Large spin systematics in CFT

20 pages; v2: version published in JHEPUsing conformal field theory (CFT) arguments we derive an infinite number of constraints on the large spin expansion of the anomalous dimensions and structure constants of higher spin operators. These arguments rely only on analiticity, unitarity, crossing-symmetry and the structure of the conformal partial wave expansion. We obtain results for both, perturbative CFT to all order in the perturbation parameter, as well as non-perturbatively. For the case of conformal gauge theories this provides a proof of the reciprocity principle to all orders in perturbation theory and provides a new "reciprocity" principle for structure constants. We argue that these results extend also to non-conformal theories.Peer reviewe

Atropselective syntheses of (-) and (+) rugulotrosin A utilizing point-to-axial chirality transfer

Chiral, dimeric natural products containing complex structures and interesting biological properties have inspired chemists and biologists for decades. A seven-step total synthesis of the axially chiral, dimeric tetrahydroxanthone natural product rugulotrosin A is described. The synthesis employs a one-pot Suzuki coupling/dimerization to generate the requisite 2,2'-biaryl linkage. Highly selective point-to-axial chirality transfer was achieved using palladium catalysis with achiral phosphine ligands. Single X-ray crystal diffraction data were obtained to confirm both the atropisomeric configuration and absolute stereochemistry of rugulotrosin A. Computational studies are described to rationalize the atropselectivity observed in the key dimerization step. Comparison of the crude fungal extract with synthetic rugulotrosin A and its atropisomer verified that nature generates a single atropisomer of the natural product.P50 GM067041 - NIGMS NIH HHS; R01 GM099920 - NIGMS NIH HHS; GM-067041 - NIGMS NIH HHS; GM-099920 - NIGMS NIH HH

't Hooft Operators in Gauge Theory from Toda CFT

We construct loop operators in two dimensional Toda CFT and calculate with them the exact expectation value of certain supersymmetric 't Hooft and dyonic loop operators in four dimensional \Ncal=2 gauge theories with SU(N) gauge group. Explicit formulae for 't Hooft and dyonic operators in \Ncal=2^* and \Ncal=2 conformal SQCD with SU(N) gauge group are presented. We also briefly speculate on the Toda CFT realization of arbitrary loop operators in these gauge theories in terms of topological web operators in Toda CFT.Comment: 49 pages, LaTeX. Typos fixed, references adde

Generalized quark-antiquark potential at weak and strong coupling

We study a two-parameter family of Wilson loop operators in N=4 supersymmetric Yang-Mills theory which interpolates smoothly between the 1/2 BPS line or circle and a pair of antiparallel lines. These observables capture a natural generalization of the quark-antiquark potential. We calculate these loops on the gauge theory side to second order in perturbation theory and in a semiclassical expansion in string theory to one-loop order. The resulting determinants are given in integral form and can be evaluated numerically for general values of the parameters or analytically in a systematic expansion around the 1/2 BPS configuration. We comment about the feasibility of deriving all-loop results for these Wilson loops.Comment: 43 pages: 15 comprising the main text and 25 for detailed appendice

Finite-size and correlation-induced effects in Mean-field Dynamics

The brain's activity is characterized by the interaction of a very large number of neurons that are strongly affected by noise. However, signals often arise at macroscopic scales integrating the effect of many neurons into a reliable pattern of activity. In order to study such large neuronal assemblies, one is often led to derive mean-field limits summarizing the effect of the interaction of a large number of neurons into an effective signal. Classical mean-field approaches consider the evolution of a deterministic variable, the mean activity, thus neglecting the stochastic nature of neural behavior. In this article, we build upon two recent approaches that include correlations and higher order moments in mean-field equations, and study how these stochastic effects influence the solutions of the mean-field equations, both in the limit of an infinite number of neurons and for large yet finite networks. We introduce a new model, the infinite model, which arises from both equations by a rescaling of the variables and, which is invertible for finite-size networks, and hence, provides equivalent equations to those previously derived models. The study of this model allows us to understand qualitative behavior of such large-scale networks. We show that, though the solutions of the deterministic mean-field equation constitute uncorrelated solutions of the new mean-field equations, the stability properties of limit cycles are modified by the presence of correlations, and additional non-trivial behaviors including periodic orbits appear when there were none in the mean field. The origin of all these behaviors is then explored in finite-size networks where interesting mesoscopic scale effects appear. This study leads us to show that the infinite-size system appears as a singular limit of the network equations, and for any finite network, the system will differ from the infinite system

The electrochemical behaviour of magnetocaloric alloys La(Fe,Mn,Si)13Hx under magnetic field conditions

The degradation mechanism of La(Fe,Mn,Si)13Hx has been examined under conditions representative of the complex operating parameters of a refrigeration cycle. The magnetic field effects are found to be dominated by magneto-transport and are most significant when the material is in its paramagnetic state - resulting in significantly accelerated corrosion rates

Expression profiling of single cells and patient cohorts identifies multiple immunosuppressive pathways and an altered NK cell phenotype in glioblastoma.

Glioblastoma (GBM) is an aggressive cancer with a very poor prognosis. Generally viewed as weakly immunogenic, GBM responds poorly to current immunotherapies. To understand this problem more clearly we used a combination of natural killer (NK) cell functional assays together with gene and protein expression profiling to define the NK cell response to GBM and explore immunosuppression in the GBM microenvironment. In addition, we used transcriptome data from patient cohorts to classify GBM according to immunological profiles. We show that glioma stem-like cells, a source of post-treatment tumour recurrence, express multiple immunomodulatory cell surface molecules and are targeted in preference to normal neural progenitor cells by natural killer (NK) cells ex vivo. In contrast, GBM-infiltrating NK cells express reduced levels of activation receptors within the tumour microenvironment, with hallmarks of transforming growth factor (TGF)-β-mediated inhibition. This NK cell inhibition is accompanied by expression of multiple immune checkpoint molecules on T cells. Single-cell transcriptomics demonstrated that both tumour and haematopoietic-derived cells in GBM express multiple, diverse mediators of immune evasion. Despite this, immunome analysis across a patient cohort identifies a spectrum of immunological activity in GBM, with active immunity marked by co-expression of immune effector molecules and feedback inhibitory mechanisms. Our data show that GBM is recognized by the immune system but that anti-tumour immunity is restrained by multiple immunosuppressive pathways, some of which operate in the healthy brain. The presence of immune activity in a subset of patients suggests that these patients will more probably benefit from combination immunotherapies directed against multiple immunosuppressive pathways

Gain control network conditions in early sensory coding

Gain control is essential for the proper function of any sensory system. However, the precise mechanisms for achieving effective gain control in the brain are unknown. Based on our understanding of the existence and strength of connections in the insect olfactory system, we analyze the conditions that lead to controlled gain in a randomly connected network of excitatory and inhibitory neurons. We consider two scenarios for the variation of input into the system. In the first case, the intensity of the sensory input controls the input currents to a fixed proportion of neurons of the excitatory and inhibitory populations. In the second case, increasing intensity of the sensory stimulus will both, recruit an increasing number of neurons that receive input and change the input current that they receive. Using a mean field approximation for the network activity we derive relationships between the parameters of the network that ensure that the overall level of activity of the excitatory population remains unchanged for increasing intensity of the external stimulation. We find that, first, the main parameters that regulate network gain are the probabilities of connections from the inhibitory population to the excitatory population and of the connections within the inhibitory population. Second, we show that strict gain control is not achievable in a random network in the second case, when the input recruits an increasing number of neurons. Finally, we confirm that the gain control conditions derived from the mean field approximation are valid in simulations of firing rate models and Hodgkin-Huxley conductance based models

Neutrino Signatures From Young Neutron Stars

After a successful core collapse supernova (CCSN) explosion, a hot dense proto-neutron star (PNS) is left as a remnant. Over a time of 20 or so seconds, this PNS emits the majority of the neutrinos that come from the CCSN, contracts, and loses most of its lepton number. This is the process by which all neutron stars in our galaxy are likely born. The emitted neutrinos were detected from supernova (SN) 1987A, and they will be detected in much greater numbers from any future galactic CCSN. These detections can provide a direct window into the properties of the dense matter encountered inside neutron stars, and they can affect nucleosynthesis in the material ejected during the CCSN. In this chapter, we review the basic physics of PNS cooling, including the basic equations of PNS structure and neutrino diffusion in dense matter. We then discuss how the nuclear equation of state, neutrino opacities in dense matter, and convection can shape the temporal behavior of the neutrino signal. We also discuss what was learned from the late-time SN 1987A neutrinos, the prospects for detection of these neutrinos from future galactic CCSNe, and the effects these neutrinos can have on nucleosynthesis