High titers of transmissible spongiform encephalopathy infectivity associated with extremely low levels of PrP in vivo

Rona Barron - ORCID: 0000-0003-4512-9177 https://orcid.org/0000-0003-4512-9177Diagnosis of transmissible spongiform encephalopathy (TSE) disease in humans and ruminants relies on the detection in post-mortem brain tissue of the protease-resistant form of the host glycoprotein PrP. The presence of this abnormal isoform (PrPSc) in tissues is taken as indicative of the presence of TSE infectivity. Here we demonstrate conclusively that high titers of TSE infectivity can be present in brain tissue of animals that show clinical and vacuolar signs of TSE disease but contain low or undetectable levels of PrPSc. This work questions the correlation between PrPSc level and the titer of infectivity and shows that tissues containing little or no proteinase K-resistant PrP can be infectious and harbor high titers of TSE infectivity. Reliance on protease-resistant PrPSc as a sole measure of infectivity may therefore in some instances significantly underestimate biological properties of diagnostic samples, thereby undermining efforts to contain and eradicate TSEs.https://doi.org/10.1074/jbc.M704329200282pubpub4

SUSY vertex algebras and supercurves

This article is a continuation of math.QA/0603633 Given a strongly conformal SUSY vertex algebra V and a supercurve X we construct a vector bundle V_X on X, the fiber of which, is isomorphic to V. Moreover, the state-field correspondence of V canonically gives rise to (local) sections of these vector bundles. We also define chiral algebras on any supercurve X, and show that the vector bundle V_X, corresponding to a SUSY vertex algebra, carries the structure of a chiral algebra.Comment: 50 page

MDL Convergence Speed for Bernoulli Sequences

The Minimum Description Length principle for online sequence estimation/prediction in a proper learning setup is studied. If the underlying model class is discrete, then the total expected square loss is a particularly interesting performance measure: (a) this quantity is finitely bounded, implying convergence with probability one, and (b) it additionally specifies the convergence speed. For MDL, in general one can only have loss bounds which are finite but exponentially larger than those for Bayes mixtures. We show that this is even the case if the model class contains only Bernoulli distributions. We derive a new upper bound on the prediction error for countable Bernoulli classes. This implies a small bound (comparable to the one for Bayes mixtures) for certain important model classes. We discuss the application to Machine Learning tasks such as classification and hypothesis testing, and generalization to countable classes of i.i.d. models.Comment: 28 page

Ab initio parametrised model of strain-dependent solubility of H in alpha-iron

The calculated effects of interstitial hydrogen on the elastic properties of alpha-iron from our earlier work are used to describe the H interactions with homogeneous strain fields using ab initio methods. In particular we calculate the H solublility in Fe subject to hydrostatic, uniaxial, and shear strain. For comparison, these interactions are parametrised successfully using a simple model with parameters entirely derived from ab initio methods. The results are used to predict the solubility of H in spatially-varying elastic strain fields, representative of realistic dislocations outside their core. We find a strong directional dependence of the H-dislocation interaction, leading to strong attraction of H by the axial strain components of edge dislocations and by screw dislocations oriented along the critical slip direction. We further find a H concentration enhancement around dislocation cores, consistent with experimental observations.Comment: part 2/2 from splitting of 1009.3784 (first part was 1102.0187), minor changes from previous version

On Convergence Properties of Shannon Entropy

Convergence properties of Shannon Entropy are studied. In the differential setting, it is shown that weak convergence of probability measures, or convergence in distribution, is not enough for convergence of the associated differential entropies. A general result for the desired differential entropy convergence is provided, taking into account both compactly and uncompactly supported densities. Convergence of differential entropy is also characterized in terms of the Kullback-Liebler discriminant for densities with fairly general supports, and it is shown that convergence in variation of probability measures guarantees such convergence under an appropriate boundedness condition on the densities involved. Results for the discrete setting are also provided, allowing for infinitely supported probability measures, by taking advantage of the equivalence between weak convergence and convergence in variation in this setting.Comment: Submitted to IEEE Transactions on Information Theor

Experimental determination of the degree of quantum polarisation of continuous variable states

We demonstrate excitation-manifold resolved polarisation characterisation of continuous-variable (CV) quantum states. In contrast to traditional characterisation of polarisation that is based on the Stokes parameters, we experimentally determine the Stokes vector of each excitation manifold separately. Only for states with a given photon number does the methods coincide. For states with an indeterminate photon number, for example Gaussian states, the employed method gives a richer and more accurate description. We apply the method both in theory and in experiment to some common states to demonstrate its advantages.Comment: 5 page

Free-of-charge medicine schemes in the NHS: A local and regional drug and therapeutic committee's experience

INTRODUCTION: Free-of-charge (FoC) medicine schemes are increasingly available and allow access to investigational treatments outside clinical trials or in advance of licensing or NHS commissioning. METHODS: We retrospectively reviewed FoC medicine schemes evaluated between 2013 and 2019 by a single NHS trust and a regional drug and therapeutics committee (DTC). The details of each locally reviewed FoC scheme, and any nationally available Medicines and Healthcare products Regulatory Agency Early Access to Medicines Scheme (MHRA EAMS) in the same period, were recorded and categorised. RESULTS: Most FoC schemes (95%) allowed access to medicines intended to address an unmet clinical need. Over 7 years, 90% were company-FoC schemes and 10% were MHRA EAMS that were locally reviewed. Phase 3 clinical trial data were available for 44% of FoC schemes, 37% had phase 2 data and 19% were supported only by phase 1 data, retrospective observational studies or preclinical data. Utilisation of company-FoC schemes increased on average by 50% per year, while MHRA EAMS schemes showed little growth. CONCLUSION: Company-FoC medicine schemes are increasingly common. This may indicate a preference for pharmaceutical companies to independently co-ordinate schemes. Motivations for company-FoC schemes remain unclear and many provide access to treatments that are yet to be evaluated in appropriately conducted clinical trials, and whose efficacy and risk of harm remain uncertain. There is no standardisation of this practice and there is no regulatory oversight. Moreover, no standardised data collection framework is in place that could demonstrate the utility of such programmes in addressing unmet clinical need or to allow generation of further evidence

The effect of alongcoast advection on pacific northwest shelf and slope water properties in relation to upwelling variability

The Northern California Current System experiences highly variable seasonal upwelling in addition to larger basin-scale variability, both of which can significantly affect its water chemistry. Salinity and temperature fields from a 7 year ROMS hindcast model of this region (43°N-50°N), along with extensive particle tracking, were used to study interannual variability in water properties over both the upper slope and the midshelf bottom. Variation in slope water properties was an order of magnitude smaller than on the shelf. Furthermore, the primary relationship between temperature and salinity anomalies in midshelf bottom water consisted of variation in density (cold/salty versus warm/fresh), nearly orthogonal to the anomalies along density levels (cold/fresh versus warm/salty) observed on the upper slope. These midshelf anomalies were well-explained (R2=0.6) by the combination of interannual variability in local and remote alongshore wind stress, and depth of the California Undercurrent (CUC) core. Lagrangian analysis of upper slope and midshelf bottom water shows that both are affected simultaneously by large-scale alongcoast advection of water through the northern and southern boundaries. The amplitude of anomalies in bottom oxygen and dissolved inorganic carbon (DIC) on the shelf associated with upwelling variability are larger than those associated with typical variation in alongcoast advection, and are comparable to observed anomalies in this region. However, a large northern intrusion event in 2004 illustrates that particular, large-scale alongcoast advection anomalies can be just as effective as upwelling variability in changing shelf water properties on the interannual scale

Approximation and learning by greedy algorithms

We consider the problem of approximating a given element $f$ from a Hilbert space $\mathcal{H}$ by means of greedy algorithms and the application of such procedures to the regression problem in statistical learning theory. We improve on the existing theory of convergence rates for both the orthogonal greedy algorithm and the relaxed greedy algorithm, as well as for the forward stepwise projection algorithm. For all these algorithms, we prove convergence results for a variety of function classes and not simply those that are related to the convex hull of the dictionary. We then show how these bounds for convergence rates lead to a new theory for the performance of greedy algorithms in learning. In particular, we build upon the results in [IEEE Trans. Inform. Theory 42 (1996) 2118--2132] to construct learning algorithms based on greedy approximations which are universally consistent and provide provable convergence rates for large classes of functions. The use of greedy algorithms in the context of learning is very appealing since it greatly reduces the computational burden when compared with standard model selection using general dictionaries.Comment: Published in at http://dx.doi.org/10.1214/009053607000000631 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org