A Birkhoff connection between quantum circuits and linear classical reversible circuits

Birkhoff's theorem tells how any doubly stochastic matrix can be decomposed as a weighted sum of permutation matrices. Similar theorems on unitary matrices reveal a connection between quantum circuits and linear classical reversible circuits. It triggers the question whether a quantum computer can be regarded as a superposition of classical reversible computers

ESR Fine Structure of Manganese Ions in Zeolite A Detects Strong Variations of the Coordination Environment

The electron spin resonance spectra of Mn 2+ exchanged zeolite A have been investigated as a function of the monovalent co-cation (K + ,Na + ,Li + ,Cs + ,or NH4 + ), Mn 2+ content, recording frequency, and temperature. Three new Mn 2+ species are observed with a well-resolved fine structure; this allows for the first time a direct quantitative determination of the zero-field splitting (ZFS) parameters in zeolites. In hydrated zeolites, three ESR active Mn 2+ species are observed, characterized by different values for the ZFS parameter D. Species I has D ) 0.035 cm -1 . Species II is closer to a regular octahedron, with D ) 0.010 cm -1 . Species III, with D ) 0.14 cm -1 , is in a strongly axially distorted coordination. Species I is dominant in MnKA, MnCsA, and MnNH4A, while II and III are found in MnNaA and MnLiA. In fully dehydrated zeolites, two species are observed. Species IV has a small hyperfine constant A and is present in dry NaA and KA. Species V is observed in dry LiA; it has axial symmetry with a large, temperature-dependent D. Species V probably represents Mn 2+ in a 3-fold coordination in a 6-ring. In partially hydrated zeolites, a tetrahedral species VI is observed. The spectroscopic data elucidate the location of manganese-( II) ions in zeolite A, particularly at relatively low metal loadings

Detection of REM Sleep Behaviour Disorder by Automated Polysomnography Analysis

Evidence suggests Rapid-Eye-Movement (REM) Sleep Behaviour Disorder (RBD) is an early predictor of Parkinson's disease. This study proposes a fully-automated framework for RBD detection consisting of automated sleep staging followed by RBD identification. Analysis was assessed using a limited polysomnography montage from 53 participants with RBD and 53 age-matched healthy controls. Sleep stage classification was achieved using a Random Forest (RF) classifier and 156 features extracted from electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) channels. For RBD detection, a RF classifier was trained combining established techniques to quantify muscle atonia with additional features that incorporate sleep architecture and the EMG fractal exponent. Automated multi-state sleep staging achieved a 0.62 Cohen's Kappa score. RBD detection accuracy improved by 10% to 96% (compared to individual established metrics) when using manually annotated sleep staging. Accuracy remained high (92%) when using automated sleep staging. This study outperforms established metrics and demonstrates that incorporating sleep architecture and sleep stage transitions can benefit RBD detection. This study also achieved automated sleep staging with a level of accuracy comparable to manual annotation. This study validates a tractable, fully-automated, and sensitive pipeline for RBD identification that could be translated to wearable take-home technology.Comment: 20 pages, 3 figure

The "quasi-stable" lipid shelled microbubble in response to consecutive ultrasound pulses

Controlled microbubble stability upon exposure to consecutive ultrasound exposures is important for increased sensitivity in contrast enhanced ultrasound diagnostics and manipulation for localised drug release. An ultra high-speed camera operating at 13 × 10 6 frames per second is used to show that a physical instability in the encapsulating lipid shell can be promoted by ultrasound, causing loss of shell material that depends on the characteristics of the microbubble motion. This leads to well characterized disruption, and microbubbles follow an irreversible trajectory through the resonance peak, causing the evolution of specific microbubble spectral signatures. © 2012 American Institute of Physics

The SOS response of Listeria monocytogenes is involved in stress resistance and mutagenesis

The SOS response is a conserved pathway that is activated under certain stress conditions and is regulated by the repressor LexA and the activator RecA. The food-borne pathogen Listeria monocytogenes contains RecA and LexA homologs, but their roles in Listeria have not been established. In this study, we identified the SOS regulon in L. monocytogenes by comparing the transcription profiles of the wild-type strain and the DeltarecA mutant strain after exposure to the DNA damaging agent mitomycin C. In agreement with studies in other bacteria, we identified an imperfect palindrome AATAAGAACATATGTTCGTTT as the SOS operator sequence. The SOS regulon of L. monocytogenes consists of 29 genes in 16 LexA regulated operons, encoding proteins with functions in translesion DNA synthesis and DNA repair. We furthermore identified a role for the product of the LexA regulated gene yneA in cell elongation and inhibition of cell division. As anticipated, RecA of L. monocytogenes plays a role in mutagenesis; DeltarecA cultures showed considerably lower rifampicin and streptomycin resistant fractions than the wild-type cultures. The SOS response is activated after stress exposure as shown by recA- and yneA-promoter reporter studies. Subsequently, stress survival studies showed DeltarecA mutant cells to be less resistant to heat, H(2)O(2), and acid exposure than wild-type cells. Our results indicate that the SOS response of L. monocytogenes contributes to survival upon exposure to a range of stresses, thereby likely contributing to its persistence in the environment and in the hos

Examining a sentiment algorithm on session patient records in an eating disorder treatment setting:a preliminary study

Background: Clinicians collect session therapy notes within patient session records. Session records contain valuable information about patients’ treatment progress. Sentiment analysis is a tool to extract emotional tones and states from text input and could be used to evaluate patients’ sentiment during treatment over time. This preliminary study aims to investigate the validity of automated sentiment analysis on session patient records within an eating disorder (ED) treatment context against the performance of human raters.Methods: A total of 460 patient session records from eight participants diagnosed with an ED were evaluated on their overall sentiment by an automated sentiment analysis and two human raters separately. The inter-rater agreement (IRR) between the automated analysis and human raters and IRR among the human raters was analyzed by calculating the intra-class correlation (ICC) under a continuous interpretation and weighted Cohen’s kappa under a categorical interpretation. Furthermore, differences regarding positive and negative matches between the human raters and the automated analysis were examined in closer detail.Results: The ICC showed a moderate automated-human agreement (ICC = 0.55), and the weighted Cohen’s kappa showed a fair automated-human (k = 0.29) and substantial human-human agreement (k = 0.68) for the evaluation of overall sentiment. Furthermore, the automated analysis lacked words specific to an ED context.Discussion/conclusion: The automated sentiment analysis performed worse in discerning sentiment from session patient records compared to human raters and cannot be used within practice in its current state if the benchmark is considered adequate enough. Nevertheless, the automated sentiment analysis does show potential in extracting sentiment from session records. The automated analysis should be further developed by including context-specific ED words, and a more solid benchmark, such as patients’ own mood, should be established to compare the performance of the automated analysis to