Exact closed form analytical solutions for vibrating cavities

For one-dimensional vibrating cavity systems appearing in the standard illustration of the dynamical Casimir effect, we propose an approach to the construction of exact closed-form solutions. As new results, we obtain solutions that are given for arbitrary frequencies, amplitudes and time regions. In a broad range of parameters, a vibrating cavity model exhibits the general property of exponential instability. Marginal behavior of the system manifests in a power-like growth of radiated energy.Comment: 17 pages, 7 figure

Exact solution for the energy density inside a one-dimensional non-static cavity with an arbitrary initial field state

We study the exact solution for the energy density of a real massless scalar field in a two-dimensional spacetime, inside a non-static cavity with an arbitrary initial field state, taking into account the Neumann and Dirichlet boundary conditions. This work generalizes the exact solution proposed by Cole and Schieve in the context of the Dirichlet boundary condition and vacuum as the initial state. We investigate diagonal states, examining the vacuum and thermal field as particular cases. We also study non-diagonal initial field states, taking as examples the coherent and Schrodinger cat states.Comment: 10 pages, 8 figure

Vibrating Cavities - A numerical approach

We present a general formalism allowing for efficient numerical calculation of the production of massless scalar particles from vacuum in a one-dimensional dynamical cavity, i.e. the dynamical Casimir effect. By introducing a particular parametrization for the time evolution of the field modes inside the cavity we derive a coupled system of first-order linear differential equations. The solutions to this system determine the number of created particles and can be found by means of numerical methods for arbitrary motions of the walls of the cavity. To demonstrate the method which accounts for the intermode coupling we investigate the creation of massless scalar particles in a one-dimensional vibrating cavity by means of three particular cavity motions. We compare the numerical results with analytical predictions as well as a different numerical approach.Comment: 28 pages, 19 figures, accepted for publication in J. Opt. B: Quantum Semiclass. Op

Investigating the Effect of Emoji in Opinion Classification of Uzbek Movie Review Comments

Opinion mining on social media posts has become more and more popular. Users often express their opinion on a topic not only with words but they also use image symbols such as emoticons and emoji. In this paper, we investigate the effect of emoji-based features in opinion classification of Uzbek texts, and more specifically movie review comments from YouTube. Several classification algorithms are tested, and feature ranking is performed to evaluate the discriminative ability of the emoji-based features.Comment: 10 pages, 1 figure, 3 table

Not all shellfish "allergy" is allergy!

The popularity of shellfish has been increasing worldwide, with a consequent increase in adverse reactions that can be allergic or toxic. The approximate prevalence of shellfish allergy is estimated at 0.5-2.5% of the general population, depending on degree of consumption by age and geographic regions. The manifestations of shellfish allergy vary widely, but it tends to be more severe than most other food allergens

Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample

EuroPineDB: a high-coverage web database for maritime pine transcriptome

Pinus pinaster is an economically and ecologically important species that is becoming a woody gymnosperm model. Its enormous genome size makes whole-genome sequencing approaches are hard to apply. Therefore, the expressed portion of the genome has to be characterised and the results and annotations have to be stored in dedicated databases

High-throughput SNP genotyping in the highly heterozygous genome of Eucalyptus: assay success, polymorphism and transferability across species

<p>Abstract</p> <p>Background</p> <p>High-throughput SNP genotyping has become an essential requirement for molecular breeding and population genomics studies in plant species. Large scale SNP developments have been reported for several mainstream crops. A growing interest now exists to expand the speed and resolution of genetic analysis to outbred species with highly heterozygous genomes. When nucleotide diversity is high, a refined diagnosis of the target SNP sequence context is needed to convert queried SNPs into high-quality genotypes using the Golden Gate Genotyping Technology (GGGT). This issue becomes exacerbated when attempting to transfer SNPs across species, a scarcely explored topic in plants, and likely to become significant for population genomics and inter specific breeding applications in less domesticated and less funded plant genera.</p> <p>Results</p> <p>We have successfully developed the first set of 768 SNPs assayed by the GGGT for the highly heterozygous genome of <it>Eucalyptus </it>from a mixed Sanger/454 database with 1,164,695 ESTs and the preliminary 4.5X draft genome sequence for <it>E. grandis</it>. A systematic assessment of <it>in silico </it>SNP filtering requirements showed that stringent constraints on the SNP surrounding sequences have a significant impact on SNP genotyping performance and polymorphism. SNP assay success was high for the 288 SNPs selected with more rigorous <it>in silico </it>constraints; 93% of them provided high quality genotype calls and 71% of them were polymorphic in a diverse panel of 96 individuals of five different species.</p> <p>SNP reliability was high across nine <it>Eucalyptus </it>species belonging to three sections within subgenus Symphomyrtus and still satisfactory across species of two additional subgenera, although polymorphism declined as phylogenetic distance increased.</p> <p>Conclusions</p> <p>This study indicates that the GGGT performs well both within and across species of <it>Eucalyptus </it>notwithstanding its nucleotide diversity ≥2%. The development of a much larger array of informative SNPs across multiple <it>Eucalyptus </it>species is feasible, although strongly dependent on having a representative and sufficiently deep collection of sequences from many individuals of each target species. A higher density SNP platform will be instrumental to undertake genome-wide phylogenetic and population genomics studies and to implement molecular breeding by Genomic Selection in <it>Eucalyptus</it>.</p

World Allergy Organization (WAO) Diagnosis and Rationale for Action against Cow's Milk Allergy (DRACMA) Guidelines update – I – Plan and definitions

Since the World Allergy Organization (WAO) Diagnosis and Rationale against Cow's Milk Allergy (DRACMA) Guidelines were published 10 years ago, new evidence has accumulated about the diagnosis, therapy, and specific immunotherapy for cow's milk allergy (CMA). For this reason, WAO has felt the need to update the guidelines. We introduce here this update. The new DRACMA guidelines aim to comprehensively address the guidance on diagnosis and therapy of both IgE non-IgE-mediated forms of cow's milk allergy in children and adults. They will be divided into 18 chapters, each of which will be dedicated to an aspect. The focus will be on the meta-analyzes and recommendations that will be expressed for the 3 most relevant clinical aspects: (a) the diagnostic identification of the condition; (b) the choice of the replacement formula in case of CMA in infancy when the mother is not able to breastfeed, and (c) the use of specific immunotherapy for cow's milk protein allergy