Differential gene expression in innate immunity between commercial broilers and layers

Tremendous improvements have been achieved in growth rates and feed efficiency in commercial broiler birds. However, fast growth broilers generally show weak immune competence and disease resistance. Innate immunity is the first line of defense providing immediate killing effects to a broad range of infectious pathogens and limiting infections to a minimum at an early stage before the activation of more specific adaptive immunity. Acute phase proteins (APPs), defensins and Toll-like receptors (TLRs) are all important innate immune molecules functioning from recognition to killing the foreign microbes. Tibial dyschondroplasia (TD) is one chicken disease associated with rapid growth in broilers. The objective of this research was to study the differential expression of innate immune related genes in liver and spleen tissues between commercial broilers and layers with the stimulation of lipopolysaccharide (LPS). Also, this study investigated mechanisms involved in the pathogenesis of TD at molecular levels. This study first identified and annotated nineteen new chicken APPs genes from the chicken genome draft with bioinformatics tools. Using a relative quantitative real-time RT-PCR method, the expression levels of all thirty-one APPs, thirteen defensins and eight TLRs genes were systemically investigated at the transcriptional level at three time points (0-, 3-, 8-hour) with the challenge of LPS. This study showed that broiler birds generally expressed significantly lower levels of all three families of innate immune related genes than layers and the inductive extent of these genes are generally smaller in broilers too. Close investigation of some important signaling transcription factors (NF-kB and IRF-3) and cytokine (IL-6) also reached the same conclusion. This study revealed that the inadequate expression of deiodinase type 2 (DIO2) contributed to the pathogenesis of TD in rapid growth broilers. All of the experimental results solidly validate the hypothesis that a compromised innate immune response or weak disease resistence is associated with fast growth broiler birds

Lymph node but not intradermal injection site macrophages are critical for germinal center formation and antibody responses to rabies vaccination.

UNLABELLED: Replication-deficient rabies virus (RABV)-based vaccines induce rapid and potent antibody responses via T cell-independent and T cell-dependent mechanisms. To further investigate early events in vaccine-induced antibody responses against RABV infections, we studied the role of macrophages as mediators of RABV-based vaccine immunogenicity. In this report, we show that a recombinant matrix gene-deleted RABV-based vaccine (rRABV-ΔM) infects and activates primary murine macrophages in vitro. Immunization of mice with live RABV-based vaccines results in accumulation of macrophages at the site of immunization, which suggests that macrophages in tissues support the development of effective anti-RABV B cell responses. However, we show that draining lymph node macrophages, but not macrophages at the site of immunization, are essential for the generation of germinal center B cells, follicular T helper cells, and RABV-specific antibodies. Our findings have implications for the design of new RABV-based vaccines for which early immunological events are important for the protection against RABV in postexposure settings. IMPORTANCE: More than two-thirds of the world\u27s population live in regions where rabies is endemic. Postexposure prophylaxis is the primary means of treating humans. Identifying immunological principles that guide the development of rapid and potent antibody responses against rabies infections will greatly increase our ability to produce more-effective rabies vaccines. Here we report that macrophages in the draining lymph node, but not in the tissue at the site of immunization are important for vaccine-induced antibody responses to rabies. Information gleaned from this study may help guide the development of a single-dose vaccine against rabies infections

Using Ipomoea aquatic as an environmental-friendly alternative to Elodea nuttallii for the aquaculture of Chinese mitten crab

Elodea nuttallii is widely used in Chinese mitten crab (CMC) rearing practice, but it is not a native aquatic plant and cannot endure high temperature. Thus, large E. nuttallii mortality and water deterioration events could occur during high-temperature seasons. The aim of this study was to identify the use of local macrophytes in CMC rearing practice, including Ipomoea aquatic and Oryza sativa. A completely randomized field experiment was conducted to investigate the crab yield, water quality, bacterioplankton community and functions in the three different systems (E. nuttallii, I. aquatic, and O. sativa). Average crab yields in the different macrophyte systems did not differ significantly. The I. aquatic and O. sativa systems significantly decreased the total nitrogen and nitrate-N quantities in the outflow waters during the rearing period compared to the E. nuttallii system, and the I. aquatic and O. sativa plants assimilated more nitrogen than the E. nuttallii plant. Moreover, the significant changes of bacterioplankton abundances and biodiversity in the three systems implied that cleanliness of rearing waters was concomitantly attributed to the differential microbial community and functions. In addition, principle component analysis successfully differentiated the bacterioplankton communities of the three macrophytes systems. Environmental factor fitting and the co-occurrence network analyses indicated that pH was the driver of bacterioplankton community structure. Functional predictions using PICRUSt (v.1.1.3) software based on evolutionary modeling indicated a higher potential for microbial denitrification in the I. aquatic and O. sativa systems. Notably, the O. sativa plants stopped growing in the middle of the rearing period. Thus, the I. aquatic system rather than the O. sativa system could be a feasible and environmental-friendly alternative to the E. nuttallii system in CMC rearing practice

Reliability Analysis of RC Slab-Column Joints under Punching Shear Load Using a Machine Learning-Based Surrogate Model

Reinforced concrete slab-column structures, despite their advantages such as architectural flexibility and easy construction, are susceptible to punching shear failure. In addition, punching shear failure is a typical brittle failure, which introduces difficulties in assessing the functionality and failure probability of slab-column structures. Therefore, the prediction of punching shear resistance and corresponding reliability analysis are critical issues in the design of reinforced RC slab-column structures. In order to enhance the computational efficiency of the reliability analysis of reinforced concrete (RC) slab-column joints, a database containing 610 experimental data is used for machine learning (ML) modelling. According to the nonlinear mapping between the selected seven input variables and the punching shear resistance of slab-column joints, four ML models, such as artificial neural network (ANN), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost) are established. With the assistance of three performance measures, such as root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2), XGBoost is selected as the best prediction model; its RMSE, MAE, and R2 are 32.43, 19.51, and 0.99, respectively. Such advantages are also reflected in the comparison with the five empirical models introduced in this paper. The prediction process of XGBoost is visualized by SHapley Additive exPlanation (SHAP); the importance sorting and feature dependency plots of the input variables explain the prediction process globally. Furthermore, this paper adopts Monte Carlo simulation with a machine learning-based surrogate model (ML-MCS) to calibrate the reliability of slab-column joints in a real engineering example. A total of 1,000,000 samples were obtained through random sampling, and the reliability index β of this practical building was calculated by Monte Carlo simulation. Results demonstrate that the target reliability index requirements under design provisions can be achieved. The sensitivity analysis of stochastic variables was then conducted, and the impact of that analysis on structural reliability was deeply examined

Reliability Analysis of RC Slab-Column Joints under Punching Shear Load Using a Machine Learning-Based Surrogate Model

Reinforced concrete slab-column structures, despite their advantages such as architectural flexibility and easy construction, are susceptible to punching shear failure. In addition, punching shear failure is a typical brittle failure, which introduces difficulties in assessing the functionality and failure probability of slab-column structures. Therefore, the prediction of punching shear resistance and corresponding reliability analysis are critical issues in the design of reinforced RC slab-column structures. In order to enhance the computational efficiency of the reliability analysis of reinforced concrete (RC) slab-column joints, a database containing 610 experimental data is used for machine learning (ML) modelling. According to the nonlinear mapping between the selected seven input variables and the punching shear resistance of slab-column joints, four ML models, such as artificial neural network (ANN), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost) are established. With the assistance of three performance measures, such as root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2), XGBoost is selected as the best prediction model; its RMSE, MAE, and R2 are 32.43, 19.51, and 0.99, respectively. Such advantages are also reflected in the comparison with the five empirical models introduced in this paper. The prediction process of XGBoost is visualized by SHapley Additive exPlanation (SHAP); the importance sorting and feature dependency plots of the input variables explain the prediction process globally. Furthermore, this paper adopts Monte Carlo simulation with a machine learning-based surrogate model (ML-MCS) to calibrate the reliability of slab-column joints in a real engineering example. A total of 1,000,000 samples were obtained through random sampling, and the reliability index β of this practical building was calculated by Monte Carlo simulation. Results demonstrate that the target reliability index requirements under design provisions can be achieved. The sensitivity analysis of stochastic variables was then conducted, and the impact of that analysis on structural reliability was deeply examined

Interpretable Machine Learning Models for Punching Shear Strength Estimation of FRP Reinforced Concrete Slabs

Fiber reinforced polymer (FRP) serves as a prospective alternative to reinforcement in concrete slabs. However, similarly to traditional reinforced concrete slabs, FRP reinforced concrete slabs are susceptible to punching shear failure. Accounts of the insufficient consideration of impact factors, existing empirical models and design provisions for punching strength of FRP reinforced concrete slabs have some problems such as high bias and variance. This study established machine learning-based models to accurately predict the punching shear strength of FRP reinforced concrete slabs. A database of 121 groups of experimental results of FRP reinforced concrete slabs are collected from a literature review. Several machine learning algorithms, such as artificial neural network, support vector machine, decision tree, and adaptive boosting, are selected to build models and compare the performance between them. To demonstrate the predicted accuracy of machine learning, this paper also introduces 6 empirical models and design codes for comparative analysis. The comparative results demonstrate that adaptive boosting has the highest predicted precision, in which the root mean squared error, mean absolute error and coefficient of determination of which are 29.83, 23.00 and 0.99, respectively. GB 50010-2010 (2015) has the best predicted performance among these empirical models and design codes, and ACI 318-19 has the similar result. In addition, among these empirical models, the model proposed by El-Ghandour et al. (1999) has the highest predicted accuracy. According to the results obtained above, SHapley Additive exPlanation (SHAP) is adopted to illustrate the predicted process of AdaBoost. SHAP not only provides global and individual interpretations, but also carries out feature dependency analysis for each input variable. The interpretation results of the model reflect the importance and contribution of the factors that influence the punching shear strength in the machine learning model

Multi-Objective Optimization Design of FRP Reinforced Flat Slabs under Punching Shear by Using NGBoost-Based Surrogate Model

Multi-objective optimization problems (MOPs) in structural engineering arise as a significant challenge in achieving a balance between prediction accuracy and efficiency of the surrogate models, which are conventionally adopted as mechanics-driven models or numerical models. Data-driven models, such as machine learning models, can be instrumental in resolving intricate structural engineering issues that cannot be tackled through mechanics-driven models. This study aims to address the challenges of multi-objective optimization punching shear design of fiber-reinforced polymer (FRP) reinforced flat slabs by using a data-driven surrogate model. Firstly, this study employs an advanced machine learning model, namely Natural Gradient Boosting (NGBoost), to predict the punching shear resistance of FRP reinforced flat slabs. The comparisons with other machine learning models, design provisions and empirical theory models illustrate that the NGBoost model has higher accuracy in predicting the punching shear resistance. Additionally, the NGBoost model is explained with Shapley Additive Explanation (SHAP), revealing that the slab’s effective depth is the primary factor affecting the punching shear resistance. Then, the formulated NGBoost model is adopted as a surrogate model in conjunction with the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) algorithm for multi-objective optimization design of FRP reinforced flat slabs subjected to punching shear. Through a case study, it is demonstrated that the Pareto-optimal set of the punching shear resistance and cost of the FRP reinforced flat slabs can be successfully obtained. By discussing the effects of design parameter changes on the results, it is also shown that increasing the slab’s effective depth is a relatively effective way to achieve higher punching shear resistance of FRP reinforced flat slabs

Ensemble Machine-Learning-Based Prediction Models for the Compressive Strength of Recycled Powder Mortar

Recycled powder (RP) serves as a potential and prospective substitute for cementitious materials in concrete. The compressive strength of RP mortar is a pivotal factor affecting the mechanical properties of RP concrete. The application of machine learning (ML) approaches in the engineering problems, particularly for predicting the mechanical properties of construction materials, leads to high prediction accuracy and low experimental costs. In this study, 204 groups of RP mortar compression experimental data are collected from the literature to establish a dataset for ML, including 163 groups in the training set and 41 groups in the test set. Four ensemble ML models, namely eXtreme Gradient-Boosting (XGBoost), Random Forest (RF), Light Gradient-Boosting Machine (LightGBM) and Adaptive Boosting (AdaBoost), were selected to predict the compressive strength of RP mortar. The comparative results demonstrate that XGBoost has the highest prediction accuracy when the a10-index, MAE, RMSE and R2 of the training set are 0.926, 1.596, 2.155 and 0.950 and the a10-index, MAE, RMSE and R2 of the test set are 0.659, 3.182, 4.285 and 0.842, respectively. SHapley Additive exPlanation (SHAP) is adopted to interpret the prediction process of XGBoost and explain the influence of influencing factors on the compressive strength of RP mortar. According to the importance of influencing factors, the order is the mass replacement rate of RP, the size of RP, the kind of RP and the water binder ratio of RP. The compressive strength of RP mortar decreases with the increase in the RP mass replacement rate. The compressive strength of RBP mortar is slightly higher than that of RCP mortar. Machine learning technologies will benefit the construction industry by facilitating the rapid and cost-effective evaluation of RP material properties