Defining stage-specific activity of potent new inhibitors of Cryptosporidium parvum growth in vitro

Currently, nitazoxanide is the only FDA-approved treatment for cryptosporidiosis; unfortunately, it is ineffective in immunocompromised patients, has varied efficacy in immunocompetent individuals, and is not approved in infants under 1 year of age. Identifying new inhibitors for the treatment of cryptosporidiosis requires standardized and quantifiable in vitro assays for assessing potency, selectivity, timing of activity, and reversibility. Here, we provide new protocols for defining which stages of the life cycle are susceptible to four highly active compound classes that likely inhibit different targets in the parasite. We also utilize a newly developed long-term culture system to define assays for monitoring reversibility as a means of defining cidal activity as a function of concentration and time of treatment. These assays should provide valuable in vitro parameters to establish conditions for efficacious in vivo treatment.Cryptosporidium parvum and Cryptosporidium hominis have emerged as major enteric pathogens of infants in the developing world, in addition to their known importance in immunocompromised adults. Although there has been recent progress in identifying new small molecules that inhibit Cryptosporidium sp. growth in vitro or in animal models, we lack information about their mechanism of action, potency across the life cycle, and cidal versus static activities. Here, we explored four potent classes of compounds that include inhibitors that likely target phosphatidylinositol 4 kinase (PI4K), phenylalanine-tRNA synthetase (PheRS), and several potent inhibitors with unknown mechanisms of action. We utilized monoclonal antibodies and gene expression probes for staging life cycle development to define the timing of when inhibitors were active during the life cycle of Cryptosporidium parvum grown in vitro. These different classes of inhibitors targeted different stages of the life cycle, including compounds that blocked replication (PheRS inhibitors), prevented the segmentation of daughter cells and thus blocked egress (PI4K inhibitors), or affected sexual-stage development (a piperazine compound of unknown mechanism). Long-term cultivation of C. parvum in epithelial cell monolayers derived from intestinal stem cells was used to distinguish between cidal and static activities based on the ability of parasites to recover from treatment. Collectively, these approaches should aid in identifying mechanisms of action and for designing in vivo efficacy studies based on time-dependent concentrations needed to achieve cidal activity

Particle Swarm Optimization and gravitational wave data analysis: Performance on a binary inspiral testbed

The detection and estimation of gravitational wave (GW) signals belonging to a parameterized family of waveforms requires, in general, the numerical maximization of a data-dependent function of the signal parameters. Due to noise in the data, the function to be maximized is often highly multi-modal with numerous local maxima. Searching for the global maximum then becomes computationally expensive, which in turn can limit the scientific scope of the search. Stochastic optimization is one possible approach to reducing computational costs in such applications. We report results from a first investigation of the Particle Swarm Optimization (PSO) method in this context. The method is applied to a testbed motivated by the problem of detection and estimation of a binary inspiral signal. Our results show that PSO works well in the presence of high multi-modality, making it a viable candidate method for further applications in GW data analysis.Comment: 13 pages, 5 figure

Imitation in Large Games

In games with a large number of players where players may have overlapping objectives, the analysis of stable outcomes typically depends on player types. A special case is when a large part of the player population consists of imitation types: that of players who imitate choice of other (optimizing) types. Game theorists typically study the evolution of such games in dynamical systems with imitation rules. In the setting of games of infinite duration on finite graphs with preference orderings on outcomes for player types, we explore the possibility of imitation as a viable strategy. In our setup, the optimising players play bounded memory strategies and the imitators play according to specifications given by automata. We present algorithmic results on the eventual survival of types

Complete Determination of the Pin1 Catalytic Domain Thermodynamic Cycle by NMR Lineshape Analysis

The phosphorylation-specific peptidyl-prolyl isomerase Pin1 catalyzes the isomerization of the peptide bond preceding a proline residue between cis and trans isomers. To best understand the mechanisms of Pin1 regulation, rigorous enzymatic assays of isomerization are required. However, most measures of isomerase activity require significant constraints on substrate sequence and only yield rate constants for the cis isomer, kciscatand apparent Michaelis constants, KAppM . By contrast, NMR lineshape analysis is a powerful tool for determining microscopic rates and populations of each state in a complex binding scheme. The isolated catalytic domain of Pin1 was employed as a first step towards elucidating the reaction scheme of the full-length enzyme. A 24-residue phosphopeptide derived from the amyloid precurser protein intracellular domain (AICD) phosphorylated at Thr668 served as a biologically-relevant Pin1 substrate. Specific 13C labeling at the Pin1-targeted proline residue provided multiple reporters sensitive to individual isomer binding and on-enzyme catalysis. We have performed titration experiments and employed lineshape analysis of phosphopeptide 13C–1H constant time HSQC spectra to determine kciscat , ktranscat , KcisD , and KtransD for the catalytic domain of Pin1 acting on this AICD substrate. The on-enzyme equilibrium value of [E·trans]/[E·cis] = 3.9 suggests that the catalytic domain of Pin1 is optimized to operate on this substrate near equilibrium in the cellular context. This highlights the power of lineshape analysis for determining the microscopic parameters of enzyme catalysis, and demonstrates the feasibility of future studies of Pin1-PPIase mutants to gain insights on the catalytic mechanism of this important enzyme

Defining the complementarities between antibodies and haptens to refine our understanding and aid the prediction of a successful binding interaction

Acknowledgments The authors would like to thank the Scottish Universities Life Sciences Alliance (SULSA) for their support.Peer reviewedPublisher PD

Tomographic approach to resolving the distribution of LISA Galactic binaries

The space based gravitational wave detector LISA is expected to observe a large population of Galactic white dwarf binaries whose collective signal is likely to dominate instrumental noise at observational frequencies in the range 10^{-4} to 10^{-3} Hz. The motion of LISA modulates the signal of each binary in both frequency and amplitude, the exact modulation depending on the source direction and frequency. Starting with the observed response of one LISA interferometer and assuming only doppler modulation due to the orbital motion of LISA, we show how the distribution of the entire binary population in frequency and sky position can be reconstructed using a tomographic approach. The method is linear and the reconstruction of a delta function distribution, corresponding to an isolated binary, yields a point spread function (psf). An arbitrary distribution and its reconstruction are related via smoothing with this psf. Exploratory results are reported demonstrating the recovery of binary sources, in the presence of white Gaussian noise.Comment: 13 Pages and 9 figures high resolution figures can be obtains from http://www.phys.utb.edu/~rajesh/lisa_tomography.pd

Analysis of Slotted ALOHA with Multipacket Messages in Clustered Surveillance Networks

This work presents an analysis of a cluster of finite population of low cost sensor nodes operating in a p-persistent S-Aloha framework with multipacket messages. Using this analytical framework, we consider the issue of partitioning the nodes and available frequencies into groups so as to maximize the system throughput. Assigning the nodes and frequencies into “groups” is important because the size of the group impacts the tradeoff between the benefits of frequency diversity and the cost of collision on the shared medium imposed by the nodes in a group. We study this tradeoff through analytical and numerical results and show how the correct choice of group sizes can vary depending on various factors like the ratio of nodes to frequencies and the overall system load

Protecting big data mining association rules using fuzzy system

Recently, big data is granted to be the solution to opening the subsequent large fluctuations of increase in fertility. Along with the growth, it is facing some of the challenges. One of the significant problems is data security. While people use data mining methods to identify valuable information following massive database, people further hold the necessary to maintain any knowledge so while not to be worked out, like delicate common itemsets, practices, taxonomy tree and the like Association rule mining can make a possible warning approaching the secrecy of information. So, association rule hiding methods are applied to evade the hazard of delicate information misuse. Various kinds of investigation already prepared on association rule protecting. However, maximum of them concentrate on introducing methods with a limited view outcome for inactive databases (with only existing information), while presently the researchers facing the problem with continuous information. Moreover, in the era of big data, this is essential to optimize current systems to be suited concerning the big data. This paper proposes the framework is achieving the data anonymization by using fuzzy logic by supporting big data mining. The fuzzy logic grouping the sensitivity of the association rules with a suitable association level. Moreover, parallelization methods which are inserted in the present framework will support fast data mining process

NMR implementation of Quantum Delayed-Choice Experiment

We report the first experimental demonstration of quantum delayed-choice experiment via nuclear magnetic resonance techniques. An ensemble of molecules each with two spin-1/2 nuclei are used as target and the ancilla qubits to perform the quantum circuit corresponding the delayed-choice setup. As expected in theory, our experiments clearly demonstrate the continuous morphing of the target qubit between particle-like and wave-like behaviors. The experimental visibility of the interference patterns shows good agreement with the theory.Comment: Revised text, more figures adde