Dynamical decoupling efficiency versus quantum non-Markovianity

We investigate the relationship between non-Markovianity and the effectiveness of a dynamical decoupling protocol for qubits undergoing pure dephasing. We consider an exact model in which dephasing arises due to a bosonic environment with a spectral density of the Ohmic class. This is parametrised by an Ohmicity parameter by changing which we can model both Markovian and non-Markovian environments. Interestingly, we find that engineering a non-Markovian environment is detrimental to the efficiency of the dynamical decoupling scheme, leading to a worse coherence preservation. We show that each dynamical decoupling pulse reverses the flow of quantum information and, on this basis, we investigate the connection between dynamical decoupling efficiency and the reservoir spectral density. Finally, in the spirit of reservoir engineering, we investigate the optimum system-reservoir parameters for achieving maximum stationary coherences.Comment: 6 pages, 4 figure

Special issue on signal processing and machine learning for biomedical data

This Special Issue is focused on advanced techniques in signal processing, analysis, modelling, and classification, applied to a variety of medical diagnostic problems. Biomedical data play a fundamental role in many fields of research and clinical practice. Very often the complexity of these data and their large volume makes it necessary to develop advanced analysis techniques and systems. Furthermore, the introduction of new techniques and methodologies for diagnostic purposes, especially in the field of medical imaging, requires new signal processing and machine learning methods. The recent progress in machine learning techniques, and in particular deep learning, revolutionized various fields of artificial vision, significantly pushing the state of the art of artificial vision systems into a wide range of high-level tasks. Such progress can help address problems in the analysis of biomedical data.This Special Issue placed particular emphasis on contributions dealing with practical, applications-led research, on the use of methods and devices in clinical diagnosis. The works that make up this special issue show a remarkable variety of applications for the detection and classification of medical imaging problems. In particular, the aforementioned works can be divided on the basis of types of techniques used, into three categories—signal processing (SP) methods, traditional machine learning (ML) methods, and deep learning (DL) methods

The phytocannabinoid, Δ(9) -tetrahydrocannabivarin, can act through 5-HT1 A receptors to produce antipsychotic effects

Funded by: •GW Pharmaceuticals Acknowledgements: The authors wish to thank Mrs Lesley Stevenson for technical support and Dr John Raymond, Dr Keith Parker and Dr Ethan Russo for providing human 5-HT1A CHO cells. This research was supported by a grant from GW Pharmaceuticals to M. G. C. and R. G. P.Peer reviewedPostprin

Deep CNN for IIF Images Classification in Autoimmune Diagnostics

The diagnosis and monitoring of autoimmune diseases are very important problem in medicine. The most used test for this purpose is the antinuclear antibody (ANA) test. An indirect immunofluorescence (IIF) test performed by Human Epithelial type 2 (HEp-2) cells as substrate antigen is the most common methods to determine ANA. In this paper we present an automatic HEp-2 specimen system based on a convolutional neural network method able to classify IIF images. The system consists of a module for features extraction based on a pre-trained AlexNet network and a classification phase for the cell-pattern association using six support vector machines and a k-nearest neighbors classifier. The classification at the image-level was obtained by analyzing the pattern prevalence at cell-level. The layers of the pre-trained network and various system parameters were evaluated in order to optimize the process. This system has been developed and tested on the HEp-2 images indirect immunofluorescence images analysis (I3A) public database. To test the generalisation performance of the method, the leave-one-specimen-out procedure was used in this work. The performance analysis showed an accuracy of 96.4% and a mean class accuracy equal to 93.8%. The results have been evaluated comparing them with some of the most representative works using the same database

Sedimentation of halloysite nanotubes from different deposits in aqueous media at variable ionic strengths

Halloysite clay is a natural nanomaterial that is attracting a growing interest in colloidal science. The halloysite aqueous dispersion stability is a key aspect for the configuration of a purification protocol as well as to establish the durability of a formulation. A physico-chemical study demonstrated the role of ionic strength and nanotube characteristic sizes on the sedimentation behavior. We highlighted the importance of the electrostatic repulsions exercised between the particles in the settling process. A protocol for image analysis has been proposed to provide robust information from time resolved optical images on the suspensions. In conclusion, we managed to correlate microscopic aspect to the peculiar sedimentation process of halloysite nanotubes

Cracks in the Melting Pot: Immigration, School Choice, and Segregation

We examine whether low-skilled immigration to the United States has contributed to immigrants\u27 residential isolation by reducing native demand for public schools. We address endogeneity in school demographics using established Mexican settlement patterns in California and use a comparison group to account for immigration\u27s broader effects. We estimate that between 1970 and 2000, the average California school district lost more than 14 non-Hispanic households with children to other districts in its metropolitan area for every 10 additional households enrolling low-English Hispanics in its public schools. By disproportionately isolating children, the native reaction to immigration may have longer-run consequences than previously thought. (JEL H75, I21, J15, J24, J61, R23

Fatal delayed diagnosis in a patient with falciparum malaria

This is a classical case of blackwater fever which is one of the most serious hematologic complications of falciparum malaria. The clinical manifestations of this acute intravascular hemolityc anemia are fulminating and delayed diagnosis is an important cause of mortality

The socio-ecology of zoonotic infections

AbstractThe resurgence of infectious diseases of zoonotic origin observed in recent years imposes a major morbidity/mortality burden worldwide, and also a major economic burden that extends beyond pure medical costs. The resurgence and epidemiology of zoonoses are complex and dynamic, being influenced by varying parameters that can roughly be categorized as human-related, pathogen-related, and climate/environment-related; however, there is significant interplay between these factors. Human-related factors include modern life trends such as ecotourism, increased exposure through hunting or pet owning, and culinary habits, industrialization sequelae such as farming/food chain intensification, globalization of trade, human intrusion into ecosystems and urbanization, significant alterations in political regimes, conflict with accompanying breakdown of public health and surveillance infrastructure, voluntary or involuntary immigration, loosening of border controls, and hierarchy issues in related decision-making, and scientific advances that allow easier detection of zoonotic infections and evolution of novel susceptible immunocompromised populations. Pathogen-related factors include alterations in ecosystems and biodiversity that influence local fauna synthesis, favouring expansion of disease hosts or vectors, pressure for virulence/resistance selection, and genomic variability. Climate/environment-related factors, either localized or extended, such as El Niño southern oscillation or global warming, may affect host–vector life cycles through varying mechanisms. Emerging issues needing clarification include the development of predictive models for the infectious disease impact of environmental projects, awareness of the risk imposed on immunocompromised populations, recognition of the chronicity burden for certain zoonoses, and the development of different evaluations of the overall stress imposed by a zoonotic infection on a household, and not strictly a person