dataesr/scientific-paragraphs-categorization huggingface dataset:
Experimental Analysis. An experimental setup has been carried out by using the PV cell configured with the proposed KenDoKu technique and the OE and the LS techniques. The electrical parameters of the PV cells under consideration are tabulated in Table  4. We have considered all shading patterns defined in Section 3. 2. Figure  21  displays the experimental setup which consists in a Keithley 2701 multimeters, a simulator, and an arc lamp source. The data were recorded with a computer. To provide nonuniform insolation to the PV module, light-emitting diode lamps are used over each PV cell as independent source. The illumination of each lamp is controlled by using potentiometers connected across a light-emitting diode lamp. This allowed us to create partial shading conditions for the PV module.
Conclusion In this research paper, the effectiveness of TCT, BL-TCT, HC-BL, HC-TCT, SP-TCT, OE, and LS configurations is evaluated, analyzed, and compared with the proposed KDK arrangement scheme of configuration under defined partial shading conditions. The proposed KDK technique redistributed shading effects over the PV module without any change in the electrical connections of the PV cells. The effectiveness of the KDK approach is evaluated in terms of P-V curve characteristics, GMP, ML (%), FF (%), and PR (%). The simulation model is developed using the bishop model and is implemented in LTSpice software. The simulated results showed that the proposed KDK configuration scheme exhibited better performance to generate maximum power output under the considered partial shading conditions. In most of the cases, the GMP of the KDK configuration is found to be superior as compared to the existing OE and LS configurations.
Then gold is transferred from the Sec-group to the catalytic cysteines inhibited by forming a Cys-Au-Cys adduct. This mechanism would imply that TrxR Sec-group could transfer the gold-moiety also to other less reactive protein targets. Additional cellular targets and mechanisms of action have been proposed so far for AF. Notably, AF was also found to inhibit several pro-inflammatory pathways, including NF-κB, signal transducer and activator of transcription (STAT)-3, Toll-like receptor (TLR) signalling, and angiogenesis  [27] [28] [29]. Currently, in the frame of the so-called "drug repurposing" strategies, AF is in clinical trials for the treatment of chronic lymphocytic leukaemia (clinicaltrials. gov/ct2/show/NCT01419691), lung (clinicaltrials. gov/ct2/show/NCT01737502), and ovarian cancer (clinicaltrials.
gov/ct2/show/NCT01747798). In ovarian cancer (OC), several studies provided evidence of AF efficacy and its ability to overcome cisplatin resistance  [10, 11, 30]. Schuh et al. [30]  confirmed that in human OC cells sensitive (A2780) and resistant (A2780/R) to cisplatin, the antiproliferative properties of this gold(I) compound are mainly due to direct inhibition of TrxR enzymes. AF was also reported to be very effective in BRCA1-defective ovarian cells due to the accumulation of unrepaired DNA damage  [31]. Importantly, substantial evidence has been gained demonstrating the synergistic enhancement of AF effects when combined with other compounds, thereby providing a rationale to reposition the FDA-approved drug AF for cancer therapy  [13, 32]. In recent years, mass spectrometry-based proteomics has become a powerful and systematic tool for large-scale protein identification of metal-based anti-cancer drug mechanisms and the associated drugprotein interactions  [33, 34].
More in detail, in this work we explored the effects of direct exposure to AF, in the A2780 ovarian cancer cell model, monitoring protein and cysteine oxidation levels by a label-free redox proteomics strategy based on the biotin switch technique. Compared to the ICAT-based study performed by Go et al. [39] , our approach provides an additional point of view, allowing us to correlate protein expression changes with cysteine oxidations. Subsequent Bioinformatics analysis disclosed a set of cellular processes most probably affected by the treatment. In this article, in particular, we offer a comprehensive interpretation of the redox proteomics dataset validating the most relevant results by direct biochemical assays.
Data processing LC-MS/MS data were processed with Protein Discoverer 2. 4. 0 (Thermo Scientific). Streptavidin bound and unbound fractions were submited to two independent database searches. Protein identification was performed with Mascot (Matrix Science) and Sequest™ HT (Thermo Scientific) search engines against the SwissProt sequence database (01/ 2020 version) with a mass tolerance at 10 ppm for MS and 0. 02 Da for MS/MS data. Trypsin digestion was set allowing up to 3 miscleavages. Met oxidation, Asn and Gln deamidations, and Cys carbamidomethylation were set as variable modifications. Peptide Spectrum Matches (PSMs) were filtered using a False Discovery Rate (FDR) of 0. 01, calculated with a concatenated target-decoy database. Cys-containing peptides for redox proteomics were considered if the associated Post Error Probability (PEP) value was lower than 0.
01. Protein identifications and quantifications were realized with the peptides detected in both streptavidin bound and unbound fractions. Peptide intensities of the streptavidin bound and unbound datasets were independently normalized and scaled using the total ion current of the most intense LC-MS/MS run as reference. The streptavidin bound and unbound search outputs were then merged, and protein quantification was recalculated in label-free mode using the three most intense peptides, excluding the cysteine containing peptides. Protein and peptide MS intensities were converted in Log2. The fold changes were calculated in Log scale (Log2) as differences between the median MS intensities of the AF-treated and untreated cell biological replicates. For each fold change, a Z-score was calculated according to the equation: Z = |D i -D| σ With Di: difference for a given protein, D : the mean difference of all protein ratios of the dataset, σ: standard deviation of all protein differences calculated in the dataset.
Bioinformatic analysis The lists of the upregulated and downregulated proteins were separately submitted to the bioinformatics platform DAVID  [43]  for the gene ontology (GO) enrichment analysis. Biological Process (GOBP) and Kegg's Pathway results were exported. In order to overcome the bias of the intrinsic selectivity of the protein extraction protocols  [44]  on GO enrichment, the whole dataset of the detected proteins was submitted to DAVID as a background list. Thus, for each GOBP/Kegg's pathway output, an enrichment fold change was calculated as the ratio between the number of enriched proteins in the downregulated/upregulated datasets and the number of enriched proteins in the background dataset. GOBP/Kegg's pathway outputs showing fold changes greater than 2 and enrichment p-values < 0.
01 were considered. The dataset combining upregulated and downregulated proteins was submitted to Ingenuity Pathway Analysis (IPA, QIAGEN). To the proteins detected only in the AF treated cells a fold change +10 was attributed, while to the protein detected exclusively in the control a fold change -10 was attributed. We used the Protein Data Bank (PDB) codes for proteins showing increased levels of oxidized form and decreased levels of reduced form for the Cpipe analysis on the cysteine chemical features (bioinformatics. akdeniz. edu. tr/cpipe/start. py), generating two test datasets. We created a background dataset (control) containing the PDB codes of all the detected proteins. This was also submitted to Cpipe to highlight eventual enrichments of chemical/physical features of the oxidized cysteines.
PVDF membranes were incubated overnight at 4 • C. Then membranes were treated with Horseradish peroxidase (HRP)-conjugated secondary antibodies (Santa Cruz), and bands were immunodetected with enhanced chemiluminescence (ECL) kit detection system (GE Healthcare) at Amersham Imager 600. For the quantification, densitometric analysis of the bands was performed using ImageJ2 software version 2. 0. 0-rc-64, and the intensities of the immunestained bands were normalized on the respective Coomassie-stained PVDF membrane.
About 3,200 proteins were quantified, and 438 were found upregulated, while 222 were downregulated (Fig. 3 , Supplementary Table  1 ). Moreover, 86 proteins were detected only in the AF-treated sample, while 5 were only detected in the untreated sample. The lists of upregulated and downregulated proteins were separately submitted to the bioinformatics tool DAVID  [43]  to perform the gene ontology enrichment analysis. The biological functions associated with DNA repair were the most represented in the upregulated dataset, while the protein import in the mitochondria was the most represented function in the downregulated dataset (Supplementary Figs. 1 2 ). A merged dataset containing the upregulated and downregulated proteins was submitted to the Ingenuity Pathway Analysis tool (IPA, Qiagen).
BRCA1 activation could be linked to the defect of DNA synthesis resulting from the inhibition of Ribonucleoside Reductase (RNR) catalytically dependent on Trx. The network analysis of our dataset by IPA revealed the enrichment of a protein network centered on RNR (Fig. 4A ). Both RNR1 and RNR2 were upregulated in AF-treated cells, highlighting the response of cells to TrxR1 inhibition. The second top-ranked upregulated network was centered on Caspase 3, proving the pro-apoptotic effects of AF (Fig. 4B ). The AF-mediated Caspase 3 activation was also previously observed  [11]. The activation of NRF2 transcription factor by AF was reported in the literature  [47] [48] [49]. The upstream analysis of our proteomic dataset did not reveal the activation of NRF2 by AF. However, we found several upregulated proteins under the NRF2 transcriptional control (TrxR1, SRX, GCLC, GCLM, GSTM2, G6PD, PGD, ME1) consistent with the activation of this well-known transcription factor.
AF induced mitochondrial stress: oxidative phosphorylation (OXPHOS) There is now considerable evidence to support that the main AFmediated cytotoxic effects are related to the mitochondrial stress impairing OXPHOS  [60, 61]. Despite this, a global unbiased proteomics approach was still missing. The bioinformatic analysis of our dataset revealed that OXPHOS was one of the affected pathways by cysteine oxidation. Moreover, Kegg's pathway analysis showed that complexes I, III, IV, and V were downregulated (Supplementary Fig. 6 ). These results were confirmed by Western blot analysis (Fig. 7A ). To confirm the impairment of the OXPHOS in AF-treated A2780 cells, we measured the level of oxygen consumption and lactate production (Fig. 7B ). The results confirm the impairment of OXPHOS and the switch to glycolytic metabolism.
AF induced mitochondrial stress: the effects on the mitochondrial membrane permeability transition pore (mPTP) A previous study reported that AF induces the mitochondrial membrane permeability transition  [71]. Voltage-Dependent Anion Channel proteins (VDAC) play an essential role in the mPTP opening. VDACs are also involved in the Cytochrome-c release mediated by Ca 2+ uptake and the BAX-BCL2 ratio  [71]. In our proteomics dataset, we found higher levels of VDAC3 in AF-treated cells. Similar independent results were found after treatment with arsenic trioxide, an alternative TrxR1 inhibitor  [72]. Peptidyl-prolyl cis-trans isomerase F, also known as Cyclophilin D, is involved in the regulation of mPTP opening. We found increased levels of reduced Cys residue Cyclophilin D C157 in AF-treated cells.
Even though no functional roles are reported in the literature, Cpipe analysis suggested that residue C157 is the most reactive Cyclophilin D Cys residue and can form an intramolecular disulfide bond with C62 (Supplementary Fig. 7 ); in addition it can be S-nitrosylated in mice. Despite the reduced C157 showed a 2. 5-fold increase in the AF samples, we estimated from our redox proteomics dataset that oxidized C157 residue still has a high site occupancy (as defined by Olsen et al. ,  [73] ): 96% in the control cells and 91% in AF-treated cells. Since Cyclophilin D is involved in mPTP opening by cysteine redox mechanisms with the less reactive C203  [74] , it is expected that C157 could also be involved, even if this hypothesis needs to be further verified by a targeted study. The mitochondrial membrane Ca 2+ carrier SLC25A13 is part of the mitochondrial permeability transition pore complex with VDAC and Cyclophilin D  [75]  and was also upregulated upon AF treatment.
Ovarian cancer cells are sensitive to AF treatment. Notably, the therapeutic efficacy of AF is now in phase 2 evaluation in patients affected by serous ovarian tumors (ClinicalTrials. gov Identifier: NCT03456700). Landini et al. characterized an AF-resistant ovarian cancer cell line and reported that the alteration of gold uptake mediated by the organic cation/anion efflux system could be one of the mechanisms underlying the drug resistance  [94]. Besides the expected cell death mechanisms involving the canonical intrinsic apoptosis pathway, the present study revealed some interesting details of the ovarian cancer cell adaptative response to AF treatment. The most significant alterations concerned GSH, BRCA1, TRAP1 and ER stress. Additional details on these biomolecules and their respective importance in cancer biology and drug mechanisms are given below.
Acknowledgments We want to thank  Dr. Aeid Igbaria  ( Ben-Gurion University of the Negev, Israel ) and  Dr. Agnes Delaunay-Moisan  ( French Alternative Energies and Atomic Energy Commission, CEA Saclay ) for the stimulating discussions concerning the results of this study. Inanc Soylu  ( Akdeniz University, Antalya, Turkey ) for his help with Cpipe software. This work was supported by the  ANR_AAP2019G  project  ERRED2  n •.
ProVerif As illustrated in Figure  1 , ProVerif takes as input a description of the protocol to verify in an extension of the pi calculus with function symbols to represent cryptography, a dialect of the applied pi calculus  [3, 2]. This language can be seen as a small, domain-specific, programming language for security protocols. Additionally, one needs to define the cryptographic primitives, using rewrite rules or equations. For instance, shared-key encryption can be represented by a free function symbol senc that takes as argument a message and a key and returns the corresponding ciphertext. The corresponding decryption function sdec can be defined by a rewrite rule sdec(senc(m, k), k) → m which means that, when we decrypt a ciphertext senc(m, k) with the correct key k, we obtain the cleartext m.
This would produce a difference in the shock radius of about 10 18 cm (corresponding to 0:5 0 at 2. 2 kpc  34  ) between region 0 and region 5 in only 250 yr. This is at odds with observations, which show a very circular shape of the shock front (see Fig. 1e ), whose radius vary less than 0:15 0 between region 0 and region 5 (see, methods subsection X-ray data analysis for details). We then consider the density modulation as a tracer of azimuthal variations in the shock compression ratio. Assuming r t = 4 in region 0 (i. e. , at θ = 90 ∘ , where the acceleration is inefficient), Fig. 3b  shows a higher compressibility in quasi-parallel conditions, where the shock compression ratio raises up to approximately r t = 7. To further constrain the observed increase of the post-shock density towards quasi-parallel conditions, we added the data from the XMM-Newton Large Program of observations of SN 1006 (approximately t exp = 750 ks).
X-ray data analysis We analyzed Chandra observations 13737, 13738, 13739, 13740, 13741, 13742, 13743, 14423, 14424, 14435 (PI F. Winkler) performed between April and June 2012, with a total exposure time of 669. 85 ks, and observation 9107 (PI R. Petre) performed on June 2008 for a total exposure time of 68. 87 ks (see Table  2 ). All observations were reprocessed with CIAO 4. 12  43  and CALDB 4. 9. 0 (https://heasarc. gsfc. nasa. gov/docs/heasarc/caldb/caldb_intro. html). Mosaic images of SN 1006 were obtained by combining the different pointings with the CIAO task merge_obs (https://cxc. cfa. harvard. edu/ciao/ahelp/merge_obs. html). In particular, we produced vignetting-corrected mosaic images of the flux (in counts s -1 cm -2 ) in the 0. 5 -1 keV band (shown in green in Fig. 1 ) and in the 2.
gsfc. nasa. gov/xanadu/xspec/manual/node195. html, based on the database AtomDB version 3. 09, see http://www. atomdb. org/Webguide/ webguide. php). Though we adopt a state-of-the-art spectral model, we acknowledge that there may be limitations in the description of the emission stemming from an under-ionized plasma with a very low ionization parameter, such as the one studied here. However, we expect this effect to introduce almost the same biases (if any) in all regions, and not be responsible for the density profile shown in Fig. 3. We found that the electron temperature in the shocked ISM is consistent with being constant over all the explored regions and fixed it to the best-fit value obtained in region 0 (kT = 1. 35 keV), where we get the most precise estimate (error bars approximately 0.
Acknowledgements We thank  F. Winkler  for kindly providing us with the H α map of SN 1006. We thank  P. Plucinsky  for helpful suggestions on the Chandra data analysis. M. M. , S. O. , F. B. , E. G. , and G. P. acknowledge financial contribution by the  PRIN INAF 2019  grant and the  INAF mainstream program. D. C. is supported by  NASA  grant  80NSSC20K1273  and  NSF  grants  AST-1909778 ,  AST-2009326  and  PHY-2010240. J. V. 's and E. G. 's work on this paper has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  101004131  (SHARP). A. D. acknowledges support by the  Centre National d'Etudes Spatiales (CNES). M. M. and R. G. acknowledge support by the  INAF Mini-Grant  "X-raying shock modification in supernova remnants".
Code availability Chandra data were processed by adopting the CIAO software package, available at https://cxc. cfa. harvard. edu/ciao/. XMM-Newton data were analyzed by using the SAS package, available at https://www. cosmos. esa. int/web/xmm-newton/download-and-install-sas. XSPEC, the software adopted for X-ray spectral analysis, is available at https://heasarc. gsfc. nasa. gov/xanadu/xspec/. The Python code that we developed to calculate the volumes is provided as Supplementary Software.
In this work, we propose an approach for the separation of coumarins from thin-layer morphological segmentation based on the acquisition of multicomponent images integrating different types of coumarins. The first step is to make a segmentation by region, by thresholding, by contour, etc. of each component of the digital image. Then, we proceeded to the calculations of parameters of the regions such as the color standard deviation, the color entropy, the average color of the pixels, the eccentricity from an algorithm on the matlab software. The mean color values at R = 91. 20 in red, at B = 213. 21 in blue showed the presence of samidin in the extract. The color entropy values H G = 5. 25 in green and H B = 4. 04 in blue also show the presence of visnadine in the leaves of Desmodium adscendens.
These values are used to consolidate the database of separation and discrimination of the types of coumarins. The relevance of our coumarin separation or coumarin recognition method has been highlighted compared to other methods, such as the one based on the calculation of frontal ratios which cannot discriminate between two coumarins having the same frontal ratio. The robustness of our method is proven with respect to the separation and identification of some coumarins, in particular samidin and anglicine.
Indeed, the pixels of a region having an average value per channel in RGB higher than a certain value (threshold) T1 and lower than a second value T2 are classified as object recognition pixels which can be used for the separation and the identification of coumarins. The approach for the automatic identification of secondary metabolites (coumarins) that we propose in this article, based on the segmentation by regions of the digital image integrating the coumarins to be identified, is an extension of the automatic identification of secondary metabolites. Parameters such as average pixel color on each channel, color entropy on each channel, standard deviation, equivalent distance, etc. of the different regions are calculated. The values obtained are stored and form a database. From this database, an algorithm is developed and executed on Matlab software version 2016  [6] , thus making it possible to perform the semi-automated and rapid identification of coumarins.
Development of the Identification Program For the different fractions, the parameters have the same values, which allows us to develop the identification program below (see Figure  10 ). We proceeded to verify the presence of coumarins, in particular samidins, in the hexane fraction of the leaves of Desmodium adscendens. The manual identification protocol on thin layer being the same, we made the segmentation of the different spots on the thin layer (see Figure  11 ). We then calculated the various parameters including color entropy (Entropy), standard deviation (std) and average pixel color (MeanIntensity)  [13]. We obtained the results recorded in Table  4. These values correspond to terpenes in the database. The distance used to recognize the objects in the image is the Euclidean distance  [13] , defined for two vectors ( ) The values of the average pixel color, the standard deviation of each component, the entropy per color component of the four fractions of the compounds are recorded in Table  5.
color (at R = 87. 01; at G = 41. 05; at B = 54. 01), of the standard deviation (s R = 6. 47; s G = 3. 58; s B = 4, 11), entropy (H R = 8. 74; H G = 3. 46; H B = 5. 08) do not correspond to the coumarin values in the database. This could be explained on the one hand, by the noise due to stray lights during the acquisition of the images, on the.
Conclusions We have exposed a new technique for the identification of coumarins on a thin layer based on the segmentation of the image and the calculation of the segmentation parameters such as the color entropy, the standard deviation and the average color of the pixels. Our method shows completely satisfactory results and has been with the results of the manual technique made from developers and the 366 nm and 254 nm UV lamp. The results of this confrontation showed a similarity between the coumarins obtained. This shows the reliability and robustness of our technique. In our next work, it will be a question of working on a large number of plants in order to have a large database allowing the identification of several coumarins.
Assessment of the certainty of evidence We constructed GRADE evidence profiles of certainty for each pairwise comparison separately and for each outcome using MAGI-Capp. The certainty of evidence is determined by the risk of bias across studies, incoherence, indirectness, imprecision, publication bias and other parameters. 37 To inform calculations of absolute effect differences, we performed proportion meta-analyses of frequencies of baseline risks/effects provided by the source studies; these are available in the online appendix. 19  used the CINeMA software to summarize risk of bias according to the contribution of each study to the network for the respective outcome. 14, 38 The overall risk of (within study) bias was based upon the highest proportion of risk of bias contributed to the network, as per CINeMA methodology.