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The application of Near Infrared Spectroscopy (NIRS) for the monitoring of the cerebral oxygen saturation within the brain is well established, albeit using temporal data that can only measure relative changes of oxygenation state of the brain from a baseline. The focus of this investigation is to demonstrate that hybridisation of existing near infrared probe designs and reconstruction techniques can pave the way to produce a system and methods that can be used to monitor the absolute oxygen saturation in the injured brain. Using registered Atlas models in simulation, a novel method is outlined by which the quantitative accuracy and practicality of NIRS for specific use in monitoring the injured brain, can be improved, with cerebral saturation being recovered to within 10.1 ± 1.8% of the expected values.

In arid and semi-arid regions of the world, including Iran, soil salinity is one of the major abiotic stresses. One of the ways to achieve high performance in these areas is to use salt-tolerant varieties of wheat. Iran is known as one of the places where the D-genome originated and evolved. In order to evaluate the salt tolerance of Iranian genotypes based on the eight indices using analysis of variance, regression and an artificial neural network (ANN), 41 Iranian wheat varieties (Trticum aestivum L.) were planted in a randomised complete block design with three replications under two saline irrigation conditions, 0.631 and 11.8 dS m−1, in Kerman, Iran. Significant differences between the varieties were observed, and the significant two-way interaction of environment × varieties in combined analysis and non-significant correlation, 0.07, between the yield in two environments (yield in non-stress conditions, Yp, and yield in stress conditions, Ys) indicates the existence of genetic variation among varieties and the different responses of the varieties in both the environments. The indices of tolerance, geometric mean product (GMP), mean product (MP), harmonic mean (HM) and stress tolerance index (STI) were calculated based on grain yield evidence of positive significant correlation with Yp and Ys. Based on the ANN results, yield stability index (YSI), MP, GMP and STI were the best indices to predict salinity-tolerant varieties. The varieties selected based on these indices, such as Bolani, Sistan, Ofogh, Pishtaz, Karchia and Arg, produced high yield in both the environments. These results show that bread wheat originating from Iran has salt tolerance potential and can also be used in studies related to salinity tolerance mechanisms.

Rapeseed ( Brassica napus L.) is regarded as one of the most important oilseed crops worldwide. An experiment was conducted to quantitatively examine the genetic parameters of oil yield, seed yield, and yield components for rapeseed by using a half diallel of nine rapeseed cultivars. The 36 F1 hybrids, their parents, and four other commercial varieties were planted in breeding nurseries in 2008 and 2009. During the growing season, the number of pods per plant (NPP), number of seeds per pod (NSP), pod length (PL), 1000 seed weight (SW), harvest index (HI), seed yield (SY), oil percent (OP), and oil yield (OY) were measured. The data were analyzed with the additive main effects and multiplicative interaction (AMMI) model and its biplots. The first two principal components (IPC1 and IPC2) were used to create a two-dimensional biplot. IPC1 accounted for 28.7% (SW in 2008) to 48.9% (OP in 2009) of the sum of squares of the male by female (M × F) interaction, while IPC2 accounted for 18.3% (PL in 2009) to 31.6% (number of seeds per pod in 2008) of the sum of squares of the M × F interaction. Two heterotic groups were identified for OY, and six between-group crosses, that is, [Talaye, Modena, Opera] × [Fornax, Orient], would provide maximum heterosis for this trait. The AMMI analysis with the half diallel method identified not only the general combining ability (GCA) and specific combining ability (SCA) effects but also the F1 crosses whose offspring may display heterosis in their offspring. The results can be used to design efficient breeding strategies in rapeseed.

Plant breeders aim to select genotypes with stable phenotypes across environments. Multienvironment trials (METs) including 3 yr and six locations for 17 genotypes of chickpea (Cicerarietinum L.) were performed in Iran. Bartlett's test of homogeneity of variances was not significant and so the combined analysis was done. This analysis indicated that the environment (E), genotype (G), and the genotype by environment (GE) interaction were significant. Thus, stability analysis was necessary, and so additive main effects and multiplicative interaction (AMMI) stability parameters and some regression methods were used for a stability analysis. According to regression method, the high-yielding genotype, G8, was more responsive (b i > 1) to improved environmental conditions than the other genotypes. Stability parameters of Tai indicated that genotypes G3 and G17 and stability statistics of AMMI analyses showed that G2 and G3 were the most stable genotypes. The first and the second factors (F1 and F2) of factor analysis explained 73.84% total variance of stability parameters, and F1 separated the D i 2 and α i from the other parameters. The F2 separated static and dynamic concepts of stability, in which the mean yield, regression coefficient, EVF, SIPCF, and AMGEF parameters had a dynamic concept of stability, and the other remaining parameters had static concept of stability. Finally, according to all stability statistics and regarding the high mean yield, genotype G8 (Flip 93-93) was recommended for national release in Iran, as it adequately demonstrated wide adaptation across environments.

Purpose: Bioluminescenceimaging is a research tool for studying gene expression levels in small animal models of human disease. Bioluminescence light, however, is strongly scattered in biological tissue and no direct image of the light-emitting reporter probe’s location can be obtained. Therefore, the authors have developed a linear image reconstruction method for bioluminescencetomography (BLT) that recovers the three-dimensional spatial bioluminescent source distribution in small animals. Methods: The proposed reconstruction method uses third-order simplified spherical harmonics ( SP 3 ) solutions to the equation of radiative transfer for modeling the bioluminescence light propagation in optically nonuniform tissue. The SP 3 equations and boundary conditions are solved with a finite-difference (FD) technique on a regular grid. The curved geometry of the animal surface was taken into account with a blocking-off region method for regular grids. Coregistered computed tomography(CT) and magnetic resonance (MR)images provide information regarding the geometry of the skin surface and internal organs. The inverse source problem is defined as an algebraic system of linear equations for the unknown source distribution and is iteratively solved given multiview and multispectral boundary measurements. The average tissue absorption parameters, which are used for the image reconstruction process, were calculated with an evolution strategy (ES) from in vivo measurements using an implanted pointlike source of known location and spectrum. Moreover, anatomical information regarding the location of the internal organs and other tissue structures within the animal’s body are provided by coregistered MRimages. Results: First, the authors recovered the wavelength-dependent absorption coefficients (average error of 14%) with the ES under ideal conditions by using a numerical mouse model. Next, they reconstructed the average absorption coefficient of a small animal by using an artificial implanted light source and the validated ES. Last, they conducted twoin vivo animal experiments and recovered the spatial location of the implanted light source and the spatial distribution of a bioluminescent reporter system located in the kidneys. The source reconstruction results were coregistered to CT and MRimages. They further found that accurate bioluminescenceimage reconstructions could be obtained when segmenting a voidlike cyst with low-scattering and absorption parameters, whereas inaccurate image reconstructions were obtained when assuming a uniform optical parameter distribution instead. The image reconstructions were completed within 23 min on a 3 GHz Intel processor. Conclusions: The authors demonstrated onin vivo examples that the combination of anatomical coregistration, accurate optical tissueproperties, multispectral acquisition, and a blocking-off FD- SP 3 solution of the radiative transfer model significantly improves the accuracy of the BLT reconstructions.

Interest in growing winter rapeseed (Brassica napus L.) in Iran is increasing due to its potential returns relative to other oilseed crops. The objectives of this study were (i) to investigate the interrelationships among different traits ofwinter rapeseed and (ii) to determine the effects ofsowing date and variety on the seed yield and other traits ofwinter rapeseed with application of the biplot methodology in visualizing agronomic research data. Four winter rapeseed varieties were grown on three sowing dates during 2 yr. The results showed that variety 1 (SLM046) produced maximum seed yield and oil content in the second (21 September) and the third sowing dates (1 October). Thousand seed weight was positively and significantly associated with seed yield and the number of pods per plant, but oil content was not correlated with yield or the other traits. The study revealed that treatment by trait (TT) biplot can graphically display the interrelationships among traits and facilitate visual comparison of treatments.

Selection of lentil (Lens culinaris Medik) cultivars with wide adaptability across diverse farming environments is important, before recommending them to achieve a high rate of cultivar adoption. Seed yield of 11 lentil genotypes, tested in a randomized complete-block design with four replicates across 20 environments in Iran, was analyzed using site regression (SREG) stability model. The biplot technique facilitates a visual evaluation of superior genotypes, which is useful for cultivar recommendation and megaenvironment identification. A substantial amount of genotype x environment (GE) interaction for lentil grain yield was detected. Location (L) and genotype x location (GL) variabilities were the predominant components of total yield variation. The first two principal components (PC1 and PC2) of the SREG model accounted for 76% of the total GE interaction. There were four winning genotypes and three megaenvironments according to the SREG model. The best genotype in one location was not always so in other test locations. According to the ideal-genotype biplot, genotype G5 was better than all other genotypes; G5 exhibited both high mean yield and high stability of performance across environments. According to G + GE sources of variations, the genotypes (G4, G7, G9, and G10) were the most suitable varieties for the lentil-producing regions in Iran.

Three-dimensional (3D) diffuse optical tomography is known to be a nonlinear, ill-posed and sometimes under-determined problem, where regularization is added to the minimization to allow convergence to a unique solution. In this work, a generalized least-squares (GLS) minimization method was implemented, which employs weight matrices for both data-model misfit and optical properties to include their variances and covariances, using a computationally efficient scheme. This allows inversion of a matrix that is of a dimension dictated by the number of measurements, instead of by the number of imaging parameters. This increases the computation speed up to four times per iteration in most of the under-determined 3D imaging problems. An analytic derivation, using the Sherman-Morrison-Woodbury identity, is shown for this efficient alternative form and it is proven to be equivalent, not only analytically, but also numerically. Equivalent alternative forms for other minimization methods, like Levenberg-Marquardt (LM) and Tikhonov, are also derived. Three-dimensional reconstruction results indicate that the poor recovery of quantitatively accurate values in 3D optical images can also be a characteristic of the reconstruction algorithm, along with the target size. Interestingly, usage of GLS reconstruction methods reduces error in the periphery of the image, as expected, and improves by 20% the ability to quantify local interior regions in terms of the recovered optical contrast, as compared to LM methods. Characterization of detector photo-multiplier tubes noise has enabled the use of the GLS method for reconstructing experimental data and showed a promise for better quantification of target in 3D optical imaging. Use of these new alternative forms becomes effective when the ratio of the number of imaging property parameters exceeds the number of measurements by a factor greater than 2.

Analysis of multienvironment trials (METs) of crops for cultivar evaluation and recommendation is an important issue in plant breeding research. Evaluating both stability of performance and high yield is essential in MET analyses. The objective of this investigation was to compare 10 nonparametric stability methods and apply nonparametric tests (which do not require distributional assumptions) for genotype-by-environment (G X E) interaction to 11 lentil (Lens culinaris Medik) genotypes. Nine improved lentil genotypes and two local cultivars were grown in 20 semiarid environments in Iran from 2002 to 2004. Results of nonparametric tests of G X E interaction and a combined ANOVA across environments showed there were both crossover and noncrossover G × E interactions and genotypes varied significantly for yield. In this study, high values of TOP (proportion of environments in which a genotype ranked in the top third) and low values of rank-sum (sum of ranks of mean yield and Shukla's stability variance) were associated with high mean yield, but the other nonparametric methods were not positively correlated with mean yield and instead characterized a static concept of stability. The results of principal component (PC) analysis and correlation analysis of nonparametric stability statistics and yield indicated that only rank-sum and TOP methods would be useful for simultaneously selecting for high yield and stability. These methods recommended FLIP 92-12L as stable and FLIP96-6L as unstable genotypes. A biplot of the first two PCs also revealed that the nonparametric methods grouped as three distinct classes that corresponded to different agronomic and biological concepts of stability.

Cultivar evaluation and mega-environment identification are the most important objectives of multienvironment trials (MET). The objective of this study was to explore the effect of genotype and genotype 3 environment interaction on the grain yield of 19 barley (Hordeum vulgare L.) genotypes via GGE (genotype plus genotype 3 environment) biplot methodology. Experiments were conducted using a randomized complete block design with four replications for 3 yr at 10 locations. The biplot analysis identified three barley mega-environments in Iran. The first mega-environment contained locations Khoy, Mashhad, Miandoab, Karaj, and Nyshabour, where genotype Bahtim7-D1/79-w40762 was the winner; the second mega-environment contained locations Tabriz, Hamedan, Ardabil, and Arak, where genotype Walfajre/W1-2291 was the winner. The location of Zanjan made up the other mega-environment, with 73-M4-30 as the winner. Genotypes Bahtim7-D1/79-w40762 and Walfajre/W1-2291 had the highest mean yield and genotype K-201/3-2 had the poorest mean yield. The estimated relative yield of genotypes at Karaj station shows that genotype Bahtim7-D1/79-w40762 had the highest yield and genotype Owb70173-2H-OH had the poorest. The performances of genotypes Star/Alger and K-201/3-2 were highly variable, whereas genotypes Cossak/Gerbel/Harmal and Toji“S”/Robur were highly stable. The results of this study indicate the possibility of improving progress from selections under diverse location conditions by applying the GGL (genotype plus genotype 3 location) biplot methodology.

Genetics of slow-rusting resistance to yellow rust (Puccinia striiformis f.sp. tritici) was studied by a half-diallel design using six wheat varieties, Tiritea (susceptible), Tancred, Kotare, Otane, Karamu, and Briscard. The parents and 15 F1 progenies were evaluated in the greenhouse by three pathotypes 7E18A ) , 38E0A + , and 134E134A + . The latent period was measured as the number of days from inoculation to the appearance of the first pustule. For each pathotype a randomized complete block design was used and data were analysed by methods of Griffing and Hayman. The range of average degree of dominance was from complete dominance to over-dominance. Positive and negative degrees of dominance were observed for each pathotype that showed the reversal of dominance. Analysis of variance showed the importance of both additive and dominance effects in controlling the latent period. Broad-sense heritabilities were 0.99 and narrow-sense heritabilities ranged from 0.85 to 0.94. Briscard and Karamu for the pathotypes 38E0A + and 134E134A + , Kotare for the pathotype 7E18A ) and Tancred for the pathotype 38E0A + had significant and positive general combining ability (GCA) (more resistance) for latent period. The crosses of Kotare with Tancred, Briscard and Karamu indicated the highest and positive specific combining ability (SCA) for the pathotype 7E18A ) . Significant additive genetic component and moderate narrow-sense heritability indicate the possibility of improving for longer latent period of stripe rust in breeding programmes.

We present a method for handling nonscattering regions within diffusing domains. The method develops from an iterative radiosity-diffusion approach using Green's functions that was computationally slow. Here we present an improved implementation using a finite element method (FEM) that is direct. The fundamental idea is to introduce extra equations into the standard diffusion FEM to represent nondiffusive light propagation across a nonscattering region. By appropriate mesh node ordering the computational time is not much greater than for diffusion alone. We compare results from this method with those from a discrete ordinate transport code, and with Monte Carlo calculations. The agreement is very good, and, in addition, our scheme allows us to easily model time-dependent and frequency domain problems.

We present an overview of time-resolved optical tomography together with the hardware and software methods that we have developed for a clinical instrument that implements this modality. The hardware is based on a multichannel photon-counting technique that records the histograms of photons time-of-flight through highly scattering and attenuating media. The software is based on a finite element model that is iteratively updated in order to minimize the difference between measured and modeled data. We have presented a first experimental reconstruction of a three-dimensional (3D) distribution of variable absorption and scattering coefficient, together with an ideal simulation of the same case. (C) 2000 John Wiley & Sons, Inc.

Bioluminescenceimaging is a powerful technique for visualizing gene expression in small animals but it suffers a serious limitation: the absorption and scattering of light in tissue. Several factors influence the image: source strength and depth, effective numerical aperture of the imaging optics, and attenuation by the tissue between the source and the camera. Our overall goal is to account for these effects and to recover the actual strength and spatial location of the bioluminescence sources in vivo. An essential first step in this research is to develop a physical model that accurately predicts the light reaching the surface of the animal for an arbitrary distribution of sources and optical absorption and scattering coefficients. The calculations must be fast, so that the model can be used eventually in an iterative algorithm to solve the inverse problem. We use the diffusion approximation, valid when scattering dominates absorption and when it is not necessary to calculate the light field close to sources. The diffusion equation expresses the light fluence rate as a function of position and the spatially dependent absorption coefficient, scattering coefficient, and source function. A finite element code called NIRFAST has been developed to generate numerical solutions. NIRFAST has been implemented in MATLAB and uses a 2 or 3 dimensional model to represent the object. The absorption and scattering coefficients are specified at each node of the mesh. We have assigned “reasonable” values of the absorption and scattering coefficients to each node based on tissue identification by x‐ray CT.