SOX2 is the determining oncogenic switch in promoting lung squamous cell carcinoma from different cells of origin

Lung squamous cell carcinoma (LSCC) is a devastating malignancy with no effective treatments, due to its complex genomic profile. Therefore, preclinical models mimicking its salient features are urgently needed. Here we describe mouse models bearing various combinations of genetic lesions predominantly found in human LSCC. We show that SOX2 but not FGFR1 overexpression in tracheobronchial basal cells combined with Cdkn2ab and Pten loss results in LSCC closely resembling the human counterpart. Interestingly, Sox2;Pten;Cdkn2ab mice develop LSCC with a more peripheral location when Club or Alveolar type 2 (AT2) cells are targeted. Our model highlights the essential role of SOX2 in commanding the squamous cell fate from different cells of origin and represents an invaluable tool for developing better intervention strategies

Tunneling into a two-dimensional electron system in a strong magnetic field

We investigate the properties of the one-electron Green's function in an interacting two-dimensional electron system in a strong magnetic field, which describes an electron tunneling into such a system. From finite-size diagonalization, we find that its spectral weight is suppressed near zero energy, reaches a maximum at an energy of about $0.2e^{2}/\epsilon l_{c}$, and decays exponentially at higher energies. We propose a theoretical model to account for the low-energy behavior. For the case of Coulomb interactions between the electrons, at even-denominator filling factors such as $\nu=1/2$, we predict that the spectral weight varies as $e^{-\omega_0/|\omega|}$, for $\omega\rightarrow 0$

How spiking neurons give rise to a temporal-feature map

A temporal-feature map is a topographic neuronal representation of temporal attributes of phenomena or objects that occur in the outside world. We explain the evolution of such maps by means of a spike-based Hebbian learning rule in conjunction with a presynaptically unspecific contribution in that, if a synapse changes, then all other synapses connected to the same axon change by a small fraction as well. The learning equation is solved for the case of an array of Poisson neurons. We discuss the evolution of a temporal-feature map and the synchronization of the single cells’ synaptic structures, in dependence upon the strength of presynaptic unspecific learning. We also give an upper bound for the magnitude of the presynaptic interaction by estimating its impact on the noise level of synaptic growth. Finally, we compare the results with those obtained from a learning equation for nonlinear neurons and show that synaptic structure formation may profit from the nonlinearity

On arbitrages arising from honest times

In the context of a general continuous financial market model, we study whether the additional information associated with an honest time gives rise to arbitrage profits. By relying on the theory of progressive enlargement of filtrations, we explicitly show that no kind of arbitrage profit can ever be realised strictly before an honest time, while classical arbitrage opportunities can be realised exactly at an honest time as well as after an honest time. Moreover, stronger arbitrages of the first kind can only be obtained by trading as soon as an honest time occurs. We carefully study the behavior of local martingale deflators and consider no-arbitrage-type conditions weaker than NFLVR.Comment: 25 pages, revised versio

Endonuclease heteroduplex mismatch cleavage for detecting mutation genetic variation of trypsin inhibitors in soybean

The objective of this work was to evaluate the genetic variation of trypsin inhibitor in cultivated (Glycine max L.) and wild (Glycine sofa Siebold & Zucc.) soybean varieties. Genetic variations of the Kunitz trypsin inhibitor, represented by a 21-kD protein (KTI), and of the Bowman-Birk trypsin chymotrypsin inhibitor (BBI) were evaluated in cultivated (G. max) and wild (G. sofa) soybean varieties. Endonuclease heteroduplex mismatch cleavage assays were performed to detect mutations in the KTI gene, with a single-stranded specific nuclease obtained from celery extracts (CEL I). The investigated soybean varieties showed low level of genetic variation in KTI and BBI. PCR-RFLP analysis divided the BBI-A type into subtypes A1 and A2, and showed that Tib type of KTI is the dominant type. Digestion with restriction enzymes was not able to detect differences between ti-null and other types of Ti alleles, while the endonuclease heteroduplex mismatch cleavage assay with CEL I could detect ti-null type. The digestion method with CEL I provides a simple and useful genetic tool for SNP analysis. The presented method can be used as a tool for fast and useful screening of desired genotypes in future breeding programs of soybean

Thermostatistics of deformed bosons and fermions

Based on the q-deformed oscillator algebra, we study the behavior of the mean occupation number and its analogies with intermediate statistics and we obtain an expression in terms of an infinite continued fraction, thus clarifying successive approximations. In this framework, we study the thermostatistics of q-deformed bosons and fermions and show that thermodynamics can be built on the formalism of q-calculus. The entire structure of thermodynamics is preserved if ordinary derivatives are replaced by the use of an appropriate Jackson derivative and q-integral. Moreover, we derive the most important thermodynamic functions and we study the q-boson and q-fermion ideal gas in the thermodynamic limit.Comment: 14 pages, 2 figure

On the selection of AGN neutrino source candidates for a source stacking analysis with neutrino telescopes

The sensitivity of a search for sources of TeV neutrinos can be improved by grouping potential sources together into generic classes in a procedure that is known as source stacking. In this paper, we define catalogs of Active Galactic Nuclei (AGN) and use them to perform a source stacking analysis. The grouping of AGN into classes is done in two steps: first, AGN classes are defined, then, sources to be stacked are selected assuming that a potential neutrino flux is linearly correlated with the photon luminosity in a certain energy band (radio, IR, optical, keV, GeV, TeV). Lacking any secure detailed knowledge on neutrino production in AGN, this correlation is motivated by hadronic AGN models, as briefly reviewed in this paper. The source stacking search for neutrinos from generic AGN classes is illustrated using the data collected by the AMANDA-II high energy neutrino detector during the year 2000. No significant excess for any of the suggested groups was found.Comment: 43 pages, 12 figures, accepted by Astroparticle Physic

Green function techniques in the treatment of quantum transport at the molecular scale

The theoretical investigation of charge (and spin) transport at nanometer length scales requires the use of advanced and powerful techniques able to deal with the dynamical properties of the relevant physical systems, to explicitly include out-of-equilibrium situations typical for electrical/heat transport as well as to take into account interaction effects in a systematic way. Equilibrium Green function techniques and their extension to non-equilibrium situations via the Keldysh formalism build one of the pillars of current state-of-the-art approaches to quantum transport which have been implemented in both model Hamiltonian formulations and first-principle methodologies. We offer a tutorial overview of the applications of Green functions to deal with some fundamental aspects of charge transport at the nanoscale, mainly focusing on applications to model Hamiltonian formulations.Comment: Tutorial review, LaTeX, 129 pages, 41 figures, 300 references, submitted to Springer series "Lecture Notes in Physics

Solar flare prediction using advanced feature extraction, machine learning and feature selection

YesNovel machine-learning and feature-selection algorithms have been developed to study: (i) the flare prediction capability of magnetic feature (MF) properties generated by the recently developed Solar Monitor Active Region Tracker (SMART); (ii) SMART's MF properties that are most significantly related to flare occurrence. Spatio-temporal association algorithms are developed to associate MFs with flares from April 1996 to December 2010 in order to differentiate flaring and non-flaring MFs and enable the application of machine learning and feature selection algorithms. A machine-learning algorithm is applied to the associated datasets to determine the flare prediction capability of all 21 SMART MF properties. The prediction performance is assessed using standard forecast verification measures and compared with the prediction measures of one of the industry's standard technologies for flare prediction that is also based on machine learning - Automated Solar Activity Prediction (ASAP). The comparison shows that the combination of SMART MFs with machine learning has the potential to achieve more accurate flare prediction than ASAP. Feature selection algorithms are then applied to determine the MF properties that are most related to flare occurrence. It is found that a reduced set of 6 MF properties can achieve a similar degree of prediction accuracy as the full set of 21 SMART MF properties