Comprehensive Analysis of Transcriptome and Metabolome Reveals Regulatory Mechanism of Intramuscular Fat Content in Beef Cattle.

The intramuscular fat (IMF) content of beef determined the meat quality, and the market value of beef varies with different breeds. To provide some new approaches for improving meat quality and cattle breed improvement, 24-month-old Qinchuan cattle (Q, n = 6), Nanyang cattle (N, n = 6), and Japanese black cattle (J, n = 6) were selected. IMF content of the J group (16.92 ± 1.08%) is remarkably higher than that of indigenous Chinese cattle (Q, 13.38 ± 1.08%, and N, 12.35 ± 1.22%). Monounsaturated fatty acids and polyunsaturated fatty acids in the J group are higher than the Q and creatine, lysine, and glutamine are the three most abundant amino acids in beef, which contribute to the flavor formation. Similarly, IMF content-related genes were enriched in four vital KEGG pathways, including fatty acid metabolism, biosynthesis of unsaturated fatty acids, fatty acid elongation, and insulin resistance. Moreover, weighted genes coexpression network analysis (WGCNA) revealed that ITGB1 is the critical gene associated with the IMF content. This study compares transcriptome and metabolome of local and high-IMF cattle breeds, providing data for native cattle breeding and improvement of beef quality.

Genome-Wide Association Study Reveals Novel Loci Associated with Body Conformation Traits in Qinchuan Cattle.

A genome-wide association study (GWAS) is an effective tool for identifying the dominant genes of complex economic traits in livestock by statistical analysis of genotype data and measured phenotype data. In this study, we rigorously measured 14 body conformation traits in 254 Qinchuan cattle, comprising body weight (BW), body height (BOH), back height (BAH), buttock height (BUH), chest depth (CD), chest width (CW), hip cross height (HCH), body length (BL), hip width (HW), rump length (RL), pin bone width (PBW), chest girth (CG), abdomen circumference (AG), and calf circumference (CC). After quality control, 281,889 SNPs were generated for GWAS with different traits. A total of 250 suggestive SNPs (p < 3.54 × 10-6) were screened and 37 candidate genes were annotated. Furthermore, we performed a linkage disequilibrium analysis of SNP loci and considered published studies, identifying the eight genes (ADAMTS17, ALDH1A3, CHSY1, MAGEL2, MEF2A, SYNM, CNTNAP5, and CTNNA3) most likely to be involved in growth traits. This study provides new insights into the regulatory mechanisms of bovine body size development, which can be very useful in the development of management and breeding strategies.

Dual-mode LiDAR SoC with an on-chip interframe filter and common optical platform design for keystone correction and auto-focus.

This paper presents an innovative methodology that incorporates direct time-of-flight technology into intelligent sensing for projectors, along with a lightweight, dual-mode optically integrated LiDAR system. The proposed LiDAR system-on-chip, which utilizes a single-photon avalanche diode and time to digital converter with 0.13 µm bipolar CMOS DMOS technology, integrates an on-chip interframe filter, a common optical platform design, and a lightweight keystone correction assist algorithm. This comprehensive integration enables the system to achieve a measurement range of 11 m with 1% relative precision (simulations indicate the potential to achieve 30 m) in auto-focus mode. Additionally, it facilitates high frame-per-second keystone correction within a range of ±30∘ with an error of ±2∘ under illumination conditions of 20 klux.

A 256 × 256 LiDAR Imaging System Based on a 200 mW SPAD-Based SoC with Microlens Array and Lightweight RGB-Guided Depth Completion Neural Network.

Light detection and ranging (LiDAR) technology, a cutting-edge advancement in mobile applications, presents a myriad of compelling use cases, including enhancing low-light photography, capturing and sharing 3D images of fascinating objects, and elevating the overall augmented reality (AR) experience. However, its widespread adoption has been hindered by the prohibitive costs and substantial power consumption associated with its implementation in mobile devices. To surmount these obstacles, this paper proposes a low-power, low-cost, single-photon avalanche detector (SPAD)-based system-on-chip (SoC) which packages the microlens arrays (MLAs) and a lightweight RGB-guided sparse depth imaging completion neural network for 3D LiDAR imaging. The proposed SoC integrates an 8 × 8 SPAD macropixel array with time-to-digital converters (TDCs) and a charge pump, fabricated using a 180 nm bipolar-CMOS-DMOS (BCD) process. Initially, the primary function of this SoC was limited to serving as a ranging sensor. A random MLA-based homogenizing diffuser efficiently transforms Gaussian beams into flat-topped beams with a 45° field of view (FOV), enabling flash projection at the transmitter. To further enhance resolution and broaden application possibilities, a lightweight neural network employing RGB-guided sparse depth complementation is proposed, enabling a substantial expansion of image resolution from 8 × 8 to quarter video graphics array level (QVGA; 256 × 256). Experimental results demonstrate the effectiveness and stability of the hardware encompassing the SoC and optical system, as well as the lightweight features and accuracy of the algorithmic neural network. The state-of-the-art SoC-neural network solution offers a promising and inspiring foundation for developing consumer-level 3D imaging applications on mobile devices.

228 × 304 200-mW lidar based on a single-point global-depth d-ToF sensor and RGB-guided super-resolution neural network.

The cutting-edge imaging system exhibits low output resolution and high power consumption, presenting challenges for the RGB-D fusion algorithm. In practical scenarios, aligning the depth map resolution with the RGB image sensor is a crucial requirement. In this Letter, the software and hardware co-design is considered to implement a lidar system based on the monocular RGB 3D imaging algorithm. A 6.4 × 6.4-mm2 deep-learning accelerator (DLA) system-on-chip (SoC) manufactured in a 40-nm CMOS is incorporated with a 3.6-mm2 TX-RX integrated chip fabricated in a 180-nm CMOS to employ the customized single-pixel imaging neural network. In comparison to the RGB-only monocular depth estimation technique, the root mean square error is reduced from 0.48 m to 0.3 m on the evaluated dataset, and the output depth map resolution matches the RGB input.

Dual pulse repetition rates with high background noise tolerance for memory-efficient SPAD LiDAR.

Single-photon avalanche diode (SPAD) sensors for flash light detection and ranging (LiDAR) typically have high memory overhead. The widely adopted memory-efficient two-step coarse-fine (CF) process suffers from degraded background noise (BGN) tolerance. To alleviate this issue, we propose a dual pulse repetition rate (DPRR) scheme while maintaining a high histogram compression ratio (HCR). The scheme involves emitting narrow laser pulses at two different high rates in two phases, generating histograms and locating their respective peaks; then the actual distance can be derived based on the peak locations and repetition rates. Additionally, in this Letter, we propose spatial filtering within neighboring pixels with different repetition rate sets to cope with multiple reflections, which can potentially confuse the derivation, owing to the existence of several possible peak combinations. Compared with the CF approach, with a same HCR of 7, the simulations and experiments demonstrate that this scheme can tolerate 2 × BGN levels, accompanied with an increase in the frame rate by 4 ×.

Integrated multi-omics analysis reveals variation in intramuscular fat among muscle locations of Qinchuan cattle.

BACKGROUND: Intramuscular fat (IMF) is closely related to the tenderness, marbling, juiciness, and flavor of meat. We used a combined transcriptome and metabolome analysis to investigate the molecular mechanisms underlying phenotypic variation among Qinchuan cattle. RESULTS: The IMF content was relatively high in the meat of Qinchuan cattle bulls and differed among muscle locations, namely the high rib (15.86%), ribeye (14%), striploin (10.44%), and tenderloin (8.67%). CCDC80 and the HOX gene cluster may regulate intramuscular adipose tissue deposition. Moreover, erucic acid (EA) was found to be the main metabolite in Qinchuan beef cattle, with a high concentration in IMF. The deposition of IMF could be regulated by the metabolic pathway for unsaturated fatty acids involving EA and the ACOX3, HACD2, and SCD5 genes. In addition, differentially expressed genes and metabolites were enriched in three major KEGG pathways: purine metabolism, pyrimidine metabolism, and the metabolism of glycine, serine, and threonine. CONCLUSIONS: We identified a significant metabolite, EA, with variation in IMF. Its closely related genes, ACOX3, HACD2, and SCD5, co-regulate the metabolism of unsaturated fatty acids, ultimately affecting the accumulation of intramuscular adipose tissue in Qinchuan cattle. Consequently, Qinchuan cattle are an elite cultivar for high-quality beef production and have great potential for breeding.

Integrative Analysis of Blood Transcriptomics and Metabolomics Reveals Molecular Regulation of Backfat Thickness in Qinchuan Cattle.

A crucial goal of reducing backfat thickness (BFT) is to indirectly improve feed conversion efficiency. This phenotype has been reported in certain papers; however, the molecular mechanism has yet to be fully revealed. Two extreme BFT groups, consisting of four Qinchuan cattle, were chosen for this study. We performed metabolite and transcriptome analyses of blood from cattle with a high BFT (H-BFT with average = 1.19) and from those with a low BFT (L-BFT with average = 0.39). In total, 1106 differentially expressed genes (DEGs) and 86 differentially expressed metabolites (DEMs) were identified in the extreme trait. In addition, serum ceramide was strongly correlated with BFT and could be used as a potential biomarker. Moreover, the most notable finding was that the functional genes (SMPD3 and CERS1) and metabolite (sphingosine 1-phosphate (S1P)) were filtered out and significantly enriched in the processes related to the sphingolipid metabolism. This investigation contributed to a better understanding of the subcutaneous fat depots in cattle. In general, our results indicated that the sphingolipid metabolism, involving major metabolites (serum ceramide and S1P) and key genes (SMPD3 and CERS1), could regulate BFT through blood circulation.

A dToF Ranging Sensor with Accurate Photon Detector Measurements for LiDAR Applications.

Direct time-of-flight (dToF) ranging sensors based on single-photon avalanche diodes (SPADs) have been used as a prominent depth-sensing devices. Time-to-digital converters (TDCs) and histogram builders have become the standard for dToF sensors. However, one of the main current issues is the bin width of the histogram, which limits the accuracy of depth without TDC architecture modifications. SPAD-based light detection and ranging (LiDAR) systems require new methods to overcome their inherent drawbacks for accurate 3D ranging. In this work, we report an optimal matched filter to process the raw data of the histogram to obtain high-accuracy depth. This method is performed by feeding the raw data of the histogram into the different matched filters and using the Center-of-Mass (CoM) algorithm for depth extraction. Comparing the measurement results of different matched filters, the filter with the highest depth accuracy can be obtained. Finally, we implemented a dToF system-on-chip (SoC) ranging sensor. The sensor is made of a configurable array of 16 × 16 SPADs, a 940 nm vertical-cavity surface-emitting laser (VCSEL), an integrated VCSEL driver, and an embedded microcontroller unit (MCU) core to implement the best matched filter. To achieve suitably high reliability and low cost, the above-mentioned features are all packaged into one module for ranging. The system resulted in a precision of better than 5 mm within 6 m with 80% reflectance of the target, and had a precision better than 8 mm at a distance within 4 m with 18% reflectance of the target.