Restriction site-associated DNA sequencing, genotyping error estimation and de novo assembly optimization for population genetic inference.

Restriction site-associated DNA sequencing (RADseq) provides researchers with the ability to record genetic polymorphism across thousands of loci for nonmodel organisms, potentially revolutionizing the field of molecular ecology. However, as with other genotyping methods, RADseq is prone to a number of sources of error that may have consequential effects for population genetic inferences, and these have received only limited attention in terms of the estimation and reporting of genotyping error rates. Here we use individual sample replicates, under the expectation of identical genotypes, to quantify genotyping error in the absence of a reference genome. We then use sample replicates to (i) optimize de novo assembly parameters within the program Stacks, by minimizing error and maximizing the retrieval of informative loci; and (ii) quantify error rates for loci, alleles and single-nucleotide polymorphisms. As an empirical example, we use a double-digest RAD data set of a nonmodel plant species, Berberis alpina, collected from high-altitude mountains in Mexico.

Dose optimization approach to fast X-ray microtomography of the lung alveoli.

A basic prerequisite for in vivo X-ray imaging of the lung is the exact determination of radiation dose. Achieving resolutions of the order of micrometres may become particularly challenging owing to increased dose, which in the worst case can be lethal for the imaged animal model. A framework for linking image quality to radiation dose in order to optimize experimental parameters with respect to dose reduction is presented. The approach may find application for current and future in vivo studies to facilitate proper experiment planning and radiation risk assessment on the one hand and exploit imaging capabilities on the other.

Single-metal deposition: optimization of this fingermark enhancement technique

Following the introduction of single-metal deposition (SMD), a simplified fingermark detection technique based on multimetal deposition, optimization studies were conducted. The different parameters of the original formula were tested and the results were evaluated based on the contrast and overall aspect of the enhanced fingermarks. The new formula for SMD was found based on the most optimized parameters. Interestingly, it was found that important variations from the base parameters did not significantly affect the outcome of the enhancement, thus demonstrating that SMD is a very robust technique. Finally, a comparison of the optimized SMD with multi-metal deposition (MMD) was carried out on different surfaces. It was demonstrated that SMD produces comparable results to MMD, thus validating the technique.

Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data.

Microstructure imaging from diffusion magnetic resonance (MR) data represents an invaluable tool to study non-invasively the morphology of tissues and to provide a biological insight into their microstructural organization. In recent years, a variety of biophysical models have been proposed to associate particular patterns observed in the measured signal with specific microstructural properties of the neuronal tissue, such as axon diameter and fiber density. Despite very appealing results showing that the estimated microstructure indices agree very well with histological examinations, existing techniques require computationally very expensive non-linear procedures to fit the models to the data which, in practice, demand the use of powerful computer clusters for large-scale applications. In this work, we present a general framework for Accelerated Microstructure Imaging via Convex Optimization (AMICO) and show how to re-formulate this class of techniques as convenient linear systems which, then, can be efficiently solved using very fast algorithms. We demonstrate this linearization of the fitting problem for two specific models, i.e. ActiveAx and NODDI, providing a very attractive alternative for parameter estimation in those techniques; however, the AMICO framework is general and flexible enough to work also for the wider space of microstructure imaging methods. Results demonstrate that AMICO represents an effective means to accelerate the fit of existing techniques drastically (up to four orders of magnitude faster) while preserving accuracy and precision in the estimated model parameters (correlation above 0.9). We believe that the availability of such ultrafast algorithms will help to accelerate the spread of microstructure imaging to larger cohorts of patients and to study a wider spectrum of neurological disorders.

Optimization of laser interferometers for the detection of gravitational waves from coalescing binaries

Coalescing compact binary systems are important sources of gravitational waves. Here we investigate the detectability of this gravitational radiation by the recently proposed laser interferometers. The spectral density of noise for various practicable configurations of the detector is also reviewed. This includes laser interferometers with delay lines and Fabry-Prot cavities in the arms, both in standard and dual recycling arrangements. The sensitivity of the detector in all those configurations is presented graphically and the signal-to-noise ratio is calculated numerically. For all configurations we find values of the detector's parameters which maximize the detectability of coalescing binaries, the discussion comprising Newtonian- as well as post-Newtonian-order effects. Contour plots of the signal-to-noise ratio are also presented in certain parameter domains which illustrate the interferometer's response to coalescing binary signals.

Optimization of Soil Stabilization with Class C Fly Ash, January 1987

Previous Iowa DOT sponsored research has shown that some Class C fly ashes are ementitious (because calcium is combined as calcium aluminates) while other Class C ashes containing similar amounts of elemental calcium are not (1). Fly ashes from modern power plants in Iowa contain significant amounts of calcium in their glassy phases, regardless of their cementitious properties. The present research was based on these findings and on the hyphothesis that: attack of the amorphous phase of high calcium fly ash could be initiated with trace additives, thus making calcium available for formation of useful calcium-silicate cements. Phase I research was devoted to finding potential additives through a screening process; the likely chemicals were tested with fly ashes representative of the cementitious and non-cementitious ashes available in the state. Ammonium phosphate, a fertilizer, was found to produce 3,600 psi cement with cementitious Neal #4 fly ash; this strength is roughly equivalent to that of portland cement, but at about one-third the cost. Neal #2 fly ash, a slightly cementitious Class C, was found to respond best with ammonium nitrate; through the additive, a near-zero strength material was transformed into a 1,200 psi cement. The second research phase was directed to optimimizing trace additive concentrations, defining the behavior of the resulting cements, evaluating more comprehensively the fly ashes available in Iowa, and explaining the cement formation mechanisms of the most promising trace additives. X-ray diffraction data demonstrate that both amorphous and crystalline hydrates of chemically enhanced fly ash differ from those of unaltered fly ash hydrates. Calciumaluminum- silicate hydrates were formed, rather than the expected (and hypothesized) calcium-silicate hydrates. These new reaction products explain the observed strength enhancement. The final phase concentrated on laboratory application of the chemically-enhanced fly ash cements to road base stabilization. Emphasis was placed on use of marginal aggregates, such as limestone crusher fines and unprocessed blow sand. The nature of the chemically modified fly ash cements led to an evaluation of fine grained soil stabilization where a wide range of materials, defined by plasticity index, could be stabilized. Parameters used for evaluation included strength, compaction requirements, set time, and frost resistance.

Optimization of Snow Drifting Mitigation and Control Methods for Iowa Conditions Part II, TR-626, 2015

Blowing and drifting of snow is a major concern for transportation efficiency and road safety in regions where their development is common. One common way to mitigate snow drift on roadways is to install plastic snow fences. Correct design of snow fences is critical for road safety and maintaining the roads open during winter in the US Midwest and other states affected by large snow events during the winter season and to maintain costs related to accumulation of snow on the roads and repair of roads to minimum levels. Of critical importance for road safety is the protection against snow drifting in regions with narrow rights of way, where standard fences cannot be deployed at the recommended distance from the road. Designing snow fences requires sound engineering judgment and a thorough evaluation of the potential for snow blowing and drifting at the construction site. The evaluation includes site-specific design parameters typically obtained with semi-empirical relations characterizing the local transport conditions. Among the critical parameters involved in fence design and assessment of their post-construction efficiency is the quantification of the snow accumulation at fence sites. The present study proposes a joint experimental and numerical approach to monitor snow deposits around snow fences, quantitatively estimate snow deposits in the field, asses the efficiency and improve the design of snow fences. Snow deposit profiles were mapped using GPS based real-time kinematic surveys (RTK) conducted at the monitored field site during and after snow storms. The monitored site allowed testing different snow fence designs under close to identical conditions over four winter seasons. The study also discusses the detailed monitoring system and analysis of weather forecast and meteorological conditions at the monitored sites. A main goal of the present study was to assess the performance of lightweight plastic snow fences with a lower porosity than the typical 50% porosity used in standard designs of such fences. The field data collected during the first winter was used to identify the best design for snow fences with a porosity of 50%. Flow fields obtained from numerical simulations showed that the fence design that worked the best during the first winter induced the formation of an elongated area of small velocity magnitude close to the ground. This information was used to identify other candidates for optimum design of fences with a lower porosity. Two of the designs with a fence porosity of 30% that were found to perform well based on results of numerical simulations were tested in the field during the second winter along with the best performing design for fences with a porosity of 50%. Field data showed that the length of the snow deposit away from the fence was reduced by about 30% for the two proposed lower-porosity (30%) fence designs compared to the best design identified for fences with a porosity of 50%. Moreover, one of the lower-porosity designs tested in the field showed no significant snow deposition within the bottom gap region beneath the fence. Thus, a major outcome of this study is to recommend using plastic snow fences with a porosity of 30%. It is expected that this lower-porosity design will continue to work well for even more severe snow events or for successive snow events occurring during the same winter. The approach advocated in the present study allowed making general recommendations for optimizing the design of lower-porosity plastic snow fences. This approach can be extended to improve the design of other types of snow fences. Some preliminary work for living snow fences is also discussed. Another major contribution of this study is to propose, develop protocols and test a novel technique based on close range photogrammetry (CRP) to quantify the snow deposits trapped snow fences. As image data can be acquired continuously, the time evolution of the volume of snow retained by a snow fence during a storm or during a whole winter season can, in principle, be obtained. Moreover, CRP is a non-intrusive method that eliminates the need to perform man-made measurements during the storms, which are difficult and sometimes dangerous to perform. Presently, there is lots of empiricism in the design of snow fences due to lack of data on fence storage capacity on how snow deposits change with the fence design and snow storm characteristics and in the estimation of the main parameters used by the state DOTs to design snow fences at a given site. The availability of such information from CRP measurements should provide critical data for the evaluation of the performance of a certain snow fence design that is tested by the IDOT. As part of the present study, the novel CRP method is tested at several sites. The present study also discusses some attempts and preliminary work to determine the snow relocation coefficient which is one of the main variables that has to be estimated by IDOT engineers when using the standard snow fence design software (Snow Drift Profiler, Tabler, 2006). Our analysis showed that standard empirical formulas did not produce reasonable values when applied at the Iowa test sites monitored as part of the present study and that simple methods to estimate this variable are not reliable. The present study makes recommendations for the development of a new methodology based on Large Scale Particle Image Velocimetry that can directly measure the snow drift fluxes and the amount of snow relocated by the fence.

Estimating parameters for generalized mass action models using constraint propagation.

As modern molecular biology moves towards the analysis of biological systems as opposed to their individual components, the need for appropriate mathematical and computational techniques for understanding the dynamics and structure of such systems is becoming more pressing. For example, the modeling of biochemical systems using ordinary differential equations (ODEs) based on high-throughput, time-dense profiles is becoming more common-place, which is necessitating the development of improved techniques to estimate model parameters from such data. Due to the high dimensionality of this estimation problem, straight-forward optimization strategies rarely produce correct parameter values, and hence current methods tend to utilize genetic/evolutionary algorithms to perform non-linear parameter fitting. Here, we describe a completely deterministic approach, which is based on interval analysis. This allows us to examine entire sets of parameters, and thus to exhaust the global search within a finite number of steps. In particular, we show how our method may be applied to a generic class of ODEs used for modeling biochemical systems called Generalized Mass Action Models (GMAs). In addition, we show that for GMAs our method is amenable to the technique in interval arithmetic called constraint propagation, which allows great improvement of its efficiency. To illustrate the applicability of our method we apply it to some networks of biochemical reactions appearing in the literature, showing in particular that, in addition to estimating system parameters in the absence of noise, our method may also be used to recover the topology of these networks.

Design and optimization of selfnanoemulsifying drug delivery systems for enhanced dissolution of gemfibrozil

Self-nanoemulsifying drug delivery systems of gemfibrozil were developed under Quality by Design approach for improvement of dissolution and oral absorption. Preliminary screening was performed to select proper components combination. BoxBehnken experimental design was employed as statistical tool to optimize the formulation variables, X1 (Cremophor® EL), X2 (Capmul® MCM-C8), and X3 (lemon essential oil). Systems were assessed for visual characteristics (emulsification efficacy), turbidity, droplet size, polydispersity index and drug release. Different pH media were also assayed for optimization. Following optimization, the values of formulation components (X1, X2, and X3) were 32.43%, 29.73% and 21.62%, respectively (16.22% of gemfibrozil). Transmission electron microscopy demonstrated spherical droplet morphology. SNEEDS release study was compared to commercial tablets. Optimized SNEDDS formulation of gemfibrozil showed a significant increase in dissolution rate compared to conventional tablets. Both formulations followed Weibull mathematical model release with a significant difference in td parameter in favor of the SNEDDS. Equally amodelistic parameters were calculated being the dissolution efficiency significantly higher for SNEDDS, confirming that the developed SNEDDS formulation was superior to commercial formulation with respect to in vitro dissolution profile. This paper provides an overview of the SNEDDS of the gemfibrozil as a promising alternative to improve oral absorption.

Optimization of Radiation Dose and Image Quality in Musculoskeletal CT: Emphasis on Iterative Reconstruction Techniques (Part 1).

Computed tomography (CT) is a modality of choice for the study of the musculoskeletal system for various indications including the study of bone, calcifications, internal derangements of joints (with CT arthrography), as well as periprosthetic complications. However, CT remains intrinsically limited by the fact that it exposes patients to ionizing radiation. Scanning protocols need to be optimized to achieve diagnostic image quality at the lowest radiation dose possible. In this optimization process, the radiologist needs to be familiar with the parameters used to quantify radiation dose and image quality. CT imaging of the musculoskeletal system has certain specificities including the focus on high-contrast objects (i.e., in CT of bone or CT arthrography). These characteristics need to be taken into account when defining a strategy to optimize dose and when choosing the best combination of scanning parameters. In the first part of this review, we present the parameters used for the evaluation and quantification of radiation dose and image quality. In the second part, we discuss different strategies to optimize radiation dose and image quality at CT, with a focus on the musculoskeletal system and the use of novel iterative reconstruction techniques.

Optimization of Radiation Dose and Image Quality in Musculoskeletal CT: Emphasis on Iterative Reconstruction Techniques (Part 2).

Computed tomography (CT) is a modality of choice for the study of the musculoskeletal system for various indications including the study of bone, calcifications, internal derangements of joints (with CT arthrography), as well as periprosthetic complications. However, CT remains intrinsically limited by the fact that it exposes patients to ionizing radiation. Scanning protocols need to be optimized to achieve diagnostic image quality at the lowest radiation dose possible. In this optimization process, the radiologist needs to be familiar with the parameters used to quantify radiation dose and image quality. CT imaging of the musculoskeletal system has certain specificities including the focus on high-contrast objects (i.e., in CT of bone or CT arthrography). These characteristics need to be taken into account when defining a strategy to optimize dose and when choosing the best combination of scanning parameters. In the first part of this review, we present the parameters used for the evaluation and quantification of radiation dose and image quality. In the second part, we discuss different strategies to optimize radiation dose and image quality of CT, with a focus on the musculoskeletal system and the use of novel iterative reconstruction techniques.

Accelerated Microstructure Imaging via Convex Optimization (AMICO) in crossing fibers

Mapping the microstructure properties of the local tissues in the brain is crucial to understand any pathological condition from a biological perspective. Most of the existing techniques to estimate the microstructure of the white matter assume a single axon orientation whereas numerous regions of the brain actually present a fiber-crossing configuration. The purpose of the present study is to extend a recent convex optimization framework to recover microstructure parameters in regions with multiple fibers.

Optimization of energy in a pulp mill

This study presents examination of ways to increase power generation in pulp mills. The main purpose was to identify and verify the best ways of power generation growth. The literature part of this study presented operation of energy pulp mill departments, energy consumption and generation by the recovery and power boilers. The second chapter of this part described the main directions for increase of electricity generation rise of black liquor dry solid content, increase of main steam parameters, flue gas heat recovery technologies, feed water and combustion air preheating. The third chapter of the literature part presented possible technical, environment and corrosion risks appeared from described alternatives. In the experimental part of this study, calculations and results of possible models with alternatives was presented. The possible combinations of alternatives were generated in 44 `models of energy pulp mill. The target of this part was define extra electricity generation after alternatives using and estimate profitability of generated models. The calculations were made by computer programme PROSIM. In the conclusions, the results were estimated on the basis of extra electricity generation and equipment design data of models. The profitability of cases was verified by their payback periods and additional incomes.

Experimental analysis of optimization techniques on the road passenger transportation problem

Analyzing the state of the art in a given field in order to tackle a new problem is always a mandatory task. Literature provides surveys based on summaries of previous studies, which are often based on theoretical descriptions of the methods. An engineer, however, requires some evidence from experimental evaluations in order to make the appropriate decision when selecting a technique for a problem. This is what we have done in this paper: experimentally analyzed a set of representative state-of-the-art techniques in the problem we are dealing with, namely, the road passenger transportation problem. This is an optimization problem in which drivers should be assigned to transport services, fulfilling some constraints and minimizing some function cost. The experimental results have provided us with good knowledge of the properties of several methods, such as modeling expressiveness, anytime behavior, computational time, memory requirements, parameters, and free downloadable tools. Based on our experience, we are able to choose a technique to solve our problem. We hope that this analysis is also helpful for other engineers facing a similar problem

Terbinafine: optimization of a LC method for quantitative analysis in pharmaceutical formulations and its application for a tablet dissolution test

A simple liquid chromatographic method was optimized for the quantitative determination of terbinafine in pharmaceutical hydroalcoholic solutions and tablets, and was also employed for a tablet dissolution test. The analysis was carried out using a RP-C18 (250 mm × 4.6 mm, 5 μm) Vertical® column, UV-Vis detection at 254 nm, and a methanol-water (95:5, v/v) mobile phase at a flow-rate of 1.2 mL min-1. Method validation investigated parameters such as linearity, precision, accuracy, robustness and specificity, which gave results within the acceptable range. The tablets dissolution was quite fast: 80% of the drug was dissolved within 15 min.