dataesr/scientific-paragraphs-categorization huggingface dataset:
e Prediction performance on the ALMANAC  13  dataset is shown for a random, NAD baseline (using only cell line and drug identities as features) and full NAD model for no targeted agents (None, i. e. , two chemotherapeutic drugs), one targeted agent plus chemo (One drug), and combinations of two targeted agents (Both drugs)(mean +/-95% CI). ANOVA P-values: *P = 1. 322e-31 for different performance between baseline and full model; #: P = 5. 584e-5 for association between model performance and number of targeted drugs in the combinations performers to limit model complexity and improve model generalizability. Despite the complexity of the problem, many teams reached the upper-bound of performance levels based on variability in experimental replicates. This was further confirmed when top-performing models were applied to an independent dataset, demonstrating robustness to assay variability, and context heterogeneity.
Furthermore, the space of therapeutic combinations should be extended to include >2 drugs, and covering targets in independent cell types, such as subclonal tumor cell populations or cells of the tumor microenvironment and immune system  3. These approaches can be complemented by adaptive and sequential strategies reactive to monitoring of the patient tumor and physiology. Success in these areas will be dependent on the availability and access to large-scale data needed for model development and validation. Public-private partnerships-as exemplified by this Challenge and AstraZeneca's generous sharing of data with the research community-will be critical to future efforts.
After incubation plates were read by the Acumen laser scanner to detect the number of Sytox Green stained cells. The total fluorescent intensity across the well was then read and the number of dead cells calculated by dividing this total fluorescence by the fluorescence of a single cell. The plates were re-read on the Acumen to give a total cell count. A live cell count was then determined by subtracting the dead cell count from the total cell count. Quantifying combination synergy and antagonism. Synergy and antagonism were quantified in an automated way using freely available Combenefit software (v1. 31)  11. The approach implemented in Combenefit is based on quantifying synergy distribution by comparing a drugs combination experimental doseresponse surface to a modeled reference based on individual drugs dose-response curves.
Briefly, for each 6-by-6 matrix, monotherapy dose-responses were extracted and modeled as a sigmoidal curve via the Hill equation  46. A reference doseresponse surface is then generated by Combenefit based on the Loewe model of additive combinations the single drug dose-response curves. The experimental combination dose-response surface is then compared by the software to the modelgenerated one, resulting in a synergy distribution in concentration space. This synergy distribution is finally further summarized by integrating the synergy distribution in logarithmic concentration space. The procedure resulted in a single score (the result of this integration) for each combination. In vivo response class definitions. Response data for 62 treatments across ~1000 PDX models were derived from Gao et al.
8  Putative drug targets from the AZ-DREAM and Gao et al. 4, 8  dataset were utilized to identify overlap. As synergy scores were not available for the Gao et al. 4, 8  dataset, 'Best Response'' (complete response-CR, partial response-PR, stable disease-SD, progressive disease-PD) for each combination-PDX pair were extracted and compared with monotherapy 'Best Response'' of each drug in the combination on the same PDX model. This was represented numerically where CR = 4, PR = 3, SD = 2, and PD = 1. Synergy was assigned to a change of +2 or more, and Antagonism to a change of -2 or less. A change of +1, 0, or -1 was assigned Additive, considering an element of experimental variability. Cases where best response has been observed as a range over time (PR→→PD), the earliest response was considered as we hypothesize this to reflect in vitro response in a more realistic sense for comparison.
Drugs were grouped into pathways and biological processes manually according to their protein targets (Supplementary Data 1). Challenge organization. The Challenge consisted of two sub-challenges, each with multiple rounds: a leaderboard, validation, bonus, and collaborative round. SC1 had four leaderboard rounds that lasted 8, 6, 5, and 5 weeks, while SC2 had three leaderboard rounds that lasted 12, 7, and 5 weeks. Participants were given a leaderboard dataset to build a model and generate three prediction files per leaderboard round. Scores were returned to participants so that they can improve their model throughout these rounds for their one submission to the final round, which was scored against a held-out dataset. The final round lasted for 2 weeks, which was then followed by a 9 week bonus round and 10 week collaborative round.
Challenge pharmacology data splits. In SC1, participants were asked to predict drug synergy of 167 combinations in the panel of 85 cell lines. The synergy data of each drug combination was partitioned into three sets: a training dataset (3/6-50%), a leaderboard set (1/6-16. 7%), and validation set (2/6-33%) of treated cell lines. SC2 leveraged data for remaining 740 drug combinations not overlapping with those used in SC1, although data for some of the same drugs (in combination with different drugs), homologous drugs (i. e. , same target, but different chemical structure), and cell lines were included. A leaderboard set (370 combinations) and a final validation set (370 combinations) were randomly split, which are mutually exclusive from each other, as well as from SC1. Primary scoring metric of Sub-Challenge 1.
Literature evidence for the shortlisted combination-biomarker associations was identified through PubMed search. The aim was to identify published evidence of (i) the combination-biomarker association, (ii) the combination but not the specific biomarker, and (iii) either one of the targets and the biomarker association. The publications were further categorized into in vitro, in vivo, and preclinical studies. Publications that discuss the specific combination-biomarker association have been highlighted in red (Supplementary Data 3). In summary, synergy biomarker were derived from best performer models, and highlighted based external validation, as well as literature support. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Independent validation on O'Neil et al. Merck screen. In order to assess the utility of features and the predictability of the learning algorithms in new contexts, we provided the participants an independent large-scale oncology combination screen published recently 4. The O'Neil et al. 4  dataset consists of 22,737 experimental endpoints covering 583 doublet combinations across 39 diverse cancer cell lines. Thirty-eight experimental drugs and approved drugs were included in this combination screen using a 4-by-4 dosing regimen. Raw cell viability measures for each combination experiment were processed through Combenefit 11  to generate synergy scores as per the Challenge dataset. While there are 6 approved drugs, 49 targets, and 10 cell lines in common between the Challenge and O'Neil et al.
4  datasets, the total number of exact experiments (Drug A-Drug B-Cell line) overlapping is below 100, giving the participants a highly independent validation set for their prediction algorithms. This information was provided to best performing teams in the Challenge, along with a dictionary of curated chemical structures and putative targets for each. Prediction models were trained on the released Challenge dataset and made synergy score predictions on the O'Neil et al. dataset. Metrics for SC1 and SC2 were used to assess prediction performance. Individual prediction models. Full description and implementation of models used by teams in the final submission to DREAM can be downloaded from:Synapse. org/ AstraZeneca_Sanger_Drug_Combination_Challenge_Leaderboards [http://www. synapse. org/AstraZeneca_Sanger_Drug_Combination_Challenge_Leaderboards].
47 vitro response class definitions. Response scores defined by the Loewe synergy model were considered in ordered to define in vitro response classes. Synergism was defined as Loewe scores ≥20, Antagonism ≤ -20, and rest are classed as Additive. Mutations were called with CAVEMAN [https://github. com/cancerit/CaVEMan/]47and PINDEL [http://gmt. genome. wustl. edu/packages/pindel/].
Acknowledgements We thank the  Genomics of Drug Sensitivity in Cancer  and COSMIC teams at the  Wellcome Trust Sanger Institute  for help with the preparation of the molecular data,  Denes Turei  for help with Omnipath, and  Katjusa Koler  for help with matching drug names across combination screens. We thank  AstraZeneca  for funding and provision of data to the  DREAM Consortium  to run the challenge, and funding from the  European Union   Horizon 2020 research  (under grant agreement No  668858 PrECISE  to J. S. R. ), the  Joint Research Center for Computational Biomedicine  (which is partially funded by  Bayer AG)  to J. S. R. ,  National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences.
Data availability The data used for this study are available from the Genomics of Drug Sensitivity in Cancer (GDSC [http://www. cancerrxgene. org/downloads/]) repository  12  , the Catalog of Somatic Mutations in Cancer (COSMIC[http://cancer. sanger. ac. uk/cell_lines]) database  55  , the European Genome-phenome Archive (EGA) EGAS00001000978, Gene Expression Omnibus (GEO) GSE68379, ArrayExpress E-MTAB-3610, Synapse database synapse. org/DrugCombinationChallenge[https://www. synapse. org/ DrugCombinationChallenge] and the AstraZeneca Open Innovation Portal openinnovation. astrazeneca. com/data-library. html[https://openinnovation. astrazeneca. com/data-library. html].
47 vitro response class definitions. Response scores defined by the Loewe synergy model were considered in ordered to define in vitro response classes. Synergism was defined as Loewe scores ≥20, Antagonism ≤ -20, and rest are classed as Additive. Mutations were called with CAVEMAN [https://github. com/cancerit/CaVEMan/]47and PINDEL [http://gmt. genome. wustl. edu/packages/pindel/] 1. Synergistic efficacy: Combination treatment leads to better tumor regression than either monotherapy. 2. Synergistic non-efficacy: Combination response is better than either monotherapy but does not result in tumor regression. 3. Additivity: Combination response same as the better of either monotherapy responses. 4. Non-synergistic efficacy: Combination response weaker than the better of the monotherapy responses but results in tumor regression.
5. Antagonism: Combination response weaker than both monotherapies. Molecular characterization. The 85 cell lines were molecularly characterized, including: 1. Mutations from whole exome sequencing with Illumina HiSeq 2000 Agilent SureSelect (EGAS00001000978) 2. Copy-number variants from Affymetrix SNP6. 0 microarrays (EGAS00001000978) 3. Gene expression from Affymetrix Human Genome U219 array plates (E- MTAB-3610) 4. DNA methylation from Infinium HumanMethylation450 v1. 2 BeadChip (GSE68379).
LITERATURE SEARCH A narrative literature search was conducted in November 2018. The PubMed database was searched using terms related to the disease areas, biologic therapies and patient access. A second search was conducted to identify publications which assessed the impact of biosimilars on patient access and healthcare budgets. The full search strings are provided in Supplementary Table  1. Only articles in English published after 2008 were included. Additional references were identified through searching the bibliographies of retrieved articles.
Both feet showed shortening of the middle phalanx of digit V with abnormal position of the joint surfaces of the distal interphalangeal joints (Figures  2 E, F ). With informed consent, in accordance with the Declaration of Helsinki and approval from the Medical University Bioethics Committee (approvals no: KE -0254/182/2015 and KE -0254/229/2016), peripheral blood was obtained from the patient and his healthy sister with the purpose of DNA isolation. Molecular analysis of the TP63 gene using direct DNA sequencing showed the missense mutation p. G314E (NP_003713. 3: p. Gly314Glu, NM_003722. 4: c. 941G>A) (Figure  3 ). This mutation is not yet registered in any database, including HGMD (the Human Gene Mutation Database). It involves a highly conserved nucleotide and amino acid. Bioinformatics analysis using SIFT and MutationTaster software showed the pathogenic nature of the identified mutation.
Importantly, the needle filament must be able to signal the secretion machine for substrate switching since these mutants are able to secrete middle and late substrates. We hypothesize that these mutants are able to complemented with FLAG-tagged WT SipD (SipD+) or a triple mutant (SipD D320R/D323K/S327R ) incapable of binding to the needle substructure (SipD-tri). (C) Cultured epithelial cell invasion ability of S. Typhimurium strains expressing the indicated constitutive PrgI mutants. Numbers represent the percentage of the inoculum that survived antibiotic treatment due to internalization and are the mean ± standard deviation of three independent experiments normalized to WT, which was set to 100%. The underlying data for this panel can be found in S3 Data. T3SS, type III secretion system; WT, wild-type.
The LI-COR Odyssey imaging system (Lincoln, Nebraska) allowed for the infrared fluorescent detection of DyLight conjugated secondary antibodies (emission 800 nm [ThermoFisher Scientific, Waltham, Massachusetts]). Quantitation of specific bands was performed with Odyssey v3. 0 software (LI-COR). To determine the secretion profile for each mutant, the abundance of each substrate was normalized relative to the wild-type control. All values represent the mean ± the standard deviation of three independent experiments.
Typhimurium lipopolysaccharide (LPS), and incubated overnight at 4 ˚C. In the following morning, the labeled bacteria were washed three times with 1 × PBS and subsequently probed with conjugated antibodies Alexa Fluor 594 anti-rabbit and Alexa Fluor 488 anti-mouse for the detection of LPS and SipD, respectively (Invitrogen, Carlsbad, California), and coverslips were mounted onto slides. Immunofluorescence microscopy was performed on a Nikon TE2000 microscope, and images were acquired using an Andor Zyla 5. 5 sCMOS camera (Belfast, United Kingdom) run by the software Micromanager (https://www. micro-manager. org).
Bioinformatic analyses All 3D models of mutants for electrostatic calculations (see below) were predicted by the SWISS-MODEL server. The 23-mer needle structure of PrgI served as the template (PDB code: 2PLZ). Electrostatic surface potentials of the models were calculated using APBS software  [60]. The molecular graphics program PyMol was used to generate all structural representations. The solvent accessible surface area of all of the residues in PrgI in relation to the needle was determined using calc-surface  [61]. For each residue, the surface area was translated into a percentage by dividing the calculated surface area of a particular residue of PrgI by a reference surface area corresponding to the residue in a Gly-X-Gly peptide (X is any residue). The atomic coordinates of the needle structure (PDB code: 2PLZ) were used for these calculations.
Cryo-EM sample freezing and data acquisition All protein samples and freezing conditions were screened on a Tecnai F20 electron microscope (FEI company) operated at 200 kV. For the imaging of fully assembled needle complexes three microliters of sample was applied onto glow-discharged holy carbon C-flat 2/1 400-mesh Cu grid (Protochips, Inc) with a thin carbon layer and plunged into liquid ethane using Vitrobot Mark III (FEI company) or IV (Thermo Fisher) at 10 ˚C and >95% humidity. Cryo-EM data were recorded on a Titan Krios electron microscope (Thermo Fisher) operating at 300 kV accelerating voltage, at a calibrated magnification of 47,000 × g using a K2 Summit direct electron detector (Gatan, Inc. ) in super resolution mode, corresponding to 1. 715 Å per physical pixel. In total, 1,424 movies with defocus values in the range of -1.
Model building and fitting The initial template was derived from in vitro polymerized PrgI filaments solved by SSNMR (PDB ID: 2LPZ). Models were docked into the EM density maps using Chimera, followed by iterative manual adjustments in Coot  [66]  and real-space refinements with Phenix  [50]. The rise (translation) and twist (rotation) for the helices of each model in SSNMR ensemble (PDB ID: 2LPZ) were calculated with Chimera (match showMatrix command) using two adjacent subunits in the models. Figures of structures were generated with Coot, PyMOL, and Chimera.
Three sections per patient were evaluated in a blinded fashion for CEACAM6 expression in a Nicon-Eclipse-E800 fluorescence microscope equipped with a Nikon DS-Ri1-digital camera. In total, 20 pictures/section were obtained, and three epithelium types were identified: VE, FAE, and adjacent-VE. The adjacent-VE is defined as the VE lining the first villus next to the FAE, and differs from the VE further away from the dome; there is higher expression of the cell adhesion protein and bacterial co-receptor CD9 in adjacent-VE compared with villi further away, which expresses no, or very little, CD9. 16 By using ImageJ software [NIH] the epithelial linings in each picture were encircled and a mean fluorescence intensity was calculated. From each patient/control, a mean value was calculated from the FAE, VE, and adjacent-VE, and then a median value was calculated for all patients/controls.
Statistical analysis According to the D'Agostino-Pearson omnibus normality test, the data were judged as non-parametric, and accordingly data are given as the median [25th-75th percentiles]. Comparisons between two groups were carried out using the Mann-Whitney U test. The Wilcoxon matched-pairs signed-rank test was used for paired comparisons [in vitro TER and individual comparisons of anti-CEACAM6 effects ex vivo]. Differences with p < 0. 05 were considered significant. Statistical analyses were performed using GraphPad Prism 7 Software [CA, USA]. 2 ]. Translocation of LF82 was increased by 42. 9% in the co-culture compared with that in Caco-2cl1, p < 0. 05 [Figure  2A ]. The greater passage of LF82 was confirmed by CFU counting of the basolateral media, which showed 50% more CFUs in the co-culture compared with in Caco-2-cl1, p < 0.
Discussion Accumulating data indicate a central role for the intestinal microbiota in triggering inflammation in CD patients. 23] 28  Here, we show for the first time that translocation of AIEC strain LF82 across the intestinal epithelium is enhanced in patients with CD compared with non-IBD controls, extending data from previous studies with epithelial cell lines. The increased uptake of wildtype LF82 in patients with CD was counteracted by anti-CEACAM6 antibodies and was not apparent in the strain lacking LPF, suggesting use of CEACAM6 or LPF antibodies could be a potential therapeutic approach to preventing AIEC-evoked recurrence in CD. Our initial in vitro experiments confirmed previous findings,  32  showing higher LF82 translocation in the FAE co-cultures compared with Caco2-cl1 mono-cultures.
Ussing experiments on human tissues revealed higher LF82 translocation compared with LF82 ∆lpfA in FAE of both CD patients and controls. In VE, there was only a significant difference in patients with CD. These findings support the contention that LPF is crucial for E. coli LF82 translocation through the intestinal mucosa; moreover, LPF may be more important in VE during inflammation, since non-IBD controls showed no significant difference in passage between LF82 and its mutant. To our knowledge, there is no data supporting a direct interaction between LPF and CEACAM6, but the fact that LPF promotes translocation in the absence of CEACAM6 suggests that LPF is not using this protein as a receptor. Our findings might be important for understanding the development of CD, since the earliest observable signs of CD are aphthoid lesions  11  preceded by ultrastructural erosions in the FAE.
8, 35 n conclusion, as illustrated in Figure  8 , this study showed higher translocation of E. coli LF82 in patients with CD compared with controls, suggesting alterations in the barrier to LF82 in patients with CD. Our results further demonstrated that both bacterial LPF and host [CEACAM6] factors facilitate uptake and translocation of LF82, especially through the FAE. Our data provide insights into CD pathophysiology and might help elucidate and define novel therapeutic targets in CD to prevent recurrence before clinical symptoms and complications occur. This article is dedicated to Professor Arlette Darfeuille-Michaud, in loving memory. 56, 57.
Acknowledgments We thank all of the patients for their participation in this study. We also thank  Mrs Ylva Braaf ,  Mr Carlos D. L. Magana  and  Mr Martin E. Winberg  at  Linköping University  for assistance in the  Ussing laboratory , and  Mr Felipe Meira de Faria ,  Linköping University , for help regarding western blot analyses. Parts of this work were presented at the Falk symposium 'IBD: Microbiota versus the barrier', Stuttgart, 2013 and at the  Falk  symposium 'IBD 2014: Thinking out of the box', Paris, 2014.
A major limitation of this encapsulation strategy has been a foreign-body reaction and a subsequent fibrosis around the capsules limiting the long-term function of the graft. Efforts have thus been made to optimize the formulation of the alginate particles  (Strand et al. , 2017). In August 2014, the company ViaCyte launched the STEP ONE* trial (NCT02239354) for evaluating the safety and efficacy of Via-Cyte's PEC-Encap in type I diabetes. The product consists of pancreatic progenitor cells delivered in a subcutaneously implanted and retrievable immune-encapsulation device (Encaptra). In June, 2018, the company reported encouraging 2-year outcomes in 19 patients with no safety issues, no evidence for allo-immunization to donor cells in the absence of immunosuppression, and differentiation into insulin-producing beta cells and glucagon-producing alpha cells in several explants.
The delivery system is now undergoing changes to enhance long-term cell engraftment, and the company hopes to resume the STEP ONE trial enrolment in 2019. While islet survival may be compromised by their destruction by endogenous immune cells, hypovascularization of the graft can further contribute to hamper their sustained therapeutic benefit. Efforts have thus been made to enhance microvascularization of the encased islets, which has translated clinically into two different approaches. In 2017, ViaCyte has started the 55-patient PEC-Direct trial (NCT03163511) in which the company's ESC-derived pancreatic progenitors are delivered in a device designed to allow their direct vascularization (Table  S1 ). A potential drawback of the open design of this device is that the patients need to be immunosuppressed.
Alternatively, Sernova has developed a cell pouch that is implanted subcutaneously to enable the creation of a vascularized environment intended to nurture islets, which are then transplanted in the pouch 2-12 weeks later. A trial testing this concept (NCT01652911) has included 3 patients and is now terminated, but no outcome data have been reported yet. A subsequent phase I/II nonrandomized, unblinded, singlearm study was announced in May 2018. A last challenge is that, in vivo, beta cells do not exist isolated but clustered within three-dimensional niches also harboring nonendocrine vascular cells and extracellular matrix components, hence the possible interest of an in vitro co-aggregation of allogeneic (human) islet cells with MSCs, which has resulted in their engraftment in the omentum of immunocompetent mice and allowed glycemic control without immunosuppression  (Westenfelder et al.