Dose and sample size determination for multi-drug combination studies

Preclinical experiment on multi-drug combination has an increasingly important role in (especially cancer) drug development because of the need to reduce development time and costs. Despite recent progress in statistical methods for assessing drug interaction, there is a lack of general methods for determining the doses comprising the combinations and the sample sizes to detect departures from additivity, especially in the case of more than two drugs. We propose a general method for dose and sample size determination for detecting departures from additivity of multiple drugs based on a semiparametric statistical model applicable to both in vivo and in vitro experiments. We show that selecting doses that comprise the combinations uniformly scattered in the experimental domain maximizes the minimum power of the F-test for detecting departures from additivity. In addition, the method applies to drugs whose relative potency is not constant. With both analytic proof and a simulation, we show the proposed design has optimal properties that are not shared by the classic designs such as the fixed ratio (ray) and the checkerboard designs. Furthermore, we show the method is dependent upon the shape of the single drug dose-response curve; therefore, different classes of drugs have to be dealt with separately. To our surprise, such an extension to multi-drug case with three or more drugs is far more difficult than it appears. Using the general methodology, we derive the dose selection and sample size specifically for a common class of drugs to derive the experimental design. We illustrate the method with the SAHA and Ara-C and Etoposide combination study.postprin

A fast em algorithm for quadratic optimization subject to Convex constraints

Convex constraints (CCs) such as box constraints and linear inequality constraints appear frequently in statistical inference and in applications. The problems of quadratic optimization (QO) subject to CCs occur in isotonic regression, shape-restricted non-parametric regression, variable selection (via the lasso algorithm and bridge regression), limited dependent variables models, image reconstruction, and so on. Existing packages for QO are not generally applicable to CCs. Although EM-type algorithms may be applied to such problems (Tian, Ng and Tan (2005)), the convergence rate/speed of these algorithms is painfully slow, especially for high-dimensional data. This paper develops a fast EM algorithm for QO with CCs. We construct a class of data augmentation schemes indexed by a 'working parameter' r (r ε R), and then optimize r over R under several convergence criteria. In addition, we use Cholesky decomposition to reduce both the number of latent variables and the dimension, leading to further acceleration of the EM. Standard errors of the restricted estimators are calculated using a non-parametric bootstrapping procedure. Simulation and comparison are performed and a complex multinomial dataset is analyzed to illustrate the proposed methods.published_or_final_versio

On the Kinematic Signature of the Galactic Warp As Revealed by the LAMOST-TGAS Data

Using a sample of about 123,000 stars with accurate 3D velocity measurements from the LAMOST-TGAS data, we confirm the kinematic signature of the Galactic warp recently found by Schonrich & Dehnen. The data reveal a clear trend of increasing mean vertical velocity Vz as a function of absolute vertical angular momentum Lz and azimuthal velocity Vφ for guiding center radius Rg between 6.0 and 10.5 kpc. The trend is consistent with a largescale Galactic warp. Similar to Schonrich & Dehnen, we also find a wave-like pattern of Vz versus Lz with an amplitude of ∼0.9 km s-1 on a scale of ∼2.0 kpc, which could arise from bending waves or a winding warp. Finally, we confirm a prominent, localized peak in Vz near Lz ∼ 2150 kpc km s-1 (corresponding to Rg ∼ 9 kpc and Vφ ∼ 255 km s-1). The additional line-of-sight velocity information from LAMOST reveals that stars in this feature have a large, inward radial velocity of VR ∼ -13.33 ± 0.59 km s-1 and a small radial velocity dispersion of σR ∼ 25.27 ± 0.89 km s-1, suggesting that a stellar stream gives rise to this feature

Counter-current chromatography for the separation of terpenoids: A comprehensive review with respect to the solvent systems employed

Copyright @ 2014 The Authors.This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.Natural products extracts are commonly highly complex mixtures of active compounds and consequently their purification becomes a particularly challenging task. The development of a purification protocol to extract a single active component from the many hundreds that are often present in the mixture is something that can take months or even years to achieve, thus it is important for the natural product chemist to have, at their disposal, a broad range of diverse purification techniques. Counter-current chromatography (CCC) is one such separation technique utilising two immiscible phases, one as the stationary phase (retained in a spinning coil by centrifugal forces) and the second as the mobile phase. The method benefits from a number of advantages when compared with the more traditional liquid-solid separation methods, such as no irreversible adsorption, total recovery of the injected sample, minimal tailing of peaks, low risk of sample denaturation, the ability to accept particulates, and a low solvent consumption. The selection of an appropriate two-phase solvent system is critical to the running of CCC since this is both the mobile and the stationary phase of the system. However, this is also by far the most time consuming aspect of the technique and the one that most inhibits its general take-up. In recent years, numerous natural product purifications have been published using CCC from almost every country across the globe. Many of these papers are devoted to terpenoids-one of the most diverse groups. Naturally occurring terpenoids provide opportunities to discover new drugs but many of them are available at very low levels in nature and a huge number of them still remain unexplored. The collective knowledge on performing successful CCC separations of terpenoids has been gathered and reviewed by the authors, in order to create a comprehensive document that will be of great assistance in performing future purifications. © 2014 The Author(s)

The Insulin-Like Growth Factor System in the Long-Lived Naked Mole-Rat.

Naked mole-rats (Heterocephalus glaber) (NMRs) are the longest living rodents known. They show negligible senescence, and are resistant to cancers and certain damaging effects associated with aging. The insulin-like growth factors (IGFs) have pluripotent actions, influencing growth processes in virtually every system of the body. They are established contributors to the aging process, confirmed by the demonstration that decreased IGF signaling results in life-extending effects in a variety of species. The IGFs are likewise involved in progression of cancers by mediating survival signals in malignant cells. This report presents a full characterization of the IGF system in the NMR: ligands, receptors, IGF binding proteins (IGFBPs), and IGFBP proteases. A particular emphasis was placed on the IGFBP protease, pregnancy-associated plasma protein-A (PAPP-A), shown to be an important lifespan modulator in mice. Comparisons of IGF-related genes in the NMR with human and murine sequences indicated no major differences in essential parts of the IGF system, including PAPP-A. The protease was shown to possess an intact active site despite the report of a contradictory genome sequence. Furthermore, PAPP-A was expressed and translated in NMRs cells and retained IGF-dependent proteolytic activity towards IGFBP-4 and IGF-independent activity towards IGFBP-5. However, experimental data suggest differential regulatory mechanisms for PAPP-A expression in NMRs than those described in humans and mice. This overall description of the IGF system in the NMR represents an initial step towards elucidating the complex molecular mechanisms underlying longevity, and how these animals have evolved to ensure a delayed and healthy aging process

Study of the corrosion rate behavior of ion implanted Fe-based alloys

We report on some studies we have made of the time evolution of the corrosion behavior of ion implanted samples of pure iron, medium carbon steel, and 18-8 Cr-Ni stainless steel. Ti, Cr, Ni, Cu, Mo and Yb were implanted at mean ion energies near 100 keV and at doses up to 1 {times} 10{sup 17} cm{sup {minus}2} using a Mevva metal ion implantation facility. A novel feature of this experiment was the simultaneous implantation with several different implanted species. The implanted samples were immersed in sulfuric acid solution at 40{degrees}C and the corrosion monitored as a function of time. The loss in mass was accurately measured using atomic absorption spectroscopy. The functional dependence of the corrosion behavior was established for all samples. The cumulative mass loss Q is given as a function of time t by Q = At{sup N}, where A and N are parameters; thus the corrosion rate V is given by V = ANt{sup N-1}. A is dominated by the initial mass loss and N reflects the long-time corrosion behavior. The values of the parameters A and N were obtained by a least-squares regression for all the samples investigated. We determined that for the samples investigated here, N > 1 always and V increases with time throughout the experimental duration. In this paper we summarize the experimental results and discuss the effect of A and N on corrosion rate and the relationship between the corrosion current density and the parameters A and N. 11 refs., 4 figs

Mechanical properties of double-layer and graded composite coatings of YSZ obtained by atmospheric plasma spraying

Double-layer and graded composite coatings of yttria-stabilized zirconia were sprayed on metallic substrates by atmospheric plasma spray. The coating architecture was built up by combining two different feedstocks: one micro- and one nanostructured. Microstructural features and mechanical properties (hardness and elastic modulus) of the coatings were determined by FE-SEM microscopy and nanoindentation technique, respectively. Additional adherence and scratch tests were carried out in order to assess the failure mechanisms occurring between the layers comprising the composites. Microstructural inspection of the coatings confirms the two-zone microstructure. This bimodal microstructure which is exclusive of the layer obtained from the nanostructured feedstock negatively affects the mechanical properties of the whole composite. Nanoindentation tests suitably reproduce the evolution of mechanical properties through coatings thickness on the basis of the position and/or amount of nanostructured feedstock used in the depositing layer. Adhesion and scratch tests show the negative effect on the coating adhesion of layer obtained from the nanostructured feedstock when this layer is deposited on the bond coat. Thus, the poor integrity of this layer results in lower normal stresses required to delaminate the coating in the adhesion test as well as minor critical load registered by using the scratch test.This work has been supported by the Spanish Ministry of Science and Innovation (Project MAT2012-38364-C03) and co-funded by ERDF (European Regional Development Funds).Carpio-Cobo, P.; Rayón Encinas, E.; Salvador Moya, MD.; Lusvarghi, L.; Sanchez, E. (2016). Mechanical properties of double-layer and graded composite coatings of YSZ obtained by atmospheric plasma spraying. 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Imbalanced Multi-Modal Multi-Label Learning for Subcellular Localization Prediction of Human Proteins with Both Single and Multiple Sites

It is well known that an important step toward understanding the functions of a protein is to determine its subcellular location. Although numerous prediction algorithms have been developed, most of them typically focused on the proteins with only one location. In recent years, researchers have begun to pay attention to the subcellular localization prediction of the proteins with multiple sites. However, almost all the existing approaches have failed to take into account the correlations among the locations caused by the proteins with multiple sites, which may be the important information for improving the prediction accuracy of the proteins with multiple sites. In this paper, a new algorithm which can effectively exploit the correlations among the locations is proposed by using Gaussian process model. Besides, the algorithm also can realize optimal linear combination of various feature extraction technologies and could be robust to the imbalanced data set. Experimental results on a human protein data set show that the proposed algorithm is valid and can achieve better performance than the existing approaches

Phenotypic Variation and Bistable Switching in Bacteria

Microbial research generally focuses on clonal populations. However, bacterial cells with identical genotypes frequently display different phenotypes under identical conditions. This microbial cell individuality is receiving increasing attention in the literature because of its impact on cellular differentiation, survival under selective conditions, and the interaction of pathogens with their hosts. It is becoming clear that stochasticity in gene expression in conjunction with the architecture of the gene network that underlies the cellular processes can generate phenotypic variation. An important regulatory mechanism is the so-called positive feedback, in which a system reinforces its own response, for instance by stimulating the production of an activator. Bistability is an interesting and relevant phenomenon, in which two distinct subpopulations of cells showing discrete levels of gene expression coexist in a single culture. In this chapter, we address techniques and approaches used to establish phenotypic variation, and relate three well-characterized examples of bistability to the molecular mechanisms that govern these processes, with a focus on positive feedback.