • • = N (M ) test ≡ Ntest.
Note that the special case Ntest = 1 corresponds to conventional approach where a given corpus is equipped with a predefined evaluation protocol. In our revised set-up we train and test anti-spoofing systems across all the collections. This yields Ntest within-corpus and (M -1) × Ntest cross-corpus experiments, per training set. As we have M training sets (one per corpus), we have a total of M × Ntest within-corpus results and M × (M -1) × Ntest cross-corpus results. In our experiments, Ntest = 20 which implies 140 within-and 840 cross-corpus experiments. This is why we fix the training partition and treat

Defining the predictors (corpus distances)

Let Dtrain and Dtest denote training and test sets that are used, respectively, to train and score any anti-spoofing system. They could be sets within the same collection or sets taken from different collections; this distinction is not important as the procedure of distance computation is the same. Let Dtrain = {(Xj, yj)} N train j=1 and Dtest = {(Xm, ym)} Ntest m=1 denote training and test waveforms X paired up with their ground-truth labels, y ∈ {0 ≡ spoof, 1 ≡ bonafide}. The jth waveform, Xj, is represented by a set of quality features, φ

(1) j , . . . , φ (Q) j
. They may have different dimensionalities and numerical ranges. For instance, φ

(1) j might be scalar-valued signal-to-noise ratio (SNR) and φ

(2) j a 512-dimensional deep speaker embedding. Each feature set corresponds to attributes suspected to influence antispoofing performance but which (ideally) should be uninformative about the class label y. For instance, one is not supposed to detect a spoofing attack based on knowledge of the speaker (at least in speaker-independent anti-spoofing setting). At the level of the corpus, however, it is useful to gauge the potential impact of speaker population upon anti-spoofing performance.

In practice, we treat each of the Q features independent of each other. We drop the feature superscript momentarily and use φj to denote any of the Q measurements of file j. The observed quality data are then Dtrain = {(φj, yj)} N train j=1 and Dtest = {(φm, ym)} Ntest m=1 , viewed as i.i.d. samples from some underlying true distribution p(φ, y). By conditioning the data distribution both by the class label (bonafide/spoof) and the data portion (train/test) we have four conditional data distributions in total, as illustrated in Fig. 2. For regression modeling, we consider all the indicated six distances, for a given set of quality measurements (thus, the maximum number of predictors is R = 6Q, obtained by cross-combining all Q quality measurements with the six different distances). The distance that we use is Chamfer distance (or modified Hausdorff distance) based on averaged Euclidean squared distances with nearest-neighbor rule. It can be computed without numerical issues for features of any dimensionality. It gives non-negative distance of two point clouds, each of which corresponds to one the four portions shown in Fig. 2. Chamfer distance is not symmetric but we compute distance to both directions and average the two values. We also normalize the distance by the dimensionality of the respective quality measurement.

The within-class distances across training and test are perhaps most easily intuitively understood. For instance, d13 measures how much bonafide data qualities between training and test data differ (likewise for spoof, d24). The remaining four cross-class distances may appear strange at first but we have a reason to include them in our models. If both bonafide and spoof are corrupted by similar nuisance variations (e.g. both are either clean or noisy) one may expect lower anti-spoofing EER as the classifier does not have to address the issue of noise. Similarly, if the training distributions of bonafide and spoof distributions are very different, the anti-spoofing system may learn to cheat (take a shortcut) by extracting information unrelated to bonafide-spoof discriminating cues -hence, potentially exhibit low generalization performance.

Experimental Setup

Spoofing corpora

We use seven publicly available corpora: SAS [START_REF]SAS: A speaker verification spoofing database containing diverse attacks[END_REF], ASVspoof 2017 v2.0 [START_REF] Delgado | Asvspoof 2017 version 2.0: meta-data analysis and baseline enhancements[END_REF], ASVspoof 2019 (LA, PA and PA Real) [START_REF] Todisco | ASVspoof 2019: Future horizons in spoofed and fake audio detection[END_REF], BTAS 2016 [START_REF]Overview of BTAS 2016 speaker anti-spoofing competition[END_REF], and ReMASC [START_REF] Gong | ReMASC: Realistic replay attack corpus for voice controlled systems[END_REF]. The SAS corpus was created for anti-spoofing research with seven voice conversion (VC) and three speech synthesis (SS) methods. The subsequent ASVspoof 2015 corpus includes the same attacks. While the ASVspoof 2017 corpus contains real replay attack recordings, the ASVspoof 2019 PA corpus consists of simulated replay attacks. PA real is a small test set that contains real replayed audio files. ReMASC [START_REF] Gong | ReMASC: Realistic replay attack corpus for voice controlled systems[END_REF] is a another publicly available corpus for replay spoofing attack research in voice controlled applications. We also include thecorpus used in BTAS 2016 anti-spoofing competition. It consists of different types of replay attacks [START_REF]Overview of BTAS 2016 speaker anti-spoofing competition[END_REF].

Random training-test protocol design

We have created multiple train-test conditions with smaller subsets. We sampled the training data to create a smaller training subset balanced according to the number of utterances and speakers. We include five speakers from each corpus, each with 10 bonafide and 50 spoofed utterances. This results to 300 training utterances per corpus. Similarly, we created 20 test sets for each of the seven corpora, each consisting of 50 bonafide and 250 spoofed utterances. The bonafide-to-spoof utterance ratio approximately corresponds to the ratio in standard evaluation protocols -there are typically far more spoofed than bonafide utterances available. We selected the speakers and the utterances from the respective pre-defined 'train' and 'evaluation' partition randomly. Due to unavailability of the speaker partitioning of training and evaluation in ReMASC and ASVspoof 2019 Real PA, we select the speakers of train and test in a disjoint manner.

Classifiers and performance measures

We use Gaussian mixture model (GMM) and convolutional neural network (CNN) as classifiers, due to their extensive use in anti-spoofing research [START_REF] Lavrentyeva | Audio replay attack detection with deep learning frameworks[END_REF][START_REF] Suthokumar | Modulation dynamic features for the detection of replay attacks[END_REF][START_REF] Kinnunen | ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech[END_REF][START_REF] Sahidullah | Introduction to voice presentation attack detection and recent advances[END_REF]. The GMMbased systems are the same as the two baseline systems used in the ASVspoof 2019 challenge. They operate on 60dimensional linear frequency cepstral coefficients (LFCCs) and 90-dimensional constant-Q cepstral coefficients (CQCCs), respectively. Two GMMs are trained to model the distribution of bonafide and spoof data using 512 mixture components. The CNN system, in turn, uses power spectrogram inputs. It is trained discriminatively to optimise cross-entropy between bonafide and spoofed class using Adam optimiser. We use the CNN architecture, training and testing approach from [START_REF] Chettri | Subband modeling for spoofing detection in automatic speaker verification[END_REF].

We evaluate classifier performance using equal error rate (EER) as a measure of bonafide-spoof discrimination. We compute EER using the public scoring toolkit used in the ASVspoof 2019 challenge. Table 1 summarises the cross-corpus performance evaluation of CNN countermeasure. As can be seen, performance is reasonable for (some) within-corpus tasks but consistently low in cross-corpus scenarios, as expected [START_REF] Korshunov | A Cross-Database Study of Voice Presentation Attack Detection[END_REF].

Quality features

We include five types of quality features. Four of them are computed with rule-based methods available in common toolkits, while one (x-vector) uses a data-driven approach, which makes feature values dependent on the training data of the extractor.

LTAS represents spectral information averaged over time. We compute 257-dimensional LTAS per utterance using 512-point FFT from 32 ms Hanning-windowed frames shifted by 10 ms. SNR is computed using waveform amplitude distribution analysis (WADA) method [START_REF] Kim | Robust signal-to-noise ratio estimation based on waveform amplitude distribution analysis[END_REF], which assumes that the amplitude of the speech can be approximated with Gamma distribution with shape parameter 0.4 and the noise by Gaussian distribution. WADA shows competitive performance compared to the DNN-based data-driven methods specially in higher SNR conditions [START_REF] Li | Frame-level signalto-noise ratio estimation using deep learning[END_REF], case relevant for our data. Noise spectrum Besides scalar-valued SNR, we also estimate noise spectral density using optimal smoothing and minimum statistics method [START_REF] Martin | Noise power spectral density estimation based on optimal smoothing and minimum statistics[END_REF]. The method estimates noise for all frequency bins in every speech frame. We average these noise spectral densities to obtain 257 coefficients per utterances. X-vector represents 512-dimensional deep speaker embedding extracted with pre-trained models trained on VoxCeleb corpus [START_REF]VoxCeleb Xvector models system 1a[END_REF], processed further with length normalization. Though x-vectors depend on training data and may contain nuisance variations [START_REF] Raj | Probing the information encoded in x-vectors[END_REF], the pre-trained model shows reasonable speaker verification EER of 3.13% on VoxCeleb1 test set. This indicates high specificity to speaker-related information. Acoustic descriptors are extracted using openSMILE toolkit 2.3 [START_REF] Eyben | Recent developments in openSMILE, the munich open-source multimedia feature extractor[END_REF]: fundamental frequency (F0), formant frequencies (F1 to F4), and loudness. The feature extraction configuration corresponds to the extended Geneva Minimalistic Standard Parameter Set [START_REF]The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for voice research and affective computing[END_REF] summarized with the mean of the descriptor at the utterance level. F0 is presented in a semitone scale. Loudness is an estimate of the perceived signal energy from an auditory spectrum from perceptual linear prediction (PLP) analysis [START_REF] Hermansky | Perceptual linear predictive (PLP) analysis of speech[END_REF].

Results

We explore the collinearities between the predictive features and the performance of classifiers using the Pearson correlation. The relation was analyzed considering the data grouped in within-and across-corpus that include 140 and 840 data points respectively, each with the six distances from the predictive features (as illustrated in Fig. 2). Figure 3 shows the EER distribution for the three classifiers and describes the dependent variable variations to be explored by the regression models. Table 2 shows, as an example, the correlation of LTAS feature distances with the EER of the CNN classifier (similar trends were observed for the other predictive features). Within-corpus correlations are stronger than across-corpus correlations. This indicates collinearity of the within-corpus distances and the performance of the classifiers. As for the distances, the four crossclass distances (d12, d14, d23, d34) have stronger correlations with EER (whether positive or negative) than the within-class distances (d13, d24). Note also that, apart from d13 on acrosscorpus case, the within-class correlations are positive. This is as expected: the larger the domain mismatch in either bonafide or spoof class, the higher the EER. We now turn our focus on the predictive features. To this end, we created multiple linear regression model for each feature to measure how well the six distances predict the corresponding EER. The coefficient of determination, or R 2 [START_REF] Casella | Statistical inference[END_REF], measures the proportion of the total variation of the dependent variable (EER) that is explained by the fitted model. The higher the number, the better the model fits the data. Adjusted-R 2 takes into account the number of predictors included in the model and how they contribute information. If the predictor is not significant, the adjusted-R 2 will compensate it by penalizing the model fit.

Table 3 presents the adjusted-R 2 for the feature models for each classifier separately for within-and across-corpus data. The values can be compared across the rows for each classifier to identify the data quality feature that better explain the EER variations. For instance, in the within-corpus data, for LFCC-GMM classifier all the feature distance models are good at ex- 3. CNN is most strongly impacted by x-vector, least by F0 (within-corpus) or noise spectrum (across-corpus).

So one may conjecture, for instance, that the CQCC-GMM system is potentially sensitive to noise (suggested earlier through simulated additive noise experiments [START_REF] Hanilc ¸i | Spoofing detection goes noisy: An analysis of synthetic speech detection in the presence of additive noise[END_REF]) and the CNN system potentially more strongly impacted by the choice of speaker population. The authors emphasize potentially: it is acknowledged that, despite speaker-discriminative training objective, x-vectors are not 'pure' speaker representations [START_REF] Raj | Probing the information encoded in x-vectors[END_REF]. Their quality depends on several factors (including the choice of training data).

Conclusions

We addressed the role of data quality in voice anti-spoofing generalization. The framework can be used to address statistical dependency between selected quality features and anti-spoofing performance. Pinpointing the potential issues can be used to design better anti-spoofing systems where the unwanted variations are suppressed in explicit ways. Our future plans include addressing further quality features and distance measures, mixed effects regression modeling, adding more powerful classifiers, and using the acquired knowledge to improve selected classifiers. In many classification tasks, performance can be improved by using additional training or adaptation data from the target domain. Our assumption, however, is that only a single training domain is available. The intention was to address 'the truly unknown' in terms of domain variation. Our findings indicate that substantial further research remains in this area.

Figure 2 :

 2 Figure 2: For each of Q quality measures, six distances are computed: within-and between-class distances of bonafide and spoof (both within and across training and test data).

Figure 3 :

 3 Figure 3: EER distribution on 20 randomly created trial lists.

Table 1 :

 1 Cross-corpus performance (EER%) of spoofing countermeasures. 2017: ASVspoof 2017 v2.0, PA: ASVspoof 2019 PA, RPA: ASVspoof 2019 Real PA, LA: ASVspoof 2019 LA. ReM: ReMASC, BT: BTAS. An EER of greater than 50% indicates chance level in a 2-class task. R R is a set of predictors suspected to influence Et. We model the assumed statistical dependency using multiple linear regression. Our prime interest is in the relative contribution of the individual predictors d

					Tested on
		SAS	LA	2017	RPA	ReM	PA	BT
	SAS	0.99	62.08 53.76 70.18 46.85 52.29 73.51
	LA	45.07 11.17 41.06	34.0	49.37 36.16 81.75
	2017 52.97 39.07 13.02 41.83 43.52 47.92	70.6
	RPA 52.01 53.75 46.16 39.35 45.46 46.52 54.77
	ReM 42.27	24.2	54.08 58.58	50.3	48.88 66.02
	PA	65.56 24.94 52.21 29.88 47.39	7.0	13.87
	BT	61.13 68.59 17.03 33.49 43.78 46.44	0.18
	only the test portions as random: despite the large number of
	EERs produced, we need to train only M = 7 anti-spoofing
	models (one per collection).
	2.2. Overview of multiple linear regression setting
	We model the dependency of anti-spoofing performance upon
			(1) t , . . . , d	(R)

data-related mismatch factors. For instance, if the training and test data consist of homogenous speakers (e.g. all have the same gender or native language) one might expect better performance compared to a situation with disjoint speaker qualities. We consider paired observations {(dt, Et) : t = 1, . . . , T } where Et is performance metric (here, bonafide-vs-spoof EER) for training-test pair indexed by t and dt = (d

(1) t , . . . , d (R) t ) T ∈ t ,

each of which is a distance between the training and test sets, formalized next.

Table 2 :

 2 Pearson correlation between LTAS distances and the equal error rate for the CNN classifier.

		d12	d13	d23	d14	d24	d34
	Within-corpus -0.605	0.162	-0.584 -0.651 0.367 -0.737
	Across-corpus	0.107	-0.115	0.044	-0.107 0.029	0.099

Table 3 :

 3 Adjusted-R 2 for grouped feature distances models of within-and across-corpus data of the three classifiers. Dimensionality of each feature set is indicated in parenthesis. Though the adjusted-R 2 are lower for across-corpus data, the same features explained the classifiers' EERs with high levels of significance. It is worth noting that our aim is to identify features that best explain the classifiers' performance, rather than searching for the best combination of different predictors. All our features explain well the variation in EER, especially for within-corpus data.So, what does Table3suggest? Due to space reasons, we arbitrarily pick the strongest and weakest individual predictors per classifier: 1. LFCC-GMM is most strongly impacted by LTAS, least by loudness; 2. CQCC-GMM is most strongly impacted by SNR, least by loudness;

		Within-corpus data	Across-corpus data
		LFCC CQCC CNN LFCC CQCC CNN
		GMM GMM		GMM GMM
	LTAS (257)	0.679	0.543	0.670	0.289	0.166	0.038
	F1..F4 (4)	0.641	0.497	0.470	0.131	0.075	0.142
	F0 (1)	0.513	0.328	0.073	0.058	0.082	0.096
	x-vec. (512)	0.558	0.642	0.817	0.067	0.149	0.202
	SNR (1)	0.593	0.715	0.187	0.160	0.227	0.075
	Noise s. (257)	0.649	0.439	0.565	0.141	0.207	0.010
	Loudness (1)	0.455	0.304	0.448	0.021	0.050	0.060
	plaining the performance, particularly LTAS distance predictors
	explain 68% of the EER's variation. Similar strong dependen-
	cies are noted with SNR for CQCC-GMM and with x-vector
	for CNN.

ASVspoof challenges contain train, development and evaluation sets; we do not differentiate between the latter two which, really, are two different test sets. During a challenge, the labels of development set are available for detector optimization while test data that lacks labels.

Acknowledgements

This work was supported in part by the Academy of Finland (Proj. No. 309629) and Nokia Foundation.