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Background Selection is one of the essential skills whereby breeders reduce the population size and increase the chance of success. Various selection methods with special applications have been developed. Superior genotypes are assessed according to interesting traits, including univariate, multivariate, phenotypic, genotypic, etc. Methods Mathematical calculation of the traits' importance based on the genetic makeup of investigated population (average degree of dominance/additive involved in the action of genes) and arbitrary genetic parameters is functional. In this paper, a general model for multivariate selection has been presented whereby the selection can be made for (a) more than one interesting trait, (b) the trait(s) with complex inheritance, (c) finding superior genotypes from among a large-scale population, (d) finding superior genotypes in segregating generations and (f) finding tolerant genotypes to stresses. This model is developed based on biometric concepts in four steps. MATLAB script is provided for the model, and users can easily apply that to identify the most suitable genotypes after data collection according to the breeding purposes. Results The main features of this model are simplicity, precision, repeatability, and speed (improving several traits simultaneously). All the steps and the analysis of the results are explained step by step in a case study.

Improvement of tomato (Lycopersicon esculentum L.) for growth in saline soils is a major goal of tomato breeders. The aim of this study was to identify the genetic combining ability and genetics of salinity tolerance in tomato. Plant materials were grown under normal (NG) and salinity stress (SSG) conditions. Results showed that the genetic controlling mechanism of salinity-related traits and fruit weight is complex and that all genetic components of additive, non-additive and maternal are involved. The nature of gene action for fruit weight and salinity-related traits was significantly affected by salinity stress. Dominance and additive gene action were predominant under NG and SSG, respectively. Under NG, the best general combiner parent for fruit weight was P3 (salt-tolerant with moderate fruit yield). Under SSG, P1 (highly salt-tolerant with low fruit yield) was the best general combiner parent for fruit weight and exhibited high genetic combining ability for K+/Na+, lipoxygenase activity, proline, relative water content, total carbohydrate and cell membrane stability. With the high frequency of genes effective in salt tolerance, the P1 parent appeared as the best specific mating partner with other parents under SSG. Simultaneous selection for fruit weight and surrogate traits (cell membrane stability, proline and relative water content) in a population derived from the P1 × P5 (susceptible with high fruit yield) cross could result in a salt-tolerant tomato genotype.

An effective culture system for producing embryos was developed from isolated microspores of sweet pepper (Capsicum annuum L.). The present work aimed to define exogenous arabinogalactan proteins (AGPs) on the embryogenesis of sweet pepper under the growth environments of donor plants. The experiments were performed with the cultivars Arancia, California wonder, Californium, Camelot, Inspiration, Plato, and Taranto under three different growth environments of the donor plants. The impacts of the different numbers of sweet pepper ovaries (0, 5, 10, and 15) were evaluated on microspore embryogenesis of cultivars in a factorial design with five replications. The simultaneous application of 10 ovaries in stress (sucrose starvation; temperature) and induction media resulted in the highest embryogenesis of the seven aforementioned cultivars under field conditions. Also, the impacts of 0, 80, 120, and 200 mg L−1 gum arabic were studied on embryogenesis in stress and induction media. The treatments containing 120 mg L−1 gum arabic showed the highest embryogenesis across all cultivars, except the California Wonder cultivar in the field conditions. Also, the simultaneous impact of the number of ovaries and concentrations of gum arabic on embryogenesis were studied. A combination of ten ovaries and 120 mg L−1 gum arabic generated the highest embryogenesis in the majority of cultivars. The results revealed that ovaries of sweet pepper and gum arabic had a significant impact on the efficiency of microspore embryogenesis in sweet pepper plants under field conditions.

Bioluminescence imaging and tomography (BLT) are used to study biologically relevant activity, typically within a mouse model. A major limitation is that the underlying optical properties of the volume are unknown, leading to the use of a "best" estimate approach often compromising quantitative accuracy.An optimization algorithm is presented that localizes the spatial distribution of bioluminescence by simultaneously recovering the optical properties and location of bioluminescence source from the same set of surface measurements.Measured data, using implanted self-illuminating sources as well as an orthotopic glioblastoma mouse model, are employed to recover three-dimensional spatial distribution of the bioluminescence source using a multi-parameter optimization algorithm.The proposed algorithm is able to recover the size and location of the bioluminescence source while accounting for tissue attenuation. Localization accuracies of1 mm are obtained in all cases, which is similar if not better than current "gold standard" methods that predict optical properties using a different imaging modality.Application of this approach, using in-vivo experimental data has shown that quantitative BLT is possible without the need for any prior knowledge about optical parameters, paving the way toward quantitative molecular imaging of exogenous and indigenous biological tumor functionality.

Non-invasive near-infrared spectral tomography (NIRST) can incorporate the structural information provided by simultaneous magnetic resonance imaging (MRI), and this has significantly improved the images obtained of tissue function. However, the process of MRI guidance in NIRST has been time consuming because of the needs for tissue-type segmentation and forward diffuse modeling of light propagation. To overcome these problems, a reconstruction algorithm for MRI-guided NIRST based on deep learning is proposed and validated by simulation and real patient imaging data for breast cancer characterization. In this approach, diffused optical signals and MRI images were both used as the input to the neural network, and simultaneously recovered the concentrations of oxy-hemoglobin, deoxy-hemoglobin, and water via end-to-end training by using 20,000 sets of computer-generated simulation phantoms. The simulation phantom studies showed that the quality of the reconstructed images was improved, compared to that obtained by other existing reconstruction methods. Reconstructed patient images show that the well-trained neural network with only simulation data sets can be directly used for differentiating malignant from benign breast tumors.

Significance: Time-domain functional near-infrared spectroscopy (TD-fNIRS) has been considered as the gold standard of noninvasive optical brain imaging devices. However, due to the high cost, complexity, and large form factor, it has not been as widely adopted as continuous wave NIRS systems. Aim: Kernel Flow is a TD-fNIRS system that has been designed to break through these limitations by maintaining the performance of a research grade TD-fNIRS system while integrating all of the components into a small modular device. Approach: The Kernel Flow modules are built around miniaturized laser drivers, custom integrated circuits, and specialized detectors. The modules can be assembled into a system with dense channel coverage over the entire head. Results: We show performance similar to benchtop systems with our miniaturized device as characterized by standardized tissue and optical phantom protocols for TD-fNIRS and human neuroscience results. Conclusions: The miniaturized design of the Kernel Flow system allows for broader applications of TD-fNIRS.

Cerebral blood flow (CBF) measures are used in clinical settings to diagnose conditions (e.g., vasospasms, sickle cell disease, stroke, dementia, confirmation of brain death), as well as in research, emerging as potential early biomarkers of declining brain health. The aim of this chapter is to provide an overview of approaches used to measure CBF for determining resting cerebral tissue perfusion and functional CBF responsiveness. We will highlight the strengths and weaknesses of each and illustrate the methodological differences that need to be considered when assessing brain vascular health. The imaging methods measuring CBF will cover: Doppler ultrasound (spanning transcranial Doppler (TCD), duplex ultrasound, and transcranial color Doppler (TCCD)), magnetic resonance imaging (MRI) (including arterial spin labeling (ASL), blood oxygenation level dependent (BOLD) signal measures and phase-contrast angiography), and finally near-infrared spectroscopy (NIRS).

Several groups, including ours, have initiated efforts to develop small-animal irradiators that mimic radiation therapy (RT) for human treatment. The major image modality used to guide irradiation is cone-beam computed tomography (CBCT). While CBCT provides excellent guidance capability, it is less adept at localizing soft tissue targets growing in a low image contrast environment. In contrast, bioluminescence imaging (BLI) provides strong image contrast and thus is an attractive solution for soft tissue targeting. However, commonly used 2D BLI on an animal surface is inadequate to guide irradiation, because optical transport from an internal bioluminescent tumor is highly susceptible to the effects of optical path length and tissue absorption and scattering. Recognition of these limitations led us to integrate 3D bioluminescence tomography (BLT) with the small animal radiation research platform (SARRP). In this chapter, we introduce quantitative BLT (QBLT) with the advanced capabilities of quantifying tumor volume for irradiation guidance. The detail of system components, calibration protocol, and step-by-step procedure to conduct the QBLT-guided irradiation are described.

In this article, a new observer design technique is proposed for the general affine class of fractional-order nonlinear multi-input/multi-output (MIMO) systems. This study is presented based on the differential mean value theorem. One significant characteristic of the suggested approach is that the nonlinear dynamic of observer error is converted into a linear parameter-varying system. Stability and convergence of observation error are shown using the Lyapunov direct method leading to feasibility and existence of a solution for some linear matrix inequalities. The performance and efficacy of the proposed method are evaluated through some illustrated simulations.

A set of deep neural network models for rheumatoid arthritis (RA) classification using a highway network, a convolutional neural network and a residual network is proposed based on the data of diffuse optical tomography (DOT) utilising near-infrared light, which ensures early diagnosis of pathophysiological changes resulting from inflammation. A numerical model of the finger is used to generate images to overcome the inherent problem of insufficient clinical DOT images available. The proposed deep neural network models are applied to automatically classify simulated DOT images of inflamed and non-inflamed joints and transfer learning is also used to improve the performance of the classification. The results demonstrate that all three deep neural network methods improve the diagnostic accuracy as compared to the widely applied support vector machine (SVM), especially for high inter-subject variability databases. In cases of distinct modelled severity of disease, residual network achieved the highest accuracy (> 99 %), and both of highway and convolutional neural networks reached 99 %, respectively. However, as the severity of the modelled disease is reduced, this accuracy is reduced to 75.2 % for residual networks. The results indicate that transfer learning can improve the performance of deep neural network methods on RA classification from DOT data and highlight their potential as a computer aided tool in DOT diagnostic systems.

The resolution of images recovered using diffuse optical tomography without spatial prior information is inherently limited due to the diffusive nature of light transport in scattering dominated biological tissue. Several studies have previously reported an improvement in depth sensitivity in near infrared spectroscopy by considering the subtraction of two neighbouring measurements. A nearest neighbour, spatial derivative based algorithm is presented, in which the difference between neighbouring source-sharing source – detector pairs is considered for tomographic reconstruction. Using a circular, inward-facing optode geometry, it is shown that deeper abnormalities in oxyhaemoglobin, deoxyhaemoglobin and water can be recovered with better localisation and contrast as compared to reconstructing using absolute values of multi-wavelength data. Relative improvements of up to 25 % in quantitative accuracy of recovered physiological values are demonstrated at better resolution for cancerous tissue in a heterogeneous breast model, when using this proposed spatial derivative based parameter reconstruction.

Objective: A new method for fitting diffusion-weighted magnetic resonance imaging (DW-MRI) data composed of an unknown number of multi-exponential components is presented and evaluated. Methods: The auto-regressive discrete acquisition points transformation (ADAPT) method is an adaption of the auto-regressive moving average system, which allows for the modeling of multi-exponential data and enables the estimation of the number of exponential components without prior assumptions. ADAPT was evaluated on simulated DW-MRI data. The optimum ADAPT fit was then applied to human brain DWI data and the correlation between the ADAPT coefficients and the parameters of the commonly used bi-exponential intravoxel incoherent motion (IVIM) method were investigated. Results: The ADAPT method can correctly identify the number of components and model the exponential data. The ADAPT coefficients were found to have strong correlations with the IVIM parameters. ADAPT(1,1)-β0 correlated with IVIM-D: ρ = 0.708, P

The cost and highly invasive nature of brain monitoring modality in traumatic brain injury patients currently restrict its utility to specialist neurological intensive care settings. We aim to test the abilities of a frequency domain near-infrared spectroscopy (FD-NIRS) device in predicting changes in invasively measured brain tissue oxygen tension. Individuals admitted to a United Kingdom specialist major trauma center were contemporaneously monitored with an FD-NIRS device and invasively measured brain tissue oxygen tension probe. Area under the curve receiver operating characteristic (AUROC) statistical analysis was utilized to assess the predictive power of FD-NIRS in detecting both moderate and severe hypoxia (20 and 10 mm Hg, respectively) as measured invasively. Sixteen individuals were prospectively recruited to the investigation. Severe hypoxic episodes were detected in nine of these individuals, with the NIRS demonstrating a broad range of predictive abilities (AUROC 0.68-0.88) from relatively poor to good. Moderate hypoxic episodes were detected in seven individuals with similar predictive performance (AUROC 0.576-0.905). A variable performance in the predictive powers of this FD-NIRS device to detect changes in brain tissue oxygen was demonstrated. Consequently, this enhanced NIRS technology has not demonstrated sufficient ability to replace the established invasive measurement.

Frequency domain (FD) high density diffuse optical tomography (HD-DOT) utilising varying or combined modulation frequencies (mFD) has shown to theoretically improve the imaging accuracy as compared to conventional continuous wave (CW) measurements. Using intensity and phase data from a solid inhomogeneous phantom (NEUROPT) with three insertable rods containing different contrast anomalies, at modulation frequencies of 78 MHz, 141 MHz and 203 MHz, HD-DOT is applied and quantitatively evaluated, showing that mFD outperforms FD and CW for both absolute (iterative) and temporal (linear) tomographic imaging. The localization error (LOCA), full width half maximum (FWHM) and effective resolution (ERES) were evaluated. Across all rods, the LOCA of mFD was 61.3% better than FD and 106.1% better than CW. For FWHM, CW was 6.0% better than FD and mFD and for ERES, mFD was 1.20% better than FD and 9.83% better than CW. Using mFD data is shown to minimize the effect of inherently noisier FD phase data whilst maximising its strengths through improved contrast.

Significance: By incorporating multiple overlapping functional near-infrared spectroscopy (fNIRS) measurements, high-density diffuse optical tomography (HD-DOT) images human brain function with fidelity comparable to functional magnetic resonance imaging (fMRI). Previous work has shown that frequency domain high-density diffuse optical tomography (FD-HD-DOT) may further improve image quality over more traditional continuous wave (CW) HD-DOT. Aim: The effects of modulation frequency on image quality as obtainable with FD-HD-DOT is investigated through simulations with a realistic noise model of functional activations in human head models, arising from 11 source modulation frequencies between CW and 1000 MHz. Approach: Simulations were performed using five representative head models with an HD regular grid of 158 light sources and 166 detectors and an empirically derived noise model. Functional reconstructions were quantitatively assessed with multiple image quality metrics including the localization error (LE), success rate, full width at half maximum, and full volume at half maximum (FVHM). All metrics were evaluated against CW-based models. Results: Compared to CW, localization accuracy is improved by >40% throughout brain depths of 13 to 25 mm below the surface with 300 to 500 MHz modulation frequencies. Additionally, the reliable field of view in brain tissue is enlarged by 35% to 48% within an optimal frequency of 300 MHz after considering realistic noise, depending on the dynamic range of the system. Conclusions: These results point to the tremendous opportunities in further development of high bandwidth FD-HD-DOT system hardware for applications in human brain mapping.

Significance: High density diffuse optical tomography (HD-DOT) as applied in functional near-infrared spectroscopy (fNIRS) is largely limited to continuous wave (CW) data. Using a single modulation frequency, frequency domain (FD) HD-DOT has recently demonstrated better localization of focal activation as compared to CW data. We show that combining CW and FD measurements and multiple modulation frequencies increases imaging performance in fNIRS. Aim: We evaluate the benefits of multiple modulation frequencies, combining different frequencies as well as CW data in fNIRS HD-DOT. Approach: A layered model was used, with activation occurring within a cortex layer. CW and FD measurements were simulated at 78, 141, and 203 MHz with and without noise. The localization error, full width half maximum, and effective resolution were evaluated. Results: Across the average of the three metrics, at 141 MHz, FD performed 8.4% better than CW, and the combination of CW and FD was 21.7% better than CW. FD measurements at 203 MHz performed 5% better than 78 MHz. Moreover, the three combined modulation frequencies of FD and CW performed up to 3.92% better than 141 MHz alone. Conclusions: We show that combining CW and FD measurements offers better performance than FD alone, with higher modulation frequencies increasing accuracy. Combining CW and FD measurements at multiple modulation frequencies yields the best overall performance.

Purpose/objective(s) The developing radio-immunotherapy has cast the use of radiation therapy (RT) into a pivotal role in the management of pancreatic cancer. There is a need to study pancreatic cancer in pre-clinical setting to advance our understanding of the role of RT. The challenge to study abdominal malignancy in vivo is lack of proper imaging guidance. Bioluminescence with its strong imaging contrast has been widely used to monitor tumor growth. We innovated 3D bioluminescence tomography (BLT) to guide irradiation for an orthotopic pancreatic ductal adenocarcinoma (PDAC), and monitor its growth and treatment response. Materials/methods To establish the orthotopic PDAC model, we implanted a fraction of BxPc3-Red-FLuc tumor into the pancreas of a nude mouse. For BLT, mice were subject to multi-spectral and multi-projection bioluminescence imaging (BLI), followed by small animal radiation research platform (SARRP) CBCT imaging. CBCT image was acquired to generate anatomical mesh for BLT reconstruction. We inserted a titanium wire to the center of tumor as the marker to validate the BLT localization accuracy. We will also quantify the accuracy of BLT reconstructed volume with MRI and ex vivo method. Volume-averaged bioluminescent power (BP), related to tumor viability, will also be assessed in comparing with surface BLI and ex vivo method, to determine if it is a proper metric for tumor monitoring and assessment. We will further irradiate the in vivo PDAC volume guided by BLT and demonstrate the application of BLT to longitudinally assess the change of PDAC volume/power in response to treatment. Results Our initial result shows 3D BLT can retrieve the orthotopic PDAC location within 1.5 mm accuracy at depth from 3-5 mm, common depth observed in pancreatic study. The BP is observed linearly by 12-fold along the tumor growth 4 weeks after tumor implantation. With BLT, we are able to reconstruct the 3D bioluminescent tumor volume using multiple-projection imaging. Conclusion The quantitative BLT system offers unique opportunities for researchers to study important pancreatic tumor in orthotopic setting to quantify its location, growth and response to therapeutic intervention.

Significance: Signal contamination is a major hurdle in functional near-infrared spectroscopy (fNIRS) of the human head as the NIR signal is contaminated with the changes corresponding to superficial tissue, therefore occluding the functional information originating from the cerebral region. For continuous wave, this is generally handled through linear regression of the shortest source-detector (SD) distance intensity measurement from all of the signals. Although phase measurements utilizing frequency domain (FD) provide deeper tissue sampling, the use of the shortest SD distance phase measurement for regression of superficial signal contamination can lead to misleading results, therefore suppressing cortical signals. Aim: An approach for FD fNIRS that utilizes a short-separation intensity signal directly to regress both intensity and phase measurements, providing a better regression of superficial signal contamination from both data-types, is proposed. Approach: Simulated data from realistic models of the human head are used, and signal regression using both intensity and phase-based components of the FD fNIRS is evaluated. Results: Intensity-based phase regression achieves a suppression of superficial signal contamination by 68% whereas phase-based phase regression is only by 13%. Phase-based phase regression is also shown to generate false-positive signals from the cortex, which are not desirable. Conclusions: Intensity-based phase regression provides a better methodology for minimizing superficial signal contamination in FD fNIRS.

Purpose Widely used cone beam computed tomography (CBCT)-guided irradiators in preclinical radiation research are limited to localize soft tissue target because of low imaging contrast. Knowledge of target volume is a fundamental need for radiation therapy (RT). Without such information to guide radiation, normal tissue can be overirradiated, introducing experimental uncertainties. This led us to develop high-contrast quantitative bioluminescence tomography (QBLT) for guidance. The use of a 3-dimensional bioluminescence signal, related to cell viability, for preclinical radiation research is one step toward biology-guided RT. Methods and Materials Our QBLT system enables multiprojection and multispectral bioluminescence imaging to maximize input data for the tomographic reconstruction. Accurate quantification of spectrum and dynamic change of in vivo signal were also accounted for the QBLT. A spectral-derivative method was implemented to eliminate the modeling of the light propagation from animal surface to detector. We demonstrated the QBLT capability of guiding conformal RT using a bioluminescent glioblastoma (GBM) model in vivo. A threshold was determined to delineate QBLT reconstructed gross target volume (GTVQBLT), which provides the best overlap between the GTVQBLT and CBCT contrast labeled GBM (GTV), used as the ground truth for GBM volume. To account for the uncertainty of GTVQBLT in target positioning and volume delineation, a margin was determined and added to the GTVQBLT to form a QBLT planning target volume (PTVQBLT) for guidance. Results The QBLT can reconstruct in vivo GBM with localization accuracy within 1 mm. A 0.5-mm margin was determined and added to GTVQBLT to form PTVQBLT, largely improving tumor coverage from 75.0% (0 mm margin) to 97.9% in average, while minimizing normal tissue toxicity. With the goal of prescribed dose 5 Gy covering 95% of PTVQBLT, QBLT-guided 7-field conformal RT can effectively irradiate 99.4 ± 1.0% of GTV. Conclusions The QBLT provides a unique opportunity for investigators to use biologic information for target delineation, guiding conformal irradiation, and reducing normal tissue involvement, which is expected to increase reproducibility of scientific discovery.

Diffuse correlation spectroscopy (DCS) is an emerging diffuse optical technique that quantifies microvascular blood flow. In spite of the wide range of clinical/research applications, DCS instrumentation is not yet standardized. In this study, we have analyzed the effect of experimental parameters as the measurement duration time and the number of detectors, at different photon count-rates, on the precision of a DCS experiment. This provides a recipe for finding device and experimental settings that optimize the precision while balancing cost and temporal resolution.

Spatial frequency domain imaging (SFDI) is an imaging modality that projects spatially modulated light patterns to determine optical property maps for absorption and reduced scattering of biological tissue via a pixel-by-pixel data acquisition and analysis procedure. The light interaction theory behind SFDI is based upon homogenous properties, with forward models calculated via analytical solutions or Monte-Carlo, also used for the optical property recovery, using only a pixel-independent nature. This is known to be limited for samples with high heterogeneity, with an increased error observed for varying optical property boundaries. NIRFAST is an image modelling and reconstruction tool based upon FEM of the diffusion model that simulates complex heterogenic tissue interactions from single and multi-wavelength systems and is routinely used in a variety of clinical and pre-clinical applications. NIRFAST has been adapted for SFDI, allowing for pixel-dependent heterogenic simulations. Image reconstruction using existing methodologies is compared to data generated from complex models with NIRFAST to quantify the optical property reconstruction accuracy, whilst heterogenous models of varying optical property values and depths further demonstrate SFDIs parameter recovery capabilities. It is shown that pixel-dependent light interaction in tissue plays an important part of accurate optical map recovery and can affect quantitative accuracy. This work demonstrates full raw image SFDI simulations for heterogenous samples working towards the use of modelbased image reconstruction to allow a coupled, pixel-dependent SFDI image modelling and parameter recovery.

We provide herein a simulation study of the effects of modulation frequency on the image quality obtainable with frequency domain high density diffuse optical tomography. Using an anatomical head model, we simulated point spread functions with modulation frequencies spanning 0-1,000 MHz throughout the optically accessible field of view. We assessed image quality using metrics of the full width at half maximum, the localization error, and the 50% success rate. Our results show higher modulation frequency improves image quality and provides access to deeper brain tissue.

A numerical simulation tool for spatial frequency domain imaging (SFDI) has been developed. Validation against current methods for parameter recovery is presented including heterogenic models demonstrating a complex light propagation tool for SFDI.

Over the recent years, a typical implementation of diffuse correlation spectroscopy (DCS) instrumentation has been adapted widely. However, there are no detailed and accepted recipes for designing such instrumentation to meet pre-defined signal-to-noise ratio (SNR) and precision targets. These require specific attention due to the subtleties of the DCS signals. Here, DCS experiments have been performed using liquid tissue simulating phantoms to study the effect of the detected photon count-rate, the number of parallel detection channels and the measurement duration on the precision and SNR to suggest scaling relations to be utilized for device design.

Genetically engineered mouse model(GEMM) that develops pancreatic ductal adenocarcinoma(PDAC) offers an experimental system to advance our understanding of radiotherapy(RT) for pancreatic cancer. Cone beam CT(CBCT)-guided small animal radiation research platform(SARRP) has been developed to mimic the RT used for human. However, we recognized that CBCT is inadequate to localize the PDAC growing in low image contrast environment. We innovated bioluminescence tomography(BLT) to guide SARRP irradiation for in vivo PDAC. Before working on the complex PDAC-GEMM, we first validated our BLT target localization using subcutaneous and orthotopic pancreatic tumor models. Our BLT process involves the animal transport between the BLT system and SARRP. We inserted a titanium wire into the orthotopic tumor as the fiducial marker to track the tumor location and to validate the BLT reconstruction accuracy. Our data shows that with careful animal handling, minimum disturbance for target position was introduced during our BLT imaging procedure(

Significance: Spatial frequency domain imaging (SFDI) is an imaging modality that projects spatially modulated light patterns to determine optical property maps for absorption and reduced scattering of biological tissue via a pixel-by-pixel data acquisition and analysis procedure. Compressive sensing (CS) is a signal processing methodology which aims to reproduce the original signal with a reduced number of measurements, addressing the pixel-wise nature of SFDI. These methodologies have been combined for complex heterogenous data in both the image detection and data analysis stage in a compressive sensing SFDI (cs-SFDI) approach, showing reduction in both the data acquisition and overall computational time. Aim: Application of CS in SFDI data acquisition and image reconstruction significantly improves data collection and image recovery time without loss of quantitative accuracy. Approach: cs-SFDI has been applied to an increased heterogenic sample from the AppSFDI data set (back of the hand), highlighting the increased number of CS measurements required as compared to simple phantoms to accurately obtain optical property maps. A novel application of CS to the parameter recovery stage of image analysis has also been developed and validated. Results: Dimensionality reduction has been demonstrated using the increased heterogenic sample at both the acquisition and analysis stages. A data reduction of 30% for the cs-SFDI and up to 80% for the parameter recover was achieved as compared to traditional SFDI, while maintaining an error of

Drought stress restricts the production of agricultural crops through morphological, physiological and biochemical changes in plants. This study explored the genetic control of physiological traits related to drought in coriander. In a diallel analysis, all six parents, their 15 F1hybrids and 15 F2populations were subjected to different irrigation regimes including well-watered, mild and severe water deficit stress. Drought stress decreased the relative chlorophyll content (RCC), the relative water content (RWC), chlorophyll a (Chla), chlorophyll b (Chlb), total chlorophyll (TChl) content, carotenoids (Car) and essential oil yield (EOY) in F1and F2generations. General combining ability (GCA) and specific combining ability effects were highly significant for all traits in F1and F2generations. Additive gene action was predominant for Chla, Chlb, TChl and Car under well-watered condition while non-additive gene effects were more important under mild and severe water deficit stresses in F1and F2generations for the above traits. Additive gene effects were more important for RCC, RWC and electrolyte leakage (EL) traits in both F1and F2generations under mild and severe water deficit stresses. In conclusion, the high narrow-sense heritability and significant genetic correlations between EOY and RCC, RWC and EL suggest that these traits can be used as surrogates to identify superior genotypes for arid and semi-arid regions. Also, the parental lines, P4and P6had the best GCA for RCC, RWC, Chla, Chlb, TChl, Car, essential oil content and EOY.

A locus for perennial growth was mapped on Lophopyrum elongatum chromosome arm 4ES and introgressed into the wheat genome. Evidence was obtained that in addition to chromosome 4E, other L. elongatum chromosomes control perennial growth. Monocarpy versus polycarpy is one of the fundamental developmental dichotomies in flowering plants. Advances in the understanding of the genetic basis of this dichotomy are important for basic biological reasons and practically for genetic manipulation of growth development in economically important plants. Nine wheat introgression lines (ILs) harboring germplasm of the Lophopyrum elongatum genome present in the octoploid amphiploid Triticum aestivum cv. Chinese Spring (subgenomes AABBDD) × L. elongatum (genomes EE) were selected from a population of ILs developed earlier. These ILs were employed here in genomic analyses of post-sexual cycle regrowth (PSCR), which is a component of polycarpy in caespitose L. elongatum. Analyses of disomic substitution (DS) lines confirmed that L. elongatum chromosome 4E confers PSCR on wheat. The gene was mapped into a short distal region of L. elongatum arm 4ES and was tentatively named Pscr1. ILs harboring recombined chromosomes with 4ES segments, including Pscr1, incorporated into the distal part of the 4DS chromosome arm were identified. Based on the location, Pscr1 is not orthologous with the rice rhizome-development gene Rhz2 located on rice chromosome Os3, which is homoeologous with chromosome 4E, but it may correspond to the Teosinte branched1 (TB1) gene, which is located in the introgressed region in the L. elongatum and Ae. tauschii genomes. A hexaploid IL harboring a large portion of the E-genome but devoid of chromosome 4E also expressed PSCR, which provided evidence that perennial growth is controlled by genes on other L. elongatum chromosomes in addition to 4E.

Coriander is an annual plant belonging to Apiaceae family that its yield is affected by drought stress. Three experiments (normal irrigation regime, sudden drought stress and gradual drought stress) were conducted according to a randomized complete block design with three replications in 2015 to introduce the most drought tolerant Iranian coriander ecotypes based on several economical traits, aiming at improving them in next generations, using univariate and multivariate tolerance indices. The economic traits having high heritability and high correlation with the yield and fruit essential oil content were used to calculate multivariate index of the Membership Function Value for Drought Stress (MFVD) and multivariate graphical analysis based on Principal Component Mediated Multivariate Index (PCAMMI). Also, genotype by environment interaction was traced for normal and stress experiments. The ecotypes, traits and indices were categorized and their occurrence in the environments was showed using a Heatmap graphical mapping. Stable genotypes and suitable indices were identified. High variation was observed among ecotypes. Stress Tolerance Index and Geometric Mean Productivity indices were suitable for identification of genotypes belonging to group B, and Tolerance Index and Mean Productivity indices were suitable for identification of genotypes belonging to group C. The multivariate index of MFVD and thousand fruit weight was categorized into the same group. Therefore, this index was not effective for separating ecotypes. Ecotypes were grouped using the PCAMMI graphical technique. In sudden drought stress, ecotypes 6, 12 and 14, and in gradual drought stress, ecotypes 1, 2, 3, 5, 8, 11 and 13 were grouped in group A.

Using Probability Hypothesis Density (PHD) filtering, a novel approach is proposed in this paper for simultaneous tracking of multiple moving targets in received data by Inverse Synthetic Aperture Radar (ISAR) system. Since PHD filtering approach is implemented successively in prediction and update steps, its performance quality will obviously be higher in “Spotlight” imaging mode than in “Stripmap”. Thus, its application to Spotlight mode is generally more logical. The idea to integrate tracking capability into ISAR system processor is to sort radar received data to correct Range Cell Migration (RCM) prior to tracking operations. Clearly, Range Cell Migration Compensation (RCMC) approach is different from this approach in image formation process, in terms of their implementation phase. However, they are implemented in a similar way. As simulation results reveal, applying Range Cell Migration Compensation to the raw data received by ISAR before tracking operation, results in high quality tracking of moving targets.

PURPOSE/OBJECTIVE(S) To advance image-guided irradiation for soft tissue targeting and treatment assessment, we collaborated with our industrial partner and developed a bioluminescence tomography system (BLT, MuriGlo) for pre-clinical radiation research. We characterize the system performance and demonstrate its capability in target localization for radiation guidance. MATERIALS/METHODS MuriGlo consists of four-mirror system, filters, lens and CCD camera, enabling a compact imaging platform and multi-projection and multi-spectral BLT. The imaging mouse bed includes a base, and a detachable and transportable bed, allowing animal imaged in MuriGlo and transferred to small animal radiation research platform (SARRP) for CBCT imaging and BLT-guided irradiation. A transparent polycarbonate plate covering the middle of the mouse bed is used to support and image animal at 1800 projection under the bed. We investigated its effects on optical signal acquisition, 3D BLT reconstruction accuracy and dosimetric impact. Imaging phantoms are designed to evaluate 2D imaging resolution, focal plane, depth of field, signal acquisition linearity, imaging uniformity and distortion. An optimization routine with pinhole camera model was developed to map 2D bioluminescence images (BLIs) to the surface of the numerical mesh of imaged object generated from the CBCT image. The mapped BLIs are used as input data for BLT reconstruction. Spectral derivative method with compressed sensing conjugate gradient algorithm was implemented for BLT volumetric reconstruction. To minimize the ambient temperature in affecting in vivo bioluminescence signal intensity and spectrum, a temperature control module was also developed to maintain consistent temperature in the MuriGlo imaging chamber. Phantom and glioblastoma (GBM) mouse model will be used to validate the localization accuracy of the BLT system for guidance. RESULTS The depth of field of MuriGlo was measured at 24mm, and the focal plane is set at 16mm above the mouse bed to have clear surface BLIs of mouse at all projections. The linearity between signal and exposure time from 0.1 to 180s is within 0.6% deviation. Our initial test shows within a given region of interest, the image uniformity is within 5% variation and image shift due to distortion is within 0.25mm. The data mapping from the 2D BLIs to 3D CBCT image is within 0.6mm. With temperature control turned on, the imaging chamber could be warmed up to 37°C from room temperature in 13 mins. Our phantom test shows MuriGlo can localize 3D bioluminescent target at 1mm accuracy and only 0.2mm deviation was observed for the case with and without transparent plate. CONCLUSION This work is the first systematic study in characterizing the commercial BLT-guided system for pre-clinical radiation research. We expect the information and methods developed in this study will support MuriGlo users, cross different institutes, in performing accurate image-guided radiation research and facilitating study reproducibility.