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Background One of the most challenging tasks for bladder cancer diagnosis is to histologically differentiate two early stages, non-invasive Ta and superficially invasive T1, the latter of which is associated with a significantly higher risk of disease progression. Indeed, in a considerable number of cases, Ta and T1 tumors look very similar under microscope, making the distinction very difficult even for experienced pathologists. Thus, there is an urgent need for a favoring system based on machine learning (ML) to distinguish between the two stages of bladder cancer. Methods A total of 1177 images of bladder tumor tissues stained by hematoxylin and eosin were collected by pathologists at University of Rochester Medical Center, which included 460 non-invasive (stage Ta) and 717 invasive (stage T1) tumors. Automatic pipelines were developed to extract features for three invasive patterns characteristic to the T1 stage bladder cancer (i.e., desmoplastic reaction, retraction artifact, and abundant pinker cytoplasm), using imaging processing software ImageJ and CellProfiler. Features extracted from the images were analyzed by a suite of machine learning approaches. Results We extracted nearly 700 features from the Ta and T1 tumor images. Unsupervised clustering analysis failed to distinguish hematoxylin and eosin images of Ta vs. T1 tumors. With a reduced set of features, we successfully distinguished 1177 Ta or T1 images with an accuracy of 91–96% by six supervised learning methods. By contrast, convolutional neural network (CNN) models that automatically extract features from images produced an accuracy of 84%, indicating that feature extraction driven by domain knowledge outperforms CNN-based automatic feature extraction. Further analysis revealed that desmoplastic reaction was more important than the other two patterns, and the number and size of nuclei of tumor cells were the most predictive features. Conclusions We provide a ML-empowered, feature-centered, and interpretable diagnostic system to facilitate the accurate staging of Ta and T1 diseases, which has a potential to apply to other types of cancer.

Objectives To identify the first time point of an MRI-verified response to certolizumab pegol (CZP) therapy in patients with rheumatoid arthritis (RA). Methods Forty-one patients with active RA despite disease-modifying antirheumatic drug therapy were randomised 2:1 to CZP (CZP loading dose 400 mg every 2 weeks at weeks 0–4; CZP 200 mg every 2 weeks at weeks 6–16) or placebo→CZP (placebo at weeks 0–2; CZP loading dose at weeks 2–6; CZP 200 mg every 2 weeks at weeks 8–16). Contrast-enhanced MRI of one hand and wrist was acquired at baseline (week 0) and weeks 1, 2, 4, 8 and 16. All six time points were read simultaneously, blinded to time, using the Outcome Measures in Rheumatology Clinical Trials RA MRI scoring system. Primary outcome was change in synovitis score in the CZP group; secondary outcomes were change in bone oedema (osteitis) and erosion scores and clinical outcome measures. Results Forty patients were treated (27 CZP, 13 placebo→CZP), and 36 (24 CZP, 12 placebo→CZP) completed week 16. In the CZP group, there were significant reductions from baseline synovitis (Hodges–Lehmann estimate of median change, −1.5, p=0.049) and osteitis scores (−2.5, p=0.031) at week 16. Numerical, but statistically insignificant, MRI inflammation reductions were observed at weeks 1–2 in the CZP group. No significant change was seen in bone erosion score. Improvements across all clinical outcomes were seen in the CZP group. Conclusions CZP reduced MRI synovitis and osteitis scores at week 16, despite small sample size and the technical challenge of reading six time points simultaneously. This study provides essential information on optimal MRI timing for subsequent trials. Trial registration number ClinicalTrials.gov, NCT01235598.

Background. This double-blind, placebo-controlled study was conducted to assess the efficacy of the nonabsorbed oral antibiotic rifaximin to prevent shigellosis in volunteers challenged with Shigella flexneri. Methods. Volunteers were randomized to receive either prophylactic rifaximin (200 mg 3 times daily for 3 days; n = 15) or placebo (n = 10) on days 0, 1, and 2. On day 1, volunteers were challenged with ∼1500 colony-forming units of S. flexneri 2a strain 2457T given orally in sodium bicarbonate buffer. Results. The incidence of diarrhea was 0 with rifaximin, compared with 60% with placebo (P = .001). The median time to onset of diarrhea was 78.5 h with placebo (P < .001). The incidence of dysentery was 0 for rifaximin and 10% for placebo (P = .4). The incidence of colonization with Shigella was 0 with rifaximin, compared with 50% with placebo (P < .005). A significant serum or mucosal immune response after challenge by at least 1 indicator (immunoglobulin A titer, immunoglobulin G titer, and immunoglobulin A antibody—secreting cell count) was 0 with rifaximin and 80% with placebo (P < .001). Conclusions. Rifaximin was effective and well tolerated, compared with placebo, in preventing shigellosis in this double-blind study of volunteers challenged with S. flexneri 2a.

Background The topological landscape of gene interaction networks provides a rich source of information for inferring functional patterns of genes or proteins. However, it is still a challenging task to aggregate heterogeneous biological information such as gene expression and gene interactions to achieve more accurate inference for prediction and discovery of new gene interactions. In particular, how to generate a unified vector representation to integrate diverse input data is a key challenge addressed here. Results We propose a scalable and robust deep learning framework to learn embedded representations to unify known gene interactions and gene expression for gene interaction predictions. These low- dimensional embeddings derive deeper insights into the structure of rapidly accumulating and diverse gene interaction networks and greatly simplify downstream modeling. We compare the predictive power of our deep embeddings to the strong baselines. The results suggest that our deep embeddings achieve significantly more accurate predictions. Moreover, a set of novel gene interaction predictions are validated by up-to-date literature-based database entries. Conclusion The proposed model demonstrates the importance of integrating heterogeneous information about genes for gene network inference. GNE is freely available under the GNU General Public License and can be downloaded from GitHub (https://github.com/kckishan/GNE).

Background The OMERACT rheumatoid arthritis (RA) magnetic resonance imaging (MRI) scoring system (RAMRIS) is a validated system for assessment of synovitis, bone edema and bone erosion. Previous studies have reported that anti-TNFs improve RAMRIS scores, but it is not known how early this occurs. The rapid clinical efficacy of certolizumab pegol (CZP), a PEGylated Fc-free anti-TNF, in RA has been reported.1 Objectives To identify the first time point of a MRI-verified response to CZP in RA patients (pts). Methods MARVELOUS (NCT01235598) is a Phase IIIb multicenter, randomized, double-blind (DB), placebo (PBO)-controlled (during initial 2 weeks [wks]) study. Pts were randomized 2:1 to CZP Q2W (400mg at Wks 0–4, then 200mg every 2 wks to Wk16) or PBO at Day 0, then CZP Q2W (400mg Wks 2–6, then 200mg every 2 wks to Wk16). Contrast enhanced MRI of one hand and wrist was acquired at baseline (Wk0) and Wks 1,2,4,8 and 16. Primary endpoint was change from Wk0 in OMERACT RAMRIS synovitis score in the CZP group. The first significance test was conducted at Wk16 and continued to earlier time points if significant. All MRIs were analyzed by an experienced reader blinded to subject identity, study treatment and time point; all time points were read simultaneously. Change in bone edema, bone erosion and clinical parameters were secondary outcomes. Last observation carried forward imputation was used for synovitis and observed data for other outcomes. Safety variables including adverse events (AEs) and laboratory parameters were assessed. Results 40 pts were randomized and treated: CZP n=27; PBO–CZP n=13. During the DB phase, 4 pts discontinued treatment (1 PBO, 3 CZP). In the CZP group a significant reduction from baseline in synovitis score was reported at Wk16 (median change= -1.5, p=0.049; Fig. 1A); reductions were not significant at Wk8, precluding testing of earlier time points. In the CZP group, bone edema score was also significantly reduced at Wk16 (-2.5, p=0.031; Fig. 1B); there was no significant change in bone erosion score. For all RAMRIS parameters very good intra-reader reliability (intra-class correlations >0.90) was observed. Most AEs were mild or moderate with low incidence of withdrawal due to AEs. There were no serious infections or deaths. Figure 1. A. Mean change from baseline in synovitis score. B. Synovitis and bone edema scores. Conclusions In this first study with multiple MRIs following initiation of biological therapy, CZP reduced OMERACT RAMRIS synovitis and bone edema scores as measured by MRI at Wk16, despite small sample size and the technical challenge of reading 6 time points simultaneously. This study documents the effect of CZP and provides essential information on the optimal timing of MRI and sample sizes for subsequent larger clinical trials. References Keystone E. Arthritis Rheum 2008;58(11):3319-3329 Acknowledgements The authors acknowledge Costello Medical Consulting for writing and editorial assistance which was funded by UCB Pharma. Disclosure of Interest M. Østergaard Grant/research support: Abbott, Pfizer and Centocor, Consultant for: Abbott, Pfizer, Merck, Roche, UCB Pharma, Speakers bureau: Abbott, Pfizer, Merck, BMS, UCB Pharma, Mundipharma, L. Jacobsson Grant/research support: Pfizer, Paid instructor for: Abbvie, BMS, MSD, Pfizer, UCB Pharma, C. Schaufelberger: None declared, M. Sejer-Hansen Consultant for: UCB Pharma, J. Bijlsma Grant/research support: UCB Pharma, Speakers bureau: UCB Pharma, A. Dudek: None declared, M. Rell-Bakalarska: None declared, F. Staelens Employee of: UCB Pharma, R. Haake Employee of: UCB Pharma, B. Sundman-Engberg Employee of: UCB Pharma, H. Bliddal Grant/research support: Abbott, Amgen, AstraZeneca, Aventis, Axellus, Bristol Myers Squibb, Cambridge Nutritional Foods, Dansk Droge, Eurovita, Ferrosan, GlaxoSmithKline, Hoechst, LEO, Lundbeck, MSD, Mundipharma, Norpharma, NOVO, NutriCare, Nycomed, Pfizer, Pharmacia, Pierre-Fabre, Proctor&Gamble, Rhone-Poulenc, Roche, Roussel, Schering-Plough, Searle, Serono, UCB Pharma, Wyeth, Consultant for: Abbott, Amgen, AstraZeneca, Aventis, Axellus, Bristol Myers Squibb, Cambridge Nutritional Foods, Dansk Droge, Eurovita, Ferrosan, GlaxoSmithKline, Hoechst, LEO, Lundbeck, MSD, Mundipharma, Norpharma, NOVO, NutriCare, Nycomed, Pfizer, Pharmacia, Pierre-Fabre, Proctor&Gamble, Rhone-Poulenc, Roche, Roussel, Schering-Plough, Searle, Serono, UCB Pharma, Wyeth DOI 10.1136/annrheumdis-2014-eular.2096

One of the key challenges for nuclear physics today is to understand from first principles the effective interaction between hadrons with different quark content. First successes have been achieved using techniques that solve the dynamics of quarks and gluons on discrete space-time lattices 1 , 2 . Experimentally, the dynamics of the strong interaction have been studied by scattering hadrons off each other. Such scattering experiments are difficult or impossible for unstable hadrons 3 – 6 and so high-quality measurements exist only for hadrons containing up and down quarks 7 . Here we demonstrate that measuring correlations in the momentum space between hadron pairs 8 – 12 produced in ultrarelativistic proton–proton collisions at the CERN Large Hadron Collider (LHC) provides a precise method with which to obtain the missing information on the interaction dynamics between any pair of unstable hadrons. Specifically, we discuss the case of the interaction of baryons containing strange quarks (hyperons). We demonstrate how, using precision measurements of proton–omega baryon correlations, the effect of the strong interaction for this hadron–hadron pair can be studied with precision similar to, and compared with, predictions from lattice calculations 13 , 14 . The large number of hyperons identified in proton–proton collisions at the LHC, together with accurate modelling 15 of the small (approximately one femtometre) inter-particle distance and exact predictions for the correlation functions, enables a detailed determination of the short-range part of the nucleon-hyperon interaction. Correlations in momentum space between hadrons created by ultrarelativistic proton–proton collisions at the CERN Large Hadron Collider provide insights into the strong interaction, particularly the short-range dynamics of hyperons—baryons that contain strange quarks.

A bstract Measurements of anisotropic flow coefficients with two- and multi-particle cumulants for inclusive charged particles in Pb-Pb collisions at s N N = 5.02 and 2.76 TeV are reported in the pseudorapidity range | η | < 0.8 and transverse momentum 0.2 < p T < 50 GeV/ c . The full data sample collected by the ALICE detector in 2015 (2010), corresponding to an integrated luminosity of 12.7 (2.0) μ b −1 in the centrality range 0-80%, is analysed. Flow coefficients up to the sixth flow harmonic ( v 6 ) are reported and a detailed comparison among results at the two energies is carried out. The p T dependence of anisotropic flow coefficients and its evolution with respect to centrality and harmonic number n are investigated. An approximate power-law scaling of the form v n ( p T ) ∼  p T n /3 is observed for all flow harmonics at low p T (0.2 < p T < 3 GeV/ c ). At the same time, the ratios v n / v m n / m are observed to be essentially independent of p T for most centralities up to about p T = 10 GeV/ c . Analysing the differences among higher-order cumulants of elliptic flow ( v 2 ), which have different sensitivities to flow fluctuations, a measurement of the standardised skewness of the event-by-event v 2 distribution P ( v 2 ) is reported and constraints on its higher moments are provided. The Elliptic Power distribution is used to parametrise P ( v 2 ), extracting its parameters from fits to cumulants. The measurements are compared to different model predictions in order to discriminate among initial-state models and to constrain the temperature dependence of the shear viscosity to entropy-density ratio.

A detailed study of pseudorapidity densities and multiplicity distributions of primary charged particles produced in proton–proton collisions, at s = 0.9, 2.36, 2.76, 7 and 8 TeV, in the pseudorapidity range | η | < 2 , was carried out using the ALICE detector. Measurements were obtained for three event classes: inelastic, non-single diffractive and events with at least one charged particle in the pseudorapidity interval | η | < 1 . The use of an improved track-counting algorithm combined with ALICE’s measurements of diffractive processes allows a higher precision compared to our previous publications. A KNO scaling study was performed in the pseudorapidity intervals | η | < 0.5, 1.0 and 1.5. The data are compared to other experimental results and to models as implemented in Monte Carlo event generators PHOJET and recent tunes of PYTHIA6, PYTHIA8 and EPOS.

The elliptic (v2), triangular (v3), and quadrangular (v4) flow coefficients of π±, K±, p+p¯¯¯,Λ+Λ¯¯¯¯,K0S, and the ϕ-meson are measured in Pb-Pb collisions at √sNN=5.02 TeV. Results obtained with the scalar product method are reported for the rapidity range |y| < 0.5 as a function of transverse momentum, pT, at different collision centrality intervals between 0–70%, including ultra-central (0–1%) collisions for π±, K±, and p+p¯¯¯. For pT < 3 GeV/c, the flow coefficients exhibit a particle mass dependence. At intermediate transverse momenta (3 < pT < 8–10 GeV/c), particles show an approximate grouping according to their type (i.e., mesons and baryons). The ϕ-meson v2, which tests both particle mass dependence and type scaling, follows p+p¯¯¯ v2 at low pT and π± v2 at intermediate pT. The evolution of the shape of vn(pT) as a function of centrality and harmonic number n is studied for the various particle species. Flow coefficients of π±, K±, and p+p¯¯¯ for pT < 3 GeV/c are compared to iEBE-VISHNU and MUSIC hydrodynamical calculations coupled to a hadronic cascade model (UrQMD). The iEBE-VISHNU calculations describe the results fairly well for pT < 2.5 GeV/c, while MUSIC calculations reproduce the measurements for pT < 1 GeV/c. A comparison to vn coefficients measured in Pb-Pb collisions at √sNN=2.76 TeV is also provided.

The pT-differential production cross section of prompt Λ c + charmed baryons was measured with the ALICE detector at the Large Hadron Collider (LHC) in pp collisions at s√=7 TeV and in p-Pb collisions at sNN−−−√=5.02 TeV at midrapidity. The Λ c + and Λ¯¯¯¯c¯¯¯ were reconstructed in the hadronic decay modes Λ c +  → pK−π+, Λ c +  → pK S 0 and in the semileptonic channel Λ c +  → e+νeΛ (and charge conjugates). The measured values of the Λ c + /D0 ratio, which is sensitive to the c-quark hadronisation mechanism, and in particular to the production of baryons, are presented and are larger than those measured previously in different colliding systems, centre-of-mass energies, rapidity and pT intervals, where the Λ c + production process may differ. The results are compared with the expectations obtained from perturbative Quantum Chromodynamics calculations and Monte Carlo event generators. Neither perturbative QCD calculations nor Monte Carlo models reproduce the data, indicating that the fragmentation of heavy-flavour baryons is not well understood. The first measurement at the LHC of the Λ c + nuclear modification factor, RpPb, is also presented. The RpPb is found to be consistent with unity and with that of D mesons within the uncertainties, and consistent with a theoretical calculation that includes cold nuclear matter effects and a calculation that includes charm quark interactions with a deconfined medium.