On Symmetry Enhancement in the psu(1,1|2) Sector of N=4 SYM

Strong evidence indicates that the spectrum of planar anomalous dimensions of N=4 super Yang-Mills theory is given asymptotically by Bethe equations. A curious observation is that the Bethe equations for the psu(1,1|2) subsector lead to very large degeneracies of 2^M multiplets, which apparently do not follow from conventional integrable structures. In this article, we explain such degeneracies by constructing suitable conserved nonlocal generators acting on the spin chain. We propose that they generate a subalgebra of the loop algebra for the su(2) automorphism of psu(1,1|2). Then the degenerate multiplets of size 2^M transform in irreducible tensor products of M two-dimensional evaluation representations of the loop algebra.Comment: 35 pages, v2: references added, sign inconsistency resolved in (5.5,5.6), v3: Section 3.4 on Hamiltonian added, minor improvements, to appear in JHE

Macrophages exert homeostatic actions in pregnancy to protect against preterm birth and fetal inflammatory injury

Macrophages are commonly thought to contribute to the pathophysiology of preterm labor by amplifying inflammation — but a protective role has not previously been considered to our knowledge. We hypothesized that given their antiinflammatory capability in early pregnancy, macrophages exert essential roles in maintenance of late gestation and that insufficient macrophages may predispose individuals to spontaneous preterm labor and adverse neonatal outcomes. Here, we showed that women with spontaneous preterm birth had reduced CD209⁺CD206⁺ expression in alternatively activated CD45⁺CD14⁺ICAM3⁻ macrophages and increased TNF expression in proinflammatory CD45⁺CD14⁺CD80⁺HLA-DR⁺ macrophages in the uterine decidua at the materno-fetal interface. In Cd11b(DTR/DTR) mice, depletion of maternal CD11b⁺ myeloid cells caused preterm birth, neonatal death, and postnatal growth impairment, accompanied by uterine cytokine and leukocyte changes indicative of a proinflammatory response, while adoptive transfer of WT macrophages prevented preterm birth and partially rescued neonatal loss. In a model of intra-amniotic inflammation–induced preterm birth, macrophages polarized in vitro to an M2 phenotype showed superior capacity over nonpolarized macrophages to reduce uterine and fetal inflammation, prevent preterm birth, and improve neonatal survival. We conclude that macrophages exert a critical homeostatic regulatory role in late gestation and are implicated as a determinant of susceptibility to spontaneous preterm birth and fetal inflammatory injury.Nardhy Gomez-Lopez, Valeria Garcia-Flores, Peck Yin Chin, Holly M. Groome, Melanie T. Bijland, Kerrilyn R. Diener, Roberto Romero, and Sarah A. Robertso

Scaling Tests of the Cross Section for Deeply Virtual Compton Scattering

We present the first measurements of the \vec{e}p->epg cross section in the deeply virtual Compton scattering (DVCS) regime and the valence quark region. The Q^2 dependence (from 1.5 to 2.3 GeV^2) of the helicity-dependent cross section indicates the twist-2 dominance of DVCS, proving that generalized parton distributions (GPDs) are accessible to experiment at moderate Q^2. The helicity-independent cross section is also measured at Q^2=2.3 GeV^2. We present the first model-independent measurement of linear combinations of GPDs and GPD integrals up to the twist-3 approximation.Comment: 5 pages, 4 figures, 2 tables. Text shortened for publication. References added. One figure remove

Exclusive Neutral Pion Electroproduction in the Deeply Virtual Regime

We present measurements of the ep->ep pi^0 cross section extracted at two values of four-momentum transfer Q^2=1.9 GeV^2 and Q^2=2.3 GeV^2 at Jefferson Lab Hall A. The kinematic range allows to study the evolution of the extracted hadronic tensor as a function of Q^2 and W. Results will be confronted with Regge inspired calculations and GPD predictions. An intepretation of our data within the framework of semi-inclusive deep inelastic scattering has also been attempted

Serre Relation and Higher Grade Generators of the AdS/CFT Yangian Symmetry

It was shown that the spin chain model coming from AdS/CFT correspondence satisfies the Yangian symmetry if we assume evaluation representation, though so far there is no explicit proof that the evaluation representation satisfies the Serre relation, which is one of the defining equations of the Yangian algebra imposing constraints on the whole algebraic structure. We prove completely that the evaluation representation adopted in the model satisfies the Serre relation by introducing a three-dimensional gamma matrix. After studying the Serre relation, we proceed to the whole Yangian algebraic structure, where we find that the conventional construction of higher grade generators is singular and we propose an alternative construction. In the discussion of the higher grade generators, a great simplification for the proof of the Serre relation is found. Using this expression, we further show that the proof is lifted to the exceptional superalgebra, which is a non-degenerate deformation of the original superalgebra.Comment: 24 pages, 1 figure, v2: on exceptional superalgebra, previous incorrect comments removed and new sections added, v3: clarifications adde

Virtual Compton Scattering and Neutral Pion Electroproduction in the Resonance Region up to the Deep Inelastic Region at Backward Angles

We have made the first measurements of the virtual Compton scattering (VCS) process via the H$(e,e'p)\gamma$ exclusive reaction in the nucleon resonance region, at backward angles. Results are presented for the $W$-dependence at fixed $Q^2=1$ GeV$^2$, and for the $Q^2$-dependence at fixed $W$ near 1.5 GeV. The VCS data show resonant structures in the first and second resonance regions. The observed $Q^2$-dependence is smooth. The measured ratio of H$(e,e'p)\gamma$ to H$(e,e'p)\pi^0$ cross sections emphasizes the different sensitivity of these two reactions to the various nucleon resonances. Finally, when compared to Real Compton Scattering (RCS) at high energy and large angles, our VCS data at the highest $W$ (1.8-1.9 GeV) show a striking $Q^2$- independence, which may suggest a transition to a perturbative scattering mechanism at the quark level.Comment: 20 pages, 8 figures. To appear in Phys.Rev.

Multi-parametric MR Imaging Biomarkers Associated to Clinical Outcomes in Gliomas: A Systematic Review

[EN] Purpose: To systematically review evidence regarding the association of multi-parametric biomarkers with clinical outcomes and their capacity to explain relevant subcompartments of gliomas. Materials and Methods: Scopus database was searched for original journal papers from January 1st, 2007 to February 20th , 2017 according to PRISMA. Four hundred forty-nine abstracts of papers were reviewed and scored independently by two out of six authors. Based on those papers we analyzed associations between biomarkers, subcompartments within the tumor lesion, and clinical outcomes. From all the articles analyzed, the twenty-seven papers with the highest scores were highlighted to represent the evidence about MR imaging biomarkers associated with clinical outcomes. Similarly, eighteen studies defining subcompartments within the tumor region were also highlighted to represent the evidence of MR imaging biomarkers. Their reports were critically appraised according to the QUADAS-2 criteria. Results: It has been demonstrated that multi-parametric biomarkers are prepared for surrogating diagnosis, grading, segmentation, overall survival, progression-free survival, recurrence, molecular profiling and response to treatment in gliomas. Quantifications and radiomics features obtained from morphological exams (T1, T2, FLAIR, T1c), PWI (including DSC and DCE), diffusion (DWI, DTI) and chemical shift imaging (CSI) are the preferred MR biomarkers associated to clinical outcomes. Subcompartments relative to the peritumoral region, invasion, infiltration, proliferation, mass effect and pseudo flush, relapse compartments, gross tumor volumes, and high-risk regions have been defined to characterize the heterogeneity. For the majority of pairwise cooccurrences, we found no evidence to assert that observed co-occurrences were significantly different from their expected co-occurrences (Binomial test with False Discovery Rate correction, alpha=0.05). The co-occurrence among terms in the studied papers was found to be driven by their individual prevalence and trends in the literature. Conclusion: Combinations of MR imaging biomarkers from morphological, PWI, DWI and CSI exams have demonstrated their capability to predict clinical outcomes in different management moments of gliomas. Whereas morphologic-derived compartments have been mostly studied during the last ten years, new multi-parametric MRI approaches have also been proposed to discover specific subcompartments of the tumors. MR biomarkers from those subcompartments show the local behavior within the heterogeneous tumor and may quantify the prognosis and response to treatment of gliomas.This work was supported by the Spanish Ministry for Investigation, Development and Innovation project with identification number DPI2016-80054-R.Oltra-Sastre, M.; Fuster García, E.; Juan -Albarracín, J.; Sáez Silvestre, C.; Perez-Girbes, A.; Sanz-Requena, R.; Revert-Ventura, A.... (2019). Multi-parametric MR Imaging Biomarkers Associated to Clinical Outcomes in Gliomas: A Systematic Review. Current Medical Imaging Reviews. 15(10):933-947. https://doi.org/10.2174/1573405615666190109100503S9339471510Louis D.N.; Perry A.; Reifenberger G.; The 2016 world health organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 2016,131(6),803-820Ostrom Q.T.; Gittleman H.; Fulop J.; CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2008-2012. Neuro-oncol 2015,17(Suppl. 4),iv1-iv62Yachida S.; Jones S.; Bozic I.; Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature 2010,467(7319),1114-1117Gerlinger M.; Rowan A.J.; Horswell S.; Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 2012,366(10),883-892Sottoriva A.; Spiteri I.; Piccirillo S.G.M.; Intratumor heterogeneityin human glioblastoma reflects cancer evolutionary dynamics. Proc Natl Acad Sci USA 2013,110(10),4009-4014Whiting P.F.; Rutjes A.W.; Westwood M.E.; QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 2011,155(8),529-536Stupp R.; Mason W.P.; van den Bent M.J.; Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 2005,352(10),987-996Ponte K.F.; Berro D.H.; Collet S.; In vivo relationship between hypoxia and angiogenesis in human glioblastoma: a multimodal imaging study. J Nucl Med 2017,58(10),1574-1579Pope W.B.; Kim H.J.; Huo J.; Recurrent glioblastoma multiforme: ADC histogram analysis predicts response to bevacizumab treatment. Radiology 2009,252(1),182-189Mörén L.; Bergenheim A.T.; Ghasimi S.; Brännström T.; Johansson M.; Antti H.; Metabolomic screening of tumor tissue and serum in glioma patients reveals diagnostic and prognostic information. Metabolites 2015,5(3),502-520Prager A.J.; Martinez N.; Beal K.; Omuro A.; Zhang Z.; Young R.J.; Diffusion and perfusion MRI to differentiate treatment-related changes including pseudoprogression from recurrent tumors in high-grade gliomas with histopathologic evidence. AJNR Am J Neuroradiol 2015,36(5),877-885Kickingereder P.; Burth S.; Wick A.; Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models. Radiology 2016,280(3),880-889Yoo R-E.; Choi S.H.; Cho H.R.; Tumor blood flow from arterial spin labeling perfusion MRI: a key parameter in distinguishing high-grade gliomas from primary cerebral lymphomas, and in predicting genetic biomarkers in high-grade gliomas. J Magn Reson Imaging 2013,38(4),852-860Liberman G.; Louzoun Y.; Aizenstein O.; Automatic multi-modal MR tissue classification for the assessment of response to bevacizumab in patients with glioblastoma. Eur J Radiol 2013,82(2),e87-e94Ramadan S.; Andronesi O.C.; Stanwell P.; Lin A.P.; Sorensen A.G.; Mountford C.E.; Use of in vivo two-dimensional MR spectroscopy to compare the biochemistry of the human brain to that of glioblastoma. Radiology 2011,259(2),540-549Xintao H.; Wong K.K.; Young G.S.; Guo L.; Wong S.T.; Support vector machine multi-parametric MRI identification of pseudoprogression from tumor recurrence in patients with resected glioblastoma. J Magn Reson Imaging 2011,33(2),296Ingrisch M.; Schneider M.J.; Nörenberg D.; Radiomic Analysis reveals prognostic information in T1-weighted baseline magnetic resonance imaging in patients with glioblastoma. Invest Radiol 2017,52(6),360-366Ulyte A.; Katsaros V.K.; Liouta E.; Prognostic value of preoperative dynamic contrast-enhanced MRI perfusion parameters for high-grade glioma patients. Neuroradiology 2016,58(12),1197-1208O’Neill A.F.; Qin L.; Wen P.Y.; de Groot J.F.; Van den Abbeele A.D.; Yap J.T.; Demonstration of DCE-MRI as an early pharmacodynamic biomarker of response to VEGF Trap in glioblastoma. J Neurooncol 2016,130(3),495-503Kickingereder P.; Bonekamp D.; Nowosielski M.; Radiogenomics of glioblastoma: machine learning-based classification of molecular characteristics by using multiparametric and multiregional mr imaging features. Radiology 2016,281(3),907-918Roberto S-R.; Antonio R-V.; Luis M-B.; Angel A-B.; Gracián G-M.; Quantitative mr perfusion parameters related to survival time in high-grade gliomas. European Radiology 2013,23(12),3456-3465Jain R.; Poisson L.; Narang J.; Genomic mapping and survival prediction in glioblastoma: molecular subclassification strengthened by hemodynamic imaging biomarkers. Radiology 2013,267(1),212-220Fathi K.A.; Mohseni M.; Rezaei S.; Bakhshandehpour G.; Saligheh R.H.; Multi-parametric (ADC/PWI/T2-W) image fusion approach for accurate semi-automatic segmentation of tumorous regions in glioblastoma multiforme. MAGMA 2015,28(1),13-22Caulo M.; Panara V.; Tortora D.; Data-driven grading of brain gliomas: a multiparametric MR imaging study. Radiology 2014,272(2),494-503Alexiou G.A.; Zikou A.; Tsiouris S.; Comparison of diffusion tensor, dynamic susceptibility contrast MRI and (99m)Tc-Tetrofosmin brain SPECT for the detection of recurrent high-grade glioma. Magn Reson Imaging 2014,32(7),854-859Van Cauter S.; De Keyzer F.; Sima D.M.; Integrating diffusion kurtosis imaging, dynamic susceptibility-weighted contrast-enhanced MRI, and short echo time chemical shift imaging for grading gliomas. Neuro-oncol 2014,16(7),1010-1021Seeger A.; Braun C.; Skardelly M.; Comparison of three different MR perfusion techniques and MR spectroscopy for multiparametric assessment in distinguishing recurrent high-grade gliomas from stable disease. Acad Radiol 2013,20(12),1557-1565Chawalparit O.; Sangruchi T.; Witthiwej T.; Diagnostic performance of advanced mri in differentiating high-grade from low-grade gliomas in a setting of routine service. J Med Assoc Thai 2013,96(10),1365-1373Li Y.; Lupo J.M.; Parvataneni R.; Survival analysis in patients with newly diagnosed glioblastoma using pre- and postradiotherapy MR spectroscopic imaging. Neuro-oncol 2013,15(5),607-617Shankar J.J.S.; Woulfe J.; Silva V.D.; Nguyen T.B.; Evaluation of perfusion CT in grading and prognostication of high-grade gliomas at diagnosis: a pilot study. AJR Am J Roentgenol 2013,200(5)Zinn P.O.; Mahajan B.; Sathyan P.; Radiogenomic mapping of edema/cellular invasion MRI-phenotypes in glioblastoma multiforme. PLoS One 2011,6(10)Matsusue E.; Fink J.R.; Rockhill J.K.; Ogawa T.; Maravilla K.R.; Distinction between glioma progression and post-radiation change by combined physiologic MR imaging. Neuroradiology 2010,52(4),297-306Juan-Albarracín J.; Fuster-Garcia E.; Manjón J.V.; Automated glioblastoma segmentation based on a multiparametric structured unsupervised classification. PLoS One 2015,10(5)Itakura H.; Achrol A.S.; Mitchell L.A.; Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities. Sci Transl Med 2015,7(303)Ion-Margineanu A.; Van Cauter S.; Sima D.M.; Tumour relapse prediction using multiparametric MR data recorded during follow-up of GBM patients. BioMed Res Int 2015,2015Durst C.R.; Raghavan P.; Shaffrey M.E.; Multimodal MR imaging model to predict tumor infiltration in patients with gliomas. Neuroradiology 2014,56(2),107-115Yoon J.H.; Kim J.H.; Kang W.J.; Grading of cerebral glioma with multi-parametric MR Imaging and 18F-FDG-PET: concordance and accuracy. European Radiol 2014,24(2),380-389Demerath T.; Simon-Gabriel C.P.; Kellner E.; Mesoscopic imaging of glioblastomas: are diffusion, perfusion and spectroscopic measures influenced by the radiogenetic phenotype? Neuroradiol J 2017,30(1),36-47Qin L.; Li X.; Stroiney A.; Advanced MRI assessment to predict benefit of anti-programmed cell death 1 protein immunotherapy response in patients with recurrent glioblastoma. Neuroradiology 2017,59(2),135-145Boult J.K.R.; Borri M.; Jury A.; Investigating intracranial tumour growth patterns with multiparametric MRI incorporating Gd-DTPA and USPIO-enhanced imaging. NMR Biomed 2016,29(11),1608-1617Server A.; Kulle B.; Gadmar Ø.B.; Josefsen R.; Kumar T.; Nakstad P.H.; Measurements of diagnostic examination performance using quantitative apparent diffusion coefficient and proton MR spectroscopic imaging in the preoperative evaluation of tumor grade in cerebral gliomas. Eur J Radiol 2011,80(2),462-470Chang P.D.; Chow D.S.; Yang P.H.; Filippi C.G.; Lignelli A.; Predicting glioblastoma recurrence by early changes in the apparent diffusion coefficient value and signal intensity on FLAIR images. AJR Am J Roentgenol 2017,208(1),57-65Yi C.; Shangjie R.; Volume of high-risk intratumoralsubregions at multi-parametric MR imaging predicts overall survival and complements molecular analysis of glioblastoma. Eur Radiol 2017,27,3583-3592Khalifa J.; Tensaouti F.; Chaltiel L.; Identification of a candidate biomarker from perfusion MRI to anticipate glioblastoma progression after chemoradiation. Eur Radiol 2016,26(11),4194-4203Prateek P.; Jay P.; Partovi S.; Madabhushi A.; Tiwari P.; Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastomamultiforme: preliminary findings. Eur Radiol 2017,27(10),4188-4197Lemasson B.; Chenevert T.L.; Lawrence T.S.; Impact of perfusion map analysis on early survival prediction accuracy in glioma patients. Transl Oncol 2013,6(6),766-774Inano R.; Oishi N.; Kunieda T.; Visualization of heterogeneity and regional grading of gliomas by multiple features using magnetic resonance-based clustered images. Sci Rep 2016,6,30344Delgado-Goñi T.; Ortega-Martorell S.; Ciezka M.; MRSI-based molecular imaging of therapy response to temozolomide in preclinical glioblastoma using source analysis. NMR Biomed 2016,29(6),732-743Cui Y.; Tha K.K.; Terasaka S.; Prognostic imaging biomarkers in glioblastoma: development and independent validation on the basis of multiregion and quantitative analysis of MR images. Radiology 2016,278(2),546-553Price S.J.; Young A.M.H.; Scotton W.J.; Multimodal MRI can identify perfusion and metabolic changes in the invasive margin of glioblastomas. J Magn Reson Imaging 2016,43(2),487-494Sauwen N.; Acou M.; Van Cauter S.; Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI. Neuroimage Clin 2016,12,753-764Jena A.; Taneja S.; Gambhir A.; Glioma recurrence versus radiation necrosis: single-session multiparametric approach using simultaneous O-(2-18F-Fluoroethyl)-L-Tyrosine PET/MRI. Clin Nucl Med 2016,41(5),e228-e236Kim H.S.; Goh M.J.; Kim N.; Choi C.G.; Kim S.J.; Kim J.H.; Which combination of MR imaging modalities is best for predicting recurrent glioblastoma? Study of diagnostic accuracy and reproducibility. Radiology 2014,273(3),831-843Christoforidis G.A.; Yang M.; Abduljalil A.; “Tumoral pseudoblush” identified within gliomas at high-spatial-resolution ultrahigh-field-strength gradient-echo MR imaging corresponds to microvascularity at stereotactic biopsy. Radiology 2012,264(1),210-217Wang S.; Kim S.; Chawla S.; Differentiation between glioblastomas, solitary brain metastases, and primary cerebral lymphomas using diffusion tensor and dynamic susceptibility contrast-enhanced MR imaging. AJNR Am J Neuroradiol 2011,32(3),507-514Hanahan D.; Weinberg R.A.; Hallmarks of cancer: the next generation. Cell 2011,144(5),646-674Macdonald D.R.; Cascino T.L.; Schold S.C.; Cairncross J.G.; Response criteria for phase II studies of supratentorial malignant glioma. J Clin Oncol 1990,8(7),1277-1280Wen P.Y.; Macdonald D.R.; Reardon D.A.; Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol 2010,28(11),1963-1972Sorensen A.G.; Batchelor T.T.; Wen P.Y.; Zhang W-T.; Jain R.K.; Response criteria for glioma. Nat Clin Pract Oncol 2008,5(11),634-644Rosenkrantz A.B.; Friedman K.; Chandarana H.; Current status of hybrid PET/MRI in oncologic imaging. AJR Am J Roentgenol 2016,206(1),162-172Castiglioni I.; Gallivanone F.; Canevari C.; Hybrid PET/MRI for In vivo imaging of cancer: current clinical experiences and recent advances. Curr Med Imaging 2016,12,106Mainta I.C.; Perani D.; Delattre B.M.A.; FDG PET/MR imaging in major neurocognitive disorders. Curr Alzheimer Res 2017,14,186-197Marner L.; Henriksen O.M.; Lundemann M.; Larsen V.A.; Law I.; Clinical PET/MRI in neurooncology: opportunities and challenges from a single-institution perspective. Clin Transl Imaging 2017,5(2),135-149R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria; 2015. Available from: https://www.R-project.org

Dynamics of the 16^{16}O(e,e'p) cross section at high missing energies

We measured the cross section and response functions (R_L, R_T, and R_LT) for the 16O(e,e'p) reaction in quasielastic kinematics for missing energies 25 60 MeV and P_miss > 200 MeV/c, the cross section is relatively constant. Calculations which include contributions from pion exchange currents, isobar currents and short-range correlations account for the shape and the transversity but only for half of the magnitude of the measured cross section

Search for jet extinction in the inclusive jet-pT spectrum from proton-proton collisions at s=8 TeV

Published by the American Physical Society under the terms of the Creative Commons Attribution 3.0 License. Further distribution of this work must maintain attribution to the author(s) and the published articles title, journal citation, and DOI.The first search at the LHC for the extinction of QCD jet production is presented, using data collected with the CMS detector corresponding to an integrated luminosity of 10.7  fb−1 of proton-proton collisions at a center-of-mass energy of 8 TeV. The extinction model studied in this analysis is motivated by the search for signatures of strong gravity at the TeV scale (terascale gravity) and assumes the existence of string couplings in the strong-coupling limit. In this limit, the string model predicts the suppression of all high-transverse-momentum standard model processes, including jet production, beyond a certain energy scale. To test this prediction, the measured transverse-momentum spectrum is compared to the theoretical prediction of the standard model. No significant deficit of events is found at high transverse momentum. A 95% confidence level lower limit of 3.3 TeV is set on the extinction mass scale

Operation and performance of the ATLAS semiconductor tracker

The semiconductor tracker is a silicon microstrip detector forming part of the inner tracking system of the ATLAS experiment at the LHC. The operation and performance of the semiconductor tracker during the first years of LHC running are described. More than 99% of the detector modules were operational during this period, with an average intrinsic hit efficiency of (99.74±0.04)%. The evolution of the noise occupancy is discussed, and measurements of the Lorentz angle, δ-ray production and energy loss presented. The alignment of the detector is found to be stable at the few-micron level over long periods of time. Radiation damage measurements, which include the evolution of detector leakage currents, are found to be consistent with predictions and are used in the verification of radiation background simulations