461 research outputs found

    Soybeans, 1976

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    Title from JPEG cover page (University of Missouri Digital Library, viewed Dec. 3, 2009)."The bulletin reposrts on Research Project 3630"--Page 2.pt. I. Corn -- pt. II. Grain sorghum -- part III. Soybeans.Includes bibliographical references

    Soybeans, 1975

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    Cover title."The bulletin reposrts on Research Project 3630"--P. 2.pt. I. Corn -- pt. II. Grain sorghum -- part III. Soybeans.Includes bibliographical references

    Corn, 1977

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    Refining Our Understanding of the Flow Through Coronary Artery Branches; Revisiting Murray's Law in Human Epicardial Coronary Arteries

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    Background: Quantification of coronary blood flow is used to evaluate coronary artery disease, but our understanding of flow through branched systems is poor. Murray’s law defines coronary morphometric scaling, the relationship between flow (Q) and vessel diameter (D) and is the basis for minimum lumen area targets when intervening on bifurcation lesions. Murray’s original law (Q α D(P)) dictates that the exponent (P) is 3.0, whilst constant blood velocity throughout the system would suggest an exponent of 2.0. In human coronary arteries, the value of Murray’s exponent remains unknown. Aim: To establish the exponent in Murray’s power law relationship that best reproduces coronary blood flows (Q) and microvascular resistances (Rmicro) in a bifurcating coronary tree. Methods and Results: We screened 48 cases, and were able to evaluate inlet Q and Rmicro in 27 branched coronary arteries, taken from 20 patients, using a novel computational fluid dynamics (CFD) model which reconstructs 3D coronary anatomy from angiography and uses pressure-wire measurements to compute Q and Rmicro distribution in the main- and side-branches. Outputs were validated against invasive measurements using a Rayflow™ catheter. A Murray’s power law exponent of 2.15 produced the strongest correlation and closest agreement with inlet Q (zero bias, r = 0.47, p = 0.006) and an exponent of 2.38 produced the strongest correlation and closest agreement with Rmicro (zero bias, r = 0.66, p = 0.0001). Conclusions: The optimal power law exponents for Q and Rmicro were not 3.0, as dictated by Murray’s Law, but 2.15 and 2.38 respectively. These data will be useful in assessing patient-specific coronary physiology and tailoring revascularisation decisions

    Comparative Analysis of the Frequency and Distribution of Stem and Progenitor Cells in the Adult Mouse Brain

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    cells (NSCs) and progenitor cells, but it cannot discriminate between these two populations. Given two assays have purported to overcome this shortfall, we performed a comparative analysis of the distribution and frequency of NSCs and progenitor cells detected in 400 m coronal segments along the ventricular neuraxis of the adult mouse brain using the neurosphere assay, the neural colony forming cell assay (N-CFCA), and label-retaining cell (LRC) approach. We observed a large variation in the number of progenitor/stem cells detected in serial sections along the neuraxis, with the number of neurosphereforming cells detected in individual 400 m sections varying from a minimum of eight to a maximum of 891 depending upon the rostral-caudal coordinate assayed. Moreover, the greatest variability occurred in the rostral portion of the lateral ventricles, thereby explaining the large variation in neurosphere frequency previously reported. Whereas the overall number of neurospheres (3730 276) or colonies (4275 124) we detected along the neuraxis did not differ significantly, LRC numbers were significantly reduced (1186 188, 7 month chase) in comparison to both total colonies and neurospheres. Moreover, approximately two orders of magnitude fewer NSC-derived colonies (50 10) were detected using the N-CFCA as compared to LRCs. Given only 5% of the LRCs are cycling (BrdU/Ki-67) or competent to divide (BrdU/Mcm-2), and proliferate upon transfer to culture, it is unclear whether this technique selectively detects endogenous NSCs. Overall, caution should be taken with the interpretation and employment of all these techniques

    Gender Differences in Russian Colour Naming

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    In the present study we explored Russian colour naming in a web-based psycholinguistic experiment (http://www.colournaming.com). Colour singletons representing the Munsell Color Solid (N=600 in total) were presented on a computer monitor and named using an unconstrained colour-naming method. Respondents were Russian speakers (N=713). For gender-split equal-size samples (NF=333, NM=333) we estimated and compared (i) location of centroids of 12 Russian basic colour terms (BCTs); (ii) the number of words in colour descriptors; (iii) occurrences of BCTs most frequent non-BCTs. We found a close correspondence between females’ and males’ BCT centroids. Among individual BCTs, the highest inter-gender agreement was for seryj ‘grey’ and goluboj ‘light blue’, while the lowest was for sinij ‘dark blue’ and krasnyj ‘red’. Females revealed a significantly richer repertory of distinct colour descriptors, with great variety of monolexemic non-BCTs and “fancy” colour names; in comparison, males offered relatively more BCTs or their compounds. Along with these measures, we gauged denotata of most frequent CTs, reflected by linguistic segmentation of colour space, by employing a synthetic observer trained by gender-specific responses. This psycholinguistic representation revealed females’ more refined linguistic segmentation, compared to males, with higher linguistic density predominantly along the redgreen axis of colour space
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