Complex genetic architecture underlying the plasticity of maize agronomic traits

Phenotypic plasticity is the ability of a given genotype to produce multiple phenotypes in response to changing environmental conditions. Understanding the genetic basis of phenotypic plasticity and establishing a predictive model is highly relevant to future agriculture under a changing climate. Here we report findings on the genetic basis of phenotypic plasticity for 23 complex traits using a diverse maize population planted at five sites with distinct environmental conditions. We found that latitude -related environmental factors were the main drivers of across-site variation in flowering time traits but not in plant architecture or yield traits. For the 23 traits, we detected 109 quantitative trait loci (QTLs), 29 for mean values, 66 for plasticity, and 14 for both parameters, and 80% of the QTLs interacted with latitude. The effects of several QTLs changed in magnitude or sign, driving variation in phenotypic plasticity. We experimentally validated one plastic gene, ZmTPS14.1, whose effect was likely mediated by the compen-sation effect of ZmSPL6 from a downstream pathway. By integrating genetic diversity, environmental vari-ation, and their interaction into a joint model, we could provide site-specific predictions with increased accuracy by as much as 9.9%, 2.2%, and 2.6% for days to tassel, plant height, and ear weight, respectively. This study revealed a complex genetic architecture involving multiple alleles, pleiotropy, and genotype-by -environment interaction that underlies variation in the mean and plasticity of maize complex traits. It provides novel insights into the dynamic genetic architecture of agronomic traits in response to changing environments, paving a practical way toward precision agriculture

Occurrence of Camallanus cotti in greatly diverse fish species from Danjiangkou Reservoir in central China

Two thousand four hundred fifty-eight fish comprised of 53 species were captured in the Danjiangkou Reservoir, in the northwestern part of Hubei Province, central China during 2004, to examine Camallanus cotti infections. We found that 19 cypriniform, 3 siluriforme, and 4 perciforme fishes were infected by the nematode. Our study revealed the species, Hemiculter bleekeri bleekeri, Culter oxycephaloide, Pseudolaubuca sinensis, Acanthobrama simony, Mylopharyngodon piceus, Ctenopharyngodon idella, Gnathopogon imberbis, G. argentatus, Saurogobio dabryi, S. dumerili, Gobiobotia ichangensis, Liobagrus marginatoides, and Ctenogobius shennongensis as new hosts of the worm. The number and range of fish host species found in this survey were much greater than any of the previous investigations. The mean prevalence, prevalence, mean abundance, and intensity of infection varied in different fish species, indicating a possible host preference. Moreover, we suggest that this nematode is a native parasite of cypriniform fishes in China, perhaps initially in the reaches of the Yangtze River.Two thousand four hundred fifty-eight fish comprised of 53 species were captured in the Danjiangkou Reservoir, in the northwestern part of Hubei Province, central China during 2004, to examine Camallanus cotti infections. We found that 19 cypriniform, 3 siluriforme, and 4 perciforme fishes were infected by the nematode. Our study revealed the species, Hemiculter bleekeri bleekeri, Culter oxycephaloide, Pseudolaubuca sinensis, Acanthobrama simony, Mylopharyngodon piceus, Ctenopharyngodon idella, Gnathopogon imberbis, G. argentatus, Saurogobio dabryi, S. dumerili, Gobiobotia ichangensis, Liobagrus marginatoides, and Ctenogobius shennongensis as new hosts of the worm. The number and range of fish host species found in this survey were much greater than any of the previous investigations. The mean prevalence, prevalence, mean abundance, and intensity of infection varied in different fish species, indicating a possible host preference. Moreover, we suggest that this nematode is a native parasite of cypriniform fishes in China, perhaps initially in the reaches of the Yangtze River

Variation of Tensor Force due to Nuclear Medium Effect

The enhancement of $J^{\pi}(T)$=3$^{+}$(0) state with isospin $T=0$ excited by the tensor force in the free $^{6}$Li nucleus has been observed, for the first time, relative to a shrinkable excitation in the $^{6}$Li cluster component inside its host nucleus. Comparatively, the excitation of $J^{\pi}(T)$=0$^{+}$(1) state with isospin $T=1$ for these two $^{6}$Li formations take on an approximately equal excitation strength. The mechanism of such tensor force effect was proposed due to the intensive nuclear medium role on isospin $T$=0 state.Comment: 6 pages, 4 figure

Aspect of Clusters Correlation at Light Nuclei Excited State

The correlation of $\alpha\alpha$ was probed via measuring the transverse momentum $p_{T}$ and width $\delta p_{T}$ of one $\alpha$, for the first time, which represents the spatial and dynamical essentialities of the initial coupling state in $^{8}$Be nucleus. The weighted interaction vertex of 3$\alpha$ reflected by the magnitudes of their relative momentums and relative emission angles proves the isosceles triangle configuration for 3$\alpha$ at the high excited energy analogous Hoyle states.Comment: 8 pages, 9 figure

Multi-alpha Boson Gas state in Fusion Evaporation Reaction and Three-body Force

The experimental evidence for the $\alpha$ Boson gas state in the $^{11}$C+$^{12}$C$\rightarrow$$^{23}$Mg$^{\ast}$ fusion evaporation reaction is presented. By measuring the $\alpha$ emission spectrum with multiplicity 2 and 3, we provide insight into the existence of a three-body force among $\alpha$ particles. The observed spectrum exhibited distinct tails corresponding to $\alpha$ particles emitted in pairs and triplets consistent well with the model-calculations of AV18-UX and chiral effective field theory of NV2-3-la*, indicating the formation of $\alpha$ clusters with three-body force in the Boson gas state.Comment: 7 pages, 6 figure

Identification of in vivo material properties of ascending thoracic aortic aneurysm: towards noninvasive risk assessment

Advances in imaging techniques and numerical methods have made it possible to investigate biomechanics of the cardiovascular system on a patient-specific level. For the four key components in a in vivo patient-specific biomechanical analysis (geometries, loading and boundary conditions, material hyperelastic properties and material failure properties), patient-specific geometries and physiological loading conditions can be obtained at a high level of spatial and temporal resolutions from clinical diagnostic imaging tools, such as CT scans, and blood pressure measurements, respectively. However, accurate identification of the unknown in vivo patient-specific hyperelastic properties, which are nonlinear and anisotropic, has been a challenging problem in the field of cardiovascular biomechanics for several decades. Furthermore, since patient-specific failure properties cannot be obtained noninvasively from clinical images, an accurate failure metric that incorporates uncertainties of failure properties, needs to be developed for patient-specific biomechanical assessment. The objective of this thesis was to develop a novel computational framework to identify in vivo patient-specific hyperleastic properties for biomechanical risk assessment of ascending thoracic aortic aneurysm (ATAA). A novel inverse method was developed for in vivo hyperleastic property identification from clinical 3D CT image data. The developed inverse approach was validated by using numerical examples as well as clinical CT images and matching tissue samples. To describe the shape probability distribution, statistical shape model (SSM) was built from ATAA geometries. A machine learning (ML) approach was investigated for fast in vivo material property identification (i.e., within seconds), virtual geometries sampled from the SSM were used to train and test the ML-model. To assess ATAA risk, a novel probabilistic and anisotropic failure metric was derived by using uniaxial failure testing data. To evaluate the performance of risk assessment methods (e.g., with and without patient-specific hyperelastic properties), ATAA risks were numerically-reconstructed by using additional patient data. The results highlighted the potentially important roles of patient-specific hyperelastic properties and probabilistic failure metric.Ph.D

A generic physics-informed neural network-based constitutive model for soft biological tissues

Constitutive modeling is a cornerstone for stress analysis of mechanical behaviors of biological soft tissues. Recently, it has been shown that machine learning (ML) techniques, trained by supervised learning, are powerful in building a direct linkage between input and output, which can be the strain and stress relation in constitutive modeling. In this study, we developed a novel generic physics-informed neural network material (NNMat) model which employs a hierarchical learning strategy by following the steps: (1) establishing constitutive laws to describe general characteristic behaviors of a class of materials; (2) determining constitutive parameters for an individual subject. A novel neural network structure was proposed which has two sets of parameters: (1) a class parameter set for characterizing the general elastic properties; and (2) a subject parameter set (three parameters) for describing individual material response. The trained NNMat model may be directly adopted for a different subject without re-training the class parameters, and only the subject parameters are considered as constitutive parameters. Skip connections are utilized in the neural network to facilitate hierarchical learning. A convexity constraint was imposed to the NNMat model to ensure that the constitutive model is physically relevant. The NNMat model was trained, cross-validated and tested using biaxial testing data of 63 ascending thoracic aortic aneurysm tissue samples, which was compared to expert-constructed models (Holzapfel-Gasser-Ogden, Gasser–Ogden–Holzapfel, and four-fiber families) using the same fitting and testing procedure. Our results demonstrated that the NNMat model has a significantly better performance in both fitting (R2 value of 0.9632 vs 0.9019, p=0.0053) and testing (R2 value of 0.9471 vs 0.8556, p=0.0203) than the Holzapfel–Gasser–Ogden model. The proposed NNMat model provides a convenient and general methodology for constitutive modeling

A Deep Learning Approach to Estimate Collagenous Tissue Nonlinear Anisotropic Stress-Strain Responses from Microscopy Images

ABSTRACT Biological collagenous tissues comprised of networks of collagen fibers are suitable for a broad spectrum of medical applications owing to their attractive mechanical properties. In this study, we developed a noninvasive approach to estimate collagenous tissue elastic properties directly from microscopy images using Machine Learning (ML) techniques. Glutaraldehyde-treated bovine pericardium (GLBP) tissue, widely used in the fabrication of bioprosthetic heart valves and vascular patches, was chosen as a representative collagenous tissue. A Deep Learning model was designed and trained to process second harmonic generation (SHG) images of collagen networks in GLBP tissue samples, and directly predict the tissue elastic mechanical properties. The trained model is capable of identifying the overall tissue stiffness with a classification accuracy of 84%, and predicting the nonlinear anisotropic stress-strain curves with average regression errors of 0.021 and 0.031. Thus, this study demonstrates the feasibility and great potential of using the Deep Learning approach for fast and noninvasive assessment of collagenous tissue elastic properties from microstructural images

Estimation of in vivo mechanical properties of the aortic wall: A multi-resolution direct search approach

The patient-specific biomechanical analysis of the aorta requires in vivo mechanical properties of individual patients. Existing approaches for estimating in vivo material properties often demand high computational cost and mesh correspondence of the aortic wall between different cardiac phases. In this paper, we propose a novel multi-resolution direct search (MRDS) approach for estimation of the nonlinear, anisotropic constitutive parameters of the aortic wall. Based on the finite element (FE) updating scheme, the MRDS approach consists of the following three steps: (1) representing constitutive parameters with multiple resolutions using principal component analysis (PCA), (2) building links between the discretized PCA spaces at different resolutions, and (3) searching the PCA spaces in a 'coarse to fine' fashion following the links. The estimation of material parameters is achieved by minimizing a node-to-surface error function, which does not need mesh correspondence. The method was validated through a numerical experiment by using the in vivo data from a patient with ascending thoracic aortic aneurysm (ATAA), the results show that the number of FE iterations was significantly reduced compared to previous methods. The approach was also applied to the in vivo CT data from an aged healthy human patient, and using the estimated material parameters, the FE-computed geometry was well matched with the image-derived geometry. This novel MRDS approach may facilitate the personalized biomechanical analysis of aortic tissues, such as the rupture risk analysis of ATAA, which requires fast feedback to clinicians