Exploring a Bayesian sparse factor model-based strategy for the genetic analysis of thousands of MIR-spectra traits for animal breeding.

With the rapid development of animal phenomics and deep phenotyping, we can get thousands of traditional but also molecular phenotypes per individual. However, there is still a lack of exploration regarding how to handle this huge amount of data in the context of animal breeding, presenting a challenge that we are likely to encounter more and more in the future. This study aimed to (1) explore the use of the Mega-scale linear mixed model (MegaLMM), a factor model-based approach, able to simultaneously estimate (co)variance components and genetic parameters in the context of thousands of milk traits, hereafter called thousand-trait (TT) models; (2) compare the phenotype values and genomic breeding values (u) predictions for focal traits (i.e., traits that are targeted for prediction, compared with secondary traits that are helping to evaluate), from single-trait (ST) and TT models, respectively; (3) propose a new approximate method of estimated genomic breeding values (U) prediction with TT models and MegaLMM. 3,421 milk mid-infrared (MIR) spectra wavepoints (called secondary traits) and 3 focal traits [average fat percent (Fat), average methane (CH4), and average somatic cell score (SCS)] collected on 3,302 first-parity Holstein cows were used. The 3,421 milk MIR wavepoints traits were composed of 311 wavepoints in 11 classes (months in lactation). Genotyping information of 564,439 SNP was available for all animals and was used to calculate the genomic relationship matrix. The MegaLMM was implemented in the framework of the Bayesian sparse factor model and solved through Gibbs sampling (Markov chain Monte Carlo). The heritabilities of the studied 3,421 milk MIR wavepoints gradually increased and then decreased in units of 311 wavepoints throughout the lactation. The genetic and phenotypic correlations between the first 311 wavepoints and the other 3,110 wavepoints were low. The accuracies of phenotype predictions from the ST model were lower than those from the TT model for Fat (0.51 vs. 0.93), CH4 (0.30 vs. 0.86), and SCS (0.14 vs. 0.33). The same trend was observed for the accuracies of u predictions: Fat (0.59 vs. 0.86), CH4 (0.47 vs. 0.78), and SCS (0.39 vs. 0.59). The average correlation between U predicted from the TT model and the new approximate method was 0.90. The new approximate method used for estimating U in MegaLMM will enhance the suitability of MegaLMM for applications in animal breeding. This study conducted an initial investigation into the application of thousands of traits in animal breeding and showed that the TT model is beneficial for the prediction of focal traits (phenotype and breeding values), especially for difficult-to-measure traits (e.g., CH4).

Highly sensitive fiber-optic chemical pH sensor based on surface modification of optical fiber with ZnCdSe/ZnS quantum dots.

The pH value plays a vital role in many biological and chemical reactions. In this work, the fiber-optic chemical pH sensors were fabricated based on carboxyl ZnCdSe/ZnS quantum dots (QDs) and tapered optical fiber. The photoluminescence (PL) intensity of QDs is pH-dependence because protonation and deprotonation can affect the process of electron-hole recombination. The evanescent wave of tapered optical fiber was used as excitation source in the process of PL. To obtain higher sensitivity, the end faces of fiber were optimized for cone region. By lengthening the cone region and shrinking the end diameter of optical fiber, evanescent wave was enhanced and the excitation times of QDs were increased, which improved the PL intensity and the sensitivity of the sensor. The sensitivity of sensor can reach as high as 0.139/pH in the range of pH 6.00-9.01. The surface functional modification was adopted to prepare sensing films. The carboxyl groups on the QDs ligands are chemically bonded to the fiber surface, which is good for response time (40 s) and stability (decreased 0.9 % for 5 min). These results demonstrated that ZnCdSe/ZnS QDs-based fiber-optic chemical pH sensors are promising approach in rapid and precise pH detection.

APPA-3D: an autonomous 3D path planning algorithm for UAVs in unknown complex environments.

Due to their high flexibility, low cost, and ease of handling, Unmanned Aerial Vehicles (UAVs) are often used to perform difficult tasks in complex environments. Stable and reliable path planning capability is the fundamental demand for UAVs to accomplish their flight tasks. Most researches on UAV path planning are carried out under the premise of known environmental information, and it is difficult to safely reach the target position in the face of unknown environment. Thus, an autonomous collision-free path planning algorithm for UAVs in unknown complex environments (APPA-3D) is proposed. An anti-collision control strategy is designed using the UAV collision safety envelope, which relies on the UAV's environmental awareness capability to continuously interact with external environmental information. A dynamic reward function of reinforcement learning combined with the actual flight environment is designed and an optimized reinforcement learning action exploration strategy based on the action selection probability is proposed. Then, an improved RL algorithm is used to simulate the UAV flight process in unknown environment, and the algorithm is trained by interacting with the environment, which finally realizes autonomous collision-free path planning for UAVs. The comparative experimental results in the same environment show that APPA-3D can effectively guide the UAV to plan a safe and collision-free path from the starting point to the target point in an unknown complex 3D environment.

High Sensitivity Temperature Sensing of Long-Period Fiber Grating for the Ocean.

In this study, a new temperature sensor with high sensitivity was achieved by four-layer Ge and B co-doped long-period fiber grating (LPFG) based on the mode coupling principle. By analyzing the mode conversion, the influence of the surrounding refractive index (SRI), the thickness and the refractive index of the film on the sensitivity of the sensor is studied. When 10 nm-thick titanium dioxide (TiO2) film is coated on the surface of the bare LPFG, the refractive index sensitivity of the sensor can be initially improved. Packaging PC452 UV-curable adhesive with a high-thermoluminescence coefficient for temperature sensitization can realize high-sensitivity temperature sensing and meet the requirements of ocean temperature detection. Finally, the effects of salt and protein attachment on the sensitivity are analyzed, which provides a reference for the subsequent application. The sensitivity of 3.8 nm/°C in the range of 5-30 °C was achieved for this new sensor, and the resolution is about 0.00026 °C, which is over 20 times higher than ordinary temperature sensors. This new sensor meets the accuracy and range of general ocean temperature measurements and could be used in various marine monitoring and environmental protection applications.

Exogenous Protein Delivery of Ionic Liquid-Mediated HMGB1 Coating on Titanium Implants.

Strategies for modifying titanium (Ti) implant surfaces are becoming increasingly popular to enhance osseointegration during acute and inflammatory healing stages. In this study, two dicationic imidazolium-based ionic liquids (IonLs) containing phenylalanine and methionine anions (IonL-Phe(1,10-bis(3-methylimidazolium-1-yl)decane diphenylalanine) and IonL-Met(1,10-bis(3-methylimidazolium-1-yl)decane dimethionine)) were investigated to stably deliver exogenous proteins on Ti to promote osseointegration. The protein selected for this study is High-Mobility Group Box 1 (HMGB1), which recruits inflammatory and mesenchymal stem cells to the implantation site, contributing to healing. To explore IonL-Ti interactions and HMGB1 stability on the IonL-coated surface, experimental characterization techniques including X-ray photoelectron spectroscopy, scanning electron microscopy, dynamic scanning calorimetry (DSC), and liquid chromatography mass spectrometry (LC-MS) were used along with molecular dynamics (MD) computer simulations to provide a detailed molecular level description. Results show well-structured IonL molecules on the Ti surface that impact protein crystallization and coating morphology. IonL cations and anions were found to bind strongly to oppositely charged residues of the protein. LC-MS/MS reveals that HMGB1 B-box lysine residues bind strongly to the IonLs. Stronger interactions of HMGB1 with Ion-Phe in contrast to IonL-Met results in greater retention capacity of HMGB1 in the IonL-Phe coating. Overall, this study provides evidence that the selected IonLs strongly interact with HMGB1, which can be a potential surface treatment for bone-implantable Ti devices.

Mega-scale Bayesian regression methods for genome-wide prediction and association studies with thousands of traits.

Large-scale phenotype data are expected to increase the accuracy of genome-wide prediction and the power of genome-wide association analyses. However, genomic analyses of high-dimensional, highly correlated traits are challenging. We developed a method for implementing high-dimensional Bayesian multivariate regression to simultaneously analyze genetic variants underlying thousands of traits. As a demonstration, we implemented the BayesC prior in the R package MegaLMM. Applied to Genomic Prediction, MegaBayesC effectively integrated hyperspectral reflectance data from 620 hyperspectral wavelengths to improve the accuracy of genetic value prediction on grain yield in a wheat dataset. Applied to Genome-Wide Association Studies, we used simulations to show that MegaBayesC can accurately estimate the effect sizes of QTL across a range of genetic architectures and causes of correlations among traits. To apply MegaBayesC to a realistic scenario involving whole-genome marker data, we developed a 2-stage procedure involving a preliminary step of candidate marker selection prior to multivariate regression. We then used MegaBayesC to identify genetic associations with flowering time in Arabidopsis thaliana, leveraging expression data from 20,843 genes. MegaBayesC selected 15 single nucleotide polymorphisms as important for flowering time, with 13 located within 100 kb of known flowering-time related genes, a higher validation rate than achieved by a single-stage analysis using only the flowering time data itself. These results demonstrate that MegaBayesC can efficiently and effectively leverage high-dimensional phenotypes in genetic analyses.

A Bayesian random regression method using mixture priors for genome-enabled analysis of time-series high-throughput phenotyping data.

The recent advancement in image-based phenotyping platforms enables the acquisition of large-scale nondestructive crop phenotypes measured at frequent intervals. To further understand the underlying genetic basis over a physiological process and improve plant breeding programs, the question of how to efficiently utilize these time-series measurements in genome-enabled analysis including genomic prediction and genome-wide association studies (GWASs) should be considered. In this paper, a Bayesian random regression model with mixture priors is developed to introduce more meaningful biological assumptions to the analysis of longitudinal traits. The mixture prior for marker effects in Bayes Cπ is implemented in our developed model (RR-BayesC) for demonstration purpose. The estimation of single-nucleotide polymorphism-specific effects that are related to the dynamic performance of crops and the accuracy of genomic prediction by RR-BayesC were studied through both simulated and real rice (Oryza sativa L.) data. For genomic prediction, three predictive scenarios were studied. In the simulated study, RR-BayesC showed a significantly higher prediction accuracy than that obtained by single-trait analysis, especially for days when heritability were low. In real data analysis, RR-BayesC showed relatively high prediction accuracy when forecast is required for phenotypes at later period (e.g., from 0.94 to 0.98 for lines with observations at an earlier period and from 0.65 to 0.67 for lines without any observations). For GWASs, inference of single markers and inference of genomic windows were conducted. In the simulated study, RR-BayesC showed its promising ability to distinguish quantitative trait loci (QTL) that are invariant to temporal covariates and QTL that interact with time. An association study of real data was also presented to demonstrate the application of RR-BayesC in real data analysis. In this paper, we develop a Bayesian random regression model that is able to incorporate mixture priors to marker effects and show improved performance of genomic prediction and GWASs for longitudinal data analysis based on both simulated and real data. The software tool JWAS offers routines to perform our proposed random regression analysis.

Immunosensor of Nitrofuran Antibiotics and Their Metabolites in Animal-Derived Foods: A Review.

Nitrofuran antibiotics have been widely used in the prevention and treatment of animal diseases due to the bactericidal effect. However, the residual and accumulation of their metabolites in vivo can pose serious health hazards to both humans and animals. Although their usage in feeding and process of food-derived animals have been banned in many countries, their metabolic residues are still frequently detected in materials and products of animal-derived food. Many sensitive and effective detection methods have been developed to deal with the problem. In this work, we summarized various immunological methods for the detection of four nitrofuran metabolites based on different types of detection principles and signal molecules. Furthermore, the development trend of detection technology in animal-derived food is prospected.

MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits.

Large-scale phenotype data can enhance the power of genomic prediction in plant and animal breeding, as well as human genetics. However, the statistical foundation of multi-trait genomic prediction is based on the multivariate linear mixed effect model, a tool notorious for its fragility when applied to more than a handful of traits. We present MegaLMM, a statistical framework and associated software package for mixed model analyses of a virtually unlimited number of traits. Using three examples with real plant data, we show that MegaLMM can leverage thousands of traits at once to significantly improve genetic value prediction accuracy.

Diabetes as a Risk Factor for Orthopedic Implant Surface Performance: A Retrieval and In Vitro Study.

Orthopedic devices are often associated with increased risk for diabetic patients due to impaired wound healing capabilities. Adverse biological responses for immunocompromised patients at the implant-tissue interface can lead to significant bone resorption that may increase failure rates. The goal of this study was to characterize the surface of implants removed from diabetic patients to determine underlying mechanisms of diabetes-induced impaired osseointegration. Thirty-nine retrieved titanium and stainless-steel orthopedic devices were obtained from diabetic and non-diabetic patients, and compared to non-implanted controls. Optical Microscopy, Scanning Electron Microscopy, Energy Dispersive X-ray Spectroscopy, and X-ray Photoelectron Spectroscopy revealed changes in morphology, chemical composition, oxidation state, and oxide thickness of the retrieval specimens, respectively. Additionally, titanium disks were immersed for 28 days in simulated in vitro diabetic conditions followed by Inductively Coupled Plasma-Optical Emission Spectroscopy to quantify metal dissolution. Electrochemical testing was performed on specimens from retrievals and in vitro study. Aside from biological deposits, retrievals demonstrated surface discoloration, pit-like formations and oxide thinning when compared to non-implanted controls, suggesting exposure to unfavorable acidic conditions. Cyclic load bearing areas on fracture-fixation screws and plates depicted cracking and delamination. The corrosion behavior was not significantly different between diabetic and non-diabetic conditions of immersed disks or implant type. However, simulated diabetic conditions elevated aluminum release. This elucidates orthopedic implant failures that potentially arise from diabetic environments at the implant-tissue interface. Design of new implant surfaces should consider specific strategies to induce constructive healing responses in immunocompromised patients while also mitigating corrosion in acidic diabetic environments.

Corrigendum: Exact Distribution of Linkage Disequilibrium in the Presence of Mutation, Selection, or Minor Allele Frequency Filtering.

[This corrects the article DOI: 10.3389/fgene.2020.00362.].

Exact Distribution of Linkage Disequilibrium in the Presence of Mutation, Selection, or Minor Allele Frequency Filtering.

Linkage disequilibrium (LD), often expressed in terms of the squared correlation (r 2) between allelic values at two loci, is an important concept in many branches of genetics and genomics. Genetic drift and recombination have opposite effects on LD, and thus r 2 will keep changing until the effects of these two forces are counterbalanced. Several approximations have been used to determine the expected value of r 2 at equilibrium in the presence or absence of mutation. In this paper, we propose a probability-based approach to compute the exact distribution of allele frequencies at two loci in a finite population at any generation t conditional on the distribution at generation t - 1. As r 2 is a function of this distribution of allele frequencies, this approach can be used to examine the distribution of r 2 over generations as it approaches equilibrium. The exact distribution of LD from our method is used to describe, quantify, and compare LD at different equilibria, including equilibrium in the absence or presence of mutation, selection, and filtering by minor allele frequency. We also propose a deterministic formula for expected LD in the presence of mutation at equilibrium based on the exact distribution of LD.