dataesr/scientific-paragraphs-categorization huggingface dataset:
OLGA runs over the translated amino acid CDR3 sequences while IGoR runs over nucleotide CDR3 (dashed blue line). In order to compare OLGA to how long it would take IGoR to compute Pgen of amino acid CDR3s we estimate by multiplying the IGoR runtime of single nucleotide sequences (dashed blue line) by the number of nucleotide sequences that translate to the given amino acid sequence (yielding the solid blue line). Monte Carlo runtime is estimated for a dataset of 100,000 sequences with an estimated coverage of 66% of sequences having at least one count. OLGA vastly outperforms both direct enumeration (est. IGoR) and Monte Carlo.
S5:  A) The runtime of Mont Carlo Pgen estimation scales as 1/Pgen while OLGA will scale with the number of sequences. This predicts a number of sequences for the 'crossing point' where the runtime of Monte Carlo Pgen estimation is comparable to OLGA Pgen for sequences with Pgen above some cutoff. For datasets with more sequences than these curves, Monte Carlo estimation may be faster (depending on the level of Poisson noise considered tolerable), while below these curves OLGA is always faster. We plot this as a function of CDR3 length where the Pgen cutoffs are determined to ensure that on average some fraction (66%, 90%, 95%, and 99%) of the sequences at that length get covered by the Monte Carlo estimation. B) log 10 (Pgen) probability density distributions for a few examples of CDR3 lengths.
These curves are used to determine the MC Pgen cutoffs per CDR3 length by determining, for a given curve, when the area under the curve and right of a Pgen cutoff matches the sequence coverage fraction. It is true that the computation time for MC estimates scale as 1/P gen and not with the number of sequences. Thus, there is a hypothetical number of sequences when MC is faster than OLGA if we are willing to accept noisy estimates and to entirely miss some fraction of the CDR3s. This 'crossing point' number of sequences is plotted in Fig  S5A  and corresponds to completely unrealistic numbers of sequences, highlighting the fact that OLGA will not only give a more reliable P gen , even for very unlikely sequences, but is also much faster than MC even for short, high P gen , sequences. So, even overlooking the drawbacks and imprecision of MC estimation, for plausible sized datasets OLGA is still dramatically faster than MC.
[38]S6: Generation probability distributions for TRB CDR3 sequences taken from three different sources (Sims et al. [38], Wu et al. [47], Wang et al. [44]), compared to DNA RepSeq data from Emerson et al. [9](black curve with standard deviation), using a model inferred from[9]. All distributions have approximately the same shape, with a slight bias in the data from Sims et al. [38], indicating how robust is the distribution. Data from Sims et al. are identified in the SI of[38]as IDs N01 (normal cortex) and G10 (glioblastoma). Data from Wang et al. is identified by Short Read Archive (SRA) accession numbers SRR030702 (naive) and SRS007450 (memory). See also Fig. S7.
2 FIG. 5 : 25 FIG. 5: Distributions of TRB generation probabilities P aa gen for sequences in the VDJdb database that bind to any epitopes of 6 different viruses (colored curves). For comparison, we plot (black curve) the same distribution for the unsorted TRB repertoire of a typical healthy subject; the 2σ variance represents biological variability across multiple individuals (data from Emerson et al. [9] ).
2 FIG. S10 : 2S10 FIG. S10:Pgen distributions for human CD4+ T cell repertoires that have been incubated with three different pathogens (Becattini et al. [1] ): the fungus Candida Albicans (CA), the bacterium Mycobacterium Tuberculosis (MT), and a toxin protein Tetanus Toxoid (TT). For comparison, the background distribution from human peripheral blood TRB sequences from Emerson et al. [9]  (with its two sigma variation across multiple individuals) is also plotted. The plotted curves are averages over data from individual donors. The sizes of the responsive T cell repertoires are quite variable: the CA dataset has 39934 clonotypes from 5 donors, the TT dataset has 26573 clonotypes from 4 donors, and the MT dataset has 5082 clonotypes from 2 donors. The generation model was infered from Emerson et al.
[38]S6: Generation probability distributions for TRB CDR3 sequences taken from three different sources (Sims et al. [38], Wu et al. [47], Wang et al. [44]), compared to DNA RepSeq data from Emerson et al. [9](black curve with standard deviation), using a model inferred from[9]. All distributions have approximately the same shape, with a slight bias in the data from Sims et al. [38], indicating how robust is the distribution. Data from Sims et al. are identified in the SI of[38]as IDs N01 (normal cortex) and G10 (glioblastoma). Data from Wang et al. is identified by Short Read Archive (SRA) accession numbers SRR030702 (naive) and SRS007450 (memory). See also Fig. S7. Probability Density 0. 10 0. 15 0. 20 0. 25 0. 30 Wu et al: active Crohn's (RNA) Sims et al: normal cortex (RNA) Sims et al: glioblastoma (RNA) Wang et al: naive (RNA) Wang et al: memory (RNA) Average baseline repertoire (DNA) Average baseline repertoire ± 2 (DNA) 0.
Conflict of interest statement: The authors have declared that no conflict of interest exists. TRIzol (Ambion), followed by clean-up using RNeasy MinElute (Qiagen). RNA integrity was determined using an Agilent 2100 Bioanalyzer (Agilent Technologies; RIN: 10 ± 0 (mean ± SD)). 1µg total RNA (n = 32) was shipped on dry ice to the Australia Genome Research Facility (AGRF) for library preparation (TruSeq Stranded mRNA Library Prep Kit, Illumina) and sequencing (Illumina HiSeq2500, 50-bp singe-end reads, v4 chemistry). The raw sequencing data are available from GEO (accession; GSE140143).
While co-administration of the JO-4 with effector molecules is an attractive model with substantial scientific support, the ability to produce a conjugatable junction opener with a specific site for covalent attachment to effector molecules would confer further production and deployment advantages. We envision that our designer junction openers would enable nanoparticle delivery to multiple cancers/tumors and may also be useful for chimeric antigen receptor (CAR) T-cell targeting. With respect to cancers, depolarization of cells during EMT as The structures are adapted from that of PDB Accession # 1H7Z_A as reported previously  11, 12. well as upregulation in DSG2 expression in tumors relative to normal tissues makes for an attractive target for our molecules. Our lead JO-4 derivative, termed JOC-x (for Junction Opener Conjugated to x), could be directly attached to tumor imaging molecules such as europium or further conjugated with radionuclides for direct ionization of the tumor microenvironment.
Domains of DSG2 are cleaved and shed by the action of matrix metalloproteinases in a process that is enhanced by treatment with our junction opener constructs. This shed DSG2 can be quantified using a simple ELISA  20. Over the course of 48 hours, JO-4, GB3 V239D , or GB7 V239D all resulted in increased DSG2 shedding compared to untreated cells (data not shown). For a more accurate measurement of DSG2 binding, we compared the avidities and association/dissociation rates of JO-x derivatives to DSG2. Surface plasmon resonance (SPR) analysis was performed by immobilizing recombinant human DSG2 (rhDSG2) onto a Biacore sensor chip and flowing JO-4, GB3 V239D , or GB7 V239D across the surface. A roughly 20-fold more avid binding of DSG2 was determined for GB7 V239D compared to JO-4 (0. 58 nM and 11.
Methods Cloning and production of JO-1 derivatives. All genes used for this study were designed in silico using the software Serial Cloner v2. 4. 1. First generation JO derivatives, designated GB1-10, were based on the original JO-1 sequence as described previously  7, 35. The plasmid pET29a (Novagen) was linearized for Gibson cloning using outward facing primers pET29a_GB_UB (5′-TCCTCTCATATGTATATCTCCTTC) and pET29_GB_DT (5′-CTTAATTAGCTGAAATCACTAGT) using a 1/10000 dilution of plasmid as template in the PCR reaction. GB sequences were appended with nucleotides corresponding to the overlap regions of pET29a entry vector and were synthesized as gBlocks ™ by Integrated DNA Technologies (Coralville, IA). Gibson cloning of gBlocks into the pET29a entry vector was performed using the Gibson Cloning Master Mix (New England Biolabs, Ipswich, MA) per manufacturer's instructions.
The following day, GFP fluorescence was measured from the bottom read orientation at 475 nm Excitation and 505 nm Emission using a SpectraMax i3x plate reader (Molecular Devices, Inc. , Sunnyvale, CA). Data were plotted using Graphpad Prism 7 (GraphPad Software, Inc. , La Jolla, CA) and IC 50 values determined using a 5-parameter non-linear fit of the sigmoidal curves in Softmax Pro software (Molecular Devices). SPR analysis of binding affinities to rhDSG2. SPR analysis was performed at 25 °C using a Biacore 3000 instrument (GE Healthcare, Pittsburgh, PA). For immobilization of DSG2, rhDSG2 (10 μg/ml) was captured on a CM5 sensor chip using amine coupling chemistry until a coupling level of 6000 RU was obtained. GB7 V239D and GB3 V239D binding were measured in 10 mM HEPES, 150 mM NaCl, 0. 005% surfactant P20, 2 mM CaCl 2 pH 7.
For association data, proteins were injected at a flow rate of 15 μL/min for 180 seconds. Following the association phase, buffer was flowed at 15 μL/min for an additional 150 seconds to measure dissociation. Binding curves were analyzed using BIAEvaluation software (GE Healthcare) and data was fit to a 1:1 Langmuir interaction model. Affinities were mathematically derived by dividing the dissociation rate by the association rate.
Size-exclusion chromatography and multi-angle light scattering analysis (SEC-MALS). Size-exclusion chromatography was performed on an Agilent 1200 HPLC system (Agilent Technologies, Santa Clara, CA) with GE HealthCare Superdex 200 Increase 10/300 GL prepacked column and 1X TBS at 1 mL/min. JO-4, GB3 V239D , and GB7 V239D were at concentrations of 2. 5 mg/mL, 2. 8 mg/mL, and 2. 5 mg/mL in PBS with 5% glycerol, respectively, with 100 μL of each injected. Protein was measured via UV absorbance at 280 nm and sizes determined based on multi-angle light scattering (MALS), with concentration determined using differential refractive index. Analyses were performed using a miniDAWN TREOS MALS detector and Optilab T-rEX differential refractometer using ASTRA software (Wyatt Technology Corporation, Santa Barbara, CA), with the weighted average of the molecular weight reported in g/mol (Mw).
Acknowledgements This research was funded by grants from the  National Institutes of Health Small Business Grants   1R43CA183379-01  and  2R44CA162582-02  awarded to D. C. and A. L. We are grateful to the  CNRS  for the PICS travelling grant between Grenoble and Seattle. We are grateful to  Dr. Jeff Guderian  at the  Infectious Disease Research Institute  for performing the HEK Blue TLR3 signaling assays. We are also grateful to the  University of Washington Mass Spectrometry Core Facility  for analyzing our JOC-DOTA conjugates.
Western blotting analysis Proteins were resolved using 4-12% n-polyacrylamide electrophoresis Bis-Tris gels (Life Technology, Carlsbad, CA, USA) and electro-transferred to nitrocellulose membranes. After blocking in phosphate-buffered saline (PBS)-0. 1% Tween-20 and bovine serum albumin (BSA), the membranes were immunostained with appropriate antibodies and horseradish peroxidase-conjugated secondary antibodies. Immunoreactive bands were visualized by enhanced chemiluminescence (PI32209; Thermo Fisher Scientific) with a Syngene camera. Densitometric analyses of immunoblots were performed using Gene-Tools software.
Immunofluorescence microscopy After the above treatments, the cells were placed onto poly-L-lysine-coated slides. Cells were then fixed in 4% formaldehyde for 8 min, and then for 5 min at 4 °C with 100% methanol. The cells were then permeabilized for 5 min with PBS containing 0. 5% Triton X-100 and saturated for 30 min in PBS-5% BSA. The LC3B antibody (2 µg/mL), in PBS-5% BSA, was added 1 h at room temperature, followed by washing and 30 min of incubation in secondary antibody conjugated with Alexa-488, Alexa-555, or Alexa-647 (Invitrogen). Slides were mounted in Prolong Gold Antifade Mountant with 4′,6-diamidino-2phenylindole (DAPI) (Invitrogen) and visualized on a Zeiss LSM710 or Zeiss LSM780 microscope using ZEN 2012 software. For quantification, fields were chosen arbitrarily based on DAPI staining and the number of LC3B dots per cell of at least 50 cells was determined using Image J software.
RNA microarray and bioinformatics analyses Total RNA were extracted form TF-1 cells expressing either the wild-type KIT or the mutant KIT D816V in independent triplicate using the Qiagen kit according to manufacturer's instructions. RNA purity was monitored with NanoDrop 1ND-1000 spectrophotometer and RNA quality was assessed through Agilent 2100 Bionalyzer with RNA 6000 Nano assay kit. No RNA degradation or contamination were detected (RIN > 9). One hundred nanograms of total RNA were analyzed on Affymetrix GeneChip© Human Gene 2. 0 ST Array using the Affymetrix GeneChip© WT Plus Reagent Kit according to the manufacturer's instructions (Manual Target Preparation for GeneChip® Whole Transcript (WT) Expression Arrays P/N 703174 Rev. 2). Arrays were washed and scanned, and the raw files generated by the scanner was transferred into Partek© Genomics Suite for preprocessing (with default partek option).
Quality control (boxplot, clustering and PCA) and differential expression analysis (with eBayes function, LIMMA package) were performed in R/CRAN environment. Mapping between transcript clusters and genes were done using annotation provided by Affymetrix (HuGene-2_0-st-v1. na34. hg19. transcript. csv) and the R/Bioconductor package hugene2. 0sttranscriptcluster. db. p-values generated by the eBayes function were adjusted to control false discovery using the Benjamini and Hochberg's procedure GGSEA analysis were performed using GSEA v3. 0 tool developed by the Broad Institute. Transcriptomic data have been deposited at the Gene Expression Omnibus database (GSE130625).
Acknowledgements We thank all members of the GAEL ( Gaël Adolescent Espoir Leucémie ) association. We acknowledge  Valérie Duplan-Eche ,  Fatima L'Faqihi-Olive , and  Manon Farcé  for their technical assistance in the flow-cytometry core facilities at INSERM  UMR1043  and  INSERM UMR1037. We are grateful to  Laetitia Ligat  for technical assistance in the imagery facility at INSERM  UMR1037. This work was supported by  Ligue nationale contre le Cancer  and  Clément Larrue  is a recipient of the  Ligue Nationale contre le Cancer. We thank  Patrice Dubreuil  ( CRCM, Marseille, France ) for providing the specific leukemic cell lines: TF-1 and TF-1  KIT D816V. This work received a grant from the  French government  under the " Investissement d'avenir" program  ( ANR-11-PHUC-001 ), the  Institut National du Cancer  ( INCA-PLBIO 2012-105 ), and the  InnaBioSanté foundation  ( RESISTAML  project).
Modulating the CXCR4/CXCR7/CXCL12 Chemokines Axis in Solid Cancers Chemokines are important signals to orient cell migration within the organism, in particular for the immune cell infiltration into the tumor  [103]. However, cancer cells can also use these signals to favor their migration into the blood stream. The CXCR4/CXCR7/CXCL12 chemokine axis is physiologically critical for hematopoiesis and T cell homing into inflammation sites. Chemokines CXCR4 and CXCR7 are overexpressed in many solid cancers and involved in metastasis towards CXCL12-rich tissues (such as lung, liver, and bone marrow)  [104, 105]. CXCR7 can also induce angiogenesis and tumor growth independently of its effect on tumor cell migration. CXCR4 blocking strategies using antibodies  [106]  or small inhibitors  [107, 108]  have proven to be effective to reduce metastatic burdens and a phase IIb clinical trial for a CXCR4 antagonist in combination with anti-PD1 therapy in advanced pancreatic cancer is ongoing (NCT02907099).
Cancer Cell Detection The most advanced molecule in non-invasive imaging is an anti-Her2 nanobody used to detect Her2 expression in breast cancers via positive electron tomography-computed tomography (PET/CT). A phase I trial demonstrated a quick elimination, allowing measurement at 60-90 min after injection  [124]. An ongoing phase II clinical trial investigates the uptake of the radiopharmaceutical 68-GaNOTA-Anti-HER2 nanobody in brain metastasis using PET/CT imaging (NCT03331601). Many nanobodies, including anti-CD20  [125] , anti-Dipeptidyl-Peptidase 6  [126] , and anti-HER3 nanobodies  [127]  are currently being studied.
Cancer Cell Detection The most advanced molecule in non-invasive imaging is an anti-Her2 nanobody used to detect Her2 expression in breast cancers via positive electron tomography-computed tomography (PET/CT). A phase I trial demonstrated a quick elimination, allowing measurement at 60-90 min after injection  [125]. An ongoing phase II clinical trial investigates the uptake of the radiopharmaceutical 68-GaNOTA-Anti-HER2 nanobody in brain metastasis using PET/CT imaging (NCT03331601). Many nanobodies, including anti-CD20  [126] , anti-Dipeptidyl-Peptidase 6  [127] , and anti-HER3 nanobodies  [128]  are currently being studied.
Implementation details Propositions 2 and 3, in appendix A, provide fast-to-evaluate expressions for the criteria, however their computation requires numerical approximations. See appendix A for more details. New points are obtained by minimizing numerically the selected criterion, we use the genetic algorithm using derivatives of  Mebane and Sekhon (2011)  to solve the optimization problem. The strategies are implemented in the R programming language (R Core  Team, 2018)  in the package KrigInv  (Chevalier et al. , 2014c). The function EGIparallel in KrigInv produces adaptive designs such as the one in figure  1b  by automatically optimizing the criterion J t2 n. KrigInv interfaces with DiceKriging  (Roustant et al. , 2012)  for GP modeling, rgenoud  (Mebane and Sekhon, 2011)  for the optimization routine and anMC  (Azzimonti and Ginsbourger, 2018)  for conservative estimates.
The latter two require large volumes of data to learn the model and verify its applicability. In crisis management, such a volume of data relating to many "similar" crises is currently non-existent. We have therefore turned to decision support models that have the ability to work with small datasets and, in addition, allow for some explanations of the proposed decisions. Finally, to make information accessible to decision-makers who are not computer scientists but who make decisions with major issues, a possibility is to use decision trees  [25]  that have the specificity of representing a set of choices, in the graphic form of a tree. The different possible decisions are located at the ends of the branches (the "leaves" of the tree), and are reached according to decisions made at each stage.
It is based on explicit knowledge, relating to the behavior of populations in case of alert or crisis. We therefore propose an approach for the design of a structured decision support tool in four steps: (1) Data collection from heterogeneous sources, (2) indicators selection and aggregation, (3) analysis based on a decision-making model and (  4 ) results interpretation (Figure  1 ). The aim is to provide a systemic view of the behavior of populations in crisis, to provide an indication of the likely behavior of a population in response to an alert for a crisis announced in a given city. The first step in the process is to build a knowledge base on past crises. This database will be a collection of data from heterogeneous sources obtained from research on social networks, field surveys based on questionnaires, vulnerability studies, weak signal sensors or individual and collective motion sensors, and search on the web.
The data collected correspond, among others, to the behavioral factors as well as to the actual behavior of the populations during the crisis. From the data collected in the first step, we determine a second step of indicator selection and aggregation to obtain a new set of indicators that are as independent as possible from each other. The selection is obtained from interviews with experts and decision-makers or from algorithms. The third step is to use this new set of indicators to analyze data on population behavior factors to determine actual behaviors for a given crisis. We choose to use algorithms to generate decision rules to perform this analysis, as this type of model allows an easily understandable reading of the results for the decision-maker. One possible representation, among others, would be a decision tree.
Step 1: (Heterogeneous) data collection The case studies were selected from the accidents listed in the ARIA database (analysis, research and information on accidents) which lists more than 46000 accidents occurred mainly in France but also abroad. The accidents involved are the result of industrial activities, the transport of hazardous materials, the distribution and use of gas, pressure equipment, underground mines and storage facilities, and dikes and dams. This database is developed by the Ministry of Ecological and Solidarity Transition. It is available free of charge on the website www. aria. developpement-durable. gouv. fr. The 9 case studies that we selected stem from events that occurred in France between 1981 and 2013 and had human and social consequences of 5 or 6 on the European scale of accidents, ranging from 0 to 6.
This scale created in 1994 for the application of the SEVESO directive (on the control of major-accident hazards involving dangerous substances) is based on 18 technical parameters intended to characterize the Figure  1 : Our decision support process effects or consequences of accidents. Each of these parameters has 6 levels. In France, the European scale is represented according to 4 indices including human and social consequences which take for example the total number of deaths, wounded with hospitalization superior to 24h, residents evacuated or confined to their homes, deprived of potable water… We have collected some data in the ARIA database. The rest of the data has been obtained from various databases made available on institutional sites and from newspaper archives: • www. georisques.
We used the ETHNO software that was specifically developed for researchers to implement Event-Structure Analysis  (Corsaro and Heise 1990). First, we coded the stakeholders' facts as they described them in their narratives. Second, we built a chronology of these facts (table  2 ). And third, we used the ETHNO software to generate an Event Structure Analysis graph. Generating such a graph requires respecting the following steps: 1) we coded all the facts of the chronology to define 'actions'; 2) we linked actions by answering Boolean questions proposed by the software such as 'Does B require A or a similar action?'. Answers must be Yes or NO; 3) the ETHNO software automatically tested the chart of actions. If any non-logical links are revealed, the software proposes some corrections to the researcher.
Data collection Our empirical data consists of publicly available information. We used two types of sources: documentation and archival records. All the data collected cover a specific period of time (from 2012 to 2016). Data were collected in their original language, namely French for the French part and English for the British part. Events used to build the project history were first based on press articles which were then cross-checked and deepened against official sources (table  1 ). On the French side, the data were drawn from the sources of the main stakeholder studied (namely EDF), supplemented by data from a key partner (namely Areva). We also gathered data from French state sources such as the French government, the National Assembly and the Court of Auditors. On the British side, our data were drawn from the British Government represented by several of the ministries involved in the project.