A double-helix neutron detector using micron-size B-10 powder

A double-helix electrode configuration is combined with a $^{10}$B powder coating technique to build large-area (9 in $\times$ 36 in) neutron detectors. The neutron detection efficiency for each of the four prototypes is comparable to a single 2-bar $^3$He drift tube of the same length (36 in). One unit has been operational continuously for 18 months and the change of efficiency is less than 1%. An analytic model for pulse heigh spectra is described and the predicted mean film thickness agrees with the experiment to within 30%. Further detector optimization is possible through film texture, power size, moderator box and gas. The estimated production cost per unit is less than 3k US\$ and the technology is thus suitable for deployment in large numbers

The uniqueness of flow in probing the aggregation behavior of clinically relevant antibodies

The development of therapeutic monoclonal antibodies (mAbs) can be hindered by their tendency to aggregate throughout their lifetime, which can illicit immunogenic responses and render mAb manufacturing unfeasible. Consequently, there is a need to identify mAbs with desirable thermodynamic stability, solubility, and lack of self‐association. These behaviors are assessed using an array of in silico and in vitro assays, as no single assay can predict aggregation and developability. We have developed an extensional and shear flow device (EFD), which subjects proteins to defined hydrodynamic forces which mimic those experienced in bioprocessing. Here, we utilize the EFD to explore the aggregation propensity of 33 IgG1 mAbs, whose variable domains are derived from clinical antibodies. Using submilligram quantities of material per replicate, wide‐ranging EFD‐induced aggregation (9‐81% protein in pellet) was observed for these mAbs, highlighting the EFD as a sensitive method to assess aggregation propensity. By comparing the EFD‐induced aggregation data to those obtained previously from 12 other biophysical assays, we show that the EFD provides distinct information compared with current measures of adverse biophysical behavior. Assessing a candidate's liability to hydrodynamic force thus adds novel insight into the rational selection of developable mAbs that complements other assays

Statistical Consequences of Devroye Inequality for Processes. Applications to a Class of Non-Uniformly Hyperbolic Dynamical Systems

In this paper, we apply Devroye inequality to study various statistical estimators and fluctuations of observables for processes. Most of these observables are suggested by dynamical systems. These applications concern the co-variance function, the integrated periodogram, the correlation dimension, the kernel density estimator, the speed of convergence of empirical measure, the shadowing property and the almost-sure central limit theorem. We proved in \cite{CCS} that Devroye inequality holds for a class of non-uniformly hyperbolic dynamical systems introduced in \cite{young}. In the second appendix we prove that, if the decay of correlations holds with a common rate for all pairs of functions, then it holds uniformly in the function spaces. In the last appendix we prove that for the subclass of one-dimensional systems studied in \cite{young} the density of the absolutely continuous invariant measure belongs to a Besov space.Comment: 33 pages; companion of the paper math.DS/0412166; corrected version; to appear in Nonlinearit

Mechanical Strength of 17 134 Model Proteins and Cysteine Slipknots

A new theoretical survey of proteins' resistance to constant speed stretching is performed for a set of 17 134 proteins as described by a structure-based model. The proteins selected have no gaps in their structure determination and consist of no more than 250 amino acids. Our previous studies have dealt with 7510 proteins of no more than 150 amino acids. The proteins are ranked according to the strength of the resistance. Most of the predicted top-strength proteins have not yet been studied experimentally. Architectures and folds which are likely to yield large forces are identified. New types of potent force clamps are discovered. They involve disulphide bridges and, in particular, cysteine slipknots. An effective energy parameter of the model is estimated by comparing the theoretical data on characteristic forces to the corresponding experimental values combined with an extrapolation of the theoretical data to the experimental pulling speeds. These studies provide guidance for future experiments on single molecule manipulation and should lead to selection of proteins for applications. A new class of proteins, involving cystein slipknots, is identified as one that is expected to lead to the strongest force clamps known. This class is characterized through molecular dynamics simulations.Comment: 40 pages, 13 PostScript figure

Cooperative folding of intrinsically disordered domains drives assembly of a strong elongated protein.

Bacteria exploit surface proteins to adhere to other bacteria, surfaces and host cells. Such proteins need to project away from the bacterial surface and resist significant mechanical forces. SasG is a protein that forms extended fibrils on the surface of Staphylococcus aureus and promotes host adherence and biofilm formation. Here we show that although monomeric and lacking covalent cross-links, SasG maintains a highly extended conformation in solution. This extension is mediated through obligate folding cooperativity of the intrinsically disordered E domains that couple non-adjacent G5 domains thermodynamically, forming interfaces that are more stable than the domains themselves. Thus, counterintuitively, the elongation of the protein appears to be dependent on the inherent instability of its domains. The remarkable mechanical strength of SasG arises from tandemly arrayed 'clamp' motifs within the folded domains. Our findings reveal an elegant minimal solution for the assembly of monomeric mechano-resistant tethers of variable length.This research was supported by Biotechnology and Biological Research Council Grants BB/J006459/1 (D.T.G. and J.C.), BB/J005029/1 (F.W. and J.R.P), BB/G019452/1 (O.E.F and D.J.B) and BB/G020671/1 (C.G.B. and J.R.P.). H.K.H.F. is supported by a studentship from a Wellcome Trust 4-year PhD programme (WT095024MA). C.M.J. is supported by the German Federal Ministry of Education and Research (BMBF), grant BIOSCAT (contract N° 05K12YE1). J.C. is a Wellcome Trust Senior Research Fellow (WT/095195). J.R.P holds a British Heart Foundation Senior Basic Science Fellowship (FS/12/36/29588). The authors acknowledge the use of EMBL SAXS beamline P12 at Petra-3 (DESY, Hamburg, Germany) and Maxim Petoukhov (EMBL) for providing a modified version of SASR EF. The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under BioStruct-X (grant agreement N°283570). The authors would like to thank Diamond Light Source for beamtime (proposal mx-7864) and Johan Turkenburg and Sam Hart for assistance with crystal testing and data collection.This is the final version of the article. It first appeared from NPG via http://dx.doi.org/10.1038/ncomms827

Sampling constrained probability distributions using Spherical Augmentation

Statistical models with constrained probability distributions are abundant in machine learning. Some examples include regression models with norm constraints (e.g., Lasso), probit, many copula models, and latent Dirichlet allocation (LDA). Bayesian inference involving probability distributions confined to constrained domains could be quite challenging for commonly used sampling algorithms. In this paper, we propose a novel augmentation technique that handles a wide range of constraints by mapping the constrained domain to a sphere in the augmented space. By moving freely on the surface of this sphere, sampling algorithms handle constraints implicitly and generate proposals that remain within boundaries when mapped back to the original space. Our proposed method, called {Spherical Augmentation}, provides a mathematically natural and computationally efficient framework for sampling from constrained probability distributions. We show the advantages of our method over state-of-the-art sampling algorithms, such as exact Hamiltonian Monte Carlo, using several examples including truncated Gaussian distributions, Bayesian Lasso, Bayesian bridge regression, reconstruction of quantized stationary Gaussian process, and LDA for topic modeling.Comment: 41 pages, 13 figure

Cooperative folding of intrinsically disordered domains drives assembly of a strong elongated protein

Bacteria exploit surface proteins to adhere to other bacteria, surfaces and host cells. Such proteins need to project away from the bacterial surface and resist significant mechanical forces. SasG is a protein that forms extended fibrils on the surface of Staphylococcus aureus and promotes host adherence and biofilm formation. Here we show that although monomeric and lacking covalent cross-links, SasG maintains a highly extended conformation in solution. This extension is mediated through obligate folding cooperativity of the intrinsically disordered E domains that couple non-adjacent G5 domains thermodynamically, forming interfaces that are more stable than the domains themselves. Thus, counterintuitively, the elongation of the protein appears to be dependent on the inherent instability of its domains. The remarkable mechanical strength of SasG arises from tandemly arrayed 'clamp' motifs within the folded domains. Our findings reveal an elegant minimal solution for the assembly of monomeric mechano-resistant tethers of variable length

BSDB: the biomolecule stretching database

We describe the Biomolecule Stretching Data Base that has been recently set up at http://www.ifpan.edu.pl/BSDB/. It provides information about mechanostability of proteins. Its core is based on simulations of stretching of 17 134 proteins within a structure-based model. The primary information is about the heights of the maximal force peaks, the force–displacement patterns, and the sequencing of the contact-rupturing events. We also summarize the possible types of the mechanical clamps, i.e. the motifs which are responsible for a protein's resistance to stretching

Discrimination of water quality monitoring sites in River Vouga using a mixed-effect state space model

The surface water quality monitoring is an important concern of public organizations due to its relevance to the public health. Statistical methods are taken as consistent and essential tools in the monitoring procedures in order to prevent and identify environmental problems. This work presents the study case of the hydrological basin of the river Vouga, in Portugal. The main goal is discriminate the water monitoring sites using the monthly dissolved oxygen concentration dataset between January 2002 and May 2013. This is achieved through the extraction of trend and seasonal components in a linear mixed-effect state space model. The parameters estimation is performed with both maximum likelihood method and distribution-free estimators in a two-step procedure. The application of the Kalman smoother algorithm allows to obtain predictions of the structural components as trend and seasonality. The water monitoring sites are discriminated through the structural components by a hierarchical agglomerative clustering procedure. This procedure identified different homogenous groups relatively to the trend and seasonality components and some characteristics of the hydrological basin are presented in order to support the results

Probabilistic change detection and visualization methods for the assessment of temporal stability in biomedical data quality

The final publication is available at Springer via http://dx.doi.org/DOI 10.1007/s10618-014-0378-6. Published online.Knowledge discovery on biomedical data can be based on on-line, data-stream analyses, or using retrospective, timestamped, off-line datasets. In both cases, changes in the processes that generate data or in their quality features through time may hinder either the knowledge discovery process or the generalization of past knowledge. These problems can be seen as a lack of data temporal stability. This work establishes the temporal stability as a data quality dimension and proposes new methods for its assessment based on a probabilistic framework. Concretely, methods are proposed for (1) monitoring changes, and (2) characterizing changes, trends and detecting temporal subgroups. First, a probabilistic change detection algorithm is proposed based on the Statistical Process Control of the posterior Beta distribution of the Jensen–Shannon distance, with a memoryless forgetting mechanism. This algorithm (PDF-SPC) classifies the degree of current change in three states: In-Control, Warning, and Out-of-Control. Second, a novel method is proposed to visualize and characterize the temporal changes of data based on the projection of a non-parametric information-geometric statistical manifold of time windows. This projection facilitates the exploration of temporal trends using the proposed IGT-plot and, by means of unsupervised learning methods, discovering conceptually-related temporal subgroups. Methods are evaluated using real and simulated data based on the National Hospital Discharge Survey (NHDS) dataset.The work by C Saez has been supported by an Erasmus Lifelong Learning Programme 2013 Grant. This work has been supported by own IBIME funds. 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