The single-cell transcriptome and chromatin accessibility datasets of peripheral blood mononuclear cells in Chinese holstein cattle.

OBJECTIVES: This study was performed in the frame of a more extensive study dedicated to the integrated analysis of the single-cell transcriptome and chromatin accessibility datasets of peripheral blood mononuclear cells (PBMCs) with a large-scale GWAS of 45 complex traits in Chinese Holstein cattle. Lipopolysaccharide (LPS) is a crucial mediator of chronic inflammation to modulate immune responses. PBMCs include primary T and B cells, natural killer (NK) cells, monocytes (Mono), and dendritic cells (DC). How LPS stimulates PBMCs at the single-cell level in dairy cattle remains largely unknown. DATA DESCRIPTION: We sequenced 30,756 estimated single cells and mapped 26,141 of them (96.05%) with approximately 60,075 mapped reads per cell after quality control for four whole-blood treatments (no, 2 h, 4 h, and 8 h LPS) by single-cell RNA sequencing (scRNA-seq) and single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq). Finally, 7,107 (no), 9,174 (2 h), 6,741 (4 h), and 3,119 (8 h) cells were generated with ~ 15,000 total genes in the whole population. Therefore, the single-cell transcriptome and chromatin accessibility datasets in this study enable a further understanding of the cell types and functions of PBMCs and their responses to LPS stimulation in vitro.

Predictions of Milk Fatty Acid Contents by Mid-Infrared Spectroscopy in Chinese Holstein Cows.

Genetic improvement of milk fatty acid content traits in dairy cattle is of great significance. However, chromatography-based methods to measure milk fatty acid content have several disadvantages. Thus, quick and accurate predictions of various milk fatty acid contents based on the mid-infrared spectrum (MIRS) from dairy herd improvement (DHI) data are essential and meaningful to expand the amount of phenotypic data available. In this study, 24 kinds of milk fatty acid concentrations were measured from the milk samples of 336 Holstein cows in Shandong Province, China, using the gas chromatography (GC) technique, which simultaneously produced MIRS values for the prediction of fatty acids. After quantification by the GC technique, milk fatty acid contents expressed as g/100 g of milk (milk-basis) and g/100 g of fat (fat-basis) were processed by five spectral pre-processing algorithms: first-order derivative (DER1), second-order derivative (DER2), multiple scattering correction (MSC), standard normal transform (SNV), and Savitzky-Golsy convolution smoothing (SG), and four regression models: random forest regression (RFR), partial least square regression (PLSR), least absolute shrinkage and selection operator regression (LassoR), and ridge regression (RidgeR). Two ranges of wavebands (4000~400 cm-1 and 3017~2823 cm-1/1805~1734 cm-1) were also used in the above analysis. The prediction accuracy was evaluated using a 10-fold cross validation procedure, with the ratio of the training set and the test set as 3:1, where the determination coefficient (R2) and residual predictive deviation (RPD) were used for evaluations. The results showed that 17 out of 31 milk fatty acids were accurately predicted using MIRS, with RPD values higher than 2 and R2 values higher than 0.75. In addition, 16 out of 31 fatty acids were accurately predicted by RFR, indicating that the ensemble learning model potentially resulted in a higher prediction accuracy. Meanwhile, DER1, DER2 and SG pre-processing algorithms led to high prediction accuracy for most fatty acids. In summary, these results imply that the application of MIRS to predict the fatty acid contents of milk is feasible.

LeGO-LOAM-SC: An Improved Simultaneous Localization and Mapping Method Fusing LeGO-LOAM and Scan Context for Underground Coalmine.

Simultaneous localization and mapping (SLAM) is one of the key technologies for coal mine underground operation vehicles to build complex environment maps and positioning and to realize unmanned and autonomous operation. Many domestic and foreign scholars have studied many SLAM algorithms, but the mapping accuracy and real-time performance still need to be further improved. This paper presents a SLAM algorithm integrating scan context and Light weight and Ground-Optimized LiDAR Odometry and Mapping (LeGO-LOAM), LeGO-LOAM-SC. The algorithm uses the global descriptor extracted by scan context for loop detection, adds pose constraints to Georgia Tech Smoothing and Mapping (GTSAM) by Iterative Closest Points (ICP) for graph optimization, and constructs point cloud map and an output estimated pose of the mobile vehicle. The test with KITTI dataset 00 sequence data and the actual test in 2-storey underground parking lots are carried out. The results show that the proposed improved algorithm makes up for the drift of the point cloud map, has a higher mapping accuracy, a better real-time performance, a lower resource occupancy, a higher coincidence between trajectory estimation and real trajectory, smoother loop, and 6% reduction in CPU occupancy, the mean square errors of absolute trajectory error (ATE) and relative pose error (RPE) are reduced by 55.7% and 50.3% respectively; the translation and rotation accuracy are improved by about 5%, and the time consumption is reduced by 2~4%. Accurate map construction and low drift pose estimation can be performed.

Impact of the Order of Legendre Polynomials in Random Regression Model on Genetic Evaluation for Milk Yield in Dairy Cattle Population.

The random regression test-day model has become the most commonly adopted model for routine genetic evaluations in dairy populations, which allows accurately accounting for genetic and environmental effects over lactation. The objective of this study was to explore appropriate random regression test-day models for genetic evaluation of milk yield in a Holstein population with a relatively small size, which is the common situation in regional or independent breeding companies to preform genetic evaluation. Data included 419,567 test-day records from 54,417 cows from the first lactation. Variance components and breeding values were estimated using a random regression test-day model with different orders (from first to fifth) of Legendre polynomials (LP) and accounted for homogeneous or heterogeneous residual variance across the lactation. Models were compared based on Akaike information criterion (AIC), Bayesian information criterion (BIC), and predictive ability. In general, models with a higher order of LP showed better goodness of fit based on AIC and BIC values. However, models with third, fourth, and fifth order of LP led to similar estimates of genetic parameters and predictive ability. Models with assumption of heterogeneous residual variances achieved better goodness of fit but yielded similar predictive ability compared with those with assumption of homogeneous residual variances. Therefore, a random regression model with third order of LP is suggested to be an appropriate model for genetic evaluation of milk yield in local Chinese Holstein populations.

Ultra-wideband all-fiber tunable Tm/Ho-co-doped laser at 2 µm.

We demonstrate an all-fiber tunable Tm/Ho-codoped laser operating in the 2 µm wavelength region. The wavelength tuning range of the Tm/Ho-codoped fiber laser (THFL) with 1-m length of Tm/Ho-codoped fiber (THDF) was from 1727 nm to 2030 nm. Efficient short wavelength operation and ultra-wide wavelength tuning range of 303 nm were both achieved. To the best of our knowledge, this is the broadest tuning range that has been reported for an all-fiber rare-earth-doped laser to date. By increasing the THDF length to 2 m, the obtainable wavelength of the THFL was further red-shifted to the range from 1768 nm to 2071 nm. The output power of the THFL was scaled up from 1810 nm to 2010 nm by using a stage of Tm/Ho-codoped fiber amplifier (THFA), which exhibited the maximum slope efficiency of 42.6% with output power of 408 mW at 1910 nm.

High-power all-fiber wavelength-tunable thulium doped fiber laser at 2 µm.

Power scaling of an all-fiber wavelength-tunable thulium doped fiber laser (TDFL) based on a monolithic master oscillator power amplifier (MOPA) system is presented. The whole configuration is comprised of a low-power seed oscillator and two stages of double-cladding thulium doped fiber amplifiers (TDFAs). The tuning of the operating wavelength is realized by inserting a spectral tunable filter into the seed oscillator. Maximum average output power of 115 W is obtained at 1950 nm with a linearly fitted slope efficiency of 51.7%. This laser has superior spectral characteristics with wavelength tunable from 1940 nm to 2070 nm. To the best of our knowledge, this is the first demonstration of an all-fiber wavelength-tunable TDFL at 2 µm with output power exceeding 100 W. The results are of great interest for many application areas.

Thirteen watt all-fiber mid-infrared supercontinuum generation in a single mode ZBLAN fiber pumped by a 2 µm MOPA system.

All-fiber-integrated mid-infrared (mid-IR) supercontinuum (SC) generation in a single mode ZBLAN (ZrF4-BaF2-LaF3-AlF3-NaF) fiber with 13 W average output power and a spectrum extending from ∼1.9 to 4.3 µm is reported, which we believe is the highest output power for mid-IR SC generation in ZBLAN fibers. The overall optical conversion efficiency from the 790 nm pump light of the last stage Tm-doped fiber (TDF) amplifier to the total SC output was 20%, and the SC power for wavelengths longer than 2.5 µm was 6.85 W with a power ratio of 52.69% with respect to the total SC power. The mid-IR SC generation in ZBLAN fiber was pumped by a 2 µm master oscillator power amplifier (MOPA) system, which is also very appropriate for high power 2-2.5 µm region SC generation. We also demonstrate high efficiency SC generation in the TDF amplifier with 62.1 W average power, 39.8% optical efficiency, and a spectrum extending from 1.9 to 2.7 µm.