dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , HMDB). For the latter, strategies for integrating metabolomics datasets may be informed by our findings defining metabolite classes which are subject to systematically across two different shipments, sent several weeks apart, to represent separate assay runs. Intra-assay precision: The percent coefficient of variation (CV%), for each annotated metabolite, between duplicate aliquots of identical parent samples within an assay run ((σ/(a 1 + b 1 /2)) × 100)  [8]. "High" precision was defined as ≤10%, "moderate" as 10% < x < 15%, "low" as ≥15%. (σ = standard deviation of duplicate metabolite measurements within a vendor; a = vendor metabolite measurement for the first identical aliquot; b = vendor metabolite measurement for the second identical aliquot). Interassay precision: The percent coefficient of variation (CV%), for each annotated metabolite, between duplicate aliquots of identical parent samples across shipments, representing separate assay runs ((σ/(a 1 + a 2 /2)) × 100).
Acknowledgments: We are immensely grateful to  Amit Etkin  and colleagues for their invaluable sample collection, as well as the  Indiana University Genetic Biobank  for their expertise in the processing and preparation of all samples. We are also greatly appreciative of the metabolomics vendors that participated in this cross-platform comparison, for providing their technical expertise and highly beneficial discussions. We are also deeply thankful to  Eugene Rakhmatulin  and the WaveAccess team for their invaluable contributions and creativity in generating the  Metabolomics Platform Exploration Tool. H. C. L. , L. E. C. , T. V. , A. T. , M. H. and A. J. are employed by  Cohen Veterans Bioscience , a nonprofit 501 (c) (3) research organization.
Acknowledgements The authors are grateful to  Intra-ACP Strengthening African Higher Education through Academic Mobility (STREAM)  for fellowships awarded to  E. L. D. Kamto ,  Pr. Bruno N. Lenta  of the  Yaounde-Bielefeld Graduate School of Natural Products with Antiparasite and Antibacterial activities (YaBiNaPA ) for MS technical support and  Mr. Victor Nana  of the  National Herbarium of Cameroon (NHC)  for the plant identification.
Statistical Analysis All sTILs and PD-L1/SP142 scores were collected in Excel (Excel Windows 10, Microsoft Corporation, Redmond, WA, USA) and imported into the IBM SPSS Statistics 26. 0 software (IBM Chicago, IL, USA) for statistical analysis. Tests for normality were performed with the Shapiro-Wilk test, which showed that both the sTILs scores and PD-L1/SP142 scores of each participant were not normally distributed (p < 0. 05) in either the proficiency set or the study set. Because of this non-normal distribution, the median (instead of the mean) values were selected for each case to serve as the "gold standard" in the study set, as a consensus diagnosis was lacking for this patient cohort. This hypothetical "median/gold standard" pathologist was designated "Px". Descriptive measures, comprising minimum, maximum, and percentiles, were calculated.
≥1%, <5% vs. ≥5%, and <10% vs. ≥10%. Each concordance rate was the percent agreement derived from a 2 × 2 contingency table created for each cut-off and pair of pathologists. The results were reported as the sample mean and sample standard deviation of these concordance rates for all pairs of pathologists. A custom code has been created for the concordance analysis using the R software environment for statistical computing and graphics (version 4. 0. 2, R Foundation, Vienna, Austria). The Mann-Whitney U test was used to explore the association between TILs scores and PD-L1 status. The Kruskal-Wallis test was used to explore the association between TILs scores and the localization of the metastases. Visualization was performed by the construction of Box-and-Whisker plots. The Spearman's Rho correlation test was used to investigate the relation between the median TILs score and the median PD-L1 score.
ACKNOWLEDGMENTS We thank the  PLATIM platform (SFR BioSciences Lyon ,  UAR3444/CNRS ,  US8/Inserm ,  ENS de Lyon ,  UCBL ) for electron microscopy technical support,  Mathilde Chopy  for kindly providing us with the petunia limb electron micrograph, and  Clément Verez  for his help in electron microscopy. We also thank  Hongzi Kong  and colleagues for agreeing that we reproduce their pictures from Nigella arvensis petals. We also thank  Michiel Vandenbussche  for critical reading of the manuscript.
Samples were separated using 1% agarose gel with electrophoresis performed in TBM buffer (0. 5× TB, 0. 1mM MgCl 2 ) at 150 V for 4 h at 4 • C. The gels were fixed in 10% trichloroacetic acid (TCA) for 10 min, dried under vacuum for 1 h and analyzed using a FLA 5000 (Fuji) scanner. Quantitative analysis of the bands was performed using ImageQuant software  (20). The Hill slope was identified by applying the values to Hill equation: Y = B max × X h K d h + X h using the GraphPad Prism 5 software. B max is the maximum specific binding, K d is the dissociation constant and h is the Hill slope. For band-shift competition assays, internally labeled WT RNA (25 000 cpm) and 10 nM unlabeled WT RNA (SA35; Figure  2A ) along with increasing concentrations of unlabeled competitor RNAs (up to 400 nM) were denatured at 90 • C followed by cooling on ice for 2 min.
15 mM dGTP, 0. 6 mM dATP, 0. 6 mM dCTP, 0. 6 mM dTTP) and 4 units of AMV RT. The cDNAs thus generated from both reactions were mixed together and sequenced using an Applied Biosystems 3130xl genetic analyzer as described earlier  (59, 60). The electropherograms obtained were analyzed with the software QuShape  (66). Normalized SHAPE reactivity data from at least 3-4 independent experiments in the absence of Pr77 Gag were used as pseudo-energy constraints to fold the RNA secondary structure of the MMTV packaging signal using the program RNAstructure version 6. 1  (67). RNA structures were then drawn using VARNAv3-93 and SHAPE reactivity was incorporated, as described previously  (68). The SHAPE reactivity obtained in the presence of 4 M Pr77 Gag was applied onto the RNA structure obtained using reactivity in the absence of Gag.
This further suggested that either both or one of the flanking sides of ssPurines plays an important role in binding to Pr77 Gag. The packaging efficiency of mutation in which the central GAAG of ssPurines was substituted with CUUC (5 GGACUUCAG3 ; AK44) was found to be slightly increased when compared to AK45 containing flanking substitutions (5 CCUGAAGUC 3 ; RPE = 0. 308 ± 0. 135; Figure  9D ). Interestingly, the mutation in which the internal AAG was substituted with UUC 6. Binding of ssPurines and its mutant RNAs to MMTV Pr77 Gag analyzed by filter-binding assay. The membrane-bound radioactivity of WT gRNA and selected mutant RNAs was quantified at increasing concentrations of MMTV Pr77 Gag. The experimental data points were fit with the Hill equation. WT, wild type. (5 GGAGUUCAG 3 ; AK50) showed packaging efficiency comparable to WT (RPE = 1.
Acknowledgments We are grateful to the  Bloomington Drosophila Stock Center  and the Drosophila Genetic Reference Panel for fly stocks and to the  SFR Biosciences  ( UMS3444/CNRS ,  US8/Inserm ,  ENS de Lyon, UCBL)  facilities:  Robert Renard  and the  Arthro-Tools  staff for fly food preparation and the  Plateau Technique d'Imagerie/Microscopie. We thank  Hafida Sellou  for her help in the preliminary observation of SD male phenotypes,  Paul Lasko  for kindly providing the anti-Vasa antibody,  Marie Delattre  for the Cy3 labeling kit,  Guillermo Orsi  and  Beatrice Horard  for critical reading of the manuscript and  Loppin's lab  for discussion and feedback.
Relaxed specificity is tolerable in hybrid HKs because the tethering of transmitter and receiver domains within a single protein increases the effective concentration of each domain in the vicinity of the other  [6] [7] [8]. Although prototypical TCS protein partners encoded by adjacent genes are frequent, isolated HK and RR genes (orphans) are also abundant, as are complex gene loci encoding multiple TCS proteins  9. This makes the identification of signaling partnerships non-trivial. The P2CS database (http:// www. p2cs. org) provides paired HK and RR phylogenetic trees, which shed light on evolutionary heritage and can additionally provide clues about protein HK-RR partnerships  10. Indeed, HKs and RRs that are known to interact, fall into corresponding subfamilies  [10] [11] [12]. This phylogenetic relationship is mainly attributed to the coevolution of the interacting domains of TCS partners  4, 13  and the need to prevent unwanted cross-talk between non-cognate TCSs.
The genome of P. brassicacearum contains homologues of the P. aeruginosa hybrid HKs, making it a relevant model for deciphering the GacS-GacA network. Here, we report how the evolutionary relationships between hybrid HKs can be used as predictors of potential intermolecular interactions. We used domain-swap genes chimeras of P. brassicacaerum GacS to assay interactions between signaling domains of hybrid HKs predicted by their evolutionary relationships. As well as showing the utility of the 'phylogenetic promiscuity' approach, we propose that additional hybrid HKs might be involved in the GacS cascade that controls phytobeneficial traits, and suggest likely new signaling interactions within the GacS multikinase network. This approach also allows us to suggest potential intermolecular hybrid HK interactions in other bacteria using phylogenetic analyses available through the P2CS database.
Results Kinase and receiver domains of 'GacS cluster' hybrid HKs have co-evolved. In the latest version of the P2CS database, paired phylogenetic trees were implemented for evolutionary analysis and identification of candidate partners for orphan TCS proteins  10. The genome of P. brassicacearum NFM421  30  was added to P2CS and analyzed to produce trees of kinase and receiver domains from encoded TCS proteins (Supplementary Fig. S1 ). The kinase domain of P. brassicacearum Psebr_a4261 (GacS) was found in a cluster (the GacS kinase cluster) alongside seven other hybrid HKs: Psebr_a4938 (LadS), Psebr_a625 (RetS), Psebr_a1633, Psebr_a1408, Psebr_a3082 and Psebr_a4122 (PA1611) (Fig. 1 , protein identifiers are abbreviated in the figure and in the rest of the manuscript). These seven proteins also cluster together in the P.
Other examples of interacting hybrid HKs have been described in Pseudomonas spp. , including the RcsC/PvrS  16  , and GacS multikinase networks. Through GacA activity, the GacS network controls the transcription of several sRNAs, which orchestrate diverse functions in gammaproteobacteria  24, 25. In all these cases, phylogenetic trees of hybrid HK kinase and receiver domains in the P2CS database show that interacting hybrid HKs fall within the same phylogenetic clusters (Supplementary Fig. S4 ). In P. brassicacearum, similar phylogenetic clustering of hybrid HK kinase and receiver domains is observed, suggesting that they may interact with each other in a multikinase network. Interconnection of hybrid HKs. Current knowledge of hybrid HKs, stipulates that a spatial tethering of kinases to their substrates relaxes evolutionary constraints on specificity  [6] [7] [8]  , implying that a covalently linked receiver domain can be functional even in the absence of specific interactions.
Multikinase network. Through in silico prediction and the use of chimeras, we have identified three proteins (1633, 3082 and 4122) whose receiver domains function interchangeably with that of GacS, enabling signal transduction via GacA and resulting in transcriptional activation of rsmX. A receiver domain of one of these proteins, 3082_3, is shown to be able to receive signal (phosphoryl group) from its cognate kinase domain both intra-and intermolecularly, suggesting that 1633, 3082 and 4122 might all be involved in the GacS network in P. brassicacearum. Further experiments are needed to confirm whether this is the case in vivo, for example by identifying signals which stimulate 1633, 3082 and 4122 signaling and testing for a GacS response to those signals. Our 'phylogenetic promiscuity' approach can be used to suggest additional hybrid HKs that may be involved in multikinase networks from publicly available data in the P2CS database.
Sequence analysis. The software MAFFT (https:// mafft. cbrc. jp/ align ment/ softw are/) was used to align kinase and receiver domain sequences from HK proteins (http:// www. p2cs. org). A threshold of 40% identity across domains was used to identify conserved amino acid residues. The multiple sequence alignment is then visualized using Jalview (https:// www. jalvi ew. org). qRT-PCR analyses. All the strains that had received the pME6032 plasmids were grown on TSB 1/20 supplemented with isopropyl β-d-1-thiogalactopyranoside (IPTG, 1 mM) and tetracycline (20 µg/ml). Total cellular RNA from 0. 5 ml of cultures was extracted using the RNA Protect Bacteria Reagent, the RNeasy Mini Kit and the RNase-Free DNase Set (Qiagen). After a second DNase treatment with the Turbo DNA-free Kit (Ambion), the yield, purity and integrity of RNA were further evaluated using a NanoVue Spectrophotometer (GE Healthcare) and 1% agarose gel electrophoresis.
Reverse transcription-PCR (RT-PCR) assays were performed using the Transcriptor First Strand cDNA Synthesis Kit (Roche). Real-time PCR runs were carried out on a LightCycler 480 System (Roche), using the LightCycler 480 Probes Master kit (Roche). The 16S rRNA gene was used as a reference for normalization. Primers and FAM/TAMRA TaqMan probes are listed in Table  S2. Fluorescence data were analyzed with the Fit Points method and the efficiencies of qPCR were measured over a dilution range framing our samples (PCR efficiency-16S rRNA: 2. 012 and rsmX: 1. 904). To test pairwise differences in rsmX expression, we performed Welch's heteroscedastic F test using the R package 'onewaytests v2. 0' , with a significance threshold to assess statistical difference at p < 0. 05.
Phylogenetic analysis of kinase and receiver domains and interaction prediction. The generation of phylogenetic trees was carried out as previously described  10. Briefly, the kinase and receiver domains of TCS proteins were identified as previously described  47  and used to construct pairwise alignments using TULIP 1. 5  48  , which uses the Smith-Waterman algorithm, with 1000 sequence shuffles, to estimate pairwise Z-values and infer a distance matrix. In the R environment, a hierarchical cluster analysis was undertaken using this distance matrix and the hclust function with 'Ward' as a linkage method. An in-house R function then plotted dendrograms to display the hierarchical relationship between domains. To predict potential interaction partners among paralogs of hybrid HKs in P. brassicacearum, we built a direct coupling analysis (DCA) model for hybrid HK kinase and receiver domains using intra-protein pairs between such domains from the P2CS database.
The fluorescence intensities were measured with a UV transilluminator (Mupid-Scope WD; Mupid, Japan) and a digital camera (IXY 220F; Canon, Japan) with an orange filter, which was connected to a computer with an image analysis software (1D Image Analysis Software; Kodak, Japan). The fluorescence intensities of DNA that was retained in the plug and released from the plug were measured by using the image analysis software. The fluorescence intensities for released DNA were proportional to the total amount of DNA fragments, which were separated by physical disconnection of the DNA fiber. The fraction of activity released (FAR) calculation equation was as follows: FAR(t) = I out (t) (I in (t) + I out (t)) , (2) where, t is the time after irradiation, I in (t) is the fluorescence intensity of DNA retained in the plug, and I out (t) is the fluorescence intensity of DNA released from the plug.
Incident Energy Spectra at the Cell Entrance To estimate the energy spectra of the incident protons at the cell entrance in the irradiation experiments for the colony assay and FAR assay, the I-value of the PMMA used in the experiments was evaluated to be 65 eV to provide the best agreement with the measured depth dose as the I-value. Figure  2  shows the relative dose distribution of 70 MeV protons scaled at 0 mm. The maximum standard errors of the experimental data are 0. 27% before the Bragg peak and 8% after the peak. The Bragg peak occurred between 32 mm and 33 mm. Thus, in this study, we selected a PMMA thickness of 32 mm to downscale the energy of the incident protons. Figure  3  shows the energy spectra of the incident protons at the entrance of the cell, scaled to the maximum of the number of protons at a 0 mm PMMA thickness.
Conclusions To evaluate the repair performance of HSGc-C5 cell against radiation induced DNA damage, the Geant4-DNA application was extended by using newly measured experimental data acquired in this study. Concerning fast-and slow-DNA rejoining, the TLK model parameters were adequately optimized (the repair speeds of each process were reasonably close to the DNA rejoining speed of the NHEJ and HR processes). The lethality rates of the DNA damage induced by complex DSBs and binary repair were approximately 3% and 40%, respectively. Using the optimized repair parameters, the Geant4-DNA simulation was able to predict the SF and the DNA repair kinetics.
I. INTRODUCTION In collaboration with the civil hospitals of Lyon ("Hospices Civils de Lyon" or HCL in French), a group of 14 hospitals in the area of Lyon (France), we have developed a virtual assistant, called CoBoy, dedicated to supporting day-to-day medical consultations. This virtual assistant was developed as a module of the Health Information System (HIS) currently used by HCL's clinicians: Easily R. Consultations are a large part of the HCL's physicians' activities (they performed 1 million of them in 2020). During these consultations, physicians make decisions repeatedly and we aim to support physicians during these customary activities. Diagnostic Decision Support Systems (DDSSs) have been developed as an attempt to support physicians during diagnosis decisions  [1] ,  [2]. Famous examples of DDSSs include MYCIN  [3] ,  [4] , an expert system dedicated to antimicrobial therapy, or INTERNIST-1/QMR  [5] ,  [6] , an expert system designed to support decisions in generalist medicine.
Accordingly, we have developed a decision support system, in the form of a virtual assistant, able to anticipate and provide pieces of information that the physician needs to reach diagnoses concerning a specific patient. This system aims to reduce the workload of physicians and to allow them to make informed decisions during medical consultations. In section II, we present the system's capabilities: how it determines which pieces of information to provide to physicians using both a rule-based classifier and a Naive Bayes classifier, how it searches for raw data associated to these pieces of information in the database of Easily R , and how it displays these pieces of information to physicians through its user interface. In section III, we present a simple use case scenario to illustrate the process of the system during a medical consultation.
II. THE PROCESS OF THE PROPOSED SYSTEM As introduced in section I, the goal of CoBoy is to determine which pieces of information on a patient could be needed by a physician for a specific consultation, to search for raw data associated to each of these pieces of information in the database of Easily R , and to display these pieces of information to physicians in an interpretable way. Fig. 1  summarizes the process of CoBoy at the beginning of a medical consultation. In this section, we detail the different steps of this process. Sub-section II-A details how the system determines, based on two classification systems, which pieces of information are needed by a physician for a specific consultation. Subsection II-B then explains how the system searches for raw data associated to each piece of information needed, given their data type.
To do so, CoBoy uses a set of probabilities learned from logs of pieces of information searched or recorded by physicians during previous consultations, and applies a naive version of the Bayes theorem (1) which assumes that each variable X ∈ X are independent of each other. P (Y = 1|X p ) = P (Y =1) X∈X P (X=x|Y =1) 1 y=0 P (Y =y) X∈X P (X=x|Y =y) (1) Therefore, if P (Y = 1|X p ) ≥ λ the label Y ∈ Y is set to 1, meaning that the physician will probably need the piece of information Y on the current patient p. The λ parameter is used as a threshold to define from which minimum probability a piece of information on a patient may be needed by a physician. By default, this threshold λ is set at 0. 5. However, the current granularity of logs recorded by Easily R for some pieces of information searched by physicians during their consultations limits the construction of a precise learning dataset.
B. Searching for raw data in the database Once the pieces of information to provide to the physician are determined for a specific medical consultation, the system searches for raw data associated to these pieces of information into the database Easily R. For each data type, CoBoy will request the HCL's database in a specific way to obtain the targeted data. For biological analyses made in medical laboratories, this is the result of the last analysis and the results history that are requested. Because biological analyses are often linked to minimum and maximum thresholds, the system also searches for these thresholds if they are available. Let us specify that results of analyses made by patients in medical laboratories outside the HCL are not available into the database of Easily R if the laboratory has not transmitted these results to the HCL.
Due to this practical limitation, CoBoy is unable to provide these results to physicians. For performance reasons, only the hundred most recent results are requested. For medical documents, such as reports of medical imaging (scanners, MRI, radiographies, etc. ), reports from other specialists following the current patient (ophtalmology, dietetic, cancerology, etc. ), or drug prescriptions, CoBoy searches for the most recent document in the database. For "general" pieces of information, such as background history, family medical history, or patient's allergies, CoBoy searches for all data about it into the database of Easily R. Therefore, CoBoy collects into the database of Easily R all the raw data associated to pieces of information that could be needed by a physician for the current patient, according to the subset determined before (see sub-section II-A).
These documents are displayed using a PDF viewer and are accessible through a tab system. Pieces of information determined using the Naive Bayes classifier (see sub-section II-A) are sorted according to the estimated probability that the physician will need them during the consultation. The more probable is the higher, but pieces of information determined by the rule-based system come first. In the case in which no data was found in the HCL's database for a piece of information, CoBoy says that it has searched for raw data for these piece of information but didn't found anything, with a message such as "I didn't find any data about HbA1c of this patient". 1) Results' explanations: Each provided piece of information is associated with explanations of how CoBoy determined that the physician could need this piece of information during the current consultation.