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Assessing Ge interaction and stability parameters for yield and related traits in soybean

Assessing Ge interaction and stability parameters for yield and related traits in soybean

difficult because it is statistically impossible to interpret the main effects. A potential genotype may sometimes fail to reach an optimum phenotypic expression that is well discernible. Thus, the suitability of genotype over time and space depends upon their capacity to minimize the impact of G x E interaction, which is their homeostatic property or buffering efficiency (Sharma, 1994). Further, the number of materials evaluated and the number of test environments required in affects the cost of plant breeding. However reduction in the number of test sites requires a thorough understanding of the genotype and GEI (Bernardo, 2002). A specific genotype does not always exhibit the same phenotypic characteristics under all environments and different genotypes respond differently to a specific environment. The main objective of soybean breeding programme has been to develop varieties that perform well over a broad spectrum of environments. Thus, assessment of the nature and extent of genotype x environment interaction and identification of phenotypic stable genotypes, showing low genotype x environment interaction, becomes important. This requires the screening of promising and stable genotypes in a set of arious biometrical procedures are now available to measure the stability of genotypes over environment. In soybean breeding, the focus of attention has been on yield increase and stability, that is, developing cultivars that are well adapted to various growing conditions. OF CURRENT RESEARCH

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Identification of Stability and Adaptability of Small Red Bean Cultivars Using AMMI Analysis

Identification of Stability and Adaptability of Small Red Bean Cultivars Using AMMI Analysis

AMMI analysis: Genotype, location and genotype by environment interaction were assessed by the additive main effect and multiplicative interaction (AMMI) model (Table 3). The analysis of variance of AMMI model for grain yield showed significant effect for genotypes, environment, and GE interaction. These result showed that 65.05% of the total sum of square was attributed to environment effects, only 10.77 and 24.17% to genotype and GE interaction effects, respectively. The effect of environment was responsible for the largest part of the variation, tailed by genotype and genotype by environment interaction. The same result was

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Genotype x environment interactions and implications for associations among soybean traits

Genotype x environment interactions and implications for associations among soybean traits

The decomposition of the sum of squares into its components, considering all the evaluated environments, allowed estimating the magnitude of the effects of GE, environment and genotype interaction on all evaluated traits. For most of them, a great magnitude of the effect of site and crop years was observed, demonstrating the influence of non-genetic factors on phenotypic expression. For grain yield, e.g., a large magnitude was observed in the effect of site. On the other hand, more than 40% of the phenotypic variation observed in the weight of 100 grains can be attributed to the effect of genotypes. It is also evident that the greater contribution of the cultivar and crop year (C x CYr) interaction was detected for most of the traits (Table 3). As observed in Table 3, the most influential character of the GE interaction was the number of grains per pod (48.61%). Plant height was the character with the lowest effect of GE interaction (4.93%). Considering grain yield, it was observed that 31.7% of the variation resulted from the effects of the GE interaction, which was superior to the effect of genotype (24.29%).

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Assessment of Genotype-Environment Interaction Using Additive Main Effects and Multiplicative Interaction Model (AMMI) in Maize (Zea mays L.) Hybrids

Assessment of Genotype-Environment Interaction Using Additive Main Effects and Multiplicative Interaction Model (AMMI) in Maize (Zea mays L.) Hybrids

Twenty one maize genotypes including check were included in the present study. The experiment was conducted at Agricultural College Farm, Bapatla during kharif 2011, late kharif 2011 and rabi 2011, thus making three environments. The experimental material was planted in randomized block design with three replications in each environment. At each environment, experimental plot consisted of three rows of 5m length each with a row to row distance of 75cm and plant to plant distance of 25cm. Yield data (g/plant) was recorded by averaging ten individual plants data for each hybrid in each location at three environments and used for the AMMI analysis, analysis of variance was performed for grain yield per plant. The analysis of variance (ANOVA) was used and the GE interaction was estimated by the AMMI model. Thus, the mean response of the genotype i in environment j (Y ij ) is modelled by

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AMMI Model for Yield Stability Analysis of Linseed Genotypes for the Highlands of Bale, Ethiopia

AMMI Model for Yield Stability Analysis of Linseed Genotypes for the Highlands of Bale, Ethiopia

AMMI model analysis: in AMMI model, principal component analysis is based on the matrix of deviation from additivity or residual will be analyzed. In this respect both the results of AMMI analysis, the genotypes and environment will be grouped based on their similar responses [9, 19, and 23]. Using ANOVA yield sum of square was partitioned into genotype, environment, and GE interaction. GE interaction was further portioned by principal component analysis (Table 4). The result if AMMI analysis indicated that 10.01% of the total variability was justified by genotypes, 75.29% by environments and 14.71% by GE interaction. A large contribution of the environment indicated that environments were diverse, with large difference among environmental means causing most of the variation in grain yield. The same result was reported by [8, 26-27]. The result of AMMI analysis also showed that the first principal component axis (IPCA1) accounted for 63.42% over the interaction SS, IPCA2 and IPCA3 explained 25.16% and 6.69% of the GE interaction SS, respectively. The first two IPCA scores were significant at (P<0.01%) and cumulatively accounted for 88.58% of the total GE interaction. This indicates that the use of AMMI model fit the data well and justifies the use of AMMI2.

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AEROBIC VERSUS ANAEROBIC GERMINATION PERFORMANCE OF SELECTED RICE (Oryza sativa L.) GENOTYPES WITH OR WITHOUT SUBMERGENCE TOLERANCE

AEROBIC VERSUS ANAEROBIC GERMINATION PERFORMANCE OF SELECTED RICE (Oryza sativa L.) GENOTYPES WITH OR WITHOUT SUBMERGENCE TOLERANCE

The presence of significant variation among the genotypes for all the traits was evident from the environment wise analyses of variance (Table 2a & 2b). The higher magnitude of experimental coefficients of variation for all the traits indicated a greater influence of oxygen stress on the various trait expressions. The pooled ANOVA over the two environments (Table 2c) revealed highly significant mean squares due to environments, indicating that anaerobic condition was quite different from the aerobic condition of germination and seedling growth of rice. Similarly, genotypic difference was also evident from highly significant mean squares for all the traits. The GE interaction component was significant for shoot length and seedling vigour index at 1% level of significance while germination (%) and seedling length was significant at 5% level, these results suggesting differential response of the genotypes in aerobic and anaerobic conditions. Table 2a Anova for the traits of the 10 selected rice genotypes under aerobic environment

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Evaluation of Genotype × Environment Interaction in Chickpea Genotypes Using Path Analysis

Evaluation of Genotype × Environment Interaction in Chickpea Genotypes Using Path Analysis

Descriptive diagrams of the measured traits (Fig. 1) showed GE interaction and high variability for all traits. GY variation was high for genotypes 12 and 17, while low for 8 and 19. Very low GE interaction was found for GY in environment 2 (Fig. 1a) indicating specific adaptation of GY in this environment. Interaction between genotype and environment of HSW was lower than that of GY and almost the same for all genotypes with a little higher in genotypes 1 and 9 (Fig. 1b). The variation and genetic interaction for NPPL trait (Fig. 1c) was higher than HSW but lower than GY. Genotypes 11 and 13 displayed lower variation in comparison to other genotypes, hence they are more stable. Variation of environments 4 and 5 was lower than others and this variation is almost similar in three initial environments. Descriptive diagram for NSPP (Fig. 1d) showed very low variation among genotypes. Only genotypes 6 and 13 showed a little higher variation among others. Farshadfar et al, (2012) showed different values of variability and GE interactions Between 14 genotypes of bread wheat (Triticum aestivum L.) in six environments for yield and yield components.

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Herpes Simplex Virus gE/gI Must Accumulate in the trans-Golgi Network at Early Times and Then Redistribute to Cell Junctions To Promote Cell-Cell Spread

Herpes Simplex Virus gE/gI Must Accumulate in the trans-Golgi Network at Early Times and Then Redistribute to Cell Junctions To Promote Cell-Cell Spread

dominately in the TGN at early times after HSV-1 infection (6 h) (13, 30, 48, 49). gE/gI TGN localization appears to be important for virus assembly and as a first step towards the selective sorting of enveloped particles to cell junctions, which promotes cell-to-cell spread. To examine whether gE CT do- main mutants accumulated in the TGN at early times, confocal microscopy was used to compare gE/gI localization to TGN46, a cellular component of the TGN (20). In HaCaT cells, wild- type gE/gI (F-BAC) (Fig. 5A, green) was found extensively in a perinuclear location colocalizing with TGN46 (red) 6 h after infection. Similarly, gE/gI was predominately perinuclear and colocalized with TGN46 in cells infected with mutants gE-519, gE-495, and gE-470 (Fig. 5A). With wild-type HSV and mu- tants gE-519, gE-495, and gE-470, there were also smaller, more punctuate gE/gI vesicles that were nearer the plasma membrane, and a fraction of these did not contain TGN46. Still, the majority of gE/gI was present in the TGN. By con- trast, gE/gI expressed in gE-448-infected cells was significantly different. There was little perinuclear accumulation, most cyto- plasmic vesicles containing gE/gI were more peripheral, and few vesicles contained TGN46 and gE/gI localized to plasma membranes, both lateral and apical (Fig. 5A).

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A Mixed-Model Approach to Mapping Quantitative Trait Loci in Barley on the Basis of Multiple Environment Data

A Mixed-Model Approach to Mapping Quantitative Trait Loci in Barley on the Basis of Multiple Environment Data

The Wald F-statistic for cofactor(M82)-by-environ- eree indicated that using fixed starting values gives more ment interaction in b was 10.84, which is significant at stable results.] At each position, I computed a Wald ␣ ⫽ 5%. Thus, the interaction term was retained. In statistic (F ) for the test of the null hypothesis of no the next model-building step, the environmental main QTL. Conditionally on the position, F asymptotically effect and interaction with M82 was regarded as random follows a ␹ 2 distribution with 1 d.f. On chromosome

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 THERMODYNAMICS OF BINARY ALLOYS OF PHARMACEUTICAL ACTIVE IMIDAZOLE WITH O- PHENYLENEDIAMINE

 THERMODYNAMICS OF BINARY ALLOYS OF PHARMACEUTICAL ACTIVE IMIDAZOLE WITH O- PHENYLENEDIAMINE

where y can be taken as any of the thermodynamic functions. In order to know the nature of interaction between the components forming binary alloy, some thermodynamic functions such as integral excess free energy (gE), excess enthalpy of mixing (hE) and excess entropy of mixing (sE) were calculated using the following equations:

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Ge stell and the Specters of the Spectrum

Ge stell and the Specters of the Spectrum

accumulation push nature, and man along with it, towards the brink of ecological collapse. What is more, as we erect structures to extract resources, we effectively exteriorize and materialize the Ge-stell mode of being into these same structures. Our will to mastery and dominion over the real returns to confront us and threatens to set upon us. The orderability of the public and public opinion in terms of calculable patterns of action and thought evince how the human will to mastery can foil easily and quietly back onto the human. Neither of these dangers – neither ecological collapse nor the standing-reserve of humanity – make themselves visible to us, and move as the undercurrent to modern technics and our mode of being with them. Our efforts, Heidegger explains, function only to alienate us from our ecological embeddedness and our human essence – in the pursuit of life, we merely succeed in laying the preconditions of our death (27). To extend and radicalize this position, we might say that, by this logic, we are dead already.

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Alterations in microRNA-124 and AMPA receptors contribute to social behavioral deficits in frontotemporal dementia

Alterations in microRNA-124 and AMPA receptors contribute to social behavioral deficits in frontotemporal dementia

To quantify sociability, we measured the time test mice spent in close interaction with social partners across trials. In Trial 2, both tTA:CHMP2B WT and tTA:CHMP2B Intron5 mice showed equal sociability, which gradually decreased in later trials, suggesting progressive loss of social interest in the familiar mouse (Fig. 1c). However, tTA:CHMP2B Intron5 mice had a more pronounced, age-dependent decrease in sociability, particularly in Trial 5, when both social partners were present (Fig. 1c). We also observed a similar age-dependence in the elevated plus maze (Supplementary Fig. 2c). In Trial 5, the proportion of time tTA:CHMP2B Intron5 mice spent interacting with Stanger 2 was similar to that of tTA:CHMP2B WT mice, suggesting that they distinguished Stranger 1 from Stranger 2 equally well (Fig. 1d). Moreover, time spent in each chamber during Trial 5 did not differ between tTA:CHMP2B Intron5 and tTA:CHMP2B WT mice (Fig. 1e), suggesting that social behavior but not exploration pattern is specifically compromised in mutant mice. Moreover, in a novel object recognition task in which exploration can be assessed independently of social cues, the times exploring identical objects during the familiarization phase as well as the familiar and novel object during the test phase were similar in tTA:CHMP2B Intron5 and tTA:CHMP2B WT mice (Supplementary Fig. 2d), confirming the specificity of the social deficits.

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Pressure dependence of the static structure of liquid GeTe based on ab initio molecular dynamics simulations

Pressure dependence of the static structure of liquid GeTe based on ab initio molecular dynamics simulations

result, Ge SB and Te SB are 3.57 and 2.78, i.e., environments around Ge and Te atoms are mostly kept as Peierls-type in the liquid state at ambient pressure. With increasing pressure, GeTe SB does not change much except for a small hump at about 12 GPa. The numbers of homopolar bonds GeGe SB and TeTe SB also do not change below about 12 GPa. However, both of them increase with increasing pressure above approximately 12 GPa. On the other hand, the numbers of covalent and anti bonding are calculated by integration of ( ) for > 0 and < 0 , respectively. The former, CB , and the latter, AB , are shown by closed circles with dashed lines and open circles with solid lines in Figs.7(d)-(f), respectively. With increasing pressure, GeTe CB do not change below about 12 GPa. Under further compression, GeTe CB decreases with increasing pressure. Hence, the difference between GeTe CB and GeTe SB decreases in the high pressure region. This difference corresponds to the weak bonding in the Peierls- type distortion, i.e., the decrease of the difference means the disappearance of the Peierls- type distortion in the high pressure region, where strong bonding is not distinguishable from

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Identification of longevity genes with systems biology approaches

Identification of longevity genes with systems biology approaches

Contrary to the reduction biology approaches, systems biology provides approaches from the top-down view. These approaches can be further divided into data-driven (from-omics to model) and hypothesis-driven (from kinetics data to mathematical model to predictions) approaches. A cell is composed of multiple structural and functional networks. The physical interactions of the cytoskeletal network, mem- brane trafficking network, and protein–protein interaction network provide the structural basis of a cell. The logic interactions of signal transduction network, gene regula- tory network, and metabolic network provide the functional basis for a cell. Searching for an interesting gene by start- ing out using the network view is emerging as a profitable approach. A group of yeast cell biologists 19 constructed a

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Effect of Ge Content on the Formation of Ge Nanoclusters in Magnetron Sputtered GeZrOx Based Structures

Effect of Ge Content on the Formation of Ge Nanoclusters in Magnetron Sputtered GeZrOx Based Structures

studied by multi-angle laser ellipsometry, Raman scattering, Auger electron spectroscopy, Fourier transform infrared spectroscopy, and X-ray diffraction for varied deposition conditions and annealing treatments. It was found that as-deposited films are homogeneous for all Ge contents, thermal treatment stimulated a phase separation and a formation of crystalline Ge and ZrO 2 . The “ start point ” of this process is in the range of 640 – 700 °C depending on

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Structure-function analysis of the gE-gI complex of feline herpesvirus: mapping of gI domains required for gE-gI interaction, intracellular transport, and cell-to-cell spread.

Structure-function analysis of the gE-gI complex of feline herpesvirus: mapping of gI domains required for gE-gI interaction, intracellular transport, and cell-to-cell spread.

either glycoprotein display a small-plaque phenotype (3, 12, 21, 28, 30, 32, 40, 48) and are impaired in virus-induced cell-cell fusion (3, 11–13, 48). The mechanism of cell-to-cell spread is by no means understood at the molecular level, but this mode of infection differs in several respects from entry via the extra- cellular route. It apparently entails the transfer of virus across cell junctions in a manner resistant to neutralizing antibodies (12). Moreover, viruses deficient in either gE or gI bind to and enter the target cell with an efficiency equal to and with kinet- ics similar to those of the parental wild-type virus (3, 12, 48). We have recently identified the genes for gE and gI of FHV and characterized their expression products both in infected cells and in the vaccinia virus vTF7-3 expression system (30). FHV gE and gI show all the characteristics of class I mem- brane proteins. In accordance with findings made for other alphaherpesviruses (43, 44, 47), the FHV proteins become N-glycosylated and oligomerize shortly after synthesis in the endoplasmic reticulum (ER) (30). The resulting gE-gI complex is then transported through the Golgi apparatus to the plasma membrane, concomitantly acquiring extensive posttransla- tional modifications, including O-glycosylation. In the absence of gE, gI is also transported to the plasma membrane, albeit inefficiently. Transport of FHV gE, however, is dependent on the presence of gI. gE, when expressed in the absence of gI, is fully retained in the ER. Similarly, in cells infected with a gI-deficient recombinant FHV only the endoglycosidase H- sensitive 83-kDa ER species is produced and maturation of gE does not occur (30). Here, we have studied gE-gI interaction in further detail. By C-terminal deletion mutagenesis of gI, we show that the N-terminal half of the gI ectodomain is sufficient * Corresponding author. Phone: 31-30-2532460. Fax: 31-30-2536723.

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Biosynthesis of glycoproteins E and I of feline herpesvirus: gE-gI interaction is required for intracellular transport.

Biosynthesis of glycoproteins E and I of feline herpesvirus: gE-gI interaction is required for intracellular transport.

FIG. 7. The biosynthesis of gE in FHV-infected cells. (a) Cells infected with FHV at a multiplicity of infection of 5 were metabolically labeled from 9 to 10 h p.i. in the absence ( 2 ) or presence ( 1 ) of DMJ. The cells were harvested either immediately (pulse) or after a 2-h chase (chase). gE was immunoprecipitated with Ra- a gE. Immunoprecipitates were treated with EndoH ( 1 ) or left untreated ( 2 ). (b) Biosynthesis of gE in FHV D gI-LZ-infected cells. (Left panel) Cells infected with FHV D gI-LZ were metabolically labeled and harvested either immediately (p) or after a 2-h chase (c). gE was immunoprecipitated with Ra- a gE ( a gE). (Right panel) As a control, RIPA was performed on lysates of cells infected with the parental strain, FHV strain B927 (wt), or its gI-defective derivative FHV D gI-LZ ( D gI) by using the gI-specific rabbit antipeptide serum ( a gI). The samples were analyzed in SDS–7.5% PAGE gels. Molecular sizes are in kilodaltons. (c) Construction of FHV D gI-LZ. The lower panel shows the genomic region of FHV containing the genes for gD, gI, and gE. The genes are shown as boxes. The gI gene was knocked out via homologous recombination between the FHV B927 genome and transfer vector pFHV D gI-LZ. Thus, the internal 0.7-kb XhoI (X)-BamHI (B) fragment of gI was replaced by an expression cassette consisting of the encephalomyocarditis virus internal ribosomal entry site (IRES) and the lacZ reporter gene.

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Solution of the Moving Oscillator Problem using the Newmark's β Method

Solution of the Moving Oscillator Problem using the Newmark's β Method

ABSTRACT: In this study, a mathematical model of the dynamic interaction between a simply supported Euler- Bernoulli beam and an oscillator moving on the beam with a constant velocity is studied. The oscillator consists of a mass, spring and damping. A set differential equation of dynamic interaction between the moving oscillator and beam was obtained by using the Lagrange equations and mode super-position method. Considering time interval the action of the moving oscillator, the coupled differential equation of the system was solved using the Newmark’s β direct time integration method.

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Identification of epigenetic interactions between miRNA and DNA methylation associated with gene expression as potential prognostic markers in bladder cancer

Identification of epigenetic interactions between miRNA and DNA methylation associated with gene expression as potential prognostic markers in bladder cancer

DNA methylation downregulates gene expression by inhibiting binding of transcription factors to DNA. Downstream, miRNA silencing, mediated by RNA poly- merase II, also works to downregulate gene expression by regulating the processing of mRNA transcripts. We expected miRNA activity targeting methylated genes to decrease expression and worsen survival outcome. We found that a common interaction was one in which there was, as expected, lower gene expression with the presence of epigenetic control, and this phenomenon would tend to lead to a worse survival outcome. Canon- ical CGI methylation is associated with gene expression silencing, but our results seem to support the previous finding that cancer cells seem to activate CGI methyla- tion of hypomethylated genes which were previously lowly expressed in normal tissues; hypermethylation did not increase the expression of the corresponding genes in cancer cells, but transcription factors were overex- pressed [27]. In some cases, the target gene maintained same or higher expression even with microRNA and methylation (UTP6, METRNL, TPCN2, and PTEN); for all these except TPCN2, this led to a worse survival

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