Search

When relativistic electrons are used to irradiate tissues, such as during FLASH pre-clinical irradiations, the electron beam energy is one of the critical parameters that determine the dose distribution. Moreover, during such irradiations, linear accelerators (linacs) usually operate with significant beam loading, where a small change in the accelerator output current can lead to beam energy reduction. Optimisation of the tuning of the accelerator’s radio frequency system is often required. We describe here a robust, easy-to-use device for non-interceptive monitoring of potential variations in the electron beam energy during every linac macro-pulse of an irradiation run. Our approach monitors the accelerated electron fringe beam using two unbiased aluminium annular charge collection plates, positioned in the beam path and with apertures (5 cm in diameter) for the central beam. These plates are complemented by two thin annular screening plates to eliminate crosstalk and equalise the capacitances of the charge collection plates. The ratio of the charge picked up on the downstream collection plate to the sum of charges picked up on the both plates is sensitive to the beam energy and to changes in the energy spectrum shape. The energy sensitivity range is optimised to the investigated beam by the choice of thickness of the first plate. We present simulation and measurement data using electrons generated by a nominal 6 MeV energy linac as well as information on the design, the practical implementation and the use of this monitor.

Significance: Line scanning-based temporal focusing multiphoton microscopy (TFMPM) has superior axial excitation confinement (AEC) compared to conventional widefield TFMPM, but the frame rate is limited due to the limitation of the single line-to-line scanning mechanism. The development of the multiline scanning-based TFMPM requires only eight multiline patterns for full-field uniform multiphoton excitation and it still maintains superior AEC. Aim: The optimized parallel multiline scanning TFMPM is developed, and the performance is verified with theoretical simulation. The system provides a sharp AEC equivalent to the line scanning-based TFMPM, but fewer scans are required. Approach: A digital micromirror device is integrated in the TFMPM system and generates the multiline pattern for excitation. Based on the result of single-line pattern with sharp AEC, we can further model the multiline pattern to find the best structure that has the highest duty cycle together with the best AEC performance. Results: The AEC is experimentally improved to 1.7 μm from the 3.5 μm of conventional TFMPM. The adopted multiline pattern is akin to a pulse-width-modulation pattern with a spatial period of four times the diffraction-limited line width. In other words, ideally only four π/2 spatial phase-shift scans are required to form a full two-dimensional image with superior AEC instead of image-size-dependent line-to-line scanning. Conclusions: We have demonstrated the developed parallel multiline scanning-based TFMPM has the multiline pattern for sharp AEC and the least scans required for full-field uniform excitation. In the experimental results, the temporal focusing-based multiphoton images of disordered biotissue of mouse skin with improved axial resolution due to the near-theoretical limit AEC are shown to clearly reduce background scattering.

To detect small-scale changes in tissue with optical techniques, small sampling volumes are required. Single fiber reflectance (SFR) spectroscopy has a sampling depth of a few hundred micrometers. SFR spectroscopy uses a single fiber to emit and collect light. The only available model to determine optical properties with SFR spectroscopy was derived for tissues with modified Henyey-Greenstein phase functions. Previously, we demonstrated that this model is inadequate for other tissue phase functions. We develop a model to relate SFR measurements to scattering properties for a range of phase functions, in the absence of absorption. Since the source and detector overlap, the reflectance cannot be accurately described by diffusion theory alone: SFR measurements are subdiffuse. Therefore, we describe the reflectance as a combination of a diffuse and a semiballistic component. We use the model of Farrell et al. for the diffuse component, solved for an overlapping source and detector fiber. For the semiballistic component, we derive a new parameter, psb, which incorporates the integrals of the phase function over 1 deg in the backward direction and 23 deg in the forward direction. Our model predicts the reflectance with a median error of 2.1%, compared to 9.0% for the currently available model..

The mesh-based Monte Carlo (MMC) algorithm is increasingly used as the gold-standard for developing new biophotonics modeling techniques in 3-D complex tissues, including both diffusion-based and various Monte Carlo (MC)-based methods. Compared to multilayered and voxel-based MCs, MMC can utilize tetrahedral meshes to gain improved anatomical accuracy but also results in higher computational and memory demands. Previous attempts of accelerating MMC using graphics processing units (GPUs) have yielded limited performance improvement and are not publicly available. We report a highly efficient MMC—MMCL—using the OpenCL heterogeneous computing framework and demonstrate a speedup ratio up to 420× compared to state-of-the-art single-threaded CPU simulations. The MMCL simulator supports almost all advanced features found in our widely disseminated MMC software, such as support for a dozen of complex source forms, wide-field detectors, boundary reflection, photon replay, and storing a rich set of detected photon information. Furthermore, this tool supports a wide range of GPUs/CPUs across vendors and is freely available with full source codes and benchmark suites at http://mcx.space/#mmc.

As photoacoustic imaging (PAI) technology matures, computational modeling will increasingly represent a critical tool for facilitating clinical translation through predictive simulation of real-world performance under a wide range of device and biological conditions. While modeling currently offers a rapid, inexpensive tool for device development and prediction of fundamental image quality metrics (e.g., spatial resolution and contrast ratio), rigorous verification and validation will be required of models used to provide regulatory-grade data that effectively complements and/or replaces in vivo testing. To address methods for establishing model credibility, we developed an integrated computational model of PAI by coupling a previously developed three-dimensional Monte Carlo model of tissue light transport with a two-dimensional (2D) acoustic wave propagation model implemented in the well-known k-Wave toolbox. We then evaluated ability of the model to predict basic image quality metrics by applying standardized verification and validation principles for computational models. The model was verified against published simulation data and validated against phantom experiments using a custom PAI system. Furthermore, we used the model to conduct a parametric study of optical and acoustic design parameters. Results suggest that computationally economical 2D acoustic models can adequately predict spatial resolution, but metrics such as signal-to-noise ratio and penetration depth were difficult to replicate due to challenges in modeling strong clutter observed in experimental images. Parametric studies provided quantitative insight into complex relationships between transducer characteristics and image quality as well as optimal selection of optical beam geometry to ensure adequate image uniformity. Multidomain PAI simulation tools provide high-quality tools to aid device development and prediction of real-world performance, but further work is needed to improve model fidelity, especially in reproducing image noise and clutter.

Since it was first demonstrated more than a decade ago, the single-pixel camera concept has been used in numerous applications in which it is necessary or advantageous to reduce the channel count, cost, or data volume. Here, three-dimensional (3-D), compressed-sensing photoacoustic tomography (PAT) is demonstrated experimentally using a single-pixel camera. A large area collimated laser beam is reflected from a planar Fabry–Pérot ultrasound sensor onto a digital micromirror device, which patterns the light using a scrambled Hadamard basis before it is collected into a single photodetector. In this way, inner products of the Hadamard patterns and the distribution of thickness changes of the FP sensor—induced by the photoacoustic waves—are recorded. The initial distribution of acoustic pressure giving rise to those photoacoustic waves is recovered directly from the measured signals using an accelerated proximal gradient-type algorithm to solve a model-based minimization with total variation regularization. Using this approach, it is shown that 3-D PAT of imaging phantoms can be obtained with compression rates as low as 10%. Compressed sensing approaches to photoacoustic imaging, such as this, have the potential to reduce the data acquisition time as well as the volume of data it is necessary to acquire, both of which are becoming increasingly important in the drive for faster imaging systems giving higher resolution images with larger fields of view.

Rank aggregation (RA), the process of combining multiple ranked lists into a single ranking, has played an important role in integrating information from individual genomic studies that address the same biological question. In previous research, attention has been focused on aggregating full lists. However, partial and/or top ranked lists are prevalent because of the great heterogeneity of genomic studies and limited resources for follow-up investigation. To be able to handle such lists, some ad hoc adjustments have been suggested in the past, but how RA methods perform on them (after the adjustments) has never been fully evaluated. In this article, a systematic framework is proposed to define different situations that may occur based on the nature of individually ranked lists. A comprehensive simulation study is conducted to examine the performance characteristics of a collection of existing RA methods that are suitable for genomic applications under various settings simulated to mimic practical situations. A non-small cell lung cancer data example is provided for further comparison. Based on our numerical results, general guidelines about which methods perform the best/worst, and under what conditions, are provided. Also, we discuss key factors that substantially affect the performance of the different methods.

X-ray luminescence computed tomography (XLCT) is an emerging hybrid imaging modality which has the potential for achieving both high sensitivity and spatial resolution simultaneously. For the narrow x-ray beam-based XLCT imaging, based on previous work, a spatial resolution of about double the x-ray beam size can be achieved using a translate/rotate scanning scheme, taking step sizes equal to the x-ray beam width. To break the current spatial resolution limit, we propose a scanning strategy achieved by reducing the scanning step size to be smaller than the x-ray beam size. We performed four sets of numerical simulations and a phantom experiment using cylindrical phantoms and have demonstrated that our proposed scanning method can greatly improve the XLCT-reconstructed image quality compared with the traditional scanning approach. In our simulations, by using a fixed x-ray beam size of 0.8 mm, we were able to successfully reconstruct six embedded targets as small as 0.5 mm in diameter and with the same edge-to-edge distances by using a scanning step as small as 0.2 mm which is a 1.6 times improvement in the spatial resolution compared with the traditional approach. Lastly, the phantom experiment further demonstrated the efficacy of our proposed method in improving the XLCT image quality, with all image quality metrics improving as the step size decreased.

Although the luminescence of water at a lower energy than the Cerenkov-light (CL) threshold has been found for various types of radiation, the fractions of the luminescence of water to the total produced light have not been obvious for radiations at a higher energy than the CL threshold because it is difficult to separate these two types of light. Thus, we used a Monte Carlo simulation to estimate the fractions of the luminescence of water for various types of radiation at a higher energy than the CL threshold to confirm the major component of the produced light. After we confirmed that the estimated light production of the luminescence of water could adequately simulate the experimental results, we calculated the produced light photons of this luminescence and the CL from water for protons (170 MeV), carbon ions (330 MeV/n), high-energy x-ray (6 MV) from a linear accelerator (LINAC), high-energy electrons (9 MeV) from LINAC, positrons (F-18, C-11, O-15, and N-13), and high-energy gamma photon radionuclides (Co-60). For protons, the major fraction of the produced light was the luminescence of water in addition to the CL from the prompt gamma photons produced by the nuclear interactions. For carbon ions, the major fraction of the produced light was the luminescence of water and the CL produced by the secondary electrons in addition to the prompt gamma photons produced by the nuclear interactions. For high-energy x-ray and electrons from LINAC, the fractions of luminescence of water were ∼0.1% to 0.2%. The fractions of luminescence of water for positrons were 0.2% to 1.5% and that for Co-60 was 0.4%. We conclude that the major fractions of light produced from x-ray and electrons from LINAC, positron radionuclides, and the Co-60 source are CL, with fractions of the luminescence of water from

Computational models predicting cell damage responses to transient temperature rises generated by exposure to lasers have implemented the damage integral (Ω), which time integrates the chemical reaction rate constant described by Arrhenius. However, few published reports of empirical temperature histories (thermal profiles) correlated with damage outcomes at the cellular level are available to validate the breadth of applicability of the damage integral. In our study, an analysis of photothermal damage rate processes in cultured retinal pigment epithelium cells indicated good agreement between temperature rise, exposure duration (τ), and threshold cellular damage. Full-frame thermograms recorded at high magnification during laser exposures were overlaid with fluorescence damage images taken 1 h postexposure. From the image overlays, pixels of the thermogram correlated with the boundary of cell death were used to extract threshold thermal profiles. Assessing photothermal responses at these boundaries standardized all data points, irrespective of laser irradiance, damage size, or optical and thermal properties of the cells. These results support the hypothesis that data from boundaries of cell death were equivalent to a minimum visible lesion, where the damage integral approached unity (Ω=1) at the end of the exposure duration. Empirically resolved Arrhenius coefficients for use in the damage integral determined from exposures at wavelengths of 2 μm and 532 nm and durations of 0.05–20 s were consistent with literature values. Varying ambient temperature (Tamb) between 20°C and 40°C during laser exposure did not change the τ-dependent threshold peak temperature (Tp). We also show that, although threshold laser irradiance varied due to pigmentation differences, threshold temperatures were irradiance independent.

International audience; We present the effects of using a single-pixel camera approach to extract optical properties with the single-snapshot spatial frequency-domain imaging method. We acquired images of a human hand for spatial frequencies ranging from 0.1 to 0.4 mm - 1 with increasing compression ratios using adaptive basis scan wavelet prediction strategy. In summary, our findings indicate that the extracted optical properties remained usable up to 99% of compression rate at a spatial frequency of 0.2 mm - 1 with errors of 5% in reduced scattering and 10% in absorption.

We compare previously reported benzoporphyrin derivative (BPD)-mediated photodynamic therapy (PDT) results for reactive singlet oxygen concentration (also called singlet oxygen dose) on mice with simulations using a computational device, Dosie™, that calculates light transport and photokinetics for PDT in near real-time. The two sets of results are consistent and validate the use of the device in PDT treatment planning to predict BPD-mediated PDT outcomes in mice animal studies based on singlet oxygen dose, which showed a much better correlation with the cure index than the conventional light dose.

An investigation of the influence of an evanescent wave on the dynamics of motion of erythrocytes in blood plasma is presented. Computer simulation of erythrocytes moving in an evanescent field and experimental demonstration of the forecasted motion substantiate the possibility for control of position of red blood cells in a solution. The range of velocities of transversal motion of erythrocytes due to the action of the optical force of the generated evanescent field is determined as a function of the angle of illumination of a cell by a linearly polarized wave with the azimuth of polarization 45 deg.

Several noninvasive imaging techniques have been developed to monitor the health of skin and enhance the diagnosis of skin diseases. Among them, skin elastography is a popular technique used to measure the elasticity of the skin. A change in the elasticity of the skin can influence its natural frequencies and mode shapes. We propose a technique to use the resonant frequencies and mode shapes of the skin to monitor its health. Our study demonstrates how the resonant frequencies and mode shapes of skin can be obtained using numerical and experimental analysis. In our study, natural frequencies and mode shapes are obtained via two methods: (1) finite element analysis: an eigensolution is performed on a finite element model of normal skin, including stratum corneum, epidermis, dermis, and subcutaneous layers and (2) digital image correlation (DIC): several in-vivo measurements have been performed using DIC. The experimental results show a correlation between the DIC and FE results suggesting a noninvasive method to obtain vibration properties of the skin. This method can be further examined to be eventually used as a method to differentiate healthy skin from diseased skin. Prevention, early diagnosis, and treatment are critical in helping to reduce the incidence, morbidity, and mortality associated with skin cancer; thus, making the current study significant and important in the field of skin biomechanics.

Pulsed dye laser irradiation in the wavelength range of 585 to 600nm is currently the gold standard for treatment of port-wine stains (PWSs). However, this treatment method is often ineffective for deeply seated blood vessels and in individuals with moderate to heavy pigmentation. Use of optical particles doped with the FDA-approved near-infrared (NIR) absorber, indocyanine green (ICG), can potentially provide an effective method to overcome these limitations. Herein, we theoretically investigate the effectiveness of particles derived from erythrocytes, which contain ICG, in mediating photothermal destruction of PWS blood vessels. We refer to these particles as NIR erythrocyte-derived transducers (NETs). Our theoretical model consists of a Monte Carlo algorithm to estimate the volumetric energy deposition, a finite elements approach to solve the heat diffusion equation, and a damage integral based on an Arrhenius relationship to quantify tissue damage. The model geometries include simulated PWS blood vessels as well as actual human PWS blood vessels plexus obtained by the optical coherence tomography. Our simulation results indicate that blood vessels containing micron- or nano-sized NETs and irradiated at 755nm have higher levels of photothermal damage as compared to blood vessels without NETs irradiated at 585nm. Blood vessels containing micron-sized NETs also showed higher photothermal damage than blood vessels containing nano-sized NETs. The theoretical model presented can be used in guiding the fabrication of NETs with patient-specific optical properties to allow for personalized treatment based on the depth and size of blood vessels as well as the pigmentation of the individual's skin.

Objective. Neural interfaces and other implantable micro-devices that use polymer-encapsulated integrated circuits will only be allowed in medical devices when their lifetimes can be estimated from experimental data. An apparatus has been developed and tested that allows hundreds of insulated samples (interdigitated combs) to be aged under accelerated conditions of high temperature and voltage stress. Occasionally, aging is paused while the sample’s impedance is measured; the impedance spectrogram may show degradation as it progresses before failure. Approach. The design was based on practical considerations which are reviewed. A Solartron Modulab provides the frequency response analyser and the femtoammeter. The apparatus can accommodate batches of samples at several temperatures and with different aging voltage waveforms. It is important to understand features of the spectra that are not due to comb–comb leakage, but come from other places (for example substrate-solution leakage); some have been observed and investigated using SPICE. Main results. The design is described in detail and test results show that it is capable of making measurements over long periods, at least up to 67 °C. Despite the size of the apparatus, background capacitance is about 1 pF and comb–comb capacitances of about 30 pF can be measured down to 10 mHz, an impedance of about 100 GΩ. An important discovery was the advantage of grounding the bathing solution, primarily in that it raises the measurement ceiling. Observation and SPICE simulation shows that leakage from the substrate to the bathing solution can give phase lags >90°, in contrast to comb–comb leakage which reduces phase lag to

The aim of the present study was to investigate the dependency of the accuracy and precision of nodule diameter measurements on the radiation dose level in chest tomosynthesis. Artificial ellipsoid-shaped nodules with known dimensions were inserted in clinical chest tomosynthesis images. Noise was added to the images in order to simulate radiation dose levels corresponding to effective doses for a standard-sized patient of 0.06 and 0.04 mSv. These levels were compared with the original dose level, corresponding to an effective dose of 0.12 mSv for a standard-sized patient. Four thoracic radiologists measured the longest diameter of the nodules. The study was restricted to nodules located in high-dose areas of the tomosynthesis projection radiographs. A significant decrease of the measurement accuracy and intraobserver variability was seen for the lowest dose level for a subset of the observers. No significant effect of dose level on the interobserver variability was found. The number of non-measurable small nodules (≤5 mm) was higher for the two lowest dose levels compared with the original dose level. In conclusion, for pulmonary nodules at positions in the lung corresponding to locations in high-dose areas of the projection radiographs, using a radiation dose level resulting in an effective dose of 0.06 mSv to a standard-sized patient may be possible in chest tomosynthesis without affecting the accuracy and precision of nodule diameter measurements to any large extent. However, an increasing number of non-measurable small nodules (≤5 mm) with decreasing radiation dose may raise some concerns regarding an applied general dose reduction for chest tomosynthesis examinations in the clinical praxis.

While novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug–target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred.

Infants are curious learners who drive their own cognitive development by imposing structure on their learning environment as they explore. Understanding the mechanisms by which infants structure their own learning is therefore critical to our understanding of development. Here we propose an explicit mechanism for intrinsically motivated information selection that maximizes learning. We first present a neurocomputational model of infant visual category learning, capturing existing empirical data on the role of environmental complexity on learning. Next we “set the model free”, allowing it to select its own stimuli based on a formalization of curiosity and three alternative selection mechanisms. We demonstrate that maximal learning emerges when the model is able to maximize stimulus novelty relative to its internal states, depending on the interaction across learning between the structure of the environment and the plasticity in the learner itself. We discuss the implications of this new curiosity mechanism for both existing computational models of reinforcement learning and for our understanding of this fundamental mechanism in early development.

Genome-wide association studies have been an important approach used to localize trait loci, with primary focus on common variants. The multiple rare variant-common disease hypothesis may explain the missing heritability remaining after accounting for identified common variants. Advances of sequencing technologies with their decreasing costs, coupled with methodological advances in the context of association studies in large samples, now make the study of rare variants at a genome-wide scale feasible. The resurgence of family-based association designs because of their advantage in studying rare variants has also stimulated more methods development, mainly based on linear mixed models (LMMs). Other tests such as score tests can have advantages over the LMMs, but to date have mainly been proposed for single-marker association tests. In this article, we extend several score tests (χcorrected2, WQLS, and SKAT) to the multiple variant association framework. We evaluate and compare their statistical performances relative with the LMM. Moreover, we show that three tests can be cast as the difference between marker allele frequencies (AFs) estimated in each of the group of affected and unaffected subjects. We show that these tests are flexible, as they can be based on related, unrelated or both related and unrelated subjects. They also make feasible an increasingly common design that only sequences a subset of affected subjects (related or unrelated) and uses for comparison publicly available AFs estimated in a group of healthy subjects. Finally, we show the great impact of linkage disequilibrium on the performance of all these tests.

Microbeam Radiation Therapy is an innovative pre-clinical strategy which uses arrays of parallel, tens of micrometres wide kilo-voltage photon beams to treat tumours. These x-ray beams are typically generated on a synchrotron source. It was shown that these beam geometries allow exceptional normal tissue sparing from radiation damage while still being effective in tumour ablation. A final biological explanation for this enhanced therapeutic ratio has still not been found, some experimental data support an important role of the vasculature. In this work, the effect of microbeams on a normal microvascular network of the cerebral cortex was assessed in computer simulations and compared to the effect of homogeneous, seamless exposures at equal energy absorption. The anatomy of a cerebral microvascular network and the inflicted radiation damage were simulated to closely mimic experimental data using a novel probabilistic model of radiation damage to blood vessels. It was found that the spatial dose fractionation by microbeam arrays significantly decreased the vascular damage. The higher the peak-to-valley dose ratio, the more pronounced the sparing effect. Simulations of the radiation damage as a function of morphological parameters of the vascular network demonstrated that the distribution of blood vessel radii is a key parameter determining both the overall radiation damage of the vasculature and the dose-dependent differential effect of microbeam irradiation.

RNA-Seq is a widely-used method for studying the behavior of genes under different biological conditions. An essential step in an RNA-Seq study is normalization, in which raw data are adjusted to account for factors that prevent direct comparison of expression measures. Errors in normalization can have a significant impact on downstream analysis, such as inflated false positives in differential expression analysis. An under-emphasized feature of normalization is the assumptions upon which the methods rely and how the validity of these assumptions can have a substantial impact on the performance of the methods. In this paper, we explain how assumptions provide the link between raw RNA-Seq read counts and meaningful measures of gene expression. We examine normalization methods from the perspective of their assumptions, as an understanding of methodological assumptions is necessary for choosing methods appropriate for the data at hand. Furthermore, we discuss why normalization methods perform poorly when their assumptions are violated and how this causes problems in subsequent analysis. To analyze a biological experiment, researchers must select a normalization method with assumptions that are met and that produces a meaningful measure of expression for the given experiment., Comment: 20 pages, 6 figures, 1 table. Supplementary information contains 9 pages, 1 table. For associated simulation code, see https://github.com/ciaranlevans/rnaSeqAssumptions

Over the last years, more stringent safety requirements for an increasing number of chemicals across many regulatory fields (e.g. industrial chemicals, pharmaceuticals, food, cosmetics,...) have triggered the need for an efficient screening strategy to prioritize the substances of highest Concern. In this context, alternative methods such as in silica (i.e. computational) techniques gain more and more importance. In the current study, a new prioritization strategy for identifying potentially mutagenic substances was developed based on the combination of multiple (quantitative) structure-activity relationship ((Q)SAR) tools. Non-evaluated substances used in printed paper and board food contact materials (FCM) were selected for a case study. By applying our strategy, 106 out of the 1723 substances were assigned 'high priority' as they were predicted mutagenic by 4 different (Q)SAR models. Information provided within the models allowed to identify 53 substances for which Ames mutagenicity prediction already has in vitro Ames test results. For further prioritization, additional support could be obtained by applying local i.e. specific models, as demonstrated here for aromatic azo compounds, typically found in printed paper and board FCM. The strategy developed here can easily be applied to other groups of chemicals facing the same need for priority ranking. (C) 2017 Elsevier Ltd. All rights reserved.

Due to the availability of data of one biological phenomenon at different levels/scales, modelling of biological systems is moving from single level/scale to multiple levels/scales, which introduces a number of challenges. Coloured Petri nets have been successfully applied to multi-level, multiscale and multi-dimensional modelling of some biological systems, addressing many of these challenges. In this paper, we first review the basics of coloured Petri nets and some popular extensions, and then their applications for multi-level, multiscale and multi-dimensional modelling of biological systems. This understanding how to use coloured Petri nets for modelling biological systems will assist readers in selecting appropriate coloured Petri net classes for specific modelling circumstances.

Exposure of finger- and toe-nails to ionizing radiation generates an Electron Paramagnetic Resonance (EPR) signal whose intensity is dose dependent and stable at room temperature for several days. The dependency of the radiation-induced signal (RIS) on the received dose may be used as the basis for retrospective dosimetry of an individual's fortuitous exposure to ionizing radiation. Two radiation-induced signals, a quasi-stable (RIS2) and stable signal (RIS5), have been identified in nails irradiated up to a dose of 50 Gy. Using X-band EPR, both RIS signals exhibit a singlet line shape with a line width around 1.0 mT and an apparent g-value of 2.0044. In this work, we seek information on the exact chemical nature of the radiation-induced free radicals underlying the signal. This knowledge may provide insights into the reason for the discrepancy in the stabilities of the two RIS signals and help develop strategies for stabilizing the radicals in nails or devising methods for restoring the radicals after decay. In this work an analysis of high field (94GHz and 240 GHz) EPR spectra of the RIS using quantum chemical calculations, the oxidation-reduction properties and the pH dependence of the signal intensities are used to show that spectroscopic and chemical properties of the RIS are consistent with a semiquinone-type radical underlying the RIS. It has been suggested that semiquinone radicals formed on trace amounts of melanin in nails are the basis for the RIS signals. However, based on the quantum chemical calculations and chemical properties of the RIS, it is likely that the radicals underlying this signal are generated from the radiolysis of L-3,4-dihydroxyphenylalanine (DOPA) amino acids in the keratin proteins. These DOPA amino acids are likely formed from the exogenous oxidation of tyrosine in keratin by the oxygen from the air prior to irradiation. We show that these DOPA amino acids can work as radical traps, capturing the highly reactive and unstable sulfur-based radicals and/or alkyl radicals generated during the radiation event and are converted to the more stable o-semiquinone anion-radicals. From this understanding of the oxidation- reduction properties of the RIS, it may be possible to regenerate the unstable RIS2 following its decay through treatment of nail clippings. However, the treatment used to recover the RIS2 also has the ability to recover an interfering, mechanically-induced signal (MIS) formed when the nail is clipped. Therefore, to use the recovered (regenerated) RIS2 to increase the detection limits and precision of the RIS measurements and, therefore, the dose estimates calculated from the RIS signal amplitudes, will require the application of methods to differentiate the RIS2 from the recovered MIS signal. © The Author 2016.

Objective. In electroencephalography (EEG) measurements, the signal of each recording electrode is contrasted with a reference electrode or a combination of electrodes. The estimation of a neutral reference is a long-standing issue in EEG data analysis, which has motivated the proposal of different re-referencing methods, among which linked-mastoid re-referencing (LMR), average re-referencing (AR) and reference electrode standardization technique (REST). In this study we quantitatively assessed the extent to which the use of a high-density montage and a realistic head model can impact on the optimal estimation of a neutral reference for EEG recordings. Approach. Using simulated recordings generated by projecting specific source activity over the sensors, we assessed to what extent AR, REST and LMR may distort the scalp topography. We examined the impact electrode coverage has on AR and REST, and how accurate the REST reconstruction is for realistic and less realistic (three-layer and single-layer spherical) head models, and with possible uncertainty in the electrode positions. We assessed LMR, AR and REST also in the presence of typical EEG artifacts that are mixed in the recordings. Finally, we applied them to real EEG data collected in a target detection experiment to corroborate our findings on simulated data. Main results. Both AR and REST have relatively low reconstruction errors compared to LMR, and that REST is less sensitive than AR and LMR to artifacts mixed in the EEG data. For both AR and REST, high electrode density yields low re-referencing reconstruction errors. A realistic head model is critical for REST, leading to a more accurate estimate of a neutral reference compared to spherical head models. With a low-density montage, REST shows a more reliable reconstruction than AR either with a realistic or a three-layer spherical head model. Conversely, with a high-density montage AR yields better results unless precise information on electrode positions is available. Significance. Our study is the first to quantitatively assess the performance of EEG re-referencing techniques in relation to the use of a high-density montage and a realistic head model. We hope our study will help researchers in the choice of the most effective re-referencing approach for their EEG studies., Journal of Neural Engineering, 12 (5), ISSN:1741-2560, ISSN:1741-2552

In this work, we propose a new approach to modeling multiphase flow and solute transport through a stack of thin porous layers. Currently, numerical simulation of thin layers involves discretization across the layer thickness. In our new approach, thin porous layers are treated as a bunch of two-dimensional (2D) interacting continua. Macroscale balance laws are formulated in terms of thickness-averaged material properties. A number of exchange terms are employed to account for exchanges of mass, momentum, energy, and entropy between two neighboring layers. The entropy inequality is then exploited for obtaining constitutive equations to close the problem under study. As an example, simplified governing equations are derived for a system of air–water flow and heat transfer through two thin porous layers. In comparison to previous macroscale models, our model possesses the following distinctive advantages: (1) it is rigorous thermodynamics-based model; (2) it is formulated in terms of layer-thickness-averaged material properties which are usually easily measurable; and (3) it reduces 3D modeling to 2D leading to a very significant reduction of computational efforts.

The thermal development of latent fingerprints on paper surfaces is a simple, safe, and chemicals-free method, based on the faster heating of the substrate underlying the print residue. Microwave heating is proposed for the first time for the development of latent fingerprints on cellulose-based substrate, in order to add to the thermal development mechanism the further characteristic of being able to heat the fingerprint residues to a different extent with respect to the substrate, due to the intrinsic difference in their dielectric properties. Numerical simulation was performed to confirm and highlight the selectivity of microwaves, and preliminary experimental results point out the great potentialities of this technique, which allowed developing both latent sebaceous-rich and latent eccrine-rich fingerprints on different porous surfaces, in less than 30 sec time with an applied output power of 500 W. Microwaves demonstrated more effectiveness in the development of eccrine-rich residues, aged up to 12 weeks.

For both model-free and model-based linkage analysis the S.A.G.E. (Statistical Analysis for Genetic Epidemiology) program package has some unique capabilities in analyzing both continuous traits and binary traits with variable age of onset. Here we highlight model-based linkage analysis of a quantitative trait (plasma dopamine β hydroxylase) that is known to be largely determined by monogenic inheritance, using a prior segregation analysis to produce the best fitting model for the trait. For a binary trait with variable age of onset (schizophrenia), we illustrate how using age of onset information to obtain a quantitative susceptibility trait leads to more statistically significant linkage signals, suggesting better power.

Linkage analysis identifies markers that appear to be co-inherited with a trait within pedigrees. The inheritance of a chromosomal segment may be probabilistically reconstructed, with missing data complicating inference. Inheritance patterns are further obscured in the analysis of complex traits, where variants in one or more genes may contribute to phenotypic variation within a pedigree. In this case, determining which relatives share a trait variant is not simple. We describe how to represent these patterns of inheritance for marker loci. We summarize how to sample patterns of inheritance consistent with genotypic and pedigree data using gl_auto, available in MORGAN v3.0. We describe identification of classes of equivalent inheritance patterns with the program IBDgraph. We finally provide an example of how these programs may be used to simplify interpretation of linkage analysis of complex traits in general pedigrees. We borrow information across loci in a parametric linkage analysis of a large pedigree. We explore the contribution of each equivalence class to a linkage signal, illustrate estimated patterns of identity-by-descent sharing, and identify a haplotype tagging the chromosomal segment driving the linkage signal. Haplotype carriers are more likely to share the linked trait variant, and can be prioritized for subsequent DNA sequencing.

Point spreading is investigated using general radiative transfer theory. We find that the single scattering anisotropy plays a significant role for point spreading together with the medium mean free path, single scattering albedo and thickness. When forward scattering dominates, the light will on average undergo more scattering events to give a specific optical response in reflectance measurements. This will significantly increase point spreading if the medium is low absorbing with large mean free path. Any fundamental and generic model of point spreading must capture the dependence on all of these medium characteristics. PaperOpt Optics of Paper and Print - Continuous Models

This article proposes an Enhanced Memetic Differential Evolution (EMDE) for designing digital filters which aim at detecting defects of the paper produced during an industrial process. Defect detection is handled by means of two Gabor filters and their design is performed by the EMDE. The EMDE is a novel adaptive evolutionary algorithm which combines the powerful explorative features of Differential Evolution with the exploitative features of three local search algorithms employing different pivot rules and neighborhood generating functions. These local search algorithms are the Hooke Jeeves Algorithm, a Stochastic Local Search, and Simulated Annealing. The local search algorithms are adaptively coordinated by means of a control parameter that measures fitness distribution among individuals of the population and a novel probabilistic scheme. Numerical results confirm that Differential Evolution is an efficient evolutionary framework for the image processing problem under investigation and show that the EMDE performs well. As a matter of fact, the application of the EMDE leads to a design of an efficiently tailored filter. A comparison with various popular metaheuristics proves the effectiveness of the EMDE in terms of convergence speed, stagnation prevention, and capability in detecting solutions having high performance.

The numerical simulation of an integrated, liquid-fuelled ramjet engine comprising supersonic air intake, subsonic combustor and a convergent-divergent nozzle has been carried out and the results are discussed in this paper. These results include cold flow studies, heat addition in the combustor and full engine analysis with coupled simulation of supersonic air-intake and combustion chamber along with the nozzle. Overall ramjet operation depends on the performance of the air intake and the combustion chamber. The coupling phenomena are very dominant and performance of air intake is affected vastly by the combustor operation and vice versa. In this paper, a numerical analysis of integrated liquid ramjet engine considering coupling phenomena between various sub-systems viz., air intake, combustor and nozzle has been reported. � 2008, DESIDOC.