Enhancement of quantum nondemolition measurements with an electro-optic feed-forward amplifier

Methods for the enhancement of optical quantum nondemolition (QND) measurements are discussed. We review the use of meter squeezing; as a QND enhancement tool and present a method of QND enhancement using an electro-optic feed-forward amplifier. By applying a linearized theory it is shown that these techniques work very well together. The combined effect of these enhancement methods is modeled for two QND systems, a squeezed light beam splitter and an optical parametric amplifier. We also discuss the conflict between the normal QND criteria and QND systems that involve noiseless amplification. We use an additional parameter to quantify the problem. A method for correcting the effects of noiseless amplification is discussed and modeled. We also discuss a special case of QND that eliminates the optical interaction between the meter and signal input beams. This system is shown to be a very effective QND device. [S1050-2947(99)06411-2]

Normal 24-hour ambulatory proximal and distal gastroesophageal reflux parameters in Chinese.

OBJECTIVE: To quantify normal proximal and distal oesophageal acid parameters in healthy Chinese. DESIGN: Observational study. SETTING: University teaching hospital, Hong Kong. SUBJECTS AND METHODS: Twenty healthy adults who were not on medication and were free from gastrointestinal symptoms were recruited by advertisement. Ambulatory oesophageal acid (pH5 minutes, 4/0; and the longest single acid exposure episode, 11.2/3.0 minutes. CONCLUSION: Physiological gastroesophageal reflux occurs in healthy Chinese. These initial data provide a preliminary reference range that could be utilised by laboratories studying Chinese subjects.published_or_final_versio

Context variability promotes generalization in reading aloud: Insight from a neural network simulation

This is the final version. Available from the Cognitive Science Society via the link in this recordHow do neural network models of quasiregular domains learn to represent knowledge that varies in its consistency with the domain, and generalize this knowledge appropriately? Recent work focusing on spelling-to-sound correspondences in English proposes that a graded “warping” mechanism determines the extent to which the pronunciation of a newly learned word should generalize to its orthographic neighbors. We explored the micro-structure of this proposal by training a network to pronounce new made-up words that were consistent with the dominant pronunciation (regulars), were comprised of a completely unfamiliar pronunciation (exceptions), or were consistent with a subordinate pronunciation in English (ambiguous). Crucially, by training the same spelling-to-sound mapping with either one or multiple items, we tested whether variation in adjacent, within-item context made a given pronunciation more able to generalize. This is exactly what we found. Context variability, therefore, appears to act as a modulator of the warping in quasiregular domains.Economic and Social Research Council (ESRC)NSERCCF

Identification of Novel Rosavirus Species That Infects Diverse Rodent Species and Causes Multisystemic Dissemination in Mouse Model

published_or_final_versio

The identification of informative genes from multiple datasets with increasing complexity

Background In microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets; (3) comparing the different gene selections and the influence of increasing the model complexity; (4) functional analysis of the informative genes. Results In this paper, we identify the most appropriate model complexity using cross-validation and independent test set validation for predicting gene expression in three published datasets related to myogenesis and muscle differentiation. Furthermore, we demonstrate that models trained on simpler datasets can be used to identify interactions among genes and select the most informative. We also show that these models can explain the myogenesis-related genes (genes of interest) significantly better than others (P < 0.004) since the improvement in their rankings is much more pronounced. Finally, after further evaluating our results on synthetic datasets, we show that our approach outperforms a concordance method by Lai et al. in identifying informative genes from multiple datasets with increasing complexity whilst additionally modelling the interaction between genes. Conclusions We show that Bayesian networks derived from simpler controlled systems have better performance than those trained on datasets from more complex biological systems. Further, we present that highly predictive and consistent genes, from the pool of differentially expressed genes, across independent datasets are more likely to be fundamentally involved in the biological process under study. We conclude that networks trained on simpler controlled systems, such as in vitro experiments, can be used to model and capture interactions among genes in more complex datasets, such as in vivo experiments, where these interactions would otherwise be concealed by a multitude of other ongoing events

Longitudinal grey and white matter changes in frontotemporal dementia and Alzheimer's disease

Behavioural variant frontotemporal dementia (bvFTD) and Alzheimer's disease (AD) dementia are characterised by progressive brain atrophy. Longitudinal MRI volumetry may help to characterise ongoing structural degeneration and support the differential diagnosis of dementia subtypes. Automated, observer-independent atlas-based MRI volumetry was applied to analyse 102 MRI data sets from 15 bvFTD, 14 AD, and 10 healthy elderly control participants with consecutive scans over at least 12 months. Anatomically defined targets were chosen a priori as brain structures of interest. Groups were compared regarding volumes at clinic presentation and annual change rates. Baseline volumes, especially of grey matter compartments, were significantly reduced in bvFTD and AD patients. Grey matter volumes of the caudate and the gyrus rectus were significantly smaller in bvFTD than AD. The bvFTD group could be separated from AD on the basis of caudate volume with high accuracy (79% cases correct). Annual volume decline was markedly larger in bvFTD and AD than controls, predominantly in white matter of temporal structures. Decline in grey matter volume of the lateral orbitofrontal gyrus separated bvFTD from AD and controls. Automated longitudinal MRI volumetry discriminates bvFTD from AD. In particular, greater reduction of orbitofrontal grey matter and temporal white matter structures after 12 months is indicative of bvFTD

Characterization of star-forming dwarf galaxies at 0.1 ≲ z ≲ 0.9 in VUDS: Probing the low-mass end of the mass-metallicity relation

We present the discovery and spectrophotometric characterization of a large sample of 164 faint ($i_{AB}$ $\sim$ $23$-$25$ mag) star-forming dwarf galaxies (SFDGs) at redshift $0.13$ $\leq z \leq$ $0.88$ selected by the presence of bright optical emission lines in the VIMOS Ultra Deep Survey (VUDS). We investigate their integrated physical properties and ionization conditions, which are used to discuss the low-mass end of the mass-metallicity relation (MZR) and other key scaling relations. We use optical VUDS spectra in the COSMOS, VVDS-02h, and ECDF-S fields, as well as deep multiwavelength photometry, to derive stellar masses, star formation rates (SFR) and gas-phase metallicities. The VUDS SFDGs are compact (median $r_{e}$ $\sim$ $1.2$ kpc), low-mass ($M_{*}$ $\sim$ $10^7-10^9$ $M_{\odot}$) galaxies with a wide range of star formation rates (SFR($H\alpha$) $\sim 10^{-3}-10^{1}$ $M_{\odot}/yr$) and morphologies. Overall, they show a broad range of subsolar metallicities (12+log(O/H)=$7.26$-$8.7$; $0.04$ $\lesssim Z/Z_{\odot} \lesssim$ $1$). The MZR of SFDGs shows a flatter slope compared to previous studies of galaxies in the same mass range and redshift. We find the scatter of the MZR partly explained in the low mass range by varying specific SFRs and gas fractions amongst the galaxies in our sample. Compared with simple chemical evolution models we find that most SFDGs do not follow the predictions of a "closed-box" model, but those from a gas regulating model in which gas flows are considered. While strong stellar feedback may produce large-scale outflows favoring the cessation of vigorous star formation and promoting the removal of metals, younger and more metal-poor dwarfs may have recently accreted large amounts of fresh, very metal-poor gas, that is used to fuel current star formation