dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2014b). Altogether, these data are compatible with the proposed role of Cys449 and Cys459 as redox sensor  (Garcia-Murria et al. , 2008)  in which chloroplastic Trxs could play a direct regulating role. Other proteins-Numerous other enzymes of the dataset were also studied at the molecular level in other species but not in Chlamydomonas. In this case, comparison of the position of target cysteines is much less informative. Indeed, redox regulatory mechanisms may significantly vary between species. For example, the activity of the autophagy cysteine protease Atg4 is regulated by Trx in both yeast  (Perez-Perez et al. , 2014)  and Chlamydomonas (Perez-Perez et al. , 2016) but through distinct disulfide bonds, i. e. located in completely different regions of the enzyme. The fact that Chlamydomonas Atg4 was not identified among Trx targets in the present study may be linked to its low abundance as suggested by its absence from our soluble proteome of Chlamydomonas cells (Supplemental Table  2 ) but also to the fact that this protease is regulated by Trx under conditions of autophagy induction while our extracts were prepared from nonautophagic conditions.
This suggests that Trx may also play some role in the regulation of β isoforms of RCA. Interestingly, the α isoform of Arabidopsis RCA was detected as nitrosylated in Arabidopsis leaves  (Lindermayr et al. , 2005)  and Cys196 and Cys148 from respectively Chlamydomonas and Arabidopsis β-RCA, were detected as S-nitrosylated  (Fares et al. , 2011; Morisse et al. , 2014b). Altogether, these data suggest that the redox regulation of RCA is likely more complex than currently established and reminds, for some aspects, the redox regulation of A-type and B-type chloroplastic GAPDHs. Nevertheless, for some proteins the position of cysteines reduced by Trx is conserved between land plants and algae and were confirmed by our Chlamydomonas reductome. This is for example the case of the Calvin-Benson cycle enzymes FBPase and SBPase  (Chiadmi et al.
, 1999; Gutle et al. , 2016; Jacquot et al. , 1997; Rodriguez-Suarez et al. , 1997) , chloroplastic magnesium chelatase CHLI  (Ikegami et al. , 2007) , 3-deoxy-D-arabinoheptulosonate 7-phosphate (DAHP) synthase  (Entus et al. , 2002)  or glucose-6-phosphate dehydrogenase  (Nee et al. , 2014; Wenderoth et al. , 1997). For some of these enzymes the reductome approach suggests, in addition to known target cysteines, the existence of additional sites of redox regulation. Interestingly some of these sites were found to undergo glutathionylation  (Zaffagnini et al. , 2012a)  or nitrosylation  (Morisse et al. , 2014b). Altogether, the Chlamydomonas reductome recapitulates most of the previously established sites of Trx-dependent regulation, strongly suggesting that other sites identified in our dataset are also relevant.
In the reductome approach, 92% of identified peptides contain only one cysteine and 7. 5% contain two cysteines in their sequence. When compared to the theoretical proteome (Supplemental Figure  4 ), this distribution suggests that peptides containing at least 2 cysteines could be underestimated, a limitation that may be linked to the MASCOT identification software  (Garcia-Santamarina et al. , 2014). The depth of proteomic analyses dedicated to post-translational modifications is generally hampered by both modification levels and protein relative abundance. Without use of robust depletion/equalization or enrichment methods, low-abundant proteins are often missed  (Doll and Burlingame, 2015; Riley et al. , 2016). Interestingly, 18. 6% of Trx targets present in our dataset (142 and 118 proteins in the affinity chromatography and the reductome approaches, respectively) are likely low-abundant proteins.
Subcellular localization The subcellular distribution of the 1188 proteins identified was determined using the PredAlgo software, a multi-subcellular localization prediction tool dedicated to algae  (Tardif et al. , 2012). As shown in Supplemental Figure  5A , Trx targets are predicted to be distributed for 30% to the chloroplast, 9% to mitochondria, 6% to the secretory pathway and 55% to other compartments. Such a distribution is not surprising despite the fact that we used a cytosolic Trx. Indeed, it is recognized that redox proteomic studies performed in vitro lack specificity and similar targets are identified regardless of the Trx isoform employed  (Buchanan et al. , 2012; Montrichard et al. , 2009)  and this is all the more true for CrTrxh1 that is able to reduce in vitro most cytosolic and chloroplastic Trx targets  (Fischer et al.
, 2009; Goyer et al. , 2002; Rivera-Madrid et al. , 1995; Stein et al. , 1995). Proteins present in the Chlamydomonas soluble proteome (Supplemental Figure  5B ) exhibited, in percentage, a subcellular distribution similar to that of Trx targets while a slight enrichment for chloroplastic proteins was observed with the total theoretical proteome encoded by the three genomes (Supplemental Figure  5C ). These data suggest that the redox regulation mediated by Trx plays an important role in the different subcellular compartments and that the chloroplast is a metabolic hub for redox regulation even under mixotrophic growth conditions where photosynthesis is dispensable for Chlamydomonas cells. These results are consistent with the established importance of thioredoxins in multiple subcellular compartments, organs and developmental stages of photosynthetic organisms (for detailed reviews see:  (Balsera et al.
Most of these proteins correspond to established partners of Trx including peroxiredoxins, glutathione peroxidases or methionine sulfoxide reductases  (Dietz, 2011; Fischer et al. , 2009; Navrot et al. , 2006; Tarrago et al. , 2009). Finally, the most prominent category corresponds to the 368 proteins of unknown function that represent 30. 9% of the Trx targets. This high proportion of proteins of unknown function illustrates our limited understanding of biological systems as recently illustrated by studies on the minimal genome by the Craig Venter Institute that assembled the smallest genome of any self-replicating organism that contains 473 genes including 149 genes (31. 5%) of unknown function  (Hutchison et al. , 2016). The percentage in our dataset is however lower than in the theoretical proteome where more than 50% of the proteins have unknown functions, suggesting that Trx targets primarily map to major functional networks.
, 2012a) , nitrosylated  (Morisse et al. , 2014b)  and were also all found here regulated by Trx. However, such multiple redox targeting may not be necessarily true for other proteins. In other words, we can wonder whether multiple redox PTMs occur on a limited number of proteins containing reactive cysteines or if each modification targets a distinct redox network. The redox proteomic datasets we have generated in Chlamydomonas provide a unique opportunity to further explore the specificity of redox PTMs at the proteome scale. Indeed, besides the extended thioredoxome reported here, we have previously identified 492 nitrosylated proteins  (Morisse et al. , 2014b)  and 225 glutathionylated proteins  (Michelet et al. , 2008; Zaffagnini et al. , 2012a). These studies were performed on the same strain of Chlamydomonas using highly similar growth conditions, i.
The specificity primarily depends on the biochemical properties of the cysteine residue that are largely linked to its microenvironment within the folded protein which can notably influence the accessibility, the acidity and the nucleophilicity of the residue  (Reddie and Carroll, 2008; Winterbourn and Hampton, 2008; Zaffagnini et al. , 2012b). Our Chlamydomonas proteomic datasets allow exploring the site-specificity of the different redox PTMs at the proteome scale. These comparisons revealed a strikingly high specificity of each modification for specific cysteine residues. Indeed, considering all cysteines experimentally identified as redox-regulated, more than 75% undergo a single modification while less than 1% are regulated by nitrosylation, glutathionylation and disulfide bond formation (Figure  7A ).
To avoid any bias due to performances of mass spectrometers used in these three studies, a second comparison was performed by restricting this cysteine dataset to Chlamydomonas proteins undergoing at least two different redox PTMs and again, similar proportions were observed (Figure  7B ). These results indicate that the Cys proteome does not represent a small subset of highly reactive cysteines that are modified through indiscriminate interaction with the molecules they encounter but represent a complex organized network of redox PTMs. The different redox PTMs appear to control different subnetworks that are strongly interconnected. Strikingly, a recent analysis of 1319 mouse liver proteins and four cysteine modifications (nitrosylation, glutathionylation, sulfenylation and Sacylation) also revealed a very high specificity of redox PTMs with limited overlap  (Gould et al.
, 2015). These results suggest that the different redox modifications are specific toward distinct interconnected protein networks. Our proteomic datasets provide a unique and comprehensive map of this network, the Cys proteome, in the model alga Chlamydomonas reinhardtii (Figure  6 ). However, this view is rather static while the redox network probably involves spatial and temporal regulation of several redox post-translational modifications on hundreds of proteins in a highly dynamic manner. This complexity is likely providing the robustness and specificity required to allow a limited number of simple molecules such as reactive oxygen and nitrogen species (ROS/RNS) to play a signaling role. The redox network is likely a major component of signal integration and constitutes the molecular signature of the ROS/RNS crosstalk whose importance in cell signaling has been recognized  (Foyer and Noctor, 2016; Frederickson Matika and Loake, 2014; Gross et al.
, 2012). In photosynthetic organisms, numerous redox PTMs are clearly linked to signaling pathways controlled by hormones  (Feng et al. , 2013; Kneeshaw et al. , 2014; Terrile et al. , 2012; Wang et al. , 2015; Waszczak et al. , 2015; Yu et al. , 2012)  or calcium  (Trapet et al. , 2015)  and in mammals, nitrosylation was reported to interfere with signaling processes mediated by phosphorylation, ubiquitylation, sumoylation, acetylation or palmitoylation  (Held and Gibson, 2012; Hess and Stamler, 2012 ). An intensive effort is therefore required to integrate redox signaling networks with other signaling pathways and to analyze their impacts on the cellular responses at multiple levels. This will require large-scale systems biology approaches and development of innovative computational methods to analyze and explore the massive datasets generated.
ACKNOWLEDGMENTS The authors thank  Matthieu Cleaz  for his technical assistance,  Camille Garcia ,  Dr. Thibaut Léger  and  Dr. Jean-Michel Camadro  ( IJM, France ) for providing an access to the IJM mass spectrometry facility and  Dr Zhou Xu  ( IBPC, France ) for the helpful discussion about cICAT quantification. This work was supported in part by an  IEF EU Marie Curie Fellowship  ( PIEF-GA-2011-298652-REDOXDYNAMICS ) (to MEPP), by  Fondation pour la Recherche Médicale  Grant  DBI2014231801  (to A. Maes), and by  Agence Nationale de la Recherche  Grant  12-BSV5-0019 REDPRO2  (to A. Mauries, SDL and CHM) and Grant  12-BSV6-011 CYNTHIOL  (to MH, SDL and CHM), by  LABEX  DYNAMO  ANR-LABX-011  and  EQUIPEX CACSICE ANR-11-EQPX-0008 , notably through funding of the  Proteomic Platform of IBPC (PPI).
An air extractor was placed behind the bee to avoid any odour contaminations of the setup. Recording. To record ball movement, two highly-sensitive optical sensors from laser mice were used (Logitech G500, Morges, Switzerland: resolution: 5700 dpi, signal rate: 1000 Hz). They were attached to the Plexiglas block at the horizontal equator of the ball and at a relative angle of 90° to each other. The body axis of the insect was always precisely aligned at an angle of 45° with respect to both mouse sensors. Mouse signals were integrated and recorded via custom-written software written in LabView2011 (National Instruments, Nanterre, France) using ManyMouse to separately handle the signals of both mouse sensors (source code by Ryan C. Gordon; http:// icculus. org/manymouse). From the recorded ball movements, custom-written software directly calculated the bee's walking path, and produced throughout the experiment several parameters such as its walking speed, turning direction and virtual heading.
e. 'during') phase, i. e. bees that never switched from an odour quadrant to a non-odour quadrant (and vice versa), were excluded. Overall, 5 out of 24 workers (21%) and 7 out of 45 queens (16%) were discarded. Ball movement data were acquired at 5 Hz frequency, so that bees' virtual position could be calculated every 200 ms, giving access to its virtual heading and the distance covered. In the figures, we represented the percentage of the time spent and of the distance travelled during each phase, either in a circular graph by 15° sectors (Figs 2A,C-4A,C), or in a boxplot for the odour quadrant and the average of the 3 other quadrants (Figs 2B,D-4B,D). To detect a significant orientation of bees toward the odour stimulus, time spent and distance travelled in the odour was compared to the average of the 3 other quadrants using Wilcoxon matched pairs tests.
Acknowledgements We are indebted to  Pascal Valois  and  Omar Kaabia  ( Beeopic, Buc, France ) for providing virgin queens for the experiments and to  Lionel Garnery  and  Damien Delalande  for beekeeping support. We thank  Andreas Brandstaetter  for the development of the walking simulator. This work was supported by the  Agence Nationale de la Recherche (ANR) ,  Paris, France  [Project  EVOLBEE ,  2010-BLAN-1712-01  to J. C. S]. G. L. thanks the  ENS Cachan and Université Paris-Saclay.
Taken together, these data suggest that ccpA-positive S. aureus provoke an increased pro-inflammatory response in infected liver tissue. The Phosphoablative ccpA Variant Ccpa_T18A/T33A Affects the Pathophysiology of S. aureus in the Murine Abscess Model Recently, it was determined that the DNA binding activity of CcpA in S. aureus is controlled by the serine/threonine kinase Stk1  (Leiba et al. , 2012). To test whether the Stk1-dependent phosphorylation of CcpA would affect the infectivity of S. aureus, C57BL/6N mice were challenged with a MST14 derivative transcomplemented with a ccpA allele having threonine to alanine mutations at positions 18 and 33 (MST14 pCN34_ccpA_Ala) and the bacterial load and cytokine profiles were determined. This CcpA_T18A/T33A variant can no longer be phosphorylated by Stk1, but retains the regulatory activity toward its target genes  (Leiba et al.
Spectral filter arrays imaging exhibits a strong similarity with color filter arrays. This permits us to embed this technology in practical vision systems with little adaptation of the existing solutions. In this communication, we define an imaging pipeline that permits high dynamic range (HDR)-spectral imaging, which is extended from color filter arrays. We propose an implementation of this pipeline on a prototype sensor and evaluate the quality of our implementation results on real data with objective metrics and visual examples. We demonstrate that we reduce noise, and, in particular we solve the problem of noise generated by the lack of energy balance. Data are provided to the community in an image database for further research.
So, we argue that such methodology could be embedded in the SFA imaging pipeline without breaking the advantages of SFA technology for computer vision. HDR multispectral acquisition is already treated by, e. g. , Brauers et al. [29]  and Simon  [30]. However, they consider the problem of HDR using individual bands acquired sequentially, so each band is treated independently for potentially different integration times. In the case of SFA, we may consider specific joined processes. We communicated preliminary qualitative results at the Scandinavian Conference on Image Analysis (SCIA 2017)  [31] , and this paper extends, generalizes, and evaluates widely our preliminary results. We compare the HDR resulting images with an SDR database of the same objects  [32]. In addition, we make our HDR database available for further research as supplementary material.
In this paper, we first generalize the imaging CFA pipeline to SFA in Section 2. This new SFA imaging pipeline embeds the HDR concept; It is based on multiple exposure spectral raw images. Then, the experimental implementation is developed in Section 3, the implementation is based on real data acquired with a prototype state-of-the-art camera  [8]  that captures visible and NIR information. Description of the database of images are provided in Section 4. Results and analysis are based on objective metric scores and visual examples in Section 5.
SFA Imaging Pipeline SFA sensors are currently investigated and developed, however beside demosaicing and application dedicated processing, the rest of the pipeline is not very well defined, thus understood, to our knowledge. We argue that a similar pipeline than CFA may be considered, which is then defined in Figure  3. In this pipeline, we still consider pre-processing as denoising and an equivalent to white balance as gain adjustment channel balance (referred to as multispectral constancy by some research). In the visualization pipeline, the color transform shall project the multispectral data into a color space representation. In this case, the pre-processing is typically performed per image, similarly to the standard dynamic range (SDR)-CFA case. Then, radiance estimation is performed based on the multiple images, providing radiance raw images.
Our HDR-SFA imaging pipeline. The radiance estimation is performed on the list of raw image taken as a whole (DB 1 in the database), which permit to create the HDR raw images (DB 2 in the database). The raw HDR image may be corrected for illumination  [41, 42]  and demosaiced by state-of-the-art methods (DB 3 in the database). After this, a visualization process projects the data into a HDR color representation CIEXYZ (DB 4 in the database), which is tone-mapped (DB 5 in the database) and processed for visualization on SDR media. Other outputs of the pipeline may be considered similarly to the previous pipelines.
Visualization and Other Output Visualization is the traditional use of HDR data. To this end, we project the radiance data into an HDR coded CIEXYZ color space according to a linear color transform computed on the 24 Macbeth ColorChecker reflectance patches, and the scene acquisition illumination measured in situ. This colorimetric image may be tone mapped by state-of-the-art algorithms. We used four tone-mapping techniques later for a representative illustration of visualization experience. We used the code furbished in the Matlab HDR Toolbox  [44]. Although the visualization process has the very well defined goal of producing a pleasant and informative visual experience, machine vision output may target several purposes and specific HDR spectral image processing must be considered depending on the task.
HDR SFA Database We created a database of images of real scenes, while using this SFA HDR pipeline on the prototype camera defined on Figure  7. A total of 18 scenes were captured using hardware and conditions shown in Table  1. Care was taken to select three adequate exposure times. A high exposure was selected for which only a few pixels are saturated in low intensities, and a low exposure was selected for which only a few pixels are saturated in high intensities. In the experiment, relatively good highlight conditions were found, and these exposure times were selected to 4, 8 and 16 ms (by doubling the amount of photons hitting the sensors between exposures). All raw images have been pre-processed to remove the dark noise and the neighboring effects due to NIR blooming, by applying the procedure according to paper  [8].
A file set from raw to tone-mapped processed data is available according to the following organization: Table  2  gives details about the content of the HDR SFA database, including the scene description, the file names and the file extensions. The demosaiced and color transformed HDR images can be, for example, visualized by any software implementation of the PFSTools framework by Mantiuk et al. [46]  (e. g. , Luminance HDR software). By using these tools, the user could visualize on his screen the 8-bits data per RGB channels, enables a HDR rendering and select a tone-mapping algorithm among several methods to visualize the file. Along with the complete database of images, a Matlab script, named Script. m is provided to load data into the workspace. The user can select which scene data to load amongst the 18 scenes available.
Database The global effect of applying our HDR pipeline can be visually appreciated on the color tone-mapped version of the image on Figure  8i, k. In terms of scene dynamic range, we know that the Macbeth ColorChecker scene is a typical low dynamic range scene. We could capture the whole dynamic range of the scene with only one exposure (i. e. , Figure  8i ) and the HDR process used permits only to reduce the noise and to solve the energy balance issue. However, for a higher dynamic range scene (like the CD scene), in addition to balancing the exposure among the pixels, we also extend the dynamic range by a certain amount. This is evaluated below. Figures  9 10 11 12 show the tone-mapped color images of the database processed with different algorithms. Namely, we applied a simple logarithmic mapping, Krawczyk et al.
We evaluated the different color images by using a state-of-the-art method called HIGRADE  [54]. HIGRADE is a dedicated method for non-reference image quality assessment. It evaluates the SDR images obtained by algorithms such as tone-mapping, multi-exposure fusion, or other dedicated processing. This method is dedicated to perceptual evaluation of HDR images and has been considered as the most efficient algorithm on a large database of HDR images, called ESPL-LIVE HDR Image Quality database  [55]. Table  5  shows the output of the algorithm for SDR images. This evaluation clearly demonstrates that the HDR versions of the images are better than the SDR one, especially when using the Krawczyk et al. [48]  technique. This technique is based on both local and global contrast enhancements, that seems to give a good general rendering, as the metric suggests.
Discussion The implementation of the HDR-SFA pipelines produce images of better visual quality that its SDR counterpart. In the previous section, we made a tentative to evaluate the quality of data which are not easy to handle by quality procedures. First because we do not have references, neither groundtruth or good knowledge on HDR scenes. Indeed, in general HDR images are evaluated visually after being tone-mapped. In our context, it is a little more complex since we also add some spectral dimension to the problem. We try in the following to discuss this according to our evaluation. One major aspect of SFA images is that we do not have ground truth, for both SDR and HDR domains. For spatial information, we are limited to the result of the demosaicing method or by the fact that the information is sparsely distributed over the whole sensor.
projects: The  Open Food System project. Open Food System  is a research project supported by  Vitagora ,  Cap Digital, Imaginove, Aquimer, Microtechnique and Agrimip , funded by the  French State  and the  Franche-Comté Region  as part of The Investments for the Future Programme managed by BPIfrance, www. openfoodsystem. fr. And the  EU-project ,  H2020-662222 , EXIST. http://cordis. europa. eu/project/rcn/198017_en. html.