dataesr/scientific-paragraphs-categorization huggingface dataset:
Model Evaluation and Fitting Procedures The mathematical models described above were implemented and analyzed using the R language of statistical computing (R Development Core Team, 2017). As indicated, the age of uninfected and infected RBC was compartmentalized leading to a tractable system of coupled ordinary differential equations with gamma-distributed lifetimes and maturation times for RBC and infected cells, respectively  (Antia et al. , 2008). In the following we used a compartment size of 4 h. The differential equations were solved using the deSolve package and models were fitted to the experimental data using the optim-fitting routine in R. In cases where a strong correlation between parameters hindered convergence of fitting algorithms, a parameter sweep was performed to find combinations of parameters that fit the data.
FUNDING This work was supported by a grant from the  FRONTIERprogram of Heidelberg University  ( ZUK 49/2 5. 2. 107 ) and by a grant from the  Ministry of Science, Research and the Arts of Baden-Württemberg  (Az:  0077. 3. 5. 2. 107 ) to FG and A-KM. NT and FG were additionally funded by the  Center for Modeling and Simulation in the Biosciences (BIOMS). A-KM is a recipient of a maternity leave stipend through the  Deutsche Zentrum für Infektionsforschung (DZIF). We acknowledge financial support by  Deutsche Forschungsgemeinschaft  within the  funding programme Open Access Publishing , by the  Baden-Württemberg Ministry of Science, Research  and the Arts and by  Ruprecht-Karls-Universität Heidelberg.
To obtain mice with a fully humanized immune system (HIS mice), newborn NOD-scid IL2Rgamma null (NSG) mice (1-3 days of age) were sublethally irradiated with 100 cGy prior to intrahepatic delivery of 10 5 HLA-A2 + CD34 + human HSCs. At 8 weeks after CD34 + cell transfer, peripheral blood was collected from all mice. Blood samples were lysed to remove red blood cells and stained with pan-leukocyte anti-human-CD45-BV510 (BD Pharmingen) and anti-mouse-CD45-PECy7 (eBioscience) antibodies. Samples were acquired on a LSR flow cytometer (BD) and analyzed by FACS Diva software (BD) to determine the level of human immune cell engraftment. Human cell engraftment typically ranged from 5-20% of viable peripheral blood cells.
Flow cytometry analysis and sorting For ex vivo P-STAT1 signaling analysis, Clec9A-AFN was injected intravenously through the retro-orbital vein in Balb/c mice (female, 8 weeks) and spleens were recovered 45 minutes later. Splenocytes were isolated, fixed, permeabilized and labelled with anti-CD11c-AlexaFluor488, anti-CD8-APC and anti-Y701-phospho-Stat1-PE antibodies (BD Biosciences)  (17). Samples were acquired on a FACS Canto (BD Biosciences) and data were analyzed using FlowJo software. For analysis of CD19 + B, CD4 + and CD8 + T cell populations in circulation, blood was collected from the tail vein with a heparinized capillary and stained for flow cytometric analysis using CD19, CD4 or CD8 antibodies (CD19 FITC, BD; CD4 APC, Immunotools; CD8 PE, eBioScience).
Samples were acquired on a BD LSR Fortessa (5-laser) and analyzed using FlowJo software. To address chemokine upregulation by tumor-resident DC, B16-bearing mice were injected with PBS or Clec9A-AFN and stained with CD16/CD32 to block Fc receptors, followed by CD11c-APC (clone N418, Biolegend). Doublets were excluded and cells were sorted based on CD11c expression using Beckman Coulter MoFlo® High Performance cell sorter. RNA of the sorted cells was isolated according to the manufacturer's protocol using RNeasy Purification kit (Qiagen) and cDNA was synthesized using PrimeScript kit (Takara); qPCR on the indicated genes was performed using Light Cycler 480 SYBR Green Master Mix (Roche). Data were normalized and quantified relative to the stable reference genes GAPDH, HPRT1 and LDHA with BioGazelle qBase software.
Tumor-draining lymph nodes and tumors were dissected three days after the last perilesional delivery of AFN and processed for flow cytometry. Fcreceptors were blocked using CD16/CD32, whereupon single cell suspensions were stained with live-dead-Fixable Aqua, CD3-PeCy7 (clone 145-2C11), CD4-PE (clone RMA-5), CD8-APC (clone 53-6. 7) (all BD Pharmingen), CD44-PerCP-Cy5. 5 (clone IMF7) and CD62L-APC-Cy7 (clone MEL-14) (both BioLegend). Effector T cells were identified based on their CD44 hi CD62L low phenotype, naive T cells based on CD44 low CD62L hi phenotype. For lymph nodes, central memory T cells were based on CD44 hi CD62L hi phenotype. Samples were acquired on an Attune NxT Acoustic Focusing Cytometer (Life Technologies) and analyzed using FlowJo software. To evaluate CTL proliferation, we used T cell receptor transgenic CD8 + T cells specifically recognizing the melanocyte differentiation antigen gp100 (Pmel-1) present on B16 tumor cells.
Gp100-specific CD8 Pmel-1 T cells were isolated from the spleens of C57BL/6 Pmel-1-Thy1. 1 mice, using the CD8α + T Cell Isolation Kit (Miltenyi Biotec) and labeled with 5 µM of CFSE (Thermo Fisher). One million of CFSE-labeled T cells were adoptively transferred to C57BL/6 mice inoculated with 6. 10 5 B16 melanoma cells. Subsequently, mice were treated with the indicated AcTakines. Six days post adoptive T cell transfer, tumor-draining lymph nodes and spleen were dissected and specific T cell proliferation was assessed by Flow Cytometry. Samples were acquired on a BD LSR Fortessa (5-laser) or on an Attune Nxt Acoustic Focusing Cytometer (Life Technologies) and analyzed using FlowJo software.
Acknowledgments We thank  Johan Grooten  for the anti-PDL1 sdAb,  Reza Hassanzadeh Ghassabeh  ( VIB Nanobody Core ) for the selection of the anti-Clec9A sdAb,  Claude Libert  for the IFNAR1 -/-mice,  Karine Breckpot  for the Pmel-1 mice,  Veronique Flamand  for the Batf3 -/-mice, Ulrich Kalinke for CD11c-IFNAR-and CD4-IFNAR-deficient mice,  Florence Apparailly  for essential support,  Tom Boterberg  for mice irradiations, and  Nico Callewaert  for critical reading and comments. We also thank the  Navelstrengbloedbank UZ Gent  for generous provision of cord blood units. J. T. , G. U. and N. K. are scientific co-founders, N. K. is CEO and J. T. is CTO of  Orionis Biosciences. J. T. , N. K. and T. W. hold stock-options in the company. Research. on November 29, 2017. © 2017 American Association for Cancer cancerres.
Specific DC-targeted IFN allows nontoxic antitumor efficacy This work was supported by  UGent Methusalem and Advanced ERC (CYRE ,  N° 340941 ) grants to J. T. ; an  FWO-V grant   G009614N  to J. T. and S. G. ; grants from  LabEx MabImprove, Institut Carnot CALYM , the  Canceropôle -Institut National du Cancer (INCa)  to G. U. ; the  SIRIC Montpellier Cancer INCa-DGOS-Inserm   6045  to F. P. ; and by  Orionis Biosciences. Research.
Processing of sequencing data and statistical analysis Read pairs were assembled into pseudoreads with PEAR, using the default settings  [16]. Sequence data was analyzed using a workflow based on the software tool QIIME (Quantitative Insights Into Microbial Ecology) version 1. 8  [17]. Reference-based chimera removal was done using UCHIME as implemented in QIIME 1. 8. OTUs consisting of only a single sequence were removed. Sequences that could not be aligned by PyNAST against the 16S reference alignment were removed. Operational taxonomy units (OTU) clustering (open reference), taxonomic assignment and reference alignment was done with the 'pick_-open_reference_otus. py' workflow script of QIIME, using 'UCLUST' as clustering method (97% identity) and Greengenes version 13. 8 as a reference  [18].
Sequence depth per sample was investigated by comparing the read counts between three different collection methods and two different DNA extraction methods. Alpha diversity was calculated using the 'alpha_rarefaction. py' workflow script. Reference-based chimera removal was done with UCHIME  [19]. The Ribosomal Database Project (RDP) classifier version 2. 2 was performed for taxonomic classification. In order to compare samples to each other we transform the read counts into relative abundance (normalization step). Taxa-specific read counts were divided by the total number of reads for that sample to which taxa belongs in order to normalized them. The resulting normalized read counts for each taxon represented a measure of the relative abundance of the various taxa identified in that sample.
The sum of the taxa relative abundance per sample is 100%. Differences in microbiota composition were determined using paired t-test or Wilcoxon signed-rank test, following by Shapiro-Wilk test for normality and inspected visually by a histogram. The statistical analyses were performed using SPSS v. 22. 0. Bonferroni correction for multiple comparisons was applied accordingly. The variation in the data was visualized with principal component analysis (PCA) and done in Canoco 5. 0. 4  [20]. Through all study we applied four comparisons: (i) Fresh+QIA vs. O24h + QIA, (ii) Fresh +QIA vs. O7d + QIA, (iii) O24h + QIA vs. O7d + QIA and (iv) O24h + QIA vs. O24h + PF.
4 15 or 30 min Comparison between storage Qiagen stool Mini qPCR Fecal samples should be [22] at RT for 15 min vs. 30 min vs. kit+ Bead-beating frozen within 15 min no buffer counting from collection. 41 (19 elderly 7 or 14 days Storage at RT within RBB Ilumina MiSeq + OMNIgene•GUT kit did not [13] + 22 infants) OMNIgene•GUT kit for 7 PicoGreen significantly impact or 14 days vs. fresh samples microbiota composition and diversity in elderly datasets after 7d of storage. It can be used instead of fresh method. 14 24 h and 7 days Comparison of storage at RT MO BIO Power Illumina MiSeq Significant influence of DNA Our within OMNIgene•GUT for Fecal DNA Isolation 16S RNA + qPCR extraction method + no study 24 h or 7 days vs freezing + Kit vs. Qiagen QIAmp on Bacteroides influence of storage within comparison of two DNA DNA Stool Mini kit+ spp.
Statistical analyses Data analyses involved the use of MYSTAT v12. 02. 00. For descriptive analyses, qualitative variables are reported with absolute and relative frequencies, and quantitative variables with mean (SD) or median (quartile 1-3 [Q1-Q3]). Normal distribution of quantitative data was assessed by the Shapiro-Wilk test. Groups were compared by 2-sample t test for normally distributed data and Mann Whitney test for non-normally distributed data. To compare qualitative data, we used chi-square test. The ICC was calculated by using MedCalc v16. 8. 4. Bonferroni correction was used for multiple comparisons (21 comparisons). Statistical significance was set at p<0. 0024.
Patient recruitment From December 2014 to May 2016, 2,292 inpatients were prospectively screened. In total, 1,466 patients were excluded because they were women and 201 because they were in hospital for reasons other than cLBP. Data for 525 males with cLBP were analyzed; 37 met the inclusion criteria and were prospectively enrolled: 24 without and 13 with active discopathy (Fig  1).
Effectiveness We expressed effectiveness in DALYs, which account both for years of life lost due to disability and years of life lost due to premature death  [30]. We calculated DALYs averted due to vaccination using three steps. The first step was to estimate dengue cases and deaths averted due to dengue based on the vaccine's effectiveness, using results of the two large-scale phase-3 efficacy trials. These were conducted in five Latin American countries on children aged at the start of follow-up between 9 and 16 years, and five Asian countries on children aged between 2 and 14 years. These trial data were used to estimate the main parameters of a transmission model: efficacy and duration of protection by age and serostatus at vaccination, transmission intensity, and interactions among serotypes.
24 ppm, for the lipidic extracts). Statistical data analysis was performed using R statistical software (https://www. r-project. org/). Metabolite identification was performed by comparison with pure compound spectra from to the Human Metabolome database (http://www. hmdb. ca), and confirmed through 2D-NMR experiments ( 1 H-1 H TOCSY, 1 H-13 C HSQC and 1 H-J-Resolved) (2). For quantification, we examined 8-20 cells magnified at x 2. 000 per group and counted the number of mitochondria per cell visualized in the sectioned image. Quantitative Real-Time PCR. For RNA analysis, total RNA from cells was extracted using TRIZOL (Life Technologies) and samples were reverse transcribed using random priming and Superscript Reverse Transcriptase (Life Technologies), according to the manufacturer's instructions.
The number of protein kinase inhibitors (PKIs) approved worldwide continues to grow steadily, with 39 drugs approved in the period between 2001 and January 2018. PKIs on the market have been the subject of many reviews, and structure-property relationships specific to this class of drugs have been inferred. However, the large number of PKIs under development is often overlooked. In this paper, we present PKIDB (Protein Kinase Inhibitor Database), a monthly-updated database gathering approved PKIs as well as PKIs currently in clinical trials. The database compiles currently 180 inhibitors ranging from phase 0 to 4 clinical trials along with annotations extracted from seven public resources. The distribution and property ranges of standard physicochemical properties are presented. They can be used as filters to better prioritize compound selection for future screening campaigns.
Several publications have already described the physicochemical properties and modes of binding of FDA-approved PKIs  [1, 2]. Likewise, a few databases recording data on PKIs are publicly available. As an example, the Kinase Profiling Inhibitor Database of University of Dundee  [3]  presents some bioactivity data for 243 commonly used signal transduction inhibitors. Robert Roskoski also compiled a small set of 39 FDA-approved protein kinase inhibitors  [4]. However, these sources of information often focus on approved inhibitors only (compounds having reached phase 4 clinical trials) and do not provide long-term visibility on the next generation of PKIs. Extending the scope to PKIs currently in the clinic (compounds under development from phase 0 to phase 3) would bring useful information on the state of the art and current trends in kinase inhibitor design.
Moreover, the databases mentioned above do not provide additional dynamic links to external databases or chemical information that could be exploited by chemoinformatics tools. Here, we have compiled the most comprehensive dataset on protein kinase inhibitors to date. We considered all protein kinase inhibitors whether approved or still in development phases and extracted relevant data available in the largest public chemical and clinical databases (see Materials and Methods section for details). By adding and updating monthly the dynamic links, our database represents a central hub to access any information related to PKIs. For example, the Protein Data Bank  [5]  ID of each PKI is collected and allows structural inspection, as well as annotation about the type of binding mode as soon as a new PDB structure is released.
An analysis of the molecular determinants of PKIs as well as their chemical and biological target spaces could provide guidelines for the design of the next generation of PKIs. With this goal in mind, we collected the largest set of kinase inhibitors complying with the following criteria: compounds should be currently in one development phase (from Phase 0 to Phase 4), and each should have a disclosed chemical structure as well as an International Nonproprietary Name (INN). We found 180 PKIs fulfilling these criteria. Physicochemical and topological properties of these compounds were computed and related to their biological target profile. Data are stored in a database named PKIDB and are updated on a monthly basis. The database is freely accessible on a dedicated website (http://www. icoa.
As observed in crystallographic structures, Type III MEK inhibitors bind the DFG-in conformation (Figure  2 , extended green surface) in complex with ATP (PDB ID 4AN2)  [9]. Finally, Type-IV inhibitors bind an allosteric pocket further away from the active site  [10]. In addition, we can distinguish two categories of inhibitors: reversible and irreversible (or covalent-reversible) inhibitors. Reversible inhibitors are typical ATP-competitive PKIs binding protein kinases through non-bonded interactions. Irreversible inhibitors (Figure  1a ,e) can covalently bind the receptor by exploiting a reactive nucleophilic cysteine proximal to the binding site. Using the above conventions, we manually annotated our PKI dataset using data from various public chemical databases. When available, crystallographic data were added, and visual inspection of the chemical structure was performed.
PKIDB Content and Interface The database (Figure  3 ) contains 180 PKIs described through twenty-one fields containing:  The PKIDB interface allows for browsing the data through a user-friendly spreadsheet-like interface. Columns can be filtered and sorted (Figure  3 ). Filters are composed of: For convenience, string filters are not case sensitive and a selection like "novartis||bayer" or "Novartis||BAYER" in column Applicants (Figure  3 , column 3) will match all inhibitors developed by the two pharmaceutical companies. A query example to select the seven approved Type 2 inhibitors is shown on Figure  3. More filtering examples are available online. In addition, sorting is available for all columns and can be used in combination with any filters. Ascending and descending sorting can be applied to sort alphabetically, numerically or alphanumerically.
We have chosen to describe the compounds collected in our PKI dataset from different viewpoints, moving beyond the mere calculation of the classical physicochemical properties. In addition to computing the distribution and boundaries of these properties, we report hereafter the results of a Principal Component Analysis aimed at mapping the chemical space of the different inhibitor types. We also compared the topology (3D shape) of the PKIs under development to the topology of approved PKIs using a Principal Moments of Inertia analysis. Then, we mapped the PKIs under development on a kinome tree to allow the exploration at a glance of the protein kinase space targeted by these compounds. Finally, we tried to capture the trends and dynamism of the pharmaceutical sector by examining the clinical pipeline of the major players in the kinase field.
Since current drugs do not always conform to Lipinski's rules, it is expected that PKIs may also offend some of the four criteria proposed by Lipinski. We checked to which extent the compounds in the PKI dataset violate the Ro5 and found that a significant fraction of them have properties falling outside Lipinski's boundaries (Table  1 ). Although 56% (or 63% depending on the method used to calculate the ClogP, Appendix A: Table  A1 ) of the compounds fully comply with all conditions, almost one third of the compounds (28%) violate a single rule and 16% of the compounds violate two rules. These numbers are quite comparable to the statistics obtained for the approved PKI subset. Going into more details, we looked at the individual components composing Lipinski's Ro5. Because the calculated logP is strongly dependent on the software used, the logarithm of the octanol/water partition coefficient was calculated using two different methods, the ClogP functionality included in the RDKit toolkit and the ClogP calculator from ChemAxon  [13]  (Figure  4f ,h and Table  2  and Appendix A: Table  A2 ).
e. , molecules in our case, into a 2D or 3D space. This space is delimited by factorial axes or principal components (PCs) formed by linear combinations of the original variables used to describe the individuals. The PCs are rank-ordered according to the fraction of the total variance accounted for by each. The graphs that are produced help understand similarities between molecules as well as correlations between variables (i. e. , descriptors). It is routinely used in the chemoinformatics field to analyze chemical datasets  [16]. We performed two separate PCAs on the following sets of compounds: the first set was built using the 180 PKIs collected in PKIDB augmented with 956 FDA-approved oral drugs. The goal was to determine whether the two classes of compounds could be efficiently discriminated into distinct groups; the second set contained only the 180 PKIs.
The second PCA plot (Figure  6 ) is a projection of the PKI dataset in the PC1/PC2 factorial plane. PKIs are labelled according to their type (Type-I, Type-I 1 2 , Type-II, Type-III and NaN for unknown kinase inhibitor Type). The variance explained by the three first principal components PC1, PC2 and PC3 is 36. 4%, 20% and 14%, respectively (56. 4% explained by PC1 and PC2 alone). The most contributing variables to PC1 are, in decreasing order of importance, the molecular weight (19. 9%), Labute's approximate surface area (18. 8%) and the number of rotatable bonds (15. 4%). Thus, PC1 primarily represents molecular size. For PC2, the most contributing variables are the number of aromatic rings (30. 8%), logP (26. 8%), and the fraction of sp3 hybridized C atoms (19. 2%). In this plot, inhibitors were colored according to their type of binding mode when the information was available.
Worldwide Pharmaceutical Companies Pipeline Overview of Kinase Inhibitors PKI clinical data could be extracted from the clinicaltrials. gov database. We queried the database using INN or generic name information of the PKIs in development or already on the market. Since most of the drugs receive an INN when they reach late-phase clinical studies, the group of PKIs in Phase 1 is most probably underrepresented. Figure  9  represents the distribution of the PKIs that we have collected, grouped by development phase and pharmaceutical company. It is important to note that the phases mentioned in Figure  9  represent the highest phase reached by a drug on any market. For instance, 37 protein kinase inhibitor drugs reached the U. S. market (FDA-approved drugs) but two other drugs, icotinib and baricitinib, entered only the Chinese and European market, respectively.
Discussion PKIDB compiles all marketed kinase inhibitors to date as well as kinase inhibitors currently in clinical trials. Only compounds that received an INN have been included in the database. Because INNs are usually requested after clinical development has been initiated, it is very likely that some compounds currently in development phase I are missing from our database. Presently, PKIDB contains 180 compounds along with annotations from multiple chemical-protein resources (see Material and Methods for details). The PKI dataset was analyzed from different perspectives to highlight features specific to these compounds. Calculation of physicochemical properties demonstrates that more than one third of the compounds violate at least one Lipinski's rule. The Rule of Five was derived from examination of the property distribution of existing orally-administrated drugs on the market before 2000s.
Gradual introduction of carbon bond saturation and stereogenic centers may help escaping from "flat land" and securing new intellectual property (IP) of future PKIs  [20]. The PMI analysis reveals that PKIs cover a narrower shape diversity as compared to oral drugs. One obvious explanation of this behavior comes from the nature of the two sets of compounds being compared: while oral drugs have shapes complementing a variety of binding pockets topologies, PKIs have been designed to target the highly conserved kinase domain. Although most kinases show an intrinsic plasticity, their binding site topology is remarkably conserved whether the protein is in DFG-in or in DFG-out conformation. Regarding the PKI dataset alone, approved PKIs cluster tightly around the rod edge with few exceptions whereas PKIs under development tend to explore wider territories.