Fragment-Based Drug Discovery for RNA Targets

Rapid development within the fields of both fragment-based drug discovery (FBDD) and medicinal targeting of RNA provides possibilities for combining technologies and methods in novel ways. This review provides an overview of fragment-based screening (FBS) against RNA targets, including a discussion of the most recently used screening and hit validation methods such as NMR spectroscopy, X-ray crystallography, and virtual screening methods. A discussion of fragment library design based on research from small-molecule RNA binders provides an overview on both the currently limited guidelines within RNA-targeting fragment library design, and future possibilities. Finally, future perspectives are provided on screening and hit validation methods not yet used in combination with both fragment screening and RNA targets.     

Provably correct runtime monitoring

Runtime monitoring is an established technique to enforce a wide range of program safety and security properties. We present a formalization of monitoring and monitor inlining, for the Java Virtual Machine. Monitors are security automata given in a special-purpose monitor specification language, ConSpec. The automata operate on finite or infinite strings of calls to a fixed API, allowing local dependencies on parameter values and heap content. We use a two-level class file annotation scheme to characterize two key properties: (i) that the program is correct with respect to the monitor as a constraint on allowed program behavior, and (ii) that the program has a copy of the given monitor embedded into it. As the main application of these results we sketch a simple inlining algorithm and show how the two-level annotations can be completed to produce a fully annotated program which is valid in the standard sense of Floyd/Hoare logic. This establishes the mediation property that inlined programs are guaranteed to adhere to the intended policy. Furthermore, validity can be checked efficiently using a weakest precondition based annotation checker, thus preparing the ground for on-device checking of policy adherence in a proof-carrying code setting.     

Improving the SMAC atmospheric correction code by analysis of Meteosat Second Generation NDVI and surface reflectance data

In order to obtain high quality data, the correction of atmospheric perturbations acting upon land surface reflectance measurements recorded by a space-based sensor is an important topic within remote sensing. For many years the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer model and the Simplified Method for Atmospheric Correction (SMAC) codes have been used for this atmospheric correction, but previous studies have shown that in a number of situations the quality of correction provided by the SMAC is low. This paper describes a method designed to improve the quality of the SMAC atmospheric correction algorithm through a slight increase in its computational complexity. Data gathered from the SEVIRI aboard Meteosat Second Generation (MSG) is used to validate the additions to SMAC, both by comparison to simulated data corrected using the highly accurate 6S method and by comparison to in-situ and 6S corrected SEVIRI data gathered for two field sites in Africa. The additions to the SMAC are found to greatly increase the quality of atmospheric correction performed, as well as broaden the range of atmospheric conditions under which the SMAC can be applied. When examining the Normalised Difference Vegetation Index (NDVI), the relative difference between SMAC and in-situ values decreases by 1.5% with the improvements in place. Similarly, the mean relative difference between SMAC and 6S reflectance values decreases by a mean of 13, 14.5 and 8.5% for Channels 1, 2 and 3 respectively. Furthermore, the processing speed of the SMAC is found to remain largely unaffected, with only a small increase in the time taken to process a full SEVIRI scene. Whilst the method described within this paper is only applicable to SEVIRI data, a similar approach can be applied to other data sources than SEVIRI, and should result in a similar accuracy improvement no matter which instrument supplies the original data.     

Service Composition Issues in Pervasive Computing

Providing new services by combining existing ones—or service composition—is an idea pervading pervasive computing. Pervasive computing technologies seek to concurrently exhibit context awareness, manage contingencies, leverage device heterogeneity, and empower users. These four goals prompt service-composition-mechanism design requirements that are unique to pervasive computing. This article catalogs service composition mechanisms and describes their variation points, which indicate how well the resulting compositions meet the four goals.     

A generalization of Paillier’s public-key system with applications to electronic voting

We propose a generalization of Paillier’s probabilistic public-key system, in which the expansion factor is reduced and which allows to adjust the block length of the scheme even after the public key has been fixed, without losing the homomorphic property. We show that the generalization is as secure as Paillier’s original system and propose several ways to optimize implementations of both the generalized and the original scheme. We construct a threshold variant of the generalized scheme as well as zero-knowledge protocols to show that a given ciphertext encrypts one of a set of given plaintexts, and protocols to verify multiplicative relations on plaintexts. We then show how these building blocks can be used for applying the scheme to efficient electronic voting. This reduces dramatically the work needed to compute the final result of an election, compared to the previously best known schemes. We show how the basic scheme for a yes/no vote can be easily adapted to casting a vote for up to t out of L candidates. The same basic building blocks can also be adapted to provide receipt-free elections, under appropriate physical assumptions. The scheme for 1 out of L elections can be optimized such that for a certain range of the other parameter values, the ballot size is logarithmic in L.     

Model selection for convolutive ICA with an application to spatiotemporal analysis of EEG

We present a new algorithm for maximum likelihood convolutive independent component analysis (ICA) in which components are unmixed using stable autoregressive filters determined implicitly by estimating a convolutive model of the mixing process. By introducing a convolutive mixing model for the components, we show how the order of the filters in the model can be correctly detected using Bayesian model selection. We demonstrate a framework for deconvolving a subspace of independent components in electroencephalography (EEG). Initial results suggest that in some cases, convolutive mixing may be a more realistic model for EEG signals than the instantaneous ICA model.     

Region-Based Memory Management

This paper describes a memory management discipline for programs that perform dynamic memory allocation and de-allocation. At runtime, all values are put intoregions. The store consists of a stack of regions. All points of region allocation and de-allocation are inferred automatically, using a type and effect based program analysis. The scheme does not assume the presence of a garbage collector. The scheme was first presented in 1994 (M. Tofte and J.-P. Talpin,in“Proceedings of the 21st ACM SIGPLAN–SIGACT Symposium on Principles of Programming Languages,” pp. 188–201); subsequently, it has been tested in The ML Kit with Regions, a region-based, garbage-collection free implementation of the Standard ML Core language, which includes recursive datatypes, higher-order functions and updatable references L. Birkedal, M. Tofte, and M. Vejlstrup, (1996),in“Proceedings of the 23 rd ACM SIGPLAN–SIGACT Symposium on Principles of Programming Languages,” pp. 171–183. This paper defines a region-based dynamic semantics for a skeletal programming language extracted from Standard ML. We present the inference system which specifies where regions can be allocated and de-allocated and a detailed proof that the system is sound with respect to a standard semantics. We conclude by giving some advice on how to write programs that run well on a stack of regions, based on practical experience with the ML Kit.     

On the secure implementation of security protocols

We consider the problem of implementing a security protocol in such a manner that secrecy of sensitive data is not jeopardized. Implementation is assumed to take place in the context of an API that provides standard cryptography and communication services. Given a dependency specification, stating how API methods can produce and consume secret information, we propose an information flow property based on the idea of invariance under perturbation, relating observable changes in output to corresponding changes in input. Besides the information flow condition itself, the main contributions of the paper are results relating the admissibility property to a direct flow property in the special case of programs which branch on secrets only in cases permitted by the dependency rules. These results are used to derive an unwinding theorem, reducing a behavioural correctness check (strong bisimulation) to an invariant.     

A functional correspondence between call-by-need evaluators and lazy abstract machines

We bridge the gap between compositional evaluators and abstract machines for the lambda-calculus, using closure conversion, transformation into continuation-passing style, and defunctionalization of continuations. This article is a followup of our article at PPDP 2003, where we consider call by name and call by value. Here, however, we consider call by need.We derive a lazy abstract machine from an ordinary call-by-need evaluator that threads a heap of updatable cells. In this resulting abstract machine, the continuation fragment for updating a heap cell naturally appears as an ‘update marker’, an implementation technique that was invented for the Three Instruction Machine and subsequently used to construct lazy variants of Krivine's abstract machine. Tuning the evaluator leads to other implementation techniques such as unboxed values. The correctness of the resulting abstract machines is a corollary of the correctness of the original evaluators and of the program transformations used in the derivation.     

Conscious and anaesthetised Göttingen mini-pigs as an in-vivo model for buccal absorption – pH-dependent absorption of metoprolol from bioadhesive tablets

The potential of buccal mucosa as a site for systemic absorption has attracted increased attention in recent years creating a need for new predictive in-vivo models. The aim of this study was to evaluate anaesthetised and conscious Göttingen mini-pigs as a model for buccal drug absorption by testing pH-dependent absorption of metoprolol from a solid dosage form. Buccal tablets buffered to pH 6.2 and pH 8.9, oral liquid and intravenous injection were tested in four conscious and anaesthetised Göttingen mini-pigs in a non-randomised cross-over study. Blood samples were collected and processed before analysis by ultra-performance liquid chromatography with tandem mass spectrometry detection. An ex-vivo flow retention model was applied to study release and retention of the bioadhesive buccal tablets. The T_max obtained from the two buccal conscious groups (55?±?5 and 35?±?5?min) were significantly different to the buccal anaesthetised groups (120?±?0 and 165?±?15?min) for buccal tablet pH 6.2 and pH 8.9, respectively. Also, the absolute bioavailability from the anaesthetised buccal tablet pH 8.9 (20.7?±?4.0%) had a significant increase compared to all other buccal tablet groups. In conclusion, this study showed a pH-dependent absolute bioavailability of metoprolol when administrated as bioadhesive buccal tablets to anaesthetised mini-pigs. The anaesthesia was found to delay the time to reach maximal plasma concentration of metoprolol as compared to the conscious pig model when administrated as buccal tablets.