Quantum and Classical Glass Transitions in LiHoxY1−xF4Li Ho_x Y_{1-x} F_4

When performed in the proper low field, low frequency limits, measurements of the dynamics and the nonlinear susceptibility in the model Ising magnet in transverse field, $\text{LiHo}_x\text{Y}_{1-x}\text{F}_4$, prove the existence of a spin glass transition for $x$ = 0.167 and 0.198. The classical behavior tracks for the two concentrations, but the behavior in the quantum regime at large transverse fields differs because of the competing effects of quantum entanglement and random fields.Comment: 5 pages, 5 figures. Updated figure 3 with corrected calibration information for thermometr

Leave-one-out prediction error of systolic arterial pressure time series under paced breathing

In this paper we show that different physiological states and pathological conditions may be characterized in terms of predictability of time series signals from the underlying biological system. In particular we consider systolic arterial pressure time series from healthy subjects and Chronic Heart Failure patients, undergoing paced respiration. We model time series by the regularized least squares approach and quantify predictability by the leave-one-out error. We find that the entrainment mechanism connected to paced breath, that renders the arterial blood pressure signal more regular, thus more predictable, is less effective in patients, and this effect correlates with the seriousness of the heart failure. The leave-one-out error separates controls from patients and, when all orders of nonlinearity are taken into account, alive patients from patients for which cardiac death occurred

A comparative study of Gaussian Graphical Model approaches for genomic data

The inference of networks of dependencies by Gaussian Graphical models on high-throughput data is an open issue in modern molecular biology. In this paper we provide a comparative study of three methods to obtain small sample and high dimension estimates of partial correlation coefficients: the Moore-Penrose pseudoinverse (PINV), residual correlation (RCM) and covariance-regularized method $(\ell_{2C})$. We first compare them on simulated datasets and we find that PINV is less stable in terms of AUC performance when the number of variables changes. The two regularized methods have comparable performances but $\ell_{2C}$ is much faster than RCM. Finally, we present the results of an application of $\ell_{2C}$ for the inference of a gene network for isoprenoid biosynthesis pathways in Arabidopsis thaliana.Comment: 7 pages, 1 figure, RevTex4, version to appear in the proceedings of 1st International Workshop on Pattern Recognition, Proteomics, Structural Biology and Bioinformatics: PR PS BB 2011, Ravenna, Italy, 13 September 201

Comparison of different multivariate calibrations and ensemble methods for estimating selected soil properties with vis-NIR reflectance spectroscopy

Sustainable soil management requires a correct assessment of soil chemi- cal and physical properties. Historically, this has been gained through conventional laboratory analyses, which are considered costly and time-consuming, particularly when a large number of soil samples need to be analysed. An alternative, faster and less expensive, approach is based on the use of reflectance spectroscopy in the vis- NIR domain. This approach implies the calibration of predictive models that relate the spectral reflectance to soil properties. The goodness of the models can be partic- ularly influenced by the multivariate methods used. In this article, we compare the performance of different multivariate and statistical ensemble methods for estimating some basic soil properties, such as sand, silt, clay, and organic carbon in the specific pedo-environmental conditions of an important agricultural area in southern Italy

Dynamic rotor mode in antiferromagnetic nanoparticles

We present experimental, numerical, and theoretical evidence for a new mode of antiferromagnetic dynamics in nanoparticles. Elastic neutron scattering experiments on 8 nm particles of hematite display a loss of diffraction intensity with temperature, the intensity vanishing around 150 K. However, the signal from inelastic neutron scattering remains above that temperature, indicating a magnetic system in constant motion. In addition, the precession frequency of the inelastic magnetic signal shows an increase above 100 K. Numerical Langevin simulations of spin dynamics reproduce all measured neutron data and reveal that thermally activated spin canting gives rise to a new type of coherent magnetic precession mode. This "rotor" mode can be seen as a high-temperature version of superparamagnetism and is driven by exchange interactions between the two magnetic sublattices. The frequency of the rotor mode behaves in fair agreement with a simple analytical model, based on a high temperature approximation of the generally accepted Hamiltonian of the system. The extracted model parameters, as the magnetic interaction and the axial anisotropy, are in excellent agreement with results from Mossbauer spectroscopy

Proof Relevant Corecursive Resolution

Resolution lies at the foundation of both logic programming and type class context reduction in functional languages. Terminating derivations by resolution have well-defined inductive meaning, whereas some non-terminating derivations can be understood coinductively. Cycle detection is a popular method to capture a small subset of such derivations. We show that in fact cycle detection is a restricted form of coinductive proof, in which the atomic formula forming the cycle plays the role of coinductive hypothesis. This paper introduces a heuristic method for obtaining richer coinductive hypotheses in the form of Horn formulas. Our approach subsumes cycle detection and gives coinductive meaning to a larger class of derivations. For this purpose we extend resolution with Horn formula resolvents and corecursive evidence generation. We illustrate our method on non-terminating type class resolution problems.Comment: 23 pages, with appendices in FLOPS 201

Laser ablation of silicon with THz bursts of femtosecond pulses

In this work, we performed an experimental investigation supported by a theoretical analysis, to improve knowledge on the laser ablation of silicon with THz bursts of femtosecond laser pulses. Laser ablated craters have been created using 200 fs pulses at a wavelength of 1030 nm on silicon samples systematically varying the burst features and comparing to the normal pulse mode (NPM). Using bursts in general allowed reducing the thermal load to the material, however, at the expense of the ablation rate. The higher the number of pulses in the bursts and the lower the intra-burst frequency, the lower is the specific ablation rate. However, bursts at 2 THz led to a higher specific ablation rate compared to NPM, in a narrow window of parameters. Theoretical investigations based on the numerical solution of the density-dependent two temperature model revealed that lower lattice temperatures are reached with more pulses and lower intra-burst frequencies, thus supporting the experimental evidence of the lower thermal load in burst mode (BM). This is ascribed to the weaker transient drop of reflectivity, which suggests that with bursts less energy is transferred from the laser to the material. This also explains the trends of the specific ablation rates. Moreover, we found that two-photon absorption plays a fundamental role during BM processing in the THz frequency range

The Immune Landscapes of Polypoid and Nonpolypoid Precancerous Colorectal Lesions.

Little is known about the immunoediting process in precancerous lesions. We explored this aspect of benign colorectal adenomas with a descriptive analysis of the immune pathways and immune cells whose regulation is linked to the morphology and size of these lesions. Two series of polypoid and nonpolypoid colorectal adenomas were used in this study: 1) 84 samples (42 lesions, each with matched samples of normal mucosa) whose gene expression data were used to quantify the tumor morphology- and size-related dysregulation of immune pathways collected in the Molecular Signature Database, using Gene Set Enrichment Analysis; 2) 40 other lesions examined with immunohistochemistry to quantify the presence of immune cells in the stromal compartment. In the analysis of transcriptomic data, 429 immune pathways displayed significant differential regulation in neoplasms of different morphology and size. Most pathways were significantly upregulated or downregulated in polypoid lesions versus nonpolypoid lesions (regardless of size). Differential pathway regulation associated with lesion size was observed only in polypoid neoplasms. These findings were mirrored by tissue immunostaining with CD4, CD8, FOXP3, MHC-I, CD68, and CD163 antibodies: stromal immune cell counts (mainly T lymphocytes and macrophages) were significantly higher in polypoid lesions. Certain markers displayed significant size-related differences regardless of lesion morphology. Multivariate analysis of variance showed that the marker panel clearly discriminated between precancerous lesions of different morphologies and sizes. Statistical analysis of immunostained cell counts fully support the results of the transcriptomic data analysis: the density of infiltration of most immune cells in the stroma of polypoid precancerous lesions was significantly higher than that observed in nonpolypoid lesions. Large neoplasms also have more immune cells in their stroma than small lesions. Immunoediting in precancerous colorectal tumors may vary with lesion morphology and stage of development, and this variability could influence a given lesion's trajectory to cancer

Discovering granger-causal features from deep learning networks

© Springer Nature Switzerland AG 2018. In this research, we propose deep networks that discover Granger causes from multivariate temporal data generated in financial markets. We introduce a Deep Neural Network (DNN) and a Recurrent Neural Network (RNN) that discover Granger-causal features for bivariate regression on bivariate time series data distributions. These features are subsequently used to discover Granger-causal graphs for multivariate regression on multivariate time series data distributions. Our supervised feature learning process in proposed deep regression networks has favourable F-tests for feature selection and t-tests for model comparisons. The experiments, minimizing root mean squared errors in the regression analysis on real stock market data obtained from Yahoo Finance, demonstrate that our causal features significantly improve the existing deep learning regression models

Lipid-coated zinc oxide nanocrystals as innovative ROS-generators for photodynamic therapy

Photodynamic Therapy (PDT) is a medical treatment that combines the administration of a nontoxic drug, called photosensitizer (PS), with light irradiation of the targeted region. It has been proposed as a new cancer therapy, promising better selectivity and fewer side-effects compared to traditional chemo- and radio-therapies. PSs indeed can accumulate specifically within the region of interest so that when the light is directly focused only in that region the therapeutic effect is highly localized. Traditional PSs, like chlorins and porphyrins, suffer from several drawbacks such as aggregation in biological media and poor biocompatibility. Thus, the development of innovative photosensitizers able to overcome these issues is crucial to the therapeutic action of PDT. Among the others, nanostructured Zinc Oxide (ZnO) has been recently proposed as new therapeutic agent and PS thanks to its semiconducting properties, biocompatible features, and ease of functionalization [1]. Nevertheless, further efforts are needed in order to improve its colloidal stability in biological media and to unravel the effective therapeutic mechanism. Here, we propose the synthesis and characterization of lipid-coated ZnO nanoparticles as new photosensitizer for cancer PDT [2]. First, by Dynamic Light Scattering (DLS) experiments, we show that the lipid-coating increases the colloidal stability of the ZnO NPs in Phosphate buffered saline (PBS). Then, using Electron Paramagnetic Resonance (EPR) coupled with the spin-trapping technique, we demonstrate and characterize the ability of bare and lipid-coated ZnO NPs to generate Reactive Oxygen Species (ROS) in water only when remotely actuated via light irradiation. Interestingly, our results aware that the surface chemistry of the NPs greatly influence the type of photo-generated ROS. Finally, we show that our NPs are effectively internalized inside human epithelial carcinoma cells (HeLa) via a lysosomal pathway and that they are able to generate ROS inside cancer cells. [1] B. Dumontel, M. Canta, H. Engelke, A. Chiodoni, L. Racca, A. Ancona, T. Limongi, G. Canavese and V. Cauda, ‎J. Mater. Chem. B. under revision. [2] A. Ancona, H. Engelke, N. Garino, B. Dumontel, W.Fazzini and V. Cauda, to be submitted. The support from ERC Starting Grant – Project N. 678151 “Trojananohorse” is gratefully acknowledged