Longitudinal genetic analysis of growth-related traits in red swamp crayfish Procambarus clarkii (Girard)

The red swamp crayfish,Procambarus clarkii,is an economically important species especially in China.Their exoskeleton places serious constraints on growth and culture management.Their growth is achieved through intermittent molting/ecdysis.The longitudinal genetic dynamics for growth-related traits at different ecdysial points in P.clarkii has been unclear to date.In this study,conditional genetic analysis was carried out for growth-related traits(body weight,body length,chela length,and cephalothorax length)based upon a mixed genetic model with conditional additive,dominance,and genotype by environment effects in P.clarkii.A complete diallel cross was made among three geographic populations of P.clarkii for the genetic mating design.Results of the conditional genetic analysis showed that from 4 th molt to 9 th molt the conditional additive variations were increased significantly whereas the conditional non-additive genetic variations(dominance and genotype by environment interaction)were decreased significantly for these growth-related traits.This indicated that lots of new expression of additive effect genes for body weight,body length,chela length,and cephalothorax length occurred during ontogeny,and environment played a signifi cant role in the expression of genes affecting these growth-related traits.Growth of the four traits was mainly affected by non-additive genetic effects in early developmental stage(prior to 4 th molt).The cumulative conditional additive variation for the growth-related traits from 4 th molt to 9 th molt accounted for a large majority of the total conditional additive variations from 2 nd molt to 9 th molt,indicating that this period was very important for the growth of this species.Using the conditional analysis method,dynamics of growth-related traits during an important ontogenetic phase of red swamp crayfish was uncovered.Our results provide valuable insights into refining production of this species.

Analysis of the Longitudinal Structure Function F_L from the Non-linear Regge Gluon Density Behavior at Low-x

The longitudinal structure function with shadowing correction according to the nonlinear effects of the gluon density behavior at low x is considered. The solution of the GLR-MQ evolution equation for the gluon density shows that the FL^g（x, Q2） behavior can be tamed by the singularity at low x values. Comparing our results with H1 data at R=4 GeV-1 shows that at very low x this behavior is completely tamed by taking shadowing correction into account.

Evolution of Collaborative Governance in the 2015,2016,and 2018 Myanmar Flood Disaster Responses:A Longitudinal Approach to a Network Analysis

In disaster response,collaboration facilitates interactions among actors,such as the government,the military,nongovernmental organizations,and civil society organizations.This study examined the longitudinal changes in collaborative governance in Myanmar’s disaster responses based on cases of flooding in 2015,2016,and 2018.To examine the mechanisms underlying this dynamic network formation,the collaborative ties of the actors involved in search and rescue activities were converted into longitudinal relational data sets,and the evolution of collaborative governance was analyzed by relying on the assumptions of social capital,transaction cost,homophily,and resource dependency theories and using a longitudinal social network analysis method.The findings show that the collaborative networks of search and rescue processes in disaster response evolved and changed over time according to the hypothesized patterns of strong,weak,and preferential tie formations.The study also revealed that the collaborative governance system assumes the form of a hierarchy rather than a generalized exchange,and the actors’reliance on military organizations is not obvious due to the emerging alternative non-military actors and diverse local actors observed in the cases.

Longitudinal Image Analysis via Path Regression on the Image Manifold

Longitudinal image analysis plays an important role in depicting the development of the brain structure,where image regression and interpolation are two commonly used techniques.In this paper,we develop an efficient model and approach based on a path regression on the image manifold instead of the geodesic regression to avoid the complexity of the geodesic computation.Concretely,first we model the deformation by diffeomorphism;then,a large deformation is represented by a path on the orbit of the diffeomorphism group action.This path is obtained by compositing several small deformations,which can be well approximated by its linearization.Second,we introduce some intermediate images as constraints to the model,which guides to form the best-fitting path.Thirdly,we propose an approximated quadratic model by local linearization method,where a closed form is deduced for the solution.It actually speeds up the algorithm.Finally,we evaluate the proposed model and algorithm on a synthetic data and a real longitudinal MRI data.The results show that our proposed method outperforms several state-of-the-art methods.

A theoretical analysis on second harmonic generation for transverse and longitudinal slice collagen fiber

Microscopic imaging based on second-harmonic generation has been proving to be a powerful tool for biomedical studies, especially in that tissues with

A nonparametric regression method for multiple longitudinal phenotypes using multivariate adaptive splines

In genetic studies of complex diseases, particularly mental illnesses, and behavior disorders, two distinct characteristics have emerged in some data sets. First, genetic data sets are collected with a large number of phenotypes that are potentially related to the complex disease under study. Second, each phenotype is collected from the same subject repeatedly over time. In this study, we present a nonparametric regression approach to study multivariate and time-repeated phenotypes together by using the technique of the multivariate adaptive regression splines for analysis of longitudinal data （MASAL）, which makes it possible to identify genes, gene-gene and gene-environment, including time, interactions associated with the phenotypes of interest. Furthermore, we propose a permutation test to assess the associations between the phenotypes and selected markers. Through simulation, we demonstrate that our proposed approach has advantages over the existing methods that examine each longitudinal phenotype separately or analyze the summarized values of phenotypes by compressing them into one-time-point phenotypes. Application of the proposed method to the Framingham Heart Study illustrates that the use of multivariate longitudinal phenotypes enhanced the significance of the association test.

Ovarian Cancer Screening Based on Mixture Change-Point Model

Ovarian cancer is one of the most deadly female genital malignant tumors in many regions while an effective early screening strategy can save numerous lives.CA125 and HE4 are tumor markers validated efficacious as well as most commonly used in recent screening research of ovarian cancer.In this paper,the authors construct a change-point and mixture model on the basis of longitudinal CA125 and HE4 levels and estimated parameters using maximum likelihood method with the preclinical duration assumed right-censored,which is more adaptive and yields comparable results in comparison to the Bayesian approach raised by Skates.Consistency of estimators is proved.The authors also run a 5-year simulation of sequential screening by calculating the risk of cancer and hypothesis testing the true incidence time respectively.Results show that diagnosis based on hypothesis test performs better in early detection.