Nadine S Schaadt

Juan Carlos

López Alfonso

Ralf Schönmeyer

Anne Grote

Germain Forestier

Cédric Wemmert

Nicole Krönke

Mechthild Stoeckelhuber

Hans H Kreipe

Haralampos Hatzikirou

Haralambos Hatzikirou

Friedrich Feuerhake
email: feuerhake.friedrich@mh-hannover.de

Jcl Alfonso

H Hatzikirou

Image analysis of immune cell patterns in the human mammary gland during the menstrual cycle refines lymphocytic lobulitis

Keywords: lymphocytic lobulitis, digital pathology, oncoimmunology, object-based image analysis, menstrual cycle, hormonal fluctuations

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

Introduction

Lymphocytic lobulitis (LLO) is an inflammatory condition previously observed in normal breast tissue [START_REF] Degnim | Immune cell quantitation in normal breast tissue lobules with and without lobulitis[END_REF], autoimmune disease [START_REF] Lammie | Sclerosing lymphocytic lobulitis of the breastevidence for an autoimmune pathogenesis[END_REF][START_REF] Schwartz | Lymphocytic mastopathy. an autoimmune disease of the breast?[END_REF], nonmalignant mammary glands from patients with germline BRCA1/2 mutations [START_REF] Hermsen | Lobulitis is a frequent finding in prophylactically removed breast tissue from women at hereditary high risk of breast cancer[END_REF], and as peritumoral phenomenon in breast cancer [START_REF] Chetty | Lymphocytic mastopathy associated with lobular breast carcinoma[END_REF][START_REF] Lee | Inflammatory infiltrate in invasive lobular and ductal carcinoma of the breast[END_REF][START_REF] Douglas-Jones | Lymphocytic lobulitis in breast core biopsy: a peritumoral phenomenon[END_REF]. So far, LLO has mainly been described by absolute immune cell numbers per lobule of the mammary gland [START_REF] Chetty | Lymphocytic mastopathy associated with lobular breast carcinoma[END_REF][START_REF] Lee | Inflammatory infiltrate in invasive lobular and ductal carcinoma of the breast[END_REF][START_REF] Hermsen | Lobulitis is a frequent finding in prophylactically removed breast tissue from women at hereditary high risk of breast cancer[END_REF][START_REF] Gulbahce | Lobulitis in nonneoplastic breast tissue from breast cancer patients: association with phenotypes that are common in hereditary breast cancer[END_REF][START_REF] Degnim | Immune cell quantitation in normal breast tissue lobules with and without lobulitis[END_REF]. While the reported descriptions that rely solely on estimated cell counts have captured important features of LLO, there remains some degree of uncertainty. The anatomical term "lobule" as a reference area can be readily identified, but size, shape, and cellular content of cross sections at different levels can be highly variable. Further, individual observers may consider different structures as "representative lobules". Moreover, the potential influence of hormonal fluctuations on immune cell infiltrates has not yet been considered. Therefore, it cannot be excluded that current definitions of LLO may miss important aspects of the dynamic temporal or spatial inflammatory tissue microenvironment (iTME) variability.

Previously, we developed context-related immune cell evaluation in single microscopic images [START_REF] Krueger | Combat or surveillance? evaluation of the heterogeneous inflammatory breast cancer microenvironment[END_REF], improved color deconvolution in digital whole slide images (WSIs) [START_REF] Wemmert | Stain unmixing in brightfield multiplexed immunohistochemistry[END_REF], and automated detection of lobular structures adjacent to tumors [START_REF] Grote | Exploring the spatial dimension of estrogen and progesterone signaling: detection of nuclear labeling in lobular epithelial cells in normal mammary glands adjacent to breast cancer[END_REF].

In this study, we implemented an advanced digital image analysis workflow specifically adjusted to iTME in breast tissue to characterize the range of normal immune presence in the healthy breast tissue and distinguish normal, potentially fluctuating immune surveillance from pathological conditions. We consider LLO as a use case for comprehensive evaluation of iTME in an anatomically well-defined context with clinical relevance. This addresses the emerging challenge of comprehensive immune cell evaluation in the era of oncoimmunology and paves the way for computer-aided breast cancer biopsy analysis. Therefore, we acquired largescale spatial data on immune cells in WSIs by combining novel technologies [START_REF] Apou | Detection of lobular structures in normal breast tissue[END_REF][START_REF] Schönmeyer | Automated Whole Slide Analysis of Differently Stained and Co-Registered Tissue Sections[END_REF] in a modular approach in breast tissue of healthy women who underwent reduction mammoplasty and relate the image-based data with the hormonal status of the patients at time of surgery.

Materials and Methods

Tissue samples

Breast tissue was obtained from 53 premenopausal healthy women who underwent reduction mammoplasty and provided a detailed reproductive clinical history [START_REF] Feuerhake | Immunohistochemical analysis of bcl-2 and bax expression in relation to cell turnover and epithelial differentiation markers in the non-lactating human mammary gland epithelium[END_REF][START_REF] Feuerhake | Cell proliferation, apoptosis, and expression of bcl-2 and bax in non-lactating human breast epithelium in relation to the menstrual cycle and reproductive history[END_REF][START_REF] Alfonso | In-silico insights on the prognostic potential of immune cell infiltration patterns in the breast lobular epithelium[END_REF]. 20 women were taking oral contraceptives at time of surgery (Table 1). The phase of the menstrual cycle at the day of the surgical procedure was determined by documentation of the periods prior and after operation (details in Section 1.1.1 of the Online Resource 1). One tissue block (one slide per staining) was evaluated from 47 patients. An additional block was used in six cases, resulting in a total number of 59 evaluated paraffin blocks. An overview of the evaluated tissue area is provided in Table 2. Tissue samples were collected during the surgery and processed immediately after, fixed in 4% phosphatebuffered formaldehyde for 24 hours, embedded in paraffin, and cut into 3 µm thick sections. The histopathological work-up included Haematoxylin-eosin (H&E), estrogen and progesterone receptors (ER/PR), Cytokeratin 5/14, and Ki-67 immunohistochemical stainings according to automated standard protocols (Ventana Benchmark Ultra, Tucson, AZ). Immunohistochemistry for CD8 (Dako M7103), CD4 (Zytomed Systems 503-3354), CD163 (medac diagnostika 163M-16), and duplex staining for CD3/CD20 (CD3: abcam ab52959, CD20: DAKO M0755) was performed using red and brown chromogenic labeling provided by Ventana.

A single representative block of the series was used for the 3D-reconstruction and robustness testing that addressed the variability of lobular structures and the concordance of immune cell evaluation across multiple sectioning levels (see Section 3.1). For this purpose, the entire block was sectioned into 3 µm thick serial sections. The first 270 levels were stained for H&E and used for 3D histology reconstruction, and subsequent 100 sections were processed in 10 sets (of 10 sections each) of which the initial 6 consecutive slides were stained for CD8/p63 (CD8: Dako M7103, p63: medac diagnostica Z2003L).

Negative controls were treated identically, but omitting the primary antibody or replacing it by nonimmune sera of the same species. WSIs were acquired by Aperio AT2 scanner (Leica Microsystems, Wetzlar, Germany) at 40x magnification (resolution 0.253 µm / pixel).

Image Analysis

The image analysis concept was focused on two aspects of immune cell evaluation where computer-assisted approaches provide an added value over visual inspection: (1) concordance testing of multiple sectioning levels across a block, and (2) evaluation of automatically detected ROIs, complementing the established practice of manually selecting representative fields for evaluation. This ROI detection approach enables expansion of the evaluated area, standardization of scoring criteria, and streamlining of the analysis process. The first aspect was addressed by a three-dimensional (3D) histology reconstruction of serial H&E sections, performed using the Voloom software (microDimensions, Munich, Germany) in one out of the 53 clinically annotated cases (follicular cycle phase; on oral contraceptives). This was done in order to analyze how variable size, shape, and immune cell content of lobular areas considered as "representative lobules" can be across multiple consecutive sectioning levels.

The second aspect was addressed by an approach resembling and expanding the established practice of analyzing single sections: The analysis allowed including all evaluable lobular areas in a WSI, provided that they can be automatically detected in an individual stained section applying reproducible and adaptable criteria (e.g., minimal accepted size). A quality assurance step ensured exclusion of technically insufficient or tangentially sectioned small parts of lobules. In order to quantify immune cell numbers in lobular structures, we developed a modular workflow (see Fig. 1), combining automated regions of interest (ROIs) detection [START_REF] Apou | Detection of lobular structures in normal breast tissue[END_REF], high-resolution cell detection with a robust analysis module for nucleus detection (part of the Tissue Phenomics framework, Definiens AG, Munich, Germany) [START_REF] Brieu | Slide specific models for segmentation of differently stained digital histopathology whole slide images[END_REF], and subsequent cell classification. For quality control, all automatically detected ROIs were verified by visual inspection of an expert. All correctly detected lobular areas (Table 2 of the Online Resource 1) were used for statistical analysis.

Detected cells were grouped into immune cells, positively stained with respective lineage markers, and negatively stained cells. The latter were further distinguished into epithelial cells and other (e.g., stromal) elements based on morphological features. The approach for Ki-67 staining was adjusted considering that this marker is not cell lineage-specific. Based on published evidence of relevant distances for cytotoxic effector functions and chemokine mediated cell-cell communication [START_REF] Huse | Shouts, whispers and the kiss of death: directional secretion in t cells[END_REF], we classified each stained immune cell into one of three categories as "in contact with an epithelial cell" (no distance to the closest epithelial cell), "short distance to an epithelial cell" (distance to the closest epithelial cell less than two average cell diameters enabling communication via chemokines), and "in lobular stroma" (within lobular area but chemokine gradient sensing could not be possible [START_REF] Lin | T cell chemotaxis in a simple microfluidic device[END_REF][START_REF] Lin | Modeling the role of homologous receptor desensitization in cell gradient sensing[END_REF]).

To investigate co-localization between different immunohistochemical markers, we applied existing co-registration methods [START_REF] Yigitsoy | Hierarchical patchbased co-registration of differently stain histopathology slides[END_REF][START_REF] Brieu | Coregistering images of needle biopsies using multiple weighted landmarks[END_REF][START_REF] Schönmeyer | Updating landmarks to improve coregistration as regions of interest are corrected[END_REF][START_REF] Schönmeyer | Automated Whole Slide Analysis of Differently Stained and Co-Registered Tissue Sections[END_REF] to allow quantitative evaluation of corresponding regions. To enable full individual analysis of every separate slide, ROIs were individually identified [START_REF] Apou | Detection of lobular structures in normal breast tissue[END_REF] and corresponding areas for joint analysis were defined as overlapping regions (Fig. 1 of the Online Resource 1). This approach addressed a challenge frequently faced in object-based biomarker analyses with multiple immunohistochemical staining methods, where the sectioning levels show corresponding tissue regions but may not exactly match in subsequent sections.

Statistical considerations

Due to the strongly varying size of automatically detected lobular areas, immune cell counts as well as the amount of Ki-67 + cells were normalized by the number of epithelial cells. Comparisons between subgroups were performed using a nonparametric test (Wilcoxon-Mann-Whitney) with accepted significance level α of 0.001. A detailed description of the statistical analysis and the definition of the menstrual cycle phases is provided in the Online Resource 1.

Results

Robustness of immune cell evaluation across multiple sectioning levels

The 3D histology reconstruction of breast glandular tissue (Fig. 2 a) confirmed that lobular structures can vary considerably across different sectioning levels. This supported the notion that a quantitatively defined reference unit complementing the descriptive term "lobule" is useful and that specifying objective criteria of epithelial areas for quantification increases robustness.

Automatically detected lobular areas including about 200-300 epithelial cells were most consistent with the lower range of "lobule" cross-sections selected for analysis by pathologists (Fig. 2 b-e). Therefore, we set our threshold for minimal size of tangentially sectioned lobular structures to 250 epithelial cells, thereby defining ROIs considered for large-scale automated image analysis.

In addition, the immune-epithelial cell ratio (IC/EC) greatly improved robustness of the analysis compared to only immune cell counts per lobule. Serial sections of duplex staining quantifying the basal epithelial cells (p63) in combination with CD8 (Fig. 2 f) showed that corresponding ROIs in consecutive sections greatly vary in their epithelial cell content (Fig. 3, left y-axis, series of strongly dotted diamonds). In contrast, their normalized immune cell density (CD8/p63) remained relatively constant, independently of lobular size, shape, and epithelial cell content (Fig. 3, left y-axis, series of faintly dotted points). Taken together, these results showed that robustness of iTME evaluation can be improved by careful definition of evaluable ROIs and appropriate normalization of immune cell counts. Given that immune cell scoring requires consideration of variation between sectioning levels even in an anatomically well-defined healthy glandular structure, we anticipate that the iTME evaluation in much more heterogeneous and poorly differentiated tumor samples will hugely benefit from our image analysis workflow.

Variable lymphocyte patterns in different phases of the menstrual cycle

Immune cell infiltration in the normal breast tissue was variable and associated with the menstrual cycle. We observed a heterogeneous occurrence of immune cells within tissue samples of an individual and between pa-tients. This was consistent with our previous results in a subset of the present series, recently described in [START_REF] Alfonso | In-silico insights on the prognostic potential of immune cell infiltration patterns in the breast lobular epithelium[END_REF]. In general, there was a high number of CD8 + Tlymphocytes, about seven per 100 epithelial cells on average. The overall mean density of CD8 + T-cells was significantly higher in the luteal than in the follicular phase of the menstrual cycle with 1.6 fold increase in patients not taking oral contraceptives (p < 0.001) and 1.4 fold increase (p < 0.001) in patients taking oral contraceptives (Fig. 4). This difference was predominantly driven by the CD8 + lymphocytes in close contact to the epithelial layer and less evident in the stromal infiltrates. The presence of potentially cytotoxic CD8 + T-cells in direct contact with morphologically vital epithelial cells suggested an inactive or resting functional state, consistent with a presumable role in continuous immunosurveillance. Further, in patients using oral contraceptive, the CD8 + density was 1.1 fold higher re- 3 Robustness of the ratio between CD8 + T-cells and p63 + basal epithelial cells in corresponding lobular areas of different size and shape in consecutive slides. Shown are three (more in Fig. 2 of the Online Resource 1) independent ROIs (different colors) in a single block of a patient who underwent reduction mammoplasty. Each dark diamond reflects an absolute p63 + basal epithelial cells count (left y-axis) and each light circle of the same color represents the CD8/p63 ratio (right y-axis) in the same ROI. Some lobular areas remained relatively constant across multiple sections (second row), whereas others changed in epithelial cell number (first and third row) gardless of the cycle phase and 1.2 fold higher in the follicular phase than in patients not taking oral contraceptives. In contrast, the overall amount of CD4 + T-helper cells was too small in the normal breast tissue (on average, about one CD4 + T-cells per 100 epithelial cells; see Fig. 3 of the Online Resource 1) to analyze their dependence on the menstrual cycle. CD20 + B-lymphocytes were also present, mostly at low numbers (on average, less than one B-cell per 100 epithelial cells), with the only exception of two cases where we observed focally higher densities of CD20 + cells in stromal localization (Fig. 4 of the Online Resource 1).

• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • Fig.

Variation of CD163 + macrophages during the menstrual cycle

CD163 + macrophages were present in high numbers, comparable with the amount of CD8 + T-lymphocytes (Fig. 5 of the Online Resource 1). The density of CD163 + macrophages was significantly lower in the luteal than in the follicular phase (Table 1 of the Online Resource 1). In contrast to CD8 + lymphocytes, macrophages were not obviously co-localized with the epithelial cells. CD163 + macrophage numbers were 1.1 fold increased in patients taking oral contraceptive compared to patients not taking oral contraceptive (significant). The spatial distribution of macrophages suggested less contact with individual epithelial cells than in the case of CD8 + Tlymphocytes. This might indicate that CD163 + macrophages have a role in permanent maintenance of the tissue microenvironment including stromal areas.

CD8 + lymphocytes in context of epithelial proliferation

Given that CD8 + lymphocytes were more frequent in the menstrual cycle phase for which higher rates of proliferation had been previously described [START_REF] Feuerhake | Cell proliferation, apoptosis, and expression of bcl-2 and bax in non-lactating human breast epithelium in relation to the menstrual cycle and reproductive history[END_REF][START_REF] Vogel | The correlation of histologic changes in the human breast with the menstrual cycle[END_REF][START_REF] Ramakrishnan | Morphological changes in breast tissue with menstrual cycle[END_REF] 2 of the Online Resource 1) in the follicular (red) and luteal (blue) phases analyzed whether focal peaks in epithelial cell turnover were co-localized with higher density of immune cells. Our results (Fig. 6 of the Online Resource 1) for nuclear Ki-67 staining were in agreement with published data, showing that proliferation is generally increased in the luteal phase [START_REF] Olsson | Proliferation of the breast epithelium in relation to menstrual cycle phase, hormonal use, and reproductive factors[END_REF]. Taking into account that this increase is especially strong in the second part of the luteal phase [START_REF] Anderson | Oral contraceptive use influences resting breast proliferation[END_REF], we split this phase in two parts for more precise statistical analysis (see Online Resource 1). Coregistration results between CD8 and Ki-67 stained sections showed that CD8 + lymphocytic infiltrates were not co-localized with peaks of epithelial proliferation (correlation coefficient: 0.030). A visual inspection by a pathologist of several images confirmed the accuracy of this automated analysis result, as shown in the representative examples of Fig. 5.

Assuming that increased cell turnover is associated with elevated risk for cell damage or aberrant cell divisions [START_REF] Liu | Measures of cell turnover (proliferation and apoptosis) and their association with survival in breast cancer[END_REF][START_REF] Labi | How cell death shapes cancer[END_REF], it would be plausible that CD8 + cells could possibly be required in higher numbers close to areas of transient proliferative activity, such that a colocalization with areas of increased cell renewal in the luteal phase [START_REF] Potten | The effect of age and menstrual cycle upon proliferative activity of the normal human breast[END_REF][START_REF] Navarrete | Assessment of the proliferative, apoptotic and cellular renovation indices of the human mammary epithelium during the follicular and luteal phases of the menstrual cycle[END_REF] could be expected. However, our results do not support this expectation and may indicate that the majority of CD8 + cells are not actively involved in processes associated with local or temporal peaks of epithelial cell turnover, consistent with a resting functional state.

Spatial distribution of immune cells in normal breast

A comprehensive repository of the spatial distribution of immune cells in normal breast tissue, confirmed by image analysis, could serve as a reference to distinguish pathological conditions from normal immunosurveillance. As a major step towards such a reference data base, our workflow captured spatial aspects such as stromal localization of immune cells and provided the technological prerequisites to combine measures for lymphocyte subsets such as local CD4/CD8 evaluation in previously defined ROIs. CD8 + T-cells were surprisingly abundant in normal breast tissue, predominantly localized in direct contact to epithelial cells (p < 0.001), and appeared to vary under hormonal influence. Differently from this distribution pattern, CD4 + T-cells were rare and in diffuse localization, and CD163 + macrophages occurred abundantly and widely spread. We propose integrating such quantitative and spatial aspects in future definitions of LLO. As one example for such combined spatial patterns, the two single lobules that were identified as "outliers" with high numbers of stromal B-cells were characterized by relatively low presence of intraepithelial CD3 + T-cells (Fig. 4 of the Online Resource 1).

As another example, we envision that the combination of focal CD4 + T-cells in predominantly stromal localization, along with CD8 + T-cell clusters in the neighborhood of destroyed or disintegrating lobular epithe-lium could be a distinguishing feature. This is based on the observation that this pattern was rarely present in the 53 normal breast samples of the current study, but repeatedly seen in an independent series of archival prophylactic mastectomy samples [START_REF] Alfonso | In-silico insights on the prognostic potential of immune cell infiltration patterns in the breast lobular epithelium[END_REF] in patients with BRCA1/2 germline mutations (see two representative examples in Fig. 7 of the Online Resource 1). We propose such combined quantitative and spatial criteria to refine differences between normal and pathological conditions by large-scale image analysis.

Discussion

In this study, we described the distribution of immune cell subpopulations in normal breast tissue with focus on markers of growing relevance for oncoimmunology (CD4, CD8, CD163) and integrate their spatial patterns applying advanced image analysis. The high number of CD8 + T-lymphocytes and their dependence on the menstrual cycle and the use of oral contraceptives suggest the permanent presence of a T-cell population related to immunosurveillance under control of hormonal fluctuations. This can have implications for those diagnostic procedures where the nonmalignant components of the iTME are evaluated, for example in diabetic mastopathy [START_REF] Thorncroft | The diagnosis and management of diabetic mastopathy[END_REF], sclerosing lymphocytic lobulitis associated with autoimmune disease [START_REF] Haj | Diabetic sclerosing lymphocytic lobulitis of the breast[END_REF], and after prophylactic mastectomy [START_REF] Hermsen | Lobulitis is a frequent finding in prophylactically removed breast tissue from women at hereditary high risk of breast cancer[END_REF].

Using the published quantitative definitions [START_REF] Gulbahce | Lobulitis in nonneoplastic breast tissue from breast cancer patients: association with phenotypes that are common in hereditary breast cancer[END_REF] accepting that one lobule is sufficient to diagnose LLO in an individual patient, more than 70% of patients in our data set would qualify for LLO. At the level of individual lobules, we estimated that a number of at least 100 CD8 + lymphocytes are contained in 17.3% of all ROIs that correspond to areas usually described as "representative lobules" with 250-2500 epithelial cells, 50-100 CD8 + lymphocytes are contained in further 27.6%. Assuming that CD8 + lymphocytes are only a subset of all included lymphocytes, more than a quarter of normal lobules show LLO based on the definition "100 lymphocytes" [START_REF] Hermsen | Lobulitis is a frequent finding in prophylactically removed breast tissue from women at hereditary high risk of breast cancer[END_REF]. Applying the definition of "more than 50 mononuclear cells per lobule" for moderate and 100 for severe LLO [START_REF] Gulbahce | Lobulitis in nonneoplastic breast tissue from breast cancer patients: association with phenotypes that are common in hereditary breast cancer[END_REF] to the CD8 and CD163 population about 81% of the normal lobules would show LLO. Since CD8 + and CD163 + cells are only a small subset of mononuclear cells, this is an underestimation. Based on the definition "at least four lymphocytes between the adjacent acini" [START_REF] Degnim | Immune cell quantitation in normal breast tissue lobules with and without lobulitis[END_REF], we estimated that about 20% of lobules in reduction mammoplasty samples would qualify for LLO. It should be questioned whether it is useful to define a potentially pathological condition like LLO in such a high percentage of asymptomatic women without clinical and histopathological abnormalities. Rather than claiming a generally applicable LLO definition, we propose to use conventional quantitative assessment to identify cases requiring further analysis, and to consider phenotypes, relative quantities, and localization of immune cells in order to contribute to a refinement of future LLO definitions. These might be different in various pathological conditions due to the individual underlying pathogenic mechanisms.

Reduction mammoplasty samples from patients considered healthy by all medical standards at the time of the surgery are strong evidence, but limitations of the approach should be mentioned. First, we cannot exclude that single patients may have been affected by breast cancer during their life after surgery. Second, it has been discussed that breast tissue from reduction mammoplasty may not be entirely representative for the "normal" female population with regard to slightly increased incidence of nonmalignant, non-proliferative lesions [START_REF] Degnim | Histologic findings in normal breast tissues: comparison to reduction mammaplasty and benign breast disease tissues[END_REF] and the occasional occurrence of premalignant lesions as described in a series from Brazil [START_REF] Pitanguy | Breast pathology and reduction mammaplasty[END_REF]. However, it can be considered the best type of tissue available for large-scale analysis, as opposed to needle biopsy specimens [START_REF] Degnim | Histologic findings in normal breast tissues: comparison to reduction mammaplasty and benign breast disease tissues[END_REF] that may miss some of the heterogeneity present in the iTME of breast glands (Fig. 2). We conclude that our study provides a solid approximation towards defining the range of normal immune cell presence. This is useful for future comparisons with different pathological conditions such as LLO in the context of genetic predisposition or manifest breast cancer.

In current practice, the evaluation of the inflammatory microenvironment in breast cancer [START_REF] Salgado | The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: recommendations by an International TILs Working Group 2014[END_REF][START_REF] Denkert | Tumor-infiltrating lymphocytes and response to neoadjuvant chemotherapy with or without carboplatin in human epidermal growth factor receptor 2-positive and triple-negative primary breast cancers[END_REF] is based on "Tumor infiltrating lymphocytes" (TILs) within the tumor mass and does neither consider the hormonal status of the patients (cycle phase, menopause, medication) nor the adjacent non-neoplastic tissue. Our results indicate that cycle phase and use of oral contraceptive therapy could potentially be relevant for evaluation, particularly in newly diagnosed patients without previous therapy-related effects. While changes up to 2-fold as observed in healthy patients may be a minor aspect for evaluation of solid tumor masses, where more than 60% of the stromal compartment can be TILs [START_REF] Salgado | The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: recommendations by an International TILs Working Group 2014[END_REF][START_REF] Denkert | Tumor-infiltrating lymphocytes and response to neoadjuvant chemotherapy with or without carboplatin in human epidermal growth factor receptor 2-positive and triple-negative primary breast cancers[END_REF], the patient history may be much more relevant for inflammatory changes in adjacent non-neoplastic tissue. For example, experience from colon cancer suggests that lymphocyte subsets at the invasive edge of tumors are more relevant for prognosis than the central tumor mass [START_REF] Galon | Towards the introduction of the immunoscore in the classification of malignant tumours[END_REF]. In this regard, our approach has the potential to support improvements of prognostic precision in the emerging era of oncoimmunology, for example, to evaluate inflammation close to benign precursor lesions [START_REF] Gulbahce | Lobulitis in nonneoplastic breast tissue from breast cancer patients: association with phenotypes that are common in hereditary breast cancer[END_REF][START_REF] Hartmann | Benign breast disease and the risk of breast cancer[END_REF][START_REF] Hussein | Analysis of the mononuclear inflammatory cell infiltrate in the normal breast, benign proliferative breast disease, in situ and infiltrating ductal breast carcinomas: preliminary observations[END_REF][START_REF] Chen | Lymphocyte subsets contribute to the degree of lobulitis and ductitis in sclerosing lymphocytic lobulitis of the breast[END_REF][START_REF] Lee | Tertiary lymphoid structures: prognostic significance and relationship with tumour-infiltrating lymphocytes in triple-negative breast cancer[END_REF] or in preexisting glandular tissue adjacent to cancer areas of small early-stage tumors, where tumor is frequently closely related to non-neoplastic preexisting tissue.

In summary, this work provides an important proofof-concept that automated recognition of ROIs is possible in breast tissue and that large-scale image analysis of WSIs provides an added value by increasing reproducibility and observer independence. We anticipate a growing demand for immune cell evaluation beyond semi-quantitative scoring of TILs, such that technologies in immunoscoring for effector T-cells [START_REF] Denkert | Tumor-infiltrating lymphocytes and response to neoadjuvant chemotherapy with or without carboplatin in human epidermal growth factor receptor 2-positive and triple-negative primary breast cancers[END_REF] and alternatively activated macrophages [START_REF] Sousa | Human breast cancer cells educate macrophages toward the m2 activation status[END_REF] will become increasingly important. As a long-term impact, further development of our approach will pave the way for broader applications of digital pathology in breast cancer biomarker evaluation, including automated detection of tumor, stroma, invasive edge, and adjacent normal tissues as relevant ROIs.

Online Resource 1

OnlineResource1.pdf contains additional information on methods as well as a collection of additional figures.

Author's contributions

NSS and FF designed the study, analyzed the results and prepared the manuscript. NSS, RS, AG, GF, and CW contributed to the concept and implementation of the image analysis workflow. NSS performed the statistical analysis. JCLA, HHK, and HH contributed to study design and edited the manuscript. NK established the staining and image acquisition workflow. MS and FF designed the collection protocol for the reduction mammoplasty material and obtained patient histories and informed patient consent. All authors approved the final manuscript.

Fig. 1

 1 Fig.1Flow chart of image analysis process: A texture and machine learning based region classification detects the region of interests[START_REF] Apou | Detection of lobular structures in normal breast tissue[END_REF] and a Definiens software module was used to segment the nuclei[START_REF] Brieu | Slide specific models for segmentation of differently stained digital histopathology whole slide images[END_REF]. Based on color information and spatial distances, the nuclei in regions of interest were classified into different cell types

Fig. 2

 2 Fig. 2 Systematic evaluation of morphological variation across multiple sectioning levels. A 3D histology reconstruction of mammary gland tissue in multiple consecutive sections confirmed lobular areas strongly varying in shape and size across consecutive slides. a: 3D-view of the whole tissue block depicting the complexity of the glandular structure. b-e: Different levels showing how a "lobular area" develops from a single lobule (a) to an epithelial area composed of two lobules (b and c, white arrows indicate the second lobule), up to an area including a tangential section of a third lobule (asterisk), together composing a lobular ROI (oval blue outline). f : Consecutive 3 µm thick immunohistochemistry slides, shown a CD8 (brown) and p63 (red) duplex stain with hematoxilin counterstain. Note the considerable variation in shape and size of the lobular components, including a shrinkage artifact in the third image (arrow)

Fig. 4

 4 Fig.[START_REF] Brieu | Coregistering images of needle biopsies using multiple weighted landmarks[END_REF] Differences between follicular and luteal cycle phase in the number of CD8 + T-cells in contact with epithelial cells (ICIC), close to epithelial cells (ICC), and in lobular stroma (ICIS) displayed as ratio per epithelial cells (IC/EC) in the same lobule. Shown are the distribution, mean value, upper and lower quantile, as well as the standard deviation. The figure compares patients without taking oral contraceptives (top row) and patients taking oral contraceptive (bottom row). Dashed lines illustrate the average values of total immune cell count (i.e., ICIC + ICC + ICIS) over all ROIs (see Table2of the Online Resource 1) in the follicular (red) and luteal (blue) phases

Fig. 5 a

 5 Fig. 5 a-b: ROI with high Ki-67 (left) and high CD8 (right) level. c-d: ROI with high Ki-67 (left) and low CD8 (right) level. e-f : ROI with low Ki-67 (left) and high CD8 (right) level

Table 1

 1 Composition of the reduction mammoplasty patient cohort and menstrual cycle phases. (*): for 5 patients in the luteal and 2 patients in the follicular phase of natural cycles (without oral contraceptives), the assignment to a cycle phase was a "best guess"-approximation due to reportedly irregular menses, (**): parity unknown for 1 patient

	Cycle phase	Number of patients	Age at surgery Median Range ceptive Oral contra-Percentage nullipara
	luteal	20* 9	29 27	21-53 no 18-34 yes	35% 37.5%**
	mid cycle	4 4	28 27	21-45 no 22-32 yes	75% 100%
	follicular	9* 7	29 26	26-48 no 19-29 yes	55.6% 85.7%

Table 2

 2 Total size of evaluated lobular area within the breast tissue (in cm 2 ) per patient after automated detection[START_REF] Apou | Detection of lobular structures in normal breast tissue[END_REF] and quality assessment of ROIs. "StdDev" = standard deviation. The last column ("Multiple blocks") shows the range and the median (in brackets) for the six patients for which two blocks were considered (see Section 2.1).

	Staining	Range	Median Mean StdDev Multiple blocks
	Ki-67	0.02-2.74 0.72	0.88	0.70	0.36-2.74 (1.18)
	CD8	0.06-3.68 0.78	0.92	0.72	0.36-1.48 (1.10)
	CD4	0.06-2.16 0.74	0.86	0.56	0.38-1.60 (1.08)
	CD163	0.06-2.74 0.74	0.86	0.62	0.38-1.72 (0.92)
	CD3/CD20 0.06-2.76 0.78	0.92	0.70	0.32-2.76 (0.86)

  , we

					without oral contraceptive without oral contraceptive without oral contraceptive without oral contraceptive without oral contraceptive
		0.20 0.20 0.20 0.20 0.20
	cells per epithelial cells cells per epithelial cells cells per epithelial cells cells per epithelial cells cells per epithelial cells	0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.06 0.08 0.10 0.12 0.14 0.16 0.18
	CD8 + CD8 + CD8 + CD8 + CD8 +	0.02 0.04 0.02 0.04 0.02 0.04 0.02 0.04 0.02 0.04
		0.00 0.00 0.00 0.00 0.00
		ICIC ICIC ICIC ICIC ICIC	ICC ICC ICC ICC ICC	ICIS ICIS ICIS ICIS ICIS	ICIC ICIC ICIC ICIC ICIC	ICC ICC ICC ICC ICC	ICIS ICIS ICIS ICIS ICIS	ICIC ICIC ICIC ICIC ICIC	ICC ICC ICC ICC ICC	ICIS ICIS ICIS ICIS ICIS
			follicular follicular follicular follicular follicular follicular follicular follicular follicular follicular follicular follicular follicular follicular			mid cycle mid cycle mid cycle mid cycle mid cycle mid cycle mid cycle mid cycle mid cycle mid cycle mid cycle mid cycle mid cycle mid cycle			luteal luteal luteal luteal luteal luteal luteal luteal luteal luteal luteal luteal luteal luteal
					oral contraceptive oral contraceptive oral contraceptive oral contraceptive oral contraceptive
		0.20 0.20 0.20 0.20 0.20
	cells per epithelial cells cells per epithelial cells cells per epithelial cells cells per epithelial cells cells per epithelial cells	0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.06 0.08 0.10 0.12 0.14 0.16 0.18
	CD8 + CD8 + CD8 + CD8 + CD8 +	0.02 0.04 0.02 0.04 0.02 0.04 0.02 0.04 0.02 0.04
		0.00 0.00 0.00 0.00 0.00
		ICIC ICIC ICIC ICIC ICIC	ICC ICC ICC ICC ICC	ICIS ICIS ICIS ICIS ICIS	ICIC ICIC ICIC ICIC ICIC	ICC ICC ICC ICC ICC	ICIS ICIS ICIS ICIS ICIS	ICIC ICIC ICIC ICIC ICIC	ICC ICC ICC ICC ICC	ICIS ICIS ICIS ICIS ICIS
			follicular follicular follicular follicular follicular follicular follicular follicular follicular follicular follicular follicular follicular follicular			mid cycle mid cycle mid cycle mid cycle mid cycle mid cycle mid cycle mid cycle mid cycle mid cycle mid cycle mid cycle mid cycle mid cycle			luteal luteal luteal luteal luteal luteal luteal luteal luteal luteal luteal luteal luteal luteal

Acknowledgements The authors thank Prof. Dr. Dr. U. Welsch, Department for Cell Biology, Biomedical Center Munich, for his kind permission to use materials from previous studies that were collected during his term as Head of the Department for Microscopic Anatomy and Cytology (Chair II), Institute for Anatomy, Ludwig Maximilians University (LMU) Munich, Germany. The authors thank Pia Unterberger for organizing and storing archival material of the reduction mammoplasty series. The authors thank Gregory Apou for collaboration and scientific discussions on image analysis. The authors thank Priv.-Doz. Dr. M. Christgen, Institute of Pathology, Hannover Medical School, for breast pathology expertise. The authors thank the team of the Immunohistochemistry Laboratory, Institute for Pathology, Hannover Medical School for technical assistance, and the team of mi-croDimensions, Munich, for help with 3D histology reconstruction.

Compliance with Ethical Standards

Funding

This study was funded by the German Federal Ministry of Education and Research (BMBF) for the collaborative SYSIMIT project, grant number 01ZX1308A-D.

Conflict of interests

Dr. Ralf Schönmeyer is a full-time employee of Definiens AG, Munich. All other authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving use of surplus tissue samples and clinical information on human participants were in accordance with the ethical standards of the institutional research committee (Reference number of the Ethical Committee/institutional review board #2063-2013) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study. This article does not contain any studies with animals performed by any of the authors.


Affoua Therese Aby
email: aby@esiee-amiens.fr

Marie-Franc ¸oise Servajean
email: m-francoise.servajean@uca.fr

Nadir Hakem
email: nadir.hakem@uqat.ca

Michel Misson
email: michel.misson@uca.fr

IMPACT OF SIMULATION ENVIRONMENT IN PERFORMANCE EVALUATION OF PROTOCOLS FOR WSNS

Keywords: WSNs, QoS, performance evaluation, simulation conditions, topologies impact, capture effect impact

Wireless sensor networks are increasingly used in many emerging applications. This type of network is composed of hundreds of low-cost sensor nodes, but with a limited budget batteries, low communication range, limited throughput, reduced computing power, low memory and low storage capacity. Communication protocols are proposed in the literature to deal with technical challenges coming from low intrinsic resources of sensor nodes. In most of these studies, simulations comparing a proposed protocol with other existing protocols are performed to show that the proposed protocol provides overall better performance. However, the environmental specification that made these comparisons is very often neglected or non-existent. In this study we show that it is essential to have the simulation environment very well defined before considering whether a protocol provides better performance than others. To do this we use two duty cycle MAC protocols, the standard IEEE 802.15.4 and SlackMAC (a protocol that we proposed). The aim of the paper is not to make an exhaustive comparison of protocols. We intuitively know that SlackMAC provides a better overall performance than the standard. What we are trying to show is the gap between performances according to simulation conditions. We will mainly focus on the topologies used and the capture effect. The results draw attention to the fact that it is essential to clearly define the simulation environment and also to reconcile the chosen conditions with the results when comparing the performances of two protocols.

Introduction

Wireless Sensor Networks (WSNs) have attracted great interest in the last decade. This explains the rich and active research about communication protocols for WSNs in order to deal with technical constraints related to sensor nodes. In most of these studies, in the absence of real conditions to validate the proposed protocols, many simulations are carried out to evaluate their performances. Mostly, these simulations are done with performance comparisons of proposed protocols compared to existing one to show that the new protocol provides better performance. However, sometimes the level of detail on the results surpasses the description of the simulation conditions used to obtain them.

In this paper we raise the curtain on this issue and we draw attention regarding the quantitative results of simulation when communication protocols are compared. Let us take the case of works that conduct to establish efficient mechanisms to ensure a long lifetime to the network. We take as examples energy-efficient MAC protocols based on sequences of active periods (during which the radio module is on) and sleep periods (during which it is off) called duty cycle. The duty cycle represents the proportion of the active period over the total duration of the cycle (active period + sleep period). The main energy-saving MAC protocol based on this mechanism is the standard IEEE 802.15.4 [START_REF]IEEE standard for local and metropolitan area networks -part 15.4: Lowrate wireless personal area networks (LR-WPANs)[END_REF] in beacon-enabled mode. In this type of protocols nodes agree on a common calendar for their periods of activity and sleep. This category is called synchronous duty-cycle MAC protocols. Other categories based on an asynchronous mechanism are also proposed in the literature. In these second categories, nodes do not have a common calendar for their period of activity and sleep. Most of these protocols are proposed as improvements to some existing protocols. However, there is a lack of important information on simulation conditions such as: radio frequency, antenna, propagation model, transmission power, topology information, data traffic information, capture effect, etc. These omission do not allow to judge the relevance of the results and also does not allow reproducibility. There are studies that show the impact of different propagation models and topologies on simulation results as in [START_REF] Kanthe | Effects of propagation models on AODV in mobile ad-hoc networks[END_REF] and [START_REF] Alduais | The performance evaluation of different logical topologies and their respective protocols for wireless sensor networks[END_REF]. However, to the best of our knowledge, no studies have shown these impacts with such a high level of detail as that in this paper. We use two energy-efficient MAC protocols, the standard IEEE 802.15.4 [START_REF]IEEE standard for local and metropolitan area networks -part 15.4: Lowrate wireless personal area networks (LR-WPANs)[END_REF] and SlackMAC [START_REF] Aby | History-based MAC protocol for low duty-cycle wireless sensor networks: the Slack-MAC protocol[END_REF][START_REF]Slack-MAC: Adaptive MAC protocol for low duty-cycle wireless sensor networks[END_REF] (a protocol that we have proposed). The aim of the paper is not to make an exhaustive comparison of protocols. We know intuitively that SlackMAC generally provides better performance than the standard. What we are trying to demonstrate through intensive simulations is the diversity in the performance gaps according to simulation conditions. Mainly, we will focus on the topologies used and the capture effect. This paper is organized as follows. In section 2, we make a summary of duty cycle MAC protocols and take note of the levels of detail on the missing simulation conditions. In section 3, we analytically compare the two protocols that serve tests in our study and give the technical details of our study. In section 4, we show simulation results from this comparative study. Finally, we conclude our work in section 5.

State-of-the-art

The MAC protocols of the literature based on the duty cycle mechanism can be classified into two main categories such as synchronous duty cycle MAC protocols and asynchronous duty cycle MAC protocols.

Synchronous duty cycle MAC protocol

In synchronous duty cycle MAC protocols synchronization can be global or local.

In the case of global synchronization, all nodes share a common schedule for their periods of activity and inactivity. The main MAC protocol based on this mechanism is the standard IEEE 802.15.4 [START_REF]IEEE standard for local and metropolitan area networks -part 15.4: Lowrate wireless personal area networks (LR-WPANs)[END_REF] in beacon enabled mode. This is also the case of protocol in [START_REF] Chalhoub | MAC specifications for a WPAN allowing both energy saving and guaranted delay -Part A: MaCARI: a synchronized tree-based MAC protocol[END_REF] and LO-MAC (Low Overhead MAC) [START_REF] Nguyen | Low overhead MAC protocol for low data rate wireless sensor networks[END_REF].

In the case of local synchronization, the nodes are synchronized by neighborhood, very often according to a tree topology as a function of their position relative to the sink. One of the first protocols based on this mechanism is S-MAC (sensor-MAC) [START_REF] Ye | An energy-efficient mac protocol for wireless sensor networks[END_REF] then D-MAC (Data-gathering MAC) [START_REF] Lu | An adaptive energy-efficient and lowlatency MAC for tree-based data gathering in sensor networks[END_REF] and TreeMAC [START_REF] Song | TreeMAC: Localized TDMA protocol for real-time high-data-rate sensor networks[END_REF]. Improvements of these protocols are proposed in ID-MAC [START_REF] Cunha | ID-MAC: An identity-based MAC protocol for wireless sensor networks[END_REF], DW-MAC (Demand Wakeup MAC) [START_REF] Sun | DW-MAC: a low latency, energy efficient demand-wakeup MAC protocol for wireless sensor networks[END_REF], DSF (Dynamic Switch-based Forwarding) [START_REF] Gu | Dynamic switching-based data forwarding for low-duty-cycle wireless sensor networks[END_REF] and iCore [START_REF] Cheng | Taming collisions for delay reduction in low-duty-cycle wireless sensor networks[END_REF].

Asynchronous duty cycle MAC protocol

In asynchronous duty cycle MAC protocols, nodes do not have a common calendar for their activity and sleep periods. A distinction can be made between sender-initiated and receiver-initiated MAC protocols.

In sender-initiated MAC protocols, most of the communication load is supported by sender nodes. The main protocol based on this principle is B-MAC protocol Berkeley MAC) [START_REF] Polastre | Versatile low power media access for wireless sensor networks[END_REF]. In B-MAC, a sender sends a long preamble before sending data frame. X-MAC [START_REF] Buettner | X-MAC: A short preamble MAC protocol for duty-cycled wireless sensor networks[END_REF] is one of the first improvement of B-MAC. Many other protocols such as BoX-MAC [START_REF] Moss | BoX-MACs: Exploiting physical and link layer boundaries in lowpower networking[END_REF] and OSX-MAC [START_REF] Kim | On-demand synchronous X-MAC protocol[END_REF] have been proposed thereafter.

Unlike sender-initiated MAC protocols, in receiver-initiated MAC protocols, most of the communication load is supported by the receiver nodes. In receiver-initiated MAC protocols the receiver initiates the communication by sending a beacon frame to express its ability to receive data frame. RI-MAC [START_REF] Sun | RI-MAC: a receiver-initiated asynchronous duty cycle MAC protocol for dynamic traffic loads in wireless sensor networks[END_REF] is the main protocol based on this mechanism. RIX-MAC (Receiver-Initiated X-MAC) [START_REF] Park | RIX-MAC: An energy-efficient receiver-initiated wakeup MAC protocol for WSNs[END_REF], ERI-MAC [START_REF] Nguyen | A receiver-initiated MAC protocol for energy harvesting sensor networks[END_REF], OC-MAC [START_REF] Wang | Opportunistic cooperation in low duty cycle wireless sensor networks[END_REF] and protocol in [START_REF] Yoo | Dynamic duty-cycle scheduling schemes for energyharvesting wireless sensor networks[END_REF] are improvement of RI-MAC.

Other hybrid MAC protocols that are both sender-initiated and receiver-initiated have been proposed to balance the communication load on the sender and receiver nodes. This is the case of protocol in [START_REF] Aby | Study of blind rendez-vous in low power wireless sensor networks[END_REF] and SlackMAC [START_REF] Aby | History-based MAC protocol for low duty-cycle wireless sensor networks: the Slack-MAC protocol[END_REF][START_REF]Slack-MAC: Adaptive MAC protocol for low duty-cycle wireless sensor networks[END_REF].

Overview of simulation conditions

In most of the protocols cited above comparisons by simulation on NS2 [START_REF]Network simulator 2[END_REF] are performed to show that the proposed protocols provide better overall performance than those of the existing. Table 1 gives a summary of simulation conditions description used in these comparisons. These simulation conditions essentially concern radio frequency, antenna (type, gain, height), propagation model, transmission power (noted TX power in table 1). It is also important to provide information about topology used (type, size, transmission range that provides connectivity, number of nodes, and sink position). Parameters such as receive power threshold and carrier sense threshold (noted RXThresh and CSThresh ), and capture threshold (noted CPThresh ) are all equally important. Traffic information such as type, size in byte and generation period of data packets are also required. It can see from Table 1 that these important elements of simulation conditions are often not fully defined and sometimes non-existent. LO-MAC [START_REF] Nguyen | Low overhead MAC protocol for low data rate wireless sensor networks[END_REF] ID-MAC [START_REF] Cunha | ID-MAC: An identity-based MAC protocol for wireless sensor networks[END_REF] DSF [START_REF] Gu | Dynamic switching-based data forwarding for low-duty-cycle wireless sensor networks[END_REF] iCore [START_REF] Cheng | Taming collisions for delay reduction in low-duty-cycle wireless sensor networks[END_REF] OSX-MAC [START_REF] Kim | On-demand synchronous X-MAC protocol[END_REF] OC-MAC [START_REF] Wang | Opportunistic cooperation in low duty cycle wireless sensor networks[END_REF] RIX-MAC [START_REF] Park | RIX-MAC: An energy-efficient receiver-initiated wakeup MAC protocol for WSNs[END_REF] [23] ERI-MAC [START_REF] Nguyen | A receiver-initiated MAC protocol for energy harvesting sensor networks[END_REF] [24] SlackMAC [START_REF]Slack-MAC: Adaptive MAC protocol for low duty-cycle wireless sensor networks[END_REF][START_REF] Aby | History-based MAC protocol for low duty-cycle wireless sensor networks: the Slack-MAC protocol[END_REF] Radio Frequency

✕ ✕ ✕ ✕ ✕ ✕ ✕ ✕ Antenna: -type -gain -height 1/3 1/3 ✕ ✕ ✕ ✕ 1/3 ✕ ✕ ✕ ✕ Propagation model ✕ ✕ ✕ ✕ ✕ TX power

✕ ✕ ✕ ✕

Topology: -type -size -TX range -number of nodes -sink position 3/5 1/5 3/5

-RXThresh -CSThresh ✕ ✕ ✕ ✕ ✕ ✕ ✕ ✕ ✕ Traffic: -type -size -period 2/3 2/3 1/3 1/3 1/3 2/3 2/3 2/3 2/3 2/3 Capture Threshold ✕ ✕ ✕ ✕ ✕ ✕ ✕ ✕ ✕ ✕ ✕ ✕ 3

Study framework

In this section we first describe the operating mechanism of the reference synchronous MAC protocol described in the standard IEEE 802.15.4 [START_REF]IEEE standard for local and metropolitan area networks -part 15.4: Lowrate wireless personal area networks (LR-WPANs)[END_REF] in beacon enabled mode and the asynchronous MAC protocol SlackMAC [START_REF]Slack-MAC: Adaptive MAC protocol for low duty-cycle wireless sensor networks[END_REF][START_REF] Aby | History-based MAC protocol for low duty-cycle wireless sensor networks: the Slack-MAC protocol[END_REF]. Then we give the technical details of the implementation of our comparative study. n 2 and n 3 ) in range. It can be seen that all the activity of the nodes is concentrated on the period SD. This reduces the communication time given to each node and increases the risk of collision depending on the number of nodes in competition to access the medium.

In SlackMAC, the medium is also accessed using the slotted CSMA/CA algorithm. The right side of Figure 1 shows an example of activity cycle for three nodes (n 1 , n 2 and n 3 ) in range with a duty cycle of 25 % ( a c × 100) in SlackMAC. Unlike the standard in SlackMAC, initially all nodes choose their activation times uniformly at random in the cycle. When a node chooses a time that yields to successful communications (reception or transmission of a frame), it memorizes it and the probability to choose this time increases, as can be seen in darker towards the right of Figure 1. It is noted that the activity of the nodes can be distributed over the whole cycle unlike the standard. Indeed, on average there are few active nodes at the same time, which reduces the risk of collision and allows a higher communication time per nodes than the standard for the same duty cycle as shown in [START_REF] Aby | History-based MAC protocol for low duty-cycle wireless sensor networks: the Slack-MAC protocol[END_REF].

According to such conditions, it is intuitively known that SlackMAC will ensure overall better performance than the standard. However, what we are trying to show in what follows is the diversity in the performance gaps according to simulation conditions.

Technical details

In this part we detail the technical environment used for our simulations. We want to highlight the fact that in a deployment study and a priori evaluation of its performance by simulation of WSNs, it is essential to be attentive and precise on the description of simulation conditions. In a simulation process we know how the choice of the propagation model is important for the relevance of the results. In this paper we will consider that this choice has been made at best and we focus on the impact of the chosen type of topology and on the effects of the given value of capture threshold parameter, often neglected in literature. The topology (or distance between nodes), the propagation model, and the capture threshold are three elements concerned to distinguish a collision of a successful reception. In a first step we propose a small synthesis of the topologies usually exploited but not often justified and/or not sufficiently detailed. On the second hand we provide some clarification on why it is important to specify the capture effect.

Topology production strategy In addition to specifying the propagation model and its associated parameters, it is also important to give the rules that allowed to generate the topology (or topologies) used for the simulation. Generally, in the used WSNs topologies for communication protocol comparison tests, nodes are positioned according to a grid or randomly distributed over a given area. Figures 2 and3 illustrate respectively the topologies in square and pseudo-linear, with in each case, one hundred nodes are positioned both in grid and randomly.

The random positioning is more closer to a realistic positioning. However, the concept of random position can change depending on how the placement is carried out. Indeed, to randomly place N nodes on a given area by hand, the person will try to cover the whole area and avoid nodes overlapping. For the same surface S and for the same number of nodes, the topologies obtained by a generator of random positions (x, y) lead to much greater diversity of solution but are not necessarily more representative for WSNs field. It can have less covered places than others, overlays and nodes that are too distant from others. To avoid the latter two situations, once the positions are randomly drawn, we use two filters in the control of network connectivity by Prim's algorithm (developed by Robert C. Prim (1957)). We set a minimum value of 1 m and a maximum of 50 m between any neighbors nodes. The surface of the coverage area (62500 m 2 ), the propagation model, the data packets production, the total number of nodes and the percentage of source nodes are identical in each case. We consider two forms of topologies (square of size 250 m x 250 m and pseudo-linear of size 625 m x 100 m) and for each type, nodes are positioned both in grid and randomly. In any case, the sink is located at the top right corner of the area.

In this work, our field of investigation is thus reduced to simulations on these 4 types of topologies (see Figures 2 and3) with different values of capture threshold.

Capture effect

The capture effect relates to the simultaneous reception of several frames with power levels such that the reception of one of them is possible. When a frame f is received with a power P f greater than or equal to the sum of the powers of the other k frames received at the same time plus a capture threshold fixed, the frame f is correctly decoded by the receiver. The capture threshold is the minimum power ratio in dB which enables the receiver in case of simultaneous reception to decode the strongest signal correctly. Therefore, if the difference between the power of a received signal and the sum of power of the other signals is greater than this threshold, reception is considered as a capture and the dominant signal is decoded correctly. Otherwise, the simultaneous reception will cause a collision and the signals can not be interpreted. This capture threshold has a direct impact on the collision rate of frames when the traffic subjected to the network becomes very significant.

Results

We carried out several simulations to show the diversity in the results as a function of simulation conditions for protocols SlackMAC [START_REF] Aby | History-based MAC protocol for low duty-cycle wireless sensor networks: the Slack-MAC protocol[END_REF][START_REF]Slack-MAC: Adaptive MAC protocol for low duty-cycle wireless sensor networks[END_REF] and the standard IEEE [START_REF]IEEE standard for local and metropolitan area networks -part 15.4: Lowrate wireless personal area networks (LR-WPANs)[END_REF]. We first describe the simulation environment and then compare the two MAC protocols.

Simulation parameters

Our simulations are performed using the network simulator NS-2 [START_REF]Network simulator 2[END_REF]. Global simulation parameters are given in Table 2. We use 10 of each type of topology of 100 nodes described in section 3 (see figures 2 and 3). We generate a convergecast communication (from the nodes to the sink), for 30 source nodes located randomly in the network. These source nodes perform periodic measurements and route them via other nodes to the sink. Nodes have a duty-cycle of 1% and the global cycle is 5 s (that is, nodes are active during A=50 ms every C=5 s). The same gradient-based routing protocol is used to route packets hop by hop towards the sink for both MAC protocols. All presented results, in each case are averaged over 10 repetitions per topology for the 10 topologies. We compute transmission power (in dBm) using outage probability method (defined in [START_REF] Mezghanni | Performance study of ieee 802.15. 4/4g waveforms over the mobile underground mine radio-channel[END_REF][START_REF] Simon | Outage performance of multiuser communication systems[END_REF]). For transmission range of 50 m and shadowing parameters defined in Table 2, the equivalent transmission power which can ensure stability of the radio links for 95% of the reception between two nodes is equal to 10.5342501084 dBm. This value is used as a transmission power in all simulations. Results with square topologies of size 250 m x 250 m In this first scenario, we perform tests using both a grid and random topologies (respectively noted SquLat and SquRnd). We vary the traffic generation period from 5 seconds to 60 seconds with capture threshold of 2 dB and 10 dB.

Figures 4 and5 show respectively the delivery ratio and the end-to-end delay of data frames, as a function of the traffic generation period (from 5 s to 60 s) for IEEE 802.15.4 and SlackMAC, when square topologies are into grid and random. For IEEE 802.15.4:

-when nodes are positioned in a grid, the delivery ratio increases from 23.43% to 66.34% for the capture threshold of 2 dB and from 14.37% to 49.56% for the capture threshold of 10 dB. The average delay decreases from 155.75 s to 10.02 s for the capture threshold of 2 dB and from 148 s to 10.52 s for the capture threshold of 10 dB.

-when nodes are randomly positioned, the delivery ratio increases from 25.18% to 66.07% for the capture threshold of 2 dB and from 15.81% to 50.2% for the capture threshold of 10 dB. The average delay decreases from 144.69 s to 7.22 s for the capture threshold of 2 dB and from 145.28 s to 9.66 s for the capture threshold of 10 dB. For SlackMAC:

-when nodes are positioned in a grid, the delivery ratio increases from 85.91% to 99.07% for the capture threshold of 2 dB and from 83.89% to 99.9% for the capture threshold of 10 dB. The average delay decreases from 59.09 s to 14.19 s for the capture threshold of 2 dB and from 53.52 s to 13.90 s for the capture threshold of 10 dB.

-when nodes are randomly positioned, the delivery ratio increases from 89.29% to 99.93% for the capture threshold of 2 dB and from 88.08% to 99.92% for the capture threshold of 10 dB. The average delay decreases from 40.14 s to 11.47 s for the capture threshold of 2 dB and from 39.96 s to 11.55 s for the capture threshold of 10 dB. The results of this scenario show that for SlackMAC, whether the nodes are positioned in grid or randomly on a square area, the delivery ratio vary very little according to the traffic and the capture threshold. However, the average delay is better in the case of random positioning when the traffic varies regardless of the capture threshold. For the standard, the results also vary very little with the type of positioning. On the other hand, there is a difference of 16% in the delivery ratio between a capture threshold of 2 dB and 10 dB for the two types of positioning. The average delay also varies little, but remains better when the positioning is random.

Results with pseudo-linear topologies of size 625 m x 100 m In this second scenario, we perform tests using both a grid and random topologies (respectively noted StrLat and StrRnd). We also vary the traffic generation period from 5 seconds to 60 seconds with capture threshold of 2 dB and 10 dB. Figures 6 and7 show respectively the delivery ratio and the end-to-end delay of data frames, as a function of the traffic generation period (from 5 s to 60 s) for IEEE 802.15.4 and SlackMAC, when pseudo-linear topologies are into grid and random. Average delay (in s) The results of this second scenario show that for SlackMAC, whether the nodes are positioned in grid or randomly on a square area, the delivery ratio vary very little according to the traffic and the capture threshold. However, unlike the square zone where the best average delay is obtained with a random positioning, that is not the case with a pseudo-linear zone in which the best average delay is with a grid positioning regardless of the capture threshold. For the standard, the results vary very little with the type of positioning for the delivery ratio and the average delay regardless of the capture threshold.

Effect of capture threshold In this last scenario, we perform tests using the two topologies area such as square and pseudo-linear. We set the traffic generation period to 60 seconds and vary the capture threshold from 2 dB to 10 dB.

Figures 8 and9 show respectively the delivery ratio and the end-to-end delay of data frames, as a function of the capture threshold (from 2 dB to 10 dB) for IEEE 802.15.4 and SlackMAC. For IEEE 802.15.4: -when topology is square, the delivery ratio decreases from 66.34% to 49.56% for grid positioning and from 66.07% to 50.20% for random positioning. The average delay increases from 10.02 s to 10.52 s for grid positioning and from 8.93 s to 9.66 s for random positioning.

-when topology is pseudo-linear, the delivery ratio decreases from 66.98% to 54.65% for grid positioning and from 66.67% to 56.48% for random positioning. -when topology is square, the delivery ratio is always around 99.9% for grid positioning and for random positioning. The average delay decreases slowly from 14.19 s to 13.9 s for grid positioning and always around 11 s for random positioning.

-when topology is pseudo-linear, the delivery ratio is always around 98.8% for grid positioning and for random positioning. The average delay decreases slowly from 24.39 s to 25.10 s for grid positioning and always around 32 s for random positioning. The results of this last scenario show that for SlackMAC, whether the nodes are positioned in grid or randomly for the two zones of topology the delivery ratio varies little with the capture threshold. However, the average delay for a square zone is better for random positioning than grid positioning and for a pseudo-linear zone the better average delay is obtained with a grid positioning. Unlike SlackMAC, the delivery ratio in the standard decreases when the capture threshold increases regardless of the zone and the positioning. The average delay increases with the capture threshold for square zone and decreases with the capture threshold for pseudo-linear zone regardless of positioning.

Conclusion

In this work, we show the need to be attentive and precise about the description of the simulation conditions comparing the performance of communication protocols for WSNs. For a given and widespread signal propagation model we pointed out how the topology choice and the capture threshold value may impact simulations results. To do this we used an asynchronous MAC protocol SlackMAC and the reference MAC protocol specified in the standard IEEE 802.15.4 in beacon-enabled mode. The aim of the paper is not to make an exhaustive comparison of protocols. We knew intuitively that the asynchronous MAC protocol would provide better overall performance than the standard. What we have demonstrated with intensive simulations, is the diversity in the results when the simulation conditions change. The conditions we have dealt with are the topology types and the capture threshold used in a WSN simulation process : (i) grid or random topologies for squared or stretched areas and (ii) capture threshold varying from 2 dB to 10 dB. The results showed great diversity such as for example for the same form of topology (square or pseudo-linear), either one of the two protocols gives better performances when the positioning is in grid and the other one rather with a random positioning. These observations are also made for two different forms of topology. These results also showed a very significant impact of the capture threshold on one protocol than the other. These remarkable differences between performances when simulation conditions change, confirm that it is essential to clearly define the simulation environment and also to reconcile the chosen conditions with the results when comparing the performances of protocols.

3. 1 Fig. 1 :

 11 Fig. 1: Example of activity of three nodes n 1 , n 2 and n 3 in range, with a duty cycle of 25 % for respectively the standard IEEE 802.15.4 (left) and SlackMAC (right).

Fig. 2 :

 2 Fig. 2: Topology of 100 nodes on square area of 250 m x 250 m with respectively a grid positioning (left) and random positioning (right).

Fig. 3 :

 3 Fig. 3: Topology of 100 nodes on square area of 625 m x 100 m with respectively a grid positioning (left) and random positioning (right).

Fig. 4 :

 4 Fig.4: The packets delivery ratio as a function of the traffic generation period with respectively the capture threshold of 2 dB (left) and 10 dB (right) for IEEE 802.15.4 and SlackMAC when square topologies are into grid and random.

Fig. 5 :

 5 Fig. 5: Average delay as a function of the traffic generation period with respectively the capture threshold of 2 dB (left) and 10 dB (right) for IEEE 802.15.4 and SlackMAC when square topologies are into grid and random.

Fig. 6 :

 6 Fig.6: The packets delivery ratio as a function of the traffic generation period with respectively the capture threshold of 2 dB (left) and 10 dB (right) for IEEE 802.15.4 and SlackMAC when pseudo-linear topologies are into grid and random.

Fig. 9 :

 9 Fig. 9: Average delay as a function of the capture threshold with respectively square topology (left) and pseudo-linear topology (right) into grid and random for IEEE 802.15.4 and SlackMAC when the traffic generation period is 60 seconds.

Table 1 :

 1 Overview on specification of NS2 simulation conditions

	Simulation conditions / Protocols	DW-MAC [12]

Table 2 :

 2 Global simulation parameters

		Topologies area				250 m x 250 m and 625 m x 100 m
		Transmission range						50 m
		Number of nodes						100
		Number of source nodes					30
		Radio frequency						2.4 GHz
		Receive threshold (RXThresh)				-85 dBm
		Carrier-sense threshold (CSThresh)			-92 GHz
		System loss (L)							1
		Antenna type						Omnidirectional
		Antenna gain (Gt, Gr)						1
		Antenna height (Z)						1.5 m
		Propagation model: shadowing	path loss exponent = 2.5 shadowing deviation = 4.0 dB
		Data traffic					Constant-bit rate (CBR)
		Data frame size							30 bytes
		Data traffic period			from 5 seconds to 60 seconds
		Maximum send queue size					20 frames
		Number of topology						10
		Number of repetitions per topology			10
		Simulation duration					3600 seconds
	4.2 Simulation results
		100							100
		80							80
	Delivery ratio (in %)	20 40 60			802.15.4 SquRnd 802.15.4 SquLat	Delivery ratio (in %)	20 40 60			802.15.4 SquRnd 802.15.4 SquLat
					SlackMAC SquRnd					SlackMAC SquRnd
					SlackMAC SquLat					SlackMAC SquLat
		0	10	20	30	40	50	60	0	10	20	30	40	50	60
			Traffic generation period (in s)				Traffic generation period (in s)

  are positioned in a grid, the delivery ratio increases from 18.34% to 66.98% for the capture threshold of 2 dB and from 15.42% to 54.65% for the capture threshold of 10 dB. The average delay decreases from 157.94 s to 29.49 s for the capture threshold of 2 dB and from 145.99 s to 19.79 s for the capture threshold of 10 dB.-when nodes are randomly positioned, the delivery ratio increases from 18.09% to 66.67% for the capture threshold of 2 dB and from 15.28% to 56.48% for the capture threshold of 10 dB. The average delay decreases from 152.20 s to 33.02 s for the capture threshold of 2 dB and from 142.32 s to 22.59 s for the capture threshold of 10 dB. For SlackMAC: -when nodes are positioned in a grid, the delivery ratio increases from 66.40% to 98.72% for the capture threshold of 2 dB and from 64.41% to 98.57% for the capture threshold of 10 dB. The average delay decreases from 164.26 s to 24.39 s for the capture threshold of 2 dB and from 171.67 s to 25.10 s for the capture threshold of 10 dB.-when nodes are randomly positioned, the delivery ratio increases from 62.55% to 98.8% for the capture threshold of 2 dB and from 61.49% to 98.73% for the capture threshold of 10 dB. The average delay decreases from 190.27 s to 32.32 s for the capture threshold of 2 dB and from 192.74 s to 32.43 s for the capture threshold of 10 dB.

	For IEEE 802.15.4:
	-when nodes
	802.15.4 StrRnd
	802.15.4 StrLat
	SlackMAC StrRnd
	SlackMAC StrLat
	Traffic generation period (in s)
	Fig. 7: Average delay as a function of the traffic generation period with respectively the
	capture threshold of 2 dB (left) and 10 dB (right) for IEEE 802.15.4 and SlackMAC
	when pseudo-linear topologies are into grid and random.

  The packets delivery ratio as a function of the capture threshold with respectively square topology (left) and pseudo-linear topology (right) into grid and random for IEEE 802.15.4 and SlackMAC when the traffic generation period is 60 seconds.

	100										100
		80										80
	Delivery ratio (in %)	20 40 60						802.15.4 StrRnd		Delivery ratio (in %)	20 40 60						802.15.4 StrRnd
								802.15.4 StrLat									802.15.4 StrLat
								SlackMAC StrRnd									SlackMAC StrRnd
							SlackMAC StrLat								SlackMAC StrLat
		0	2	3	4	5	6	7	8	9	10	0	2	3	4	5	6	7	8	9	10
				Capture threshold (in dB)							Capture threshold (in dB)
	Fig. 8:
																			The average
	delay decreases from 29.49 s to 19.79 s for grid positioning and from 33.02 s to 22.59 s
	for random positioning.
	For SlackMAC: