Inflorescence architecture in plants is often complex and challenging to quantify, particularly for inflorescences of cereal grasses. Methods for capturing inflorescence architecture and for analyzing the resulting data are limited to a few easily captured parameters that may miss the rich underlying diversity.Here, we apply X-ray computed tomography combined with detailed morphometrics, offering new imaging and computational tools to analyze three-dimensional inflorescence architecture. To show the power of this approach, we focus on the panicles of Sorghum bicolor, which vary extensively in numbers, lengths, and angles of primary branches, as well as the three-dimensional shape, size, and distribution of the seed.We imaged and comprehensively evaluated the panicle morphology of 55 sorghum accessions that represent the five botanical races in the most common classification system of the species, defined by genetic data. We used our data to determine the reliability of the morphological characters for assigning specimens to race and found that seed features were particularly informative.However, the extensive overlap between botanical races in multivariate trait space indicates that the phenotypic range of each group extends well beyond its overall genetic background, indicating unexpectedly weak correlation between morphology, genetic identity, and domestication history.

Background 3D imaging, such as X-ray CT and MRI, has been widely deployed to study plant root structures. Many computational tools exist to extract coarse-grained features from 3D root images, such as total volume, root number and total root length. However, methods that can accurately and efficiently compute fine-grained root traits, such as root number and geometry at each hierarchy level, are still lacking. These traits would allow biologists to gain deeper insights into the root system architecture. Results We present TopoRoot, a high-throughput computational method that computes fine-grained architectural traits from 3D images of maize root crowns or root systems. These traits include the number, length, thickness, angle, tortuosity, and number of children for the roots at each level of the hierarchy. TopoRoot combines state-of-the-art algorithms in computer graphics, such as topological simplification and geometric skeletonization, with customized heuristics for robustly obtaining the branching structure and hierarchical information. TopoRoot is validated on both CT scans of excavated field-grown root crowns and simulated images of root systems, and in both cases, it was shown to improve the accuracy of traits over existing methods. TopoRoot runs within a few minutes on a desktop workstation for images at the resolution range of 400^3, with minimal need for human intervention in the form of setting three intensity thresholds per image. Conclusions TopoRoot improves the state-of-the-art methods in obtaining more accurate and comprehensive fine-grained traits of maize roots from 3D imaging. The automation and efficiency make TopoRoot suitable for batch processing on large numbers of root images. Our method is thus useful for phenomic studies aimed at finding the genetic basis behind root system architecture and the subsequent development of more productive crops.

Background: Cholinesterase (CHE) is a routine serum biomarker in gastric cancer (GC). However, little research has been done on its clinical value in advanced GC. In addition, it is not clear whether it can be used as biomarker for the response and prognosis of advanced GC patients.Methods: Between Jan. 2013 and Dec. 2016, a total of 150 patients with advanced GC treated with first-line chemotherapy were admitted to Changzhou Tumor Hospital Affiliated to Soochow University. We retrospectively identified serum CHE level on the day before chemotherapy and at the end of chemotherapy and abstracted clinicopathologic features and treatment outcomes. Univariate and multivariate survival analyses were performed to assess the relationship between serum CHE levels and progression-free survival (PFS) and overall survival (OS).Results: A total of 150 advanced GC patients were included and divided into serum level ≥5,000 IU/L and serum level <5,000 IU/L. CHE level lower than 5,000 IU/L was associated with poorer PFS (HR, 1.60; 95% CI, 1.141–2.243; p = 0.006), poorer OS (HR, 1.76; 95% CI, 1.228–2.515; p = 0.002) and trend of poorer response (HR, 0.56; 95% CI, 0.272–1.129; p = 0.104). In univariate and multivariate logistic regression analysis, only liver metastasis and PS score were significantly associated with objective response (p < 0.05). The medium PFS was 8.0 months in patients with post-treatment CHE increased vs. 3.8 months in patients with CHE decreased after chemotherapy (HR, 1.82; 95% CI 1.28–2.57; p = 0.0002). The medium OS was 13.1 months in patients with increased post-treatment CHE vs. 8.1 months in patients with decreased post-treatment CHE (HR, 1.87; 95% CI 1.29–2.71; p = 0.0002).Conclusion: Advanced GC with CHE levels below 5,000 IU/L was significantly associated with poor PFS and OS. The results suggested that CHE analysis before chemotherapy was a promising prognostic marker for advanced GC.

Momentum and reversal investment strategy are based on cross trade mechanism, which was not available until Aril, 2010 in China. Since then, countless papers in this area have been benefited from a mass amount of data. Using the stock market data since April 2010, this paper sets focus on the existence of the momentum effect and reversal effect on Chinese stock market. From the empirical research results, we find that there exist the short-term momentum effect and the mid-term reversal effect on Chinese stock market. Based on the BSV model, this paper makes an effective explanation of the momentum and reversal effect on Chinese stock market. Key words: momentum effect; reverse effect; BSV model; HS model; Chinese A-share market IntroductionSince the 1990s, many scholars have done empirical researches on the momentum and reversal effect of the international capital market, most of which showed that reversal strategy and momentum strategy could get excess returns. For the domestic stock market, researches also demonstrated that the momentum effect and reversal effect exist. Base on the hypothesis that the short selling mechanism exists, much studies of the domestic market were pushed. However, until April 2010 when China came to do some margin trading and the stock index futures trading, short selling mechanism emerged in China. Therefore, this article contributes to exploration of existence of the momentum effect and reversal effect on Chinese stock market and tries to explain it after the presence of short-selling mechanism. The first time that the reversal effect on foreign securities market was found out was in 1985, De Bondt and Thaler 1 built the winner portfolio and loser portfolio using the

Background: 3D imaging, such as X-ray CT and MRI, has been widely deployed to study plant root structures. Many computational tools exist to extract coarse-grained features from 3D root images, such as total volume, root number and total root length. However, methods that can accurately and efficiently compute fine-grained root traits, such as root number and geometry at each hierarchy level, are still lacking. These traits would allow biologists to gain deeper insights into the root system architecture (RSA). Results: We present TopoRoot, a high-throughput computational method that computes fine-grained architectural traits from 3D X-ray CT images of field-excavated maize root crowns. These traits include the number, length, thickness, angle, tortuosity, and number of children for the roots at each level of the hierarchy. TopoRoot combines state-of-the-art algorithms in computer graphics, such as topological simplification and geometric skeletonization, with customized heuristics for robustly obtaining the branching structure and hierarchical information. TopoRoot is validated on both real and simulated root images, and in both cases it was shown to improve the accuracy of traits over existing methods. We also demonstrate TopoRoot in differentiating a maize root mutant from its wild type segregant using fine-grained traits. TopoRoot runs within a few minutes on a desktop workstation for volumes at the resolution range of 400^3, without need for human intervention. Conclusions: TopoRoot improves the state-of-the-art methods in obtaining more accurate and comprehensive fine-grained traits of maize roots from 3D CT images. The automation and efficiency makes TopoRoot suitable for batch processing on a large number of root images. Our method is thus useful for phenomic studies aimed at finding the genetic basis behind root system architecture and the subsequent development of more productive crops.