Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-calibration for lab-μCT using space-time regularized projection-based DVC and model reduction

An online calibration procedure for x-ray lab-CT is developed using projection-based digital volume correlation. An initial reconstruction of the sample is positioned in the 3D space for every angle so that its projection matches the initial one. This procedure allows a space-time displacement field to be estimated for the scanned sample, which is regularized with (i) rigid body motions in space and (ii) modal time shape functions computed using model reduction techniques (i.e. proper generalized decomposition). The result is an accurate identification of the position of the sample adapted for each angle, which may deviate from the desired perfect rotation required for standard reconstructions. An application of this procedure to a 4D in situ mechanical test is shown. The proposed correction leads to a much improved tomographic reconstruction quality.
Unexpectedly strong effect of supergranulation on the detectability of Earth twins orbiting Sun-like stars with radial velocities

Context. Magnetic activity and surface flows at different scales pertub radial velocity measurements. This affects the detectability of low-mass exoplanets. Aims. In these flows, the effect of supergranulation is not as well characterized as the other flows, and we wish to estimate its effect on the detection of Earth-like planets in the habitable zone of Sun-like stars. Methods. We produced time series of radial velocities due to oscillations, granulation, and supergranulation, and estimated the detection limit for a G2 star and a period of 300 days. We also studied in detail the behavior of the power when the signal of a 1 MEarth planet was superposed on the signal from the stellar flows. Results. We find that the detection rate does not reach 100% except for the supergranulation level we assume, which is still optimistic, and for an excellent sampling. Conclusions. We conclude that with current knowledge, it is a very challenging task to find Earth twins around Sun-like stars with our current capabilities.
Measuring Entanglement Negativity

Entanglement not only plays a crucial role in quantum technologies, but is key to our understanding of quantum correlations in many-body systems. However, in an experiment, the only way of measuring entanglement in a generic mixed state is through reconstructive quantum tomography, requiring an exponential number of measurements in the system size. Here, we propose an operational scheme to measure the entanglement — as given by the negativity — between arbitrary subsystems of size NA and NB , with O(NA +NB) measurements, and without any prior knowledge of the state. We propose how to experimentally measure the partially transposed moments of a density matrix, and using just the first few of these, extract the negativity via Chebyshev approximation or machine learning techniques. Our procedure will allow entanglement measurements in a wide variety of systems, including strongly interacting many body systems in both equilibrium and non-equilibrium regimes.
Ein Health Technology Assessment - Was ist wissenschaftlich gesichert zur Prävention und Kontrolle von MRSA?

judgements, consensus statements, or reports. Few hygiene measures have proven to be evidence-based and appropriate to control the spread of MRSA, or have proven to be cost-effective in various acute-care settings (especially screening of patients at risk). Isolation of MRSA patients is generally recommended, and improving hand hygiene is of high importance.
Passive Polarimetric Imaging

Passive electro-optical polarimetric imaging is a form of remote sensing in which the properties associated with electromagnetic field orientation are exploited as a means to discriminate between objects in an extended scene. The purpose of this chapter is to introduce some fundamental concepts in the science of imaging polarimetry. These concepts include the Stokes-Mueller description of polarized light, the physical mechanisms that contribute to polarimetric image contrast, a mathematical description of several polarimetric imaging systems, and an example target detection algorithm. Polarimetric image contrast is discussed in terms of reflected, emitted, and scattered light. Special emphasis is placed upon atmospheric effects and, separately, material properties in terms of the polarimetric bidirectional reflectance distribution function. Division-of-time and division-of-focal-plane modulated imaging polarimeters are considered from both the data reduction matrix and Fourier analysis points-of-view. Procedures for the radiometric and polarimetric calibration of imaging systems are also provided.
Visual saliency detection based on in-depth analysis of sparse representation

Abstract. Visual saliency detection has been receiving great attention in recent years since it can facilitate a wide range of applications in computer vision. A variety of saliency models have been proposed based on different assumptions within which saliency detection via sparse representation is one of the newly arisen approaches. However, most existing sparse representation-based saliency detection methods utilize partial characteristics of sparse representation, lacking of in-depth analysis. Thus, they may have limited detection performance. Motivated by this, this paper proposes an algorithm for detecting visual saliency based on in-depth analysis of sparse representation. A number of discriminative dictionaries are first learned with randomly sampled image patches by means of inner product-based dictionary atom classification. Then, the input image is partitioned into many image patches, and these patches are classified into salient and nonsalient ones based on the in-depth analysis of sparse coding coefficients. Afterward, sparse reconstruction errors are calculated for the salient and nonsalient patch sets. By investigating the sparse reconstruction errors, the most salient atoms, which tend to be from the most salient region, are screened out and taken away from the discriminative dictionaries. Finally, an effective method is exploited for saliency map generation with the reduced dictionaries. Comprehensive evaluations on publicly available datasets and comparisons with some state-of-the-art approaches demonstrate the effectiveness of the proposed algorithm.
A Review of Therapeutic Nerve Decompression for Neuropathy in Hansen's Disease with Research Suggestions

Nerve decompression surgery for leprosy neuritis has a long history and large literature. New understanding of the high frequency of spontaneous recovery from nerve function impairment requires re-evaluation of the value of decompression in acute nerve dysfunction with strong evidence-based protocols. Several reports and theoretical considerations suggest research avenues that might offer hope for prevention of long-term complications and relief of impairment and disabilities.
Bayesian multiresolution dynamic models

Dynamic process models such as state space models or diffusion process models are widely used for many natural dynamic phenomena. Statistical modelling has been essential tool for performing inference and prediction for processes in the various application areas including physics, economics, engineering, environment and biological sciences, climatology, etc. However, nonstationary and/or nonGaussian process models remain challenging. I consider a class of nonstationary dynamic process models with time-varying coefficients. The key to the approach is the development of a hierarchical model for the main process of interest conditional upon coefficients which are themselves modeled via state space models. These parameter models are allowed to have different time scales. Bayesian inference for such a multi-resolution dynamic process model is applied to various examples such as surface temperature data. Bayesian model selection and model uncertainty problems are also discussed. Spatiotemporal processes in the physical, environmental, and biological sciences often exhibit complicated and diverse patterns across different space-time scales. Both scientific understanding and observed data also vary in form and content across these scales. I develop a Bayesian hierarchical framework by which the combination of information sources accross spatiotemporal scale can be accomplished. The approach restricts a few essential scales when such a restriction justify the trade-off between
Anatomy of a Cooling Flow: The Feedback Response to Pure Cooling in the Core of the Phoenix Cluster

We present new, deep observations of the Phoenix cluster from Chandra, the Hubble Space Telescope, and the Karl Jansky Very Large Array. These data provide an order-of-magnitude improvement in depth and/or angular resolution over previous observations at X-ray, optical, and radio wavelengths. We find that the one-dimensional temperature and entropy profiles are consistent with expectations for pure-cooling models. In particular, the entropy profile is well fit by a single power law at all radii, with no evidence for excess entropy in the core. In the inner ∼10 kpc, the cooling time is shorter than any other known cluster by an order of magnitude, while the ratio of the cooling time to freefall time (tcool/tff) approaches unity, signaling that the intracluster medium is unable to resist multiphase condensation on kpc scales. The bulk of the cooling in the inner ∼20 kpc is confined to a low-entropy filament extending northward from the central galaxy, with tcool/tff ∼ 1 over the length of the filament. In this filament, we find evidence for ∼1010 M⊙ in cool (∼104 K) gas (as traced by the [O ii]λλ3726,3729 doublet), which is coincident with the low-entropy filament and absorbing soft X-rays. The bulk of this cool gas is draped around and behind a pair of X-ray cavities, presumably bubbles that have been inflated by radio jets. These data support a picture in which active galactic nucleus feedback is promoting the formation of a multiphase medium via uplift of low-entropy gas, either via ordered or chaotic (turbulent) motions.
Surface enhanced Raman spectroscopic substrate utilizing gold nanoparticles on carbon nanotubes

This paper reports a new low cost technique for fabricating Surface Enhanced Raman Spectroscopy substrates. A Gold (Au) nano-metallic structure for surface enhancement is created by depositing Au nanoparticles on a Multi-wall Carbon Nanotube layer previously deposited on the etched Aluminum foil. A low cost, simple method is used to deposit the nanotubes. Huge enhancements have been observed in both in vitro and in vivo measurements.
Dynamics and protection of quantum discord via cavity engineering

Exploring strategies to protect quantum correlations from the detrimental effects of their surroundings is crucial in the field of quantum computing and information sciences. For this purpose, we consider a simple engineered environment consisting of two noninteracting separated qubits, each embedded in a single-mode lossy cavity which is further connected to another lossy cavity. We investigate the dynamics and protection of quantum discord for the initially maximum entangled state in the weak and strong coupling regimes between the qubits and their corresponding cavities. It is found that the quantum discord can be preserved efficiently for a much longer time by adjusting the hopping strengths between the two coupled cavities. In addition, if the secondary cavity is perfect then a stationary quantum discord between the two qubits arises after certain damping oscillations. Interestingly, the magnitude of the stationary quantum discord can be maximized by increasing the coupling strengths of the two cavities.
Emergence of Compositional Language with Deep Generational Transmission

Consider a collaborative task that requires communication. Two agents are placed in an environment and must create a language from scratch in order to coordinate. Recent work has been interested in what kinds of languages emerge when deep reinforcement learning agents are put in such a situation, and in particular in the factors that cause language to be compositional-i.e. meaning is expressed by combining words which themselves have meaning. Evolutionary linguists have also studied the emergence of compositional language for decades, and they find that in addition to structural priors like those already studied in deep learning, the dynamics of transmitting language from generation to generation contribute significantly to the emergence of compositionality. In this paper, we introduce these cultural evolutionary dynamics into language emergence by periodically replacing agents in a population to create a knowledge gap, implicitly inducing cultural transmission of language. We show that this implicit cultural transmission encourages the resulting languages to exhibit better compositional generalization and suggest how elements of cultural dynamics can be further integrated into populations of deep agents.
Brain Invaders Adaptive versus Non-Adaptive P300 Brain-Computer Interface dataset

We describe the experimental procedures for a dataset that we have made publicly available at https://doi.org/10.5281/zenodo.1494163 in mat and csv formats. This dataset contains electroencephalographic (EEG) recordings of 24 subjects doing a visual P300 Brain-Computer Interface experiment on PC. The visual P300 is an event-related potential elicited by visual stimulation, peaking 240-600 ms after stimulus onset. The experiment was designed in order to compare the use of a P300-based brain-computer interface on a PC with and without adaptive calibration using Riemannian geometry. The brain-computer interface is based on electroencephalography (EEG). EEG data were recorded thanks to 16 electrodes. Data were recorded during an experiment taking place in the GIPSA-lab, Grenoble, France, in 2013 (Congedo, 2013). Python code for manipulating the data is available at https://github.com/plcrodrigues/py.BI.EEG.2013-GIPSA. The ID of this dataset is BI.EEG.2013-GIPSA.
Unterschiedliche Entitäten mit ähnlichen Symptomen - Differenzialdiagnose und -therapie von Schlafapnoe und Hypoventilation

S chlafapnoe und Hypoventilation sind zwei unterschiedliche Entitäten, die aber durchaus gemeinsam beim gleichen Patienten vorkommen können. Die Begriffe ‘Apnoe’ oder ‘Hypopnoe’ bezeichnen den fehlenden oder herabgesetzten Atemfluss, der mithilfe eines Sensors an Nase oder Mund gemessen wird. Eine exakte Differenzierung obstruktiver und zentraler Apnoen ist mithilfe der transdiaphragmalen Druckmessung (pdi) möglich. Der Begriff ‘Hypoventilation’ steht dagegen für eine Reduktion der alveolären Ventilation, die immer mit einer Hyperkapnie und einer sekundären Hypoxämie einhergeht. Ursache für eine Reduktion der alveolären Ventilation (VA) ist eine Verminderung des Atemminutenvolumens (VE) oder eine Erhöhung der Totraumventilation (VD): VA = VE VD. Eine Hypoventilation kann auch ohne Apnoen auftreten. Da der Sauerstoffpartialdruck (pO2) im Blut mit dem Kohlensäurepartialdruck (pCO2) invers korreliert, hat jeder Patient mit Hyperkapnie somit auch eine (sekundäre) Hypoxämie (Abb. 1). Allerdings ist der Sauerstoffpartialdruck keine präzise Messgröße für eine Hypoventilation, da ihn eventuell zusätzliche Störgrößen (Diffusionsstörungen, Verteilungsstörungen, Shunt) weiter beeinträchtigen (Abb. 2). Die Hypoventilation lässt sich daher nur über den Kohlensäurepartialdruck messen. Als Messmethoden kommen die Blutgasanalyse, die Kapnografie oder eine transkutane pCO2-Messung in Betracht. Folge der Hyperkapnie ist eine alveoläre Hypoxie, die zur Polyzythämie, zur pulmonalen Hypertonie und damit zum Cor pulmonale führt. Bei weiterer Zunahme kann eine respiratorische Dekompensation auftreten (Abb. 3).
Zeit ist Myokard - Frühbehandlung des akuten Koronarsyndroms von besonderer Bedeutung

D ie Entwicklung der Vorstellungen zur Pathogenese, Diagnostik, Akuttherapie und Prävention des akuten Koronarsyndroms (ACS) erfährt seit mehr als 25 Jahren eine enorme Dynamik, die erstaunlicherweise bis in die jüngste Zeit anhält. Dabei ist der Umfang der Literatur zu neuen Studienergebnissen für den Einzelnen fast nicht mehr überschaubar. Leitlinien versuchen daher – mit dem Nachteil eines gewissen Mangels an Aktualität – die wesentlichen Erkenntnisse zusammenzufassen und in einer mehr oder weniger komprimierten Form dem vor Ort tätigen Arzt zugänglich zu machen. In den letzten fünf Jahren haben sowohl die Europäische Gesellschaft für Kardiologie (ESC), die „American Heart Association“ (AHA) zusammen mit dem „American College of Cardiology“ (ACC), die Deutsche Gesellschaft für Kardiologie und der European Resuscitation Council (ERC) Leitlinien entwickelt (1, 2, 5, 17, 18, 29), die zum Teil mehr allgemein, zum Teil aber auch gezielt auf die Frühphase der Behandlung eingehen. Gerade die Frühphase ist von besonderer Bedeutung: Einerseits gefährden tachykarde lebensbedrohliche Rhythmusstörungen den Patienten mit den Symptomen eines akuten Koronarsyndroms akut, andererseits eröffnet die Frühdiagnostik die Möglichkeit einer gezielten, schnellstmöglichen kausalen Therapie, die das Ausmaß der Folgeschäden möglichst gering zu erhalten vermag. Da der vorliegende Artikel sich spezifisch mit der Frühdiagnostik und Frühbehandlung in den ersten Stunden nach der Manifestation des akuten Koronarsyndroms entsprechend den jüngsten Empfehlungen des ERC beschäftigt, wird bewusst auf die Darstellung der zusätzlichen Perspektiven mittelfristiger und langfristiger Behandlungskonzepte verzichtet.
Bayesian Methods in Brain Connectivity Change Point Detection with EEG Data and Genetic Algorithm

Human brain is processing a great amount of information everyday, and our brain regions are organized optimally for this information processing. There have been increasing number of studies focusing on functional or effective connectivity in human brain regions in the last decade. In this dissertation, Bayesian methods in Brain connectivity change point detection are discussed. First, a review of state-of-the-art Bayesian-inference-based methods applied to functional magnetic resonance imaging (fMRI) data is carried out, three methods are reviewed and compared. Second, the Bayesian connectivity change point model is extended to change point analysis in electroencephalogram (EEG) data, and the ability of EEG measures of frontal and temporo-parietal activity during mindfulness therapy to track response to dysfunctional anxiety patients’ treatment is tested successfully. Then an optimized method for Bayesian connectivity change point model with genetic algorithm (GA) is proposed and proved to be more efficient in change point detection. And due to the good parallel performance of GA, the change point detection method can be parallelized in GPU or multi-processor computers as a future work. Furthermore, a more advanced Bayesian bi-cluster connectivity change point model is developed to simultaneously detect change point of each subject within a group, and cluster subjects into different groups according to their change point distribution and connectivity dynamics. The method is also validated on experimental datasets. After discussing brain change point detection, a review of Bayesian analysis of complex mutations in HBV HCV and HIV studies is also included as part of my Ph.D. work. Finally, conclusions are drawn and future work is discussed. INDEX WORDS: Bayesian inference, Brain network, Change point, Connectivity dynamics, Markov chain Monte Carlo, Electroencephalography, Genetic algorithm BAYESIAN METHODS IN BRAIN CONNECTIVITY CHANGE POINT DETECTION WITH EEG DATA AND GENETIC ALGORITHM
Iterative Identification of Hammerstein Parameter Varying Systems With Parameter Uncertainties Based on the Variational Bayesian Approach

The identification of the multiple model-based Hammerstein parameter varying systems is studied in this paper. The parameters of the considered systems vary as the systems perform on different operating conditions. For each local model, the input nonlinear output-error structure is introduced to describe the dynamical property. Allocating an exponential weighting function to each local model, the nonlinear dynamics of the global system is approximated by combining all local models. The variational Bayesian (VB) approach is adopted to find the solution to the problem of parameter estimation. For the parameter uncertainties, instead of the point estimation, the posterior distribution of each model parameters is obtained under the framework of the VB approach. Two numerical simulation examples and an experiment carried on a multitank system have been employed to demonstrate that the proposed approach can work effectively.
Analysis of losses in superconducting magnets based on the Nb3Sn Rutherford cable configuration for future gantries

Proton therapy for the treatment of cancers adopts a rotating gantry to irradiate the tumor from any direction. The gantry system consists of different beamline magnets that bend the proton beam towards the patient. The use of superconducting magnets allows reducing the size and weight of the last bending section. During the gantry operation, it is necessary to change the magnetic field of the last bending section in order to vary the proton penetration depth. This change determines electrodynamic transients in the superconducting strands and cables that generate losses. A thorough computation of these losses is essential for a safe design of the cryogenic system. Two main types of losses must be accounted for when dealing with multistrand superconducting cables, related to the magnetization and coupling of the superconducting filaments (intrastrand losses) and to the current loops induced between different strands during electrodynamic transients (interstrand losses). This work describes the methodologies and numerical codes adopted to compute the hysteresis and coupling losses in an innovative magnet system designed by the Paul Scherrer Institute for future superconducting gantries. In this design the superconducting coils are wound using Nb3Sn Rutherford cables. The validation of the numerical tools versus analytical results is presented for simplified cases with uniform magnetic flux density applied to the conductor. The results of the losses calculation and the impact of the different contributions are then presented for the actual proposed magnet system configuration.
CRISPR KNOCKOUT OF TAX1BP3 REDUCES EXPRESSION OF P53 IN HUMAN PAPILLOMA NEGATIVE AND HUMAN PAPILLOMA POSITIVE CERVICAL CARCINOMA CELLS.

Mona El Helaly El Sherbeny 1* , Hussein Abdelaziz Abdalla 1 , El Said Mohamed Abd El Hady 2 , Ian Noel Hampson ,Anthony.W. Oliver, Souad Mohamed Abo Azma, and Muhamad Muhamad Said. 1. Department of Medical Biochemistry, Faculty of Medicine, Mansoura University, El Gomhouria Street, Dakahlia Governorate, Egypt. 2. Department of Obstetrics and Gynecology, Faculty of Medicine, Mansoura University, El Gomhouria Street, Dakahlia Governorate, Egypt. 3. School of Cancer and Imaging Science, Research Floor, St Mary's Hospital, Hathersage Road, Manchester M13 0JH. UK. Manuscript Info Abstract . Manuscript History