Role of topology in determining the precision of a finite thermometer

Temperature fluctuations of a finite system follow the Landau bound \u3b4T 2 = T 2/C(T ) where C(T ) is the heat capacity of the system. In turn, the same bound sets a limit to the precision of temperature estimation when the system itself is used as a thermometer. In this paper, we employ graph theory and the concept of Fisher information to assess the role of topology on the thermometric performance of a given system. We find that low connectivity is a resource to build precise thermometers working at low temperatures, whereas highly connected systems are suitable for higher temperatures. Upon modeling the thermometer as a set of vertices for the quantum walk of an excitation, we compare the precision achievable by position measurement to the optimal one, which itself corresponds to energy measurement

Continuous-time quantum walks in the presence of a quadratic perturbation

We address the properties of continuous-time quantum walks with Hamiltonians of the form H = L + \u3bbL2, with L the Laplacian matrix of the underlying graph and the perturbation \u3bbL2 motivated by its potential use to introduce next-nearest-neighbor hopping. We consider cycle, complete, and star graphs as paradigmatic models with low and high connectivity and/or symmetry. First, we investigate the dynamics of an initially localized walker. Then we devote attention to estimating the perturbation parameter \u3bb using only a snapshot of the walker dynamics. Our analysis shows that a walker on a cycle graph spreads ballistically independently of the perturbation, whereas on complete and star graphs one observes perturbation-dependent revivals and strong localization phenomena. Concerning the estimation of the perturbation, we determine the walker preparations and the simple graphs that maximize the quantum Fisher information. We also assess the performance of position measurement, which turns out to be optimal, or nearly optimal, in several situations of interest. Besides fundamental interest, our study may find applications in designing enhanced algorithms on graphs

Spotting Insects from Satellites: Modeling the Presence of Culicoides Imicola Through Deep CNNs

Nowadays, Vector-Borne Diseases (VBDs) raise a severe threat for public health, accounting for a considerable amount of human illnesses. Recently, several surveillance plans have been put in place for limiting the spread of such diseases, typically involving on-field measurements. Such a systematic and effective plan still misses, due to the high costs and efforts required for implementing it. Ideally, any attempt in this field should consider the triangle vectors-host-pathogen, which is strictly linked to the environmental and climatic conditions. In this paper, we exploit satellite imagery from Sentinel-2 mission, as we believe they encode the environmental factors responsible for the vector's spread. Our analysis - conducted in a data-driver fashion - couples spectral images with ground-truth information on the abundance of Culicoides imicola. In this respect, we frame our task as a binary classification problem, underpinning Convolutional Neural Networks (CNNs) as being able to learn useful representation from multi-band images. Additionally, we provide a multi-instance variant, aimed at extracting temporal patterns from a short sequence of spectral images. Experiments show promising results, providing the foundations for novel supportive tools, which could depict where surveillance and prevention measures could be prioritized

The structure of DNA by direct imaging

The structure of DNA was determined in 1953 by x-ray fiber diffraction. Several attempts have been made to obtain a direct image of DNA with alternative techniques. The direct image is intended to allow a quantitative evaluation of all relevant characteristic lengths present in a molecule. A direct image of DNA, which is different from diffraction in the reciprocal space, is difficult to obtain for two main reasons: the intrinsic very low contrast of the elements that form the molecule and the difficulty of preparing the sample while preserving its pristine shape and size. We show that through a preparation procedure compatible with the DNA physiological conditions, a direct image of a single suspended DNA molecule can be obtained. In the image, all relevant lengths of A-form DNA are measurable. A high-resolution transmission electron microscope that operates at 80 keV with an ultimate resolution of 1.5 Å was used for this experiment. Direct imaging of a single molecule can be used as a method to address biological problems that require knowledge at the single-molecule level, given that the average information obtained by x-ray diffraction of crystals or fibers is not sufficient for detailed structure determination, or when crystals cannot be obtained from biological molecules or are not sufficient in understanding multiple protein configurations

Science communication and concept of risk in bio-tech-sciences: Is it a part of neo-liberalism, or foucaultian bio-politics?

In this work a Raman flow cytometer is presented. It consists of a microfluidic device that takes advantages of the basic principles of Raman spectroscopy and flow cytometry. The microfluidic device integrates calibrated microfluidic channels- where the cells can flow one-by-one -, allowing single cell Raman analysis. The microfluidic channel integrates plasmonic nanodimers in a fluidic trapping region. In this way it is possible to perform Enhanced Raman Spectroscopy on single cell. These allow a label-free analysis, providing information about the biochemical content of membrane and cytoplasm of the each cell. Experiments are performed on red blood cells (RBCs), peripheral blood lymphocytes (PBLs) and myelogenous leukemia tumor cells (K562)

Outbreak of unusualSalmonella entericaserovar Typhimurium monophasic variant 1,4 [5],12:i:-, Italy, June 2013 to September 2014

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A New Weighted Degree Centrality Measure: The Application in an Animal Disease Epidemic.

In recent years researchers have investigated a growing number of weighted heterogeneous networks, where connections are not merely binary entities, but are proportional to the intensity or capacity of the connections among the various elements. Different degree centrality measures have been proposed for this kind of networks. In this work we propose weighted degree and strength centrality measures (WDC and WSC). Using a reducing factor we correct classical centrality measures (CD) to account for tie weights distribution. The bigger the departure from equal weights distribution, the greater the reduction. These measures are applied to a real network of Italian livestock movements as an example. A simulation model has been developed to predict disease spread into Italian regions according to animal movements and animal population density. Model's results, expressed as infected regions and number of times a region gets infected, were related to weighted and classical degree centrality measures. WDC and WSC were shown to be more efficient in predicting node's risk and vulnerability. The proposed measures and their application in an animal network could be used to support surveillance and infection control strategy plans