Heng Zhang

Elisa Fromont

Sébastien Lefevre

Bruno Avignon

Guided Attentive Feature Fusion for Multispectral Pedestrian Detection

Multispectral image pairs can provide complementary visual information, making pedestrian detection systems more robust and reliable. To benefit from both RGB and thermal IR modalities, we introduce a novel attentive multispectral feature fusion approach. Under the guidance of the inter-and intra-modality attention modules, our deep learning architecture learns to dynamically weigh and fuse the multispectral features. Experiments on two public multispectral object detection datasets demonstrate that the proposed approach significantly improves the detection accuracy at a low computation cost.

Introduction

Real world pedestrian detection applications require accurate detection performance under various conditions, such as darkness, rain, fog, etc. In these conditions, it is difficult to perform precise detection using only standard RGB cameras. Instead, multispectral systems try to combine the information coming from e.g. thermal and visible cameras to improve the reliability of the detections.

Deep learning-based methods, more specifically, twostream convolutional neural networks, nowadays largely dominate the field of multispectral pedestrian detection [START_REF] Guan | Fusion of multispectral data through illumination-aware deep neural networks for pedestrian detection[END_REF][START_REF] König | Fully convolutional region proposal networks for multispectral person detection[END_REF][START_REF] Li | Multispectral pedestrian detection via simultaneous detection and segmentation[END_REF][START_REF] Li | Illumination-aware faster R-CNN for robust multispectral pedestrian detection[END_REF][START_REF] Liu | Multispectral deep neural networks for pedestrian detection[END_REF][START_REF] Wagner | Multispectral pedestrian detection using deep fusion convolutional neural networks[END_REF][START_REF] Zhang | Multispectral Fusion for Object Detection with Cyclic Fuse-and-Refine Blocks[END_REF][START_REF] Zhang | Cross-modality interactive attention network for multispectral pedestrian detection[END_REF]. As illustrated in Fig. 1, a typical two-stream pedestrian detection network consists of two separate spectra-specific feature extraction branches, a multispectral feature fusion module and a pedestrian detection network operating on the fused features. The system uses some aligned thermal-visible image pairs as input and outputs the joint detection results on each image pair.

Thermal and visible cameras have different imaging characteristics under different conditions. As shown in Fig. 2, visible cameras provide precise visual details (such as color and texture) in a well-lit environment, while thermal cameras are sensitive to temperature changes, which is extremely useful for nighttime or shadow detection. An adaptive fusion of thermal and visible features should take such differences into account, and should identify and lever- age the information from the most relevant modality.

An intuitive solution to adapt the feature fusion to the different weather and lighting conditions is to manually identify multiple usage scenarios and design a specific solution for each scenario. For example, [START_REF] Guan | Fusion of multispectral data through illumination-aware deep neural networks for pedestrian detection[END_REF] proposes an illumination-aware network consisting of a day illumination sub-network and a night illumination sub-network. The detection results from the two sub-networks are then fused according to the prediction of the illumination context. Such a kind of hand-crafted fusion mechanism improves the resilience of the model to a certain extent, nonetheless, there are still two limitations: firstly, cherry-picked scenarios may not cover all conditions, e.g., different illumination/season/weather conditions; Secondly, the situation may be completely different even in the same usage scenario, e.g., at nighttime, lighting conditions in urban areas are different from those in rural areas.

In this paper, we propose a novel and fully adaptive multispectral feature fusion approach, named Guided Attentive Feature Fusion (GAFF). By combining the intra-and intermodality attention modules, the proposed approach allows the network to learn the adaptive weighing and fusion of multispectral features. These two attention mechanisms are guided by the prediction and comparison of the pedestrian masks in the multispectral feature fusion stage. Specifically, at each spatial position, thermal or visible features are enhanced when they are located in the area of a pedestrian (intra-modality attention) or when they possess a higher quality than in the other modality (inter-modality attention). To the best of our knowledge, GAFF is the first work that regards the multispectral feature fusion as a sub-task in the network optimization and that introduces a specific guidance in this task to improve the multispectral pedestrian detection. Extensive experiments on KAIST multispectral pedestrian detection dataset [START_REF] Hwang | Multispectral pedestrian detection: Benchmark dataset and baselines[END_REF] and FLIR ADAS dataset [START_REF]Free flir thermal dataset for algorithm training[END_REF] demonstrate that, compared with common feature fusion methods (such as addition or concatenation), GAFF brings important accuracy gains at a low computational cost.

This paper is organized as follows: Section 2 reviews some representative work applying static/adaptive feature fusion for multispectral pedestrian detection; Section 3 introduces implementation details on how to integrate GAFF into a typical two-stream convolutional neural network; In Section 4, we evaluate our methods on two public multispectral object detection datasets [START_REF] Hwang | Multispectral pedestrian detection: Benchmark dataset and baselines[END_REF][START_REF]Free flir thermal dataset for algorithm training[END_REF], then we provide an extensive ablation study and visualization results to discuss the reasons of the accuracy improvements; Section 5 concludes the paper.

Related Work

Static multispectral feature fusion

KAIST released the first large-scale multispectral pedestrian detection dataset [START_REF] Hwang | Multispectral pedestrian detection: Benchmark dataset and baselines[END_REF], which contains approximately 95k well-aligned and manually annotated thermal-visible image pairs captured during daytime and nighttime. Some example image pairs are shown in Fig. 2. Then [START_REF] Wagner | Multispectral pedestrian detection using deep fusion convolutional neural networks[END_REF] demonstrated the first application of deep learning-based solutions in multispectral pedestrian detection. They compared the early and late fusion architectures and found that the late fusion architecture is superior to the early one and the traditional ACF method [START_REF] Dollar | Fast feature pyramids for object detection[END_REF]. This late-stage fusion architecture can be regarded as a prototype of a two-stream neural network, in which multispectral features are fused through concatenation operations. Both [START_REF] Liu | Multispectral deep neural networks for pedestrian detection[END_REF] and [START_REF] König | Fully convolutional region proposal networks for multispectral person detection[END_REF] adapted Faster R-CNN [START_REF] Shaoqing Ren | Faster R-CNN: towards real-time object detection with region proposal networks[END_REF] to a two-stream network architecture for multispectral pedestrian detection. They compare different multispectral fusion stages and came to the conclusion that the fusion in the middle stage outperforms the fusion in the early or late stage. Based on this, MSDS-RCNN [START_REF] Li | Multispectral pedestrian detection via simultaneous detection and segmentation[END_REF] adopted a two-stream middle-level fusion architecture and combined the pedestrian detection task and the semantic segmentation task to further improve the detection accuracy.

Adaptive multispectral feature fusion

As mentioned in Section 1, thermal and visible cameras have different imaging characteristics and the adaptive multispectral fusion can improve the resilience and the detection accuracy of the system. This has become the main focus of the multispectral pedestrian detection research in recent years. Both [START_REF] Li | Illumination-aware faster R-CNN for robust multispectral pedestrian detection[END_REF] and [START_REF] Guan | Fusion of multispectral data through illumination-aware deep neural networks for pedestrian detection[END_REF] use the illumination information as a clue for the adaptive fusion: they train a separate network to estimate the illumination value from a given image pair, then [START_REF] Li | Illumination-aware faster R-CNN for robust multispectral pedestrian detection[END_REF] uses the predicted illumination value to weigh the detection results from both the thermal and visible images. [START_REF] Guan | Fusion of multispectral data through illumination-aware deep neural networks for pedestrian detection[END_REF] uses the illumination value to weigh the detection results from a day illumination sub-network and a night illumination sub-network. As mentioned in the previous section, such a handcrafted weighing scheme is limited and produces sub-optimal performance. CIAN [START_REF] Zhang | Cross-modality interactive attention network for multispectral pedestrian detection[END_REF] applies the channel-level attention in the multispectral feature fusion stage to model the cross-modality interaction and weigh each feature map extracted from the different spectrum. This network realizes a fully adaptive fusion of thermal and visible features, however, in this approach, the fusion module is optimized directly while solving the pedestrian detection task which means that the network uses information about what (pedestrian or background) and where (bounding box) relevant elements are in the images but it does not use the fact that some features may contain more relevant information than others. We believe and we show that with these additional information (that we include in our method through the guidance mechanism), we can improve the detection precision.

Proposed approach

The proposed Guided Attentive Feature Fusion (GAFF), shown in Fig. 3, takes place in the multispectral feature fusion stage of a two-stream convolutional neural network. It consists of two components: an intra-modality attention module and an inter-modality one.

Intra-modality attention module

The intra-modality attention module aims at enhancing the thermal or visible features in a monospectral view. Specifically, as illustrated by the yellow paths on Fig. 3, features of an area with a pedestrian are highlighted by multiplying the learnt features with the predicted pedestrian mask. Moreover, in order to avoid directly affecting the thermal or visible features, the highlighted features are added as a residual to enhance the mono-spectral features. This procedure can be formalized as:

f t intra = f t ⊗ (1 + m t intra ) f v intra = f v ⊗ (1 + m v intra ) (1)
where

m t intra = σ(F t intra (f t )) m v intra = σ(F v intra (f v )) (2)
Superscripts (t or v) denote the thermal (t) or visible (v) modality; ⊗ denotes the element-wise multiplication; σ represents the sigmoid function; F intra represents a convolution operation to predict the intra-modality attention masks (pedestrian masks) m intra ; f and f intra represent the original and enhanced features, respectively.

The prediction of the pedestrian masks is supervised by the semantic segmentation loss, where the ground truth mask (m gt intra ) is converted from the object detection annotations. As illustrated in Fig. 3 the bounding box annotations are transformed into some filled ellipses to approximate the shape of the true pedestrians.

Inter-modality attention module

Thermal and visible cameras have their own imaging characteristics, and under certain conditions, one sensor has superior imaging quality (i.e. is more relevant for the considered task) than the other. To leverage both modalities, we propose the inter-modality attention module, which adaptively selects thermal or visible features according to the dynamic comparison of their feature quality. Concretely, an inter-modality attention mask is predicted based on the combination of thermal and visible features. This predicted mask has two values for each pixel, corresponding to the weights for thermal and visible features (summing to 1). This attention module is illustrated as the red paths in Fig. 3. It can be formulated as:

f t inter = f t ⊗ (1 + m t inter ) f v inter = f v ⊗ (1 + m v inter ) (3)
where

m t inter , m v inter = δ(F inter ([f t , f v ])) (4)
Here, δ denotes the softmax function; [•] denotes the feature concatenation operation; F inter represents a convolution operation to predict the inter-modality attention mask m inter . At each spatial position of the mask, the sum of m t inter and m v inter equals to 1. Note that this formalization could theoretically allow for more than two modalities to be fuse following the same principles.

The inter-modality attention module allows the network to adaptively select the most reliable modality. However, in order to train this module, we should need a costly ground truth information about the best pixel-level modality quality. Our solution to relieve the annotation cost is to assign labels according to the prediction of the pedestrian masks from the intra-modality attention module, i.e., we force the network to select one modality if its intra-modality mask prediction is better (i.e. closer to the ground truth pedestrian mask) than the other. Specifically, we first calculate an error mask for each spectrum with the following formula:

e t intra = | m t intra -m gt intra | e v intra = | m v intra -m gt intra | (5)
then the label for the modality selection is defined as:

m gt inter =    1, 0 if (e v intra -e t intra ) > margin 0, 1
if (e t intra -e v intra ) > margin ignored otherwise (6) Here, | • | denotes the absolute function; e intra represents the error mask, defined by the L1 distance between the predicted intra-modality mask m intra and the ground truth intra-modality mask m gt intra ; m gt inter is the ground truth mask for inter-modality attention (2 values at each mask position); margin is a hyper-parameter to be tuned.

An example of the label assignment for the intermodality attention mask is shown in Fig. 3. If the intramodality pedestrian masks are predicted as shown in the yellow paths, the inter-modality (weak) ground truth masks are then defined as the ones shown on the red paths, where white, black and gray areas denote the classification labels 1,0 and ignored, respectively. Here, the thermal features produce a better intra-modality mask prediction for the pedestrians on the left side of the input images in Fig. 3. Therefore, according to Eq. 6, the label for the intermodality mask on this area is assigned as 1,0 (1 for the thermal mask and 0 for the visible mask). For regions where the two intra-modality masks have comparable prediction qualities (i.e., the difference between prediction errors is smaller than the predefined margin), the optimization of the inter-modality attention mask prediction on these areas are ignored (i.e., do not participate in the loss calculation).

Combining intra-and inter-modality attention

The intra-modality attention module enhances features on areas with pedestrians and the inter-modality attention module adaptively selects features from the most reliable modality. When these two modules are combined, the fused features are obtained by:

f f used = f t hybrid + f v hybrid 2 (7)
where

f t hybrid = f t ⊗ (1 + m t intra ) ⊗ (1 + m t inter ) f v hybrid = f v ⊗ (1 + m v intra ) ⊗ (1 + m v inter ) (8)
Here, m intra and m inter are predicted intra-and intermodality attention masks from Eq. 2 and Eq. 4; f hybrid represents features enhanced by both attention modules; f f used represents the final fused features.

As mentioned in Section 2, the optimization of the multispectral feature fusion task may not benefit enough from the sole optimization of the object detection task (as done e.g. in [START_REF] Zhang | Cross-modality interactive attention network for multispectral pedestrian detection[END_REF]). In GAFF, we propose two specific feature fusion losses, including the pedestrian segmentation loss for the intra-modality attention and the modality selection loss for the inter-modality attention, to guide the multispectral feature fusion task. These losses are jointly optimized with the object detection loss. The final training loss L total is:

L total = L det + L intra + L inter (9)
where, L det , L intra and L inter are the pedestrian detection, the intra-and inter-modality attention loss, respectively.

Experiments

In this section, we conduct experiments on KAIST Multispectral Pedestrian Detection Dataset [START_REF] Hwang | Multispectral pedestrian detection: Benchmark dataset and baselines[END_REF] and FLIR ADAS Dataset [START_REF]Free flir thermal dataset for algorithm training[END_REF] to evaluate the effectiveness of the proposed method. Moreover, we attempt to interpret the reasons for improvements by visualizing the predicted attention masks. Finally, we provide inference speed analysis on two different target platforms.

Datasets

KAIST dataset contains 7,601 training image pairs and 2,252 pairs testing ones. Some example image pairs from this dataset are shown in Fig. 2. [START_REF] Li | Multispectral pedestrian detection via simultaneous detection and segmentation[END_REF] proposes a "sanitized" version of the annotations, where numerous annotation errors are removed. Our experiments are conducted with the original as well as the "sanitized" version of annotations for fair comparisons with our competitors. We found out that the "sanitized" annotations substantially improve the detection accuracy for different network architectures. All models are evaluated with the improved testing annotations from [START_REF] Liu | Multispectral deep neural networks for pedestrian detection[END_REF] and the usual pedestrian detection metric: logaverage Miss Rate over the range of [10 -2 , 10 0 ] false positives per image (FPPI) under a "reasonable" setting [START_REF] Dollar | Pedestrian detection: An evaluation of the state of the art[END_REF], i.e., only pedestrians taller than 50 pixels under no or partial occlusions are considered 1 .

We also conduct experiments on FLIR ADAS Dataset [START_REF]Free flir thermal dataset for algorithm training[END_REF]. [START_REF] Zhang | Multispectral Fusion for Object Detection with Cyclic Fuse-and-Refine Blocks[END_REF] proposed an "aligned" version of the dataset for multispectral object detection. This new version contains 5,142 well-aligned multispectral image pairs (4,129 pairs for training and 1,013 pairs for testing). FLIR covers three object categories: "person", "car" and "bicycle". Models are evaluated with the usual object detection metric introduced with MS-COCO [START_REF] Lin | Microsoft COCO: Common objects in context[END_REF]: the mean Average Precision (mAP) averaged over ten different IoU thresholds.

Implementation details

The proposed GAFF module can be included in any type of two-stream convolutional neural networks. In these experiments, we choose RetinaNet [START_REF] Lin | Focal loss for dense object detection[END_REF] as our base detector. It is transformed into a two-stream convolutional neural network by adding an additional branch for the extraction of thermal features. A ResNet18 [START_REF] He | Deep residual learning for image recognition[END_REF] or a VGG16 [START_REF] Simonyan | Very deep convolutional networks for large-scale image recognition[END_REF] network is pre-trained on ImageNet [START_REF] Deng | ImageNet: A Large-Scale Hierarchical Image Database[END_REF], then adopted as our backbone network. The input image resolution is fixed to 640×512 for training and evaluation. Our baseline detector applies the basic addition as the multispectral feature fusion method. GAFF is implemented by adding the intra-and inter-modality attention modules, corresponding to the yellow and the red branches in Fig. 3. Focal loss [START_REF] Lin | Focal loss for dense object detection[END_REF] and Balanced L1 loss [START_REF] Pang | Libra r-cnn: Towards balanced learning for object detection[END_REF] are adopted as the classification loss and the bounding box regression loss to optimize the object detection task. In order to introduce our specific guidance, we adopt the DICE [START_REF] Dice | Measures of the amount of ecologic association between species[END_REF] loss as the pedestrian segmentation loss (L intra in Eq. 9) and the cross-entropy loss as the modality selection loss (L inter in Eq. 9). by comparing in Table 2 the Miss Rate of GAFF where the attention masks are directly applied to mono-spectral features (f intra = f ⊗ m intra and f inter = f ⊗ m inter ) or added as residual (as in Eq. 1 and Eq. 3).

Ablation study

Necessity of attention.

We compare in Tab. 3 the detection accuracy on KAIST dataset with different attention settings, different backbone networks, and different annotation settings (original and "sanitized"). When conducting experiments with inter-modality but without intra-modality attention, the pedestrian masks are predicted but are not multiplied with the corresponding mono-spectral features. For each backbone network or annotation setting, both intraand inter-modality attention modules consistently improve the baseline detection accuracy, and their combination leads to the lowest overall Miss Rate under all experimental settings. The present findings confirm the effectiveness of the proposed guided attentive feature fusion modules.

Necessity of guidance. To explore the effect of the proposed multispectral feature fusion guidance, we compare our guided approach to one with a similar architecture as ours but where the optimization of the specific fusion losses (L intra and L inter in Eq. 9) are removed from the training process, i.e., the fusion is only supervised by the object detection loss (as done with [START_REF] Zhang | Cross-modality interactive attention network for multispectral pedestrian detection[END_REF]). We report in Tab. 4 the detection performance with and without guidance, under different backbone networks and annotations settings. The results confirm our assumption that the object detection loss is not relevant enough for the multispectral feature fusion task: even though the non-guided attentive fusion module improves the baseline Miss Rate to some degree (e.g., with the "sanitized" annotations and VGG16 backbone, non-guided model improves the base detector's Miss Rate from 9.28% to 8.38%), it could be further improved when the specific fusion guidance is added (from 8.38% to 6.48%).

Attention mask interpretation. Fig. 4 provides the visualization results of the intra-modality, the inter-modality and the hybrid attention masks during daytime and nighttime. For each figure, the top and bottom two rows of images are visualization results of guided and non-guided attentive feature fusions, respectively. We can see on the intra-modality attention masks that the guided attention mechanism focuses on pedestrian areas, even though, sometimes, it is not accurate from a single mono-spectral view. For example, the traffic cone is misclassified as a pedestrian [START_REF] Hwang | Multispectral pedestrian detection: Benchmark dataset and baselines[END_REF] with both annotation settings.

due to its human-like shape on the thermal image of Fig. 4a, and the pedestrian in the middle right position is missed due to insufficient lighting on the RGB image of Fig. 4b. For inter-modality attention masks, it appears that the guided attentive fusion tends to select visible features on well-lit areas (such as upside of images in Fig. 4b) and brightly coloured areas (e.g., traffic cone, road sign, speed bump, car tail light, etc), and to select thermal features on dark areas and uniform areas (such as sky and road). Note that these attention preferences are automatically learnt via our intermodality attention guidance. On the contrary, despite the fact that the non-guided attention mechanism brings some accuracy improvements, the predicted attention masks are quite difficult to interpret. More visualization results are shown in Fig. 5. Besides, an interesting error case is shown in Fig. 5c, where the pedestrian on the steps is not detected with the guided model but detected with the non-guided model. As mentioned earlier, GAFF selects thermal features on uniform areas, which is intuitive since thermal cameras are sensitive to temperature change and there exist few objects on uniform areas of the thermal image. However, in this particular case, the pedestrian is not captured on the thermal image, which leads to the final detection error. From the plot, we can conclude that thermal images are generally better for recognition than visible images. This observation is consistent with our mono-spectral experiments, where thermal-only model reaches 18.8% of Miss Rate while visible-only model achieves 20.74% (both trained with "sanitized" annotations). Interestingly, as the segmentation accuracy increases for both images, the modality selection task becomes more and more challenging. Note that this accuracy is irrelevant at the beginning of the training, where predicted pedestrian masks are almost zero for both thermal and visible features, thus the difference between their error masks is minor and the set of margin makes most areas ignored for modality selection optimization. Such mechanism avoids the "cold start" problem.

Runtime analysis In Tab. 5 we report the total number of learnable parameters and the average inference runtime on two different computation platforms. Specifically, the models are implemented with Pytorch (TensorRT) framework for an inference time testing on the Nvidia GTX 1080Ti (Nvidia TX2) platform. Since GAFF only involves 3 convolution layers, the additional parameters and computation cost is low, i.e., it represents less than 0.1% of additional parameters and around 0.5ms (1.5ms) of inference time on 1080Ti (TX2). Note that the time for post-processing treatments (such as Non-Maximum Suppression) is not taken into account for the benchmarking. Our model meets the requirement of real-time treatment on embedded devices, which is essential for many applications.

Comparison with State-of-the-art Multispectral Pedestrian Detection Methods

KAIST Dataset Tab. 6 shows the detection results of existing methods and our GAFF with the original and "sanitized" annotations on KAIST. It can be observed that GAFF achieves state-of-the-art performance on this dataset (it is slightly less accurate than CFR [START_REF] Zhang | Multispectral Fusion for Object Detection with Cyclic Fuse-and-Refine Blocks[END_REF], which applies cascaded Fuse-and-Refine blocks for sequential feature enhancement and needs more computation than GAFF (see Methods Platform Runtime ACF+T+THOG [START_REF] Hwang | Multispectral pedestrian detection: Benchmark dataset and baselines[END_REF] MATLAB 2730ms Halfway Fusion [START_REF] Liu | Multispectral deep neural networks for pedestrian detection[END_REF] Titan X 430ms Fusion RPN+BF [START_REF] Liu | Multispectral deep neural networks for pedestrian detection[END_REF] MATLAB 800ms IAF R-CNN [START_REF] Li | Illumination-aware faster R-CNN for robust multispectral pedestrian detection[END_REF] Titan X 210ms IATDNN+IASS [START_REF] Guan | Fusion of multispectral data through illumination-aware deep neural networks for pedestrian detection[END_REF] Titan X 250ms CIAN [START_REF] Zhang | Cross-modality interactive attention network for multispectral pedestrian detection[END_REF] 1080Ti 70ms MSDS-RCNN [START_REF] Li | Multispectral pedestrian detection via simultaneous detection and segmentation[END_REF] Titan X 220ms CFR [START_REF] Zhang | Multispectral Fusion for Object Detection with Cyclic Fuse-and-Refine Blocks[END_REF] 1080Ti 50ms GAFF (ours) 1080Ti 9.34ms Table 8: Detection results on FLIR dataset [START_REF]Free flir thermal dataset for algorithm training[END_REF].

Table 7). According to Tab. 7, thanks to the lightweight design of GAFF, our model has substantial advantage in terms of inference speed compared to e.g. [START_REF] Zhang | Multispectral Fusion for Object Detection with Cyclic Fuse-and-Refine Blocks[END_REF].

FLIR Dataset Tab. 8 reports the detection results with and without GAFF on FLIR dataset. We can observe that the average precision is improved for all IoU thresholds with GAFF (around 1% of mAP improvement for both backbone networks), which shows that our method can generalize well to different types of images. For comparison, the more costly CFR [START_REF] Zhang | Multispectral Fusion for Object Detection with Cyclic Fuse-and-Refine Blocks[END_REF] reaches 72.39% of AP50 on this dataset, whereas our best result is 72.9%.

Conclusion

We argue that the lack guidance is a limitation for efficient and effective multispectral feature fusion, and we propose Guided Attentive Feature Fusion (GAFF) to guide this fusion process. Without hand-crafted assumptions or additional annotations, GAFF realizes a fully adaptive fusion of thermal and visible features. Experiments on KAIST and FLIR datasets demonstrate the effectiveness of GAFF and the necessity of attention and guidance in the feature fusion stage. We noticed that certain thermal-visible image pairs are slightly misaligned in the above datasets, such a problem could be more critical in real life applications. Our future research is devoted to the development of a real-time feature calibration module based on the predicted attention masks from GAFF.

Figure 1 :

 1 Figure 1: Multispectral pedestrian detection via a twostream convolutional neural network.

Figure 2 :

 2 Figure 2: Typical examples of thermal-visible image pairs captured during the day (first two rows) and night (bottom row). For each pair, the thermal image is on the left and the RGB image is on the right.

Figure 3 :

 3 Figure 3: The overall architecture of Guided Attentive Feature Fusion (GAFF). Green, blue and purple blocks represent thermal, visible and fused features. Yellow and red paths represent the intra-and inter-modality attention modules, respectively.

Figure 4 :

 4 Figure 4: Visualization examples of attention masks on KAIST dataset. Zoom in to see details.

Figure 5 :

 5 Figure 5: More visualization examples of attention masks on KAIST dataset. Zoom in to see details.

  inter-modality attention accuracy evolution Modality selection accuracy

Figure 6 :

 6 Figure 6: Intra-and inter-modality attention accuracy evolution during training.

Table 1 :

 1 Detection results of GAFF with different margin values in the inter-modality attention module.

	M argin	All	Miss Rate Day	Night
	0.05	6.92% 8.47% 3.68%
	0.1	6.48% 8.35% 3.46%
	0.2	7.47% 9.31% 4.22%
	Hyper-parameter tuning. As reported in Table 1, we
	conduct experiments with different margin values in the
	inter-modality attention module on KAIST dataset [8] with
	"sanitized" annotations. The Miss Rate scores on the
	Reasonable-all, Reasonable-day and Reasonable-night sub-
	sets are listed. We observe that the optimal Miss Rate is
	achieved when margin = 0.1. Thus, we use margin =
	0.1 for all the following experiments.

Residual attention. As mentioned in Section 3, attention enhanced features are added as residual to avoid directly affecting the thermal or visible features. We verify this choice

Table 2 :

 2 Detection results of GAFF where the attention masks are directly applied or added as residual.

Table 3

 3

	: Ablation study of two attentive fusion modules on
	KAIST dataset [8] with original (top) or "sanitized" (bot-
	tom) annotations.

Table 4 :

 4 Comparison between guided and non-guided models on KAIST dataset

Table 5 :

 5 Runtime on different computing platforms.

	Methods	All	Miss Rate Day	Night
	ACF+T+THOG [8]	47.24% 42.44% 56.17%
	Halfway Fusion [14] 26.15% 24.85% 27.59%
	Fusion RPN+BF [14] 16.53% 16.39% 18.16%
	IAF R-CNN [11]	16.22% 13.94% 18.28%
	IATDNN+IASS [6]	15.78% 15.08% 17.22%
	CIAN [20]	14.12% 14.77% 11.13%
	MSDS-RCNN [10]	11.63% 10.60% 13.73%
	CFR [19]	10.05%	9.72%	10.80%
	GAFF (ours)	10.62% 10.82% 10.14%
	(a) Original annotations
	Methods	All	Miss Rate Day	Night
	MSDS-RCNN [10]	7.49%	8.09%	5.92%
	CFR [19]	6.13%	7.68%	3.19%
	GAFF(ours)	6.48%	8.35%	3.46%
	(b) "Sanitized" annotations

Table 6 :

 6

Detection results on KAIST dataset

[START_REF] Hwang | Multispectral pedestrian detection: Benchmark dataset and baselines[END_REF]

with original (top) or "sanitized" (bottom) annotations.

Table 7 :

 7 Runtime comparisons with different methods on KAIST dataset[START_REF] Hwang | Multispectral pedestrian detection: Benchmark dataset and baselines[END_REF].

	Backbone GAFF	mAP	AP75	AP50
	ResNet18	36.6% 31.9% 72.8% 37.5% 32.9% 72.9%
	VGG16	36.3% 30.2% 71.9% 37.3% 30.9% 72.7%

We use the evaluation code provided by[START_REF] Li | Multispectral pedestrian detection via simultaneous detection and segmentation[END_REF]: https://github.com/Li-Chengyang/MSDS-RCNN/tree/master/lib/datasets/KAISTdevkit-matlabwrapper


Giovanni Busco

Fabienne Fasani

Sébastien Dozias

Loïck Ridou

Claire Douat

Jean-Michel Pouvesle

Eric Robert

Catherine Grillon

T Trpms

CHANGES IN OXYGEN LEVEL UPON COLD PLASMA TREATMENTS: CONSEQUENCES FOR RONS PRODUCTION

Keywords: Non-equilibrium plasma sources, Plasma-liquid interactions, Plasma-cell interactions, Oxygen, RONS

Despite the large number of papers showing the biological effect of Cold Atmospheric Plasmas (CAPs) on skin treatment and although their ability to produce a cocktail of reactive oxygen and nitrogen species is largely known, the deep knowledge of these ionized gas sources is far to be uncovered. Many parameters take part in the complex mechanisms induced by plasma, such as voltage, frequency, feeding gas flow rate, temperature, air humidity and atmospheric gas composition. In this work we focus our attention on the influence of oxygen dissolved in the medium in reactive oxygen and nitrogen species (RONS) generation during CAP treatment. We evaluate RONS production in Dulbecco's Phosphate-Buffered Saline equilibrated with two different percentages of oxygen: 18% and 3% O2. The former oxygen concentration is that usually used in most of cell culture and treatment (normoxia) while the latter is close to the physiological level measured inside human skin (physioxia). Intracellular RONS generation upon CAP treatment was also measured in two human skin cell lines maintained either in normoxic or in physioxic conditions. Our data suggest that RONS generation induced by plasma either in the medium or inside cells is influenced by oxygen dissolved in the treated target.

I. INTRODUCTION

HE different cell types composing human body, with their heterogeneous metabolism and blood supply, create a microenvironment that is characteristic of each tissue. Chemical and physical extracellular parameters act together to create a unique, organ-specific, physiological condition. Among the factors influencing the extracellular microenvironment, oxygen plays a relevant role. Its local partial pressure is a typical signature that varies from one tissue to another. Organ physiological oxygenation is called physioxia and ranges between 14.5% in pulmonary alveoli and 1% in hair follicles [START_REF] Carreau | Why is the partial oxygen pressure of human tissues a crucial parameter? Small molecules and hypoxia[END_REF]. However, in vitro cell cultures are currently performed in classic incubator under atmospheric oxygen pressure conditions (37°C, 5% CO2 and 95% of air, i.e. 18-19% O2). This gas level is far from the physiological oxygenation of any human tissue and this has consequences on cell activities [START_REF] Carreau | Why is the partial oxygen pressure of human tissues a crucial parameter? Small molecules and hypoxia[END_REF][START_REF] Grillon | News on microenvironmental physioxia to revisit skin cell targeting approaches[END_REF][START_REF] Nadim | Physioxia and microRNAs as key factors in the skin microenvironment[END_REF]. Attempts have to be done to respect tissue physioxia in vitro, especially to avoid excess of oxidative stress. In fact, although oxygen is of vital importance for aerobic organisms, it possesses a dark side. During mitochondrial respiration, thanks to oxygen, cells produce and store energy in one hand and create toxic catabolites in the other hand. The oxygen derived catabolites are called Reactive Oxygen Species (ROS). Despite their well-known toxicity when produced in high level, ROS also display beneficial effects protecting cells from pathogens or activating signaling pathways [START_REF] Valko | Free radicals and antioxidants in normal physiological functions and human disease[END_REF][START_REF] Winterbourn | Redox reactions and microbial killing in the neutrophil phagosome[END_REF][START_REF] Onyango | Endogenous Generation of Singlet Oxygen and Ozone in Human and Animal Tissues: Mechanisms, Biological Significance, and Influence of Dietary Components[END_REF]. In physiological conditions, ROS are kept at the right amount by the cellular antioxidant system. When ROS production overcomes the anti-oxidant defenses, it induces an oxidative stress that can lead to pathological effects on the organism [START_REF] Valko | Free radicals and antioxidants in normal physiological functions and human disease[END_REF][START_REF] Kettle | Oxidation contributes to low glutathione in the airways of children with cystic fibrosis[END_REF][START_REF] Yang | Oxidative stress mediates chemical hypoxia-induced injury and inflammation by activating NFkappab-COX-2 pathway in HaCaT cells[END_REF][START_REF] Uttara | Oxidative stress and neurodegenerative diseases: a review of upstream and downstream antioxidant therapeutic options[END_REF]. Cold atmospheric plasma (CAP) is known to generate reactive oxygen and nitrogen species (RONS). Depending on the treated cell type and on the treatment conditions, plasmadelivered reactive species can generate opposite effect: stimulate normal cell growth or induce apoptosis in tumor cells [START_REF] Kim | Cold atmospheric plasma jetgenerated RONS and their selective effects on normal and carcinoma cells[END_REF][START_REF] Vandamme | ROS implication in a new antitumor strategy based on non-thermal plasma[END_REF][START_REF] Graves | The emerging role of reactive oxygen and nitrogen species in redox biology and some implications for plasma applications to medicine and biology[END_REF]. At the molecular level, RONS are generated by plasma directly inside the ionized gas but also by interactions with the liquid with which it comes in contact [START_REF] Gorbanev | Non-Thermal Plasma in Contact with Water: The Origin of Species[END_REF]. Despite the wellknown importance of oxygen in RONS production, the role of oxygen partial pressure in the medium or in in vitro cell culture has almost not been taken into account in the study of CAP mechanisms and its biological effects [START_REF] Oh | How plasma induced oxidation, oxygenation, and de-oxygenation influences viability of skin cells[END_REF]. Here we show the effect of our He-Plasma Gun on dissolved oxygen in a treated Dulbecco's Phosphate-Buffered Saline (DPBS), on ROS and nitrite generation in the medium and finally on intracellular RONS generation in two human skin cell lines maintained in an atmosphere containing either 18% or 3% O2.

II. MATERIALS AND METHODS

Materials

giovanni.busco@cnrs-orleans.fr) is funded by the PLASMACOSM project N°2015-00103497 from Cosmetosciences. 2´,7´-dichlorodihydrofluorescein diacetate (DCDHF-DA) was from Molecular Probes® (Thermo Fisher Scientific). Dulbecco's Phosphate-Buffered Saline (DPBS) (GIBCO®) was supplemented with 1mM CaCl2 and 0.5mM MgCl2. N-(1-Naphthyl) ethylene-diamine dihydrochloride (NEDD), sulfanilamide and methylene blue were purchased from SIGMA Aldrich.

Plasma Gun device and treatment

The plasma jet used in this work is delivered by the so-called Plasma Gun (PG) [START_REF] Robert | Characterization of pulsed atmosphericpressure plasma streams (PAPS) generated by a plasma gun[END_REF]. The plasma is generated in a dielectric barrier discharge reactor based on a glass tube, 4 mm inner diameter, flushed with a moderate rare gas flow, and equipped with an inner hollowed powered electrode and an outer grounded annular electrode. Ionization wave allows for plasma propagation in the capillary, 10 cm in length in this work, before expansion in the ambient air where plasma plume emerges at the capillary outlet. The glass tube inner diameter is tapered to 1.5 mm in the 2 cm wide section at the outlet of the capillary. In this work, negative polarity high voltage pulses having a peak amplitude of -14 kV, full with half maximum 2 µs duration, and delivered at frequency ranging from 250 Hz to 2 kHz have been used, while helium gas flow rate was set either at 0.5 or 1 L/min. Plasma treatments were performed for 60 seconds.

Oxygen partial pressure and temperature measurements

Oxygen partial pressure (pO2) and temperature variations in DPBS were measured during plasma treatment by a calibrated OxyLite fiber optic sensor coupled with a T-type thermocouple (OxyLite™ probe, pO2-Series Sensor, BF/OT/E, Oxford Optronix Ltd., Oxford, U.K.). The oxygen sensor is based on the ruthenium fluorescence lifetime decay, which is inversely proportional to the pO2 dissolved in the medium. In order to measure pO2 and temperature, the probe was inserted inside a micro-well of a 96 well plate containing 200 µL of DPBS equilibrated at 18% or 3% O2 (Fig. 1a). The microwell plate was placed inside a plate heater set at 37°C. Room temperature was between 22 and 24°C. Data were recorded by the OxyLite™ 2000E pO2 (Oxford Optronix Ltd).

Cell culture

HaCaT cell line comes by spontaneously immortalized human keratinocytes [START_REF] Boukamp | Normal keratinization in a spontaneously immortalized aneuploid human keratinocyte cell line[END_REF] . MSU 1.1 cell line was obtained by v-myc oncogene transformation of foreskin fibroblast [START_REF] Hurlin | Malignant transformation of human fibroblasts caused by expression of a transfected T24 HRAS oncogene[END_REF]. HaCaT and MSU 1.1 were cultured at 37°C in an incubator with 5% CO2 and 95% air atmosphere (18% O2) or inside the HypOxystation incubator (HypOxystation® H35, Don Whitley Scientific) at 37°C in a 5% CO2, 3% O2 and 92 % N2 atmosphere). All cells were cultured in high glucose DMEM supplemented with 10% of fetal bovine serum (FBS), 100 U/mL-100µg/mL of penicillin-streptomycin (Invitrogen) and 2 mM of L-Glutamine (Eurobio).

Measurement of nitrites by Griess test

200 µl of DPBS previously equilibrated at 18% or 3% O2 were treated by CAP in a 96-well microplate. Low oxygen concentration in DPBS (3%) was maintained by means of micro-caps placed on each well, caps were removed for the treatment and immediately replaced. Experiments with airbubbled DPBS were carried out using a peristaltic pump delivering air (0.24 mL/min) inside the micro-wells during the whole treatment by means of a capillary tube. After treatment, 40 µl of medium from each well were transferred in a new 96well microplate and 80 µl of sulfanilamide 3 mM solution and 80 µl of NEDD 0.2 mM were added to each well to start the reaction. NaNO2 standard solutions were used to make a calibration curve. After 10 min of incubation, absorbance of the pink color developed in the presence of nitrites was measured at 560 nm with a microplate reader spectrofluorometer (Victor 3 V, PerkinElmer®).

Reactive oxygen species measurement

Reactive oxygen species formation such as O3, O2 •-, O2 2-and • OH were measured by methylene blue discoloration. 200 µl of DPBS solution containing 0.01 g/L of methylene blue and previously equilibrated at 18% or 3% O2 were submitted to plasma in a 96-well microplate. Absorbance was read at 664 nm with the microplate reader spectrofluorometer. As ROS production is inversely proportional to methylene blue absorbance, arbitrary relative values were calculated as OD not treated /OD treated.

Measurement of RONS inside cells

Intracellular RONS formation upon plasma treatment was measured by the DCDHF-DA probe. The molecule penetrates cell membranes and is hydrolyzed by cell esterases to 2´,7´dichlorodihydrofluorescein (DCDHF) and then oxidized by intracellular RONS to give 2´,7´-dichlorofluorescein (DCF), a highly fluorescent compound. Cells (1.5x10 4 /well) were seeded in 96-well microplates and placed in the incubator at 18% or 3% O2 for 15 h. The day after, cells were washed with DPBS and then incubated with DCDHF-DA 10 µM in DPBS for 20 minutes at 37°C in 18% or 3% O2. Cells were washed twice with DPBS to remove the excess of extracellular probe. Each well was filled with 200 µl of DPBS previously equilibrated at the corresponding oxygen condition. Cells cultivated in physioxia were kept in low oxygen condition during treatment by capping each well with micro-caps, each micro-cap was removed for plasma treatment and immediately replaced. During all the treatment, the microwell plate was maintained at 37°C by a plate heater. After the treatment, intracellular fluorescence induced by RONS was measured with the microplate reader spectrofluorometer (excitation at 490 nm; emission at 535 nm).

Data analysis and statistics

One-way ANOVA and two-way ANOVA with Bonferroni's multiple comparison tests for the two oxygenation conditions were performed with GraphPad Prism 6.

III. RESULTS

To evaluate the effect of CAP on oxygen dissolved in a liquid medium and on temperature, air-equilibrated DPBS, kept warm by a plate heater set to 37°C, was treated at a frequency of 1 kHz with two different He flows: 0.5 L/min and 1 L/min (Fig. 1a). A typical experiment displaying the evolution of pO2 and temperature in DPBS before, during and after plasma treatment is shown in Figure 1b. Oxygen partial pressure decreases rapidly in the first seconds of treatment. The minimum reached at the end of the treatment remains stable for few seconds before recovering rapidly. Air-saturated DPBS (18% O2) shows a pO2 between 150 and 200 mmHg (19.7-26% O2) meaning that the ruthenium probe is oxygen saturated. Plasma treatment results in a rapid drop of pO2 to around 30 mm Hg with 0.5 L/min He and to 25 mm Hg with 1.0 L/min He, which corresponds to 3 and 2.5% of oxygen, respectively. A drop to 2% and 1% O2 is observed when DPBS is treated by He alone at 0.5 L/min and 1.0 L/min, respectively (Fig. 1c). Temperature also falls down during treatment with a speed rate not easy to measure due to the electromagnetic perturbation of the thermocouple signal during plasma operation. The final temperature value is clearly visible at the end when plasma is turned off. Plasma treatment with the two He flow rates has a cooling effect on DPBS leading the temperature to fall from 27°C to around 20-23 °C. Helium gas alone is able to cool down DPBS to 20°C at 0.5 L/min and to 15°C at 1 L/min (Fig. 1d). At the end of the treatment, DPBS temperature recovers slowly although the 96-well microplate is placed on a plate heater. The measured slow increase of temperature can be mainly attributed to the slow equilibrium dynamic between the small well volume and the plate heater separated by an air layer and also to the thermocouple thermal inertia [START_REF] Alexandros | Thermocouple thermal inertia effects on impingement heat transfer experiments using the transient liquid crystal technique[END_REF]. We measured the ability of plasma to generate RONS in DPBS under 18% or 3% O2 using different frequencies. During plasma treatment we measured a small evaporation of the DPBS: 7.7 ± 1.2 % with a He flow rate of 1 L/min and 4 ± 0.8 % when medium was treated by plasma 0.5 L/min of helium (data measured at different frequencies). In order to minimize medium evaporation during plasma treatment, we chose to feed plasma with a He flow rate of 0.5 L/min. Nitrite production was measured to evaluate nitrogen species formation and methylene blue discoloration to assess oxygen-derived species production. Fig. 2a shows CAP generated nitrites in DPBS equilibrated with 18% or 3% O2. In atmospheric oxygen-equilibrated DPBS, nitrite production gradually increases together with the plasma delivered frequencies. In DPBS containing low oxygen, nitrite generation increases also with the frequency but producing significant smaller nitrite quantity compared to the more oxygenated DPBS. Methylene blue discoloration test was used to evaluate oxygen derived radicals formed upon plasma treatment.. The blue color solution is discolored by oxidation until it becomes transparent (leuco form). The dye is sensitive to reactive oxygen species such as O3, O2 -• [START_REF] Huang | Analysis of the degradation mechanism of methylene blue by atmospheric pressure dielectric barrier discharge plasma[END_REF] and it is also used to detect reactive species with a very short half-life time such as • OH. [START_REF] Yan | Principles of using Cold Atmospheric Plasma Stimulated Media for Cancer Treatment[END_REF]. ROS produced in DPBS upon plasma treatment increases gradually with the applied frequency in DPBS equilibrated with 18% of O2. Treatment of DPBS with lower percentage of dissolved O2 results in a similar increase of ROS for low frequencies and in a significant decrease compared to the airequilibrated DPBS when frequencies higher than 1.0 kHz are applied (Fig. 2b). In order to exclude the role of UV in • OH photochemical formation, control experiments were performed using a MgF2 window (supplemental figure S1). To better understand the role of oxygen in reactive species generation, we also evaluated the effect of the CAP treatment in DPBS either air-equilibrated or air-bubbled (0.24 mL/min). After the treatments, nitrites were measured in both conditions. Interestingly we found that air introduction in the liquid during plasma treatment increases nitrite formation (Fig. 3). To assess the role of oxygen on the treatment of cell cultures by CAP, we measured intracellular RONS generated in two human skin cell lines, HaCaT keratinocytes and MSU 1.1 fibroblasts (Fig. 4 a-b). RONS formation inside cells upon plasma treatment was measured using the DCDHF-DA probe. Interestingly we found that, in basal condition, HaCaT and MSU 1.1 cells grown in 3% O2 display already a lower level of intracellular RONS than the same cells raised in 18% O2. For both cell lines, CAP treatment induced an increase in intracellular RONS production. The increase of frequency, between 0.25 kHz and 1.25 kHz, results in a RONS increase, which is largely higher at 18% O2 (about 2-fold at 0.75 kHz for both cell lines) when compared to 3% O2. For higher frequency treatments, RONS generation shows a lower increase and the difference between both oxygen conditions is not significant.

IV. DISCUSSION

Cold atmospheric plasmas with temperatures below 40°C are well suited for plasma medicine applications [START_REF] Isbary | Non-thermal plasma-More than five years of clinical experience[END_REF]. Our plasma gun showed a cooling effect on treated warm liquids (DPBS). As noted before, the DPBS temperature drop is bigger with helium flow alone. The slightly higher temperature obtained (3 to 5 °C) when plasma is "on" is due to the energy transferred from the plasma to the liquid, which partially compensates the DPBS cooling due to the gas flow. Oxygen measurement during CAP treatment showed an outgassing effect of DPBS. Oxygen content in atmospheric, air-equilibrated DPBS, (18% O2) falls down to around 3% when the liquid is treated for 60 sec at a He flow rate of 0.5-1.0 L/min. Similar pO2 values were measured using higher frequencies 1.5 kHz and 2 kHz with a flow rate of 0.5 L/min. With a He flow rate of 1 L/min, at the same higher frequencies, pO2 can drop to 10-15 mm Hg (1.3-2% of O2) (data not shown). The DPBS de-oxygenation induced by plasma is lower than the one observed when DPBS is treated with only helium gas at the same flow rate. CAP mitigation on helium sparging effect has already been observed by Oh and colleagues who attribute it to the oxidative species introduced in the medium by the plasma jet [START_REF] Oh | How plasma induced oxidation, oxygenation, and de-oxygenation influences viability of skin cells[END_REF]. Using DPBS differently oxygenated (18% and 3% O2), we evaluated the effect on RONS production of the CAP treatment using a helium flow rate of 0.5 L/min and frequencies between 0.25 and 2.0 kHz. Nitrite increase in DPBS correlates with the increase of the applied frequency. Plasma treatment on DPBS with a lower percentage of dissolved oxygen generates a lower quantity of nitrites compared to DPBS containing 18% O2 when treated at the same frequency. Similar results were found for ROS measurement assessed using methylene blue. ROS production increases proportionally with the applied frequencies in airexposed DPBS whereas in 3% pO2 medium ROS generation significantly decreases for frequencies higher than 1.0 kHz. To explain the decrease of ROS observed at higher frequencies, we speculate that, during CAP treatment, two opposite effects occur. In one hand, helium degasses the liquid leading dissolved oxygen to the surface. In the other hand, the plasma plume flowing downward creates a drop in pressure on the surrounding air layer (Venturi effect). This negative pressure around the plume attracts air from outer layers so that atmospheric oxygen enters in contact with the plasma plume. At frequencies up to 1 kHz the Venturi effect counterbalances the helium sparging effect, ROS are generated from oxygen coming from the liquid and oxygen coming from the attracted atmosphere. At higher frequencies plasma increases the helium flow preventing surrounding air to enter in contact with the plume, in this case ROS are mainly generated by oxygen coming from the DPBS. Recently Darny and colleagues have demonstrated how a negative polarity CAP at 1 kHz generates a straight and stronger helium flow compared to the flow observed at lower frequencies [START_REF] Darny | Plasma action on helium flow in cold atmospheric pressure plasma jet experiments[END_REF]. Moreover oxygen was shown to play a dual role in CAP-induced RONS formation. Increasing quantities of oxygen (0.1-2%) introduced directly in the helium feeding gas leads to a proportional decrease of • OH and nitrites/nitrates production in the plasma [START_REF] Norberg | Atmospheric pressure plasma jets interacting with liquid covered tissue: touching and not-touching the liquid[END_REF]. Reuter and colleagues demonstrated how a nitrogen-oxygen gas curtain placed around the plasma jet effluent influences nitrite generation in PBS. A linear increase in nitrites was observed when they used oxygen up to 50% and a decrease with higher oxygen percentages [START_REF] Reuter | From RONS to ROS: Tailoring Plasma Jet Treatment of Skin Cells[END_REF]. Here, our data suggest that oxygen dissolved inside the liquid medium can influence RONS production. The importance of tissue-dissolved oxygen and its influence on RONS generation and cell response upon plasma treatment has been already highlighted by Oh and colleagues in a soft agar skin model or in human skin cells [START_REF] Oh | How plasma induced oxidation, oxygenation, and de-oxygenation influences viability of skin cells[END_REF][START_REF] Oh | How to assess the plasma delivery of RONS into tissue fluid and tissue[END_REF]. Although we found evidences of the influence of dissolved oxygen on reactive species generation, the differences measured are sometimes low between the two oxygenated conditions. This is mainly due to the small volume of the treated medium. Oxygen is in fact rapidly depleted even in DPBS equilibrated to the atmospheric air (18% O2). To confirm our hypothesis, we measured nitrites generated during plasma treatment at three different frequencies, in air-balanced DPBS (18%) or in DPBS continuously air-bubbled during the treatment. Plasma plume fed with oxygen coming from the airbubbled medium delivers higher quantities of nitrites when compared to medium only air-equilibrated that is rapidly depleted of oxygen in the first seconds of plasma treatment (Fig. 3). Oxygen partial pressure is a parameter that should be taken into account also in cell culture [START_REF] Carreau | Why is the partial oxygen pressure of human tissues a crucial parameter? Small molecules and hypoxia[END_REF]. Oxygen is not only a source of reactive species during plasma treatment but its partial pressure can directly affect cell physiology [START_REF] Carreau | Why is the partial oxygen pressure of human tissues a crucial parameter? Small molecules and hypoxia[END_REF][START_REF] Mccormick | Manipulation of environmental oxygen modifies reactive oxygen and nitrogen species generation during myogenesis[END_REF]. Our data showed that human skin cells maintained in physioxic conditions (3% O2) generate a lower quantity of intracellular RONS compared to cells grown in an air-balanced medium. When treated by plasma, both cell lines respond to the treatment, increasing intracellular RONS generation with the increase of the applied frequency. The observed increase is significantly lower in cells maintained at 3% O2 when frequencies between 0.25 kHz and 1.25 kHz are used (Fig. 4 ab). Treatment at frequencies between 1.5 kHz and 2.0 kHz showed not only no significant differences in cells exposed to both oxygenated conditions but also an inverted trend with intracellular RONS that decrease with the increase of frequency. The decrease of intracellular RONS at higher frequencies is coherent with the decrease of ROS measured in DPBS. Furthermore, although RONS generated in both cell lines appeared quantitatively lower in physioxia compared to normoxia condition, the CAP treatment at the different frequencies induces a higher relative increase of intracellular reactive species in physioxia compared to normoxia . Our results show how RONS generated by plasma treatment depend on the interplay of frequency and oxygen dissolved in the medium. The composition and the amount of generated reactive species can induce different physiological consequences on cells. RONS dose-dependent effect (hormesis) has already been demonstrated in HaCaT cells [START_REF] Szili | The hormesis effect of plasmaelevated intracellular ROS on HaCaT cells[END_REF]. Moreover, in vivo plasma treatment was shown to increase oxygen release from the deeper layer of the treated tissue [START_REF] Collet | Plasma jet-induced tissue oxygenation: potentialities for new therapeutic strategies[END_REF], gases coming from underneath blood vessels could also be a feeding source for RONS formation.

V. CONCLUSION

The control of helium flow rate, frequency and treatment duration is of great importance to generate the right dose of RONS and achieve the desired effect on a tissue. The same cell type could be positively stimulated or destroyed by CAP treatment. Our data suggest that tissue oxygenation should also be taken into account when planning a plasma treatment session. The intracellular RONS increase upon CAP treatment is very different when experiments are performed either in normoxic or in physioxic conditions. This underlines the very different stress response of the different cell lines but also the drastic impact of the oxygen level during plasma treatment. This may be of importance to design relevant in vitro skin cell plasma jet treatments likely to be translated to real in vivo protocols where tissue oxygenation levels are definitively different from those usually used during cell culture and experiments. The different intracellular RONS production achieved with the two different cell lines, also open up opportunities to select plasma treatment conditions likely to differentially activate/stimulate the different skin cells, which may be crucial for some biomedical applications.

  This work was supported by Cosmetosciences, a global training and research program dedicated to the cosmetic industry. Located in the heart of the Cosmetic Valley, this program led by University of Orléans is funded by the Région Centre-Val de Loire, France. *Giovanni Busco postdoc fellow at the Centre de Biophysique Moleculaire, UPR4301 CNRS, rue Charles Sadron, 45071 Orléans, FRANCE (e-mail:

Fig. 1 .

 1 Fig. 1. Effect of CAP treatment on oxygen partial pressure in DPBS. DPBS was treated with CAP (frequency 1 kHz) or with helium alone for 1 min using two helium flows: 0.5 and 1 L/min. pO2 and temperature were monitored using OxyLite probes. (a) OxyLite system used to measure dissolved oxygen. (b) A typical experiment showing oxygen and temperature evolution during and after plasma treatment; (c) Effect of CAP or helium alone on oxygen partial pressure in DPBS; (d) Effect of CAP or helium alone on temperature in DPBS. Data are the mean ± SD of N = 3 independent experiments. One way ANOVA *** p ≤ 0.001, **** p ≤ 0.0001.

Fig. 2 .

 2 Fig. 2. Effect of CAP on RONS production in DPBS according to pO2. DPBS previoulsy equilibrated at 18 or 3% O2 was treated by CAP using various frequencies. (a) Nitrite production measured by Griess test; (b) •OH radical production measured by methylene blue discoloration. Data are the mean ± SD of N = 3 independent experiments (each experiment in triplicate). Two way ANOVA comparing frequency effect on the two oxygenation conditions * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001, and **** p ≤ 0.0001.

Fig. 3 .Fig. 4 .

 34 Fig. 3. Effect of CAP on nitrites formation in DPBS according to oxygen saturation. DPBS either airequilibrated or continuously air-bubbled were treated by CAP. Nitrites were measured by Griess test. Data are the mean ± SD of N = 6 independent experiments. One way ANOVA ** p ≤ 0.01