Progress on deep learning in genomics.

With the rapid growth of data driven by high-throughput sequencing technologies, genomics has entered an era characterized by big data, which presents significant challenges for traditional bioinformatics methods in handling complex data patterns. At this critical juncture of technological progress, deep learning-an advanced artificial intelligence technology-offers powerful capabilities for data analysis and pattern recognition, revitalizing genomic research. In this review, we focus on four major deep learning models: Convolutional Neural Network(CNN), Recurrent Neural Network(RNN), Long Short-Term Memory(LSTM), and Generative Adversarial Network(GAN). We outline their core principles and provide a comprehensive review of their applications in DNA, RNA, and protein research over the past five years. Additionally, we also explore the use of deep learning in livestock genomics, highlighting its potential benefits and challenges in genetic trait analysis, disease prevention, and genetic enhancement. By delivering a thorough analysis, we aim to enhance precision and efficiency in genomic research through deep learning and offer a framework for developing and applying livestock genomic strategies, thereby advancing precision livestock farming and genetic breeding technologies.

Development and application of a 1K functional liquid chip for lactation performance in Bactrian camels.

Introduction: The advancement of high-throughput, high-quality, flexible, and cost-effective genotyping platforms is crucial for the progress of dairy breeding in Bactrian camels. This study focuses on developing and evaluating a 1K functional liquid single nucleotide polymorphism (SNP) array specifically designed for milk performance in Bactrian camels. Methods: We utilized RNA sequencing data from 125 lactating camels to identify and select 1,002 loci associated with milk production traits for inclusion in the SNP array. The array's performance was then assessed using 24 randomly selected camels. Additionally, the array was employed to genotype 398 individuals, which allowed for population validation to assess the polymorphism of SNP sites. Results: The SNP array demonstrated high overall SNP call rates (> 99%) and a remarkable 100% consistency in genotyping. Population validation results indicate that camels from six breeding areas in Northwest China share a similar genetic background regarding lactation functionality. Discussion: This study highlights the potential of the SNP array to accelerate the breeding process of lactating Bactrian camels and provides a robust technical foundation for improving lactation performance.

Three-dimensional ranging system based on Fresnel incoherent correlation holography.

We proposed a three-dimensional (3D) ranging system based on Fresnel incoherent correlation holography (FINCH). Distinct from the displacement measurement based on coherent digital holography (DH), our system simultaneously achieves a 3D range measurement using incoherent illumination. The observation range is obtained by the holographic reconstruction, while the in-plane range is determined using the two-dimensional digital imaging correlation (2D-DIC) technique. Experimental results on the resolution target demonstrate precise 3D ranging determination and improved measurement accuracy.

Progress on CRISPR/Cas9 system in the genetic improvement of livestock and poultry.

CRISPR/Cas9 gene editing technology, as a highly efficient genome editing method, has been extensively employed in the realm of animal husbandry for genetic improvement. With its remarkable efficiency and precision, this technology has revolutionized the field of animal husbandry. Currently, CRISPR/Cas9-based gene knockout, gene knock-in and gene modification techniques are widely employed to achieve precise enhancements in crucial production traits of livestock and poultry species. In this review, we summarize the operational principle and development history of CRISPR/Cas9 technology. Additionally, we highlight the research advancements utilizing this technology in muscle growth and development, fiber growth, milk quality composition, disease resistance breeding, and animal welfare within the livestock and poultry sectors. Our aim is to provide a more comprehensive understanding of the application of CRISPR/Cas9 technology in gene editing for livestock and poultry.

Massively Parallel CRISPR-Cas9 Knockout Screening in Sheep Granulosa Cells for FSH Response Genes.

Follicle-stimulating hormone (FSH) regulates ovarian follicle development through specific gene expression programs. Granulosa cells (GCs) are somatic cells surrounding the oocytes, secreting gonadotropins to regulate ovulation and promote follicular development. By analyzing the effects of different doses of FSH on the proliferation of GCs, we found that adding 10 ng/mL of FSH, as the optimal concentration, could promote the growth of GCs. Furthermore, we have successfully constructed the first CRISPR-Cas9 knockout library targeting the genes on chromosomes 2 and 3 and the X chromosomes of the sheep massively parallel coding gene, as well as an ovarian GCs knockout cell library. For the first time, we have exposed the knockout cell library to a concentration of 10 ng/mL FSH to explore the underlying mechanisms. Through this screening, we have identified 836 positive-negative screening genes that are responsive to FSH, thereby revealing the regulatory mechanisms and screening the functionality of candidate genes. Next, RNA-Seq of control (0 ng/mL), low (10 ng/mL), and high (100 ng/mL) doses of FSH revealed 1708 differentially expressed genes, and combined with 836 genes, we obtained 129 FSH dose-dependent genes with extremely significant differences. This enables us to delve deeper into investigating and identifying the mechanisms by which FSH regulates GCs. More generally, we have discovered new regulatory factors and identified reproductivity-associated major effectors. These findings provide novel research directions for further studies on sheep reproduction.

Edge enhancement in three-dimensional vortex imaging based on FINCH by Bessel-like spiral phase modulation.

Edge enhancement, as an important part of image processing, has played an essential role in amplitude-contrast and phase-contrast object imaging. The edge enhancement of three-dimensional (3D) vortex imaging has been successfully implemented by Fresnel incoherent correlation holography (FINCH), but the background noise and image contrast effects are still not satisfactory. To solve these issues, the edge enhancement of FINCH by employing Bessel-like spiral phase modulation is proposed and demonstrated. Compared with the conventional spiral phase modulated FINCH, the proposed technique can achieve high-quality edge enhancement 3D vortex imaging with lower background noise, higher contrast and resolution. The significantly improved imaging quality is mainly attributed to the effective sidelobes' suppression in the generated optical vortices with the Bessel-like modulation technique. Experimental results of the small circular aperture, resolution target, and the Drosophila melanogaster verify its excellent imaging performance. Moreover, we also proposed a new method for selective edge enhancement of 3D vortex imaging by breaking the symmetry of the spiral phase in the algorithmic model of isotropic edge enhancement. The reconstructed images of the circular aperture show that the proposed method is able to enhance the edges of the given objects selectively in any desired direction.

Design of a Two-Dimensional Conveyor Platform with Cargo Pose Recognition and Adjustment Capabilities.

Linear conveyors, traditional tools for cargo transportation, have faced criticism due to their directional constraints, inability to adjust poses, and single-item conveyance, making them unsuitable for modern flexible logistics demands. This paper introduces a platform designed to convey and adjust cargo boxes according to their spatial positions and orientations. Additionally, a cargo pose recognition algorithm that integrates image and point cloud data are presented. By aligning depth camera data, the axis-aligned bounding box (AABB) point serves as the image's region of interest (ROI). Peaks extracted from the image's Hough transform are refined using RANSAC-based point cloud linear fitting, then integrated with the point cloud's oriented bounding box (OBB). Notably, the algorithm eliminates the need for deep learning and registration, enabling its use in rectangular cargo boxes of various sizes. A comparative experiment using accelerometer sensors for pose acquisition revealed a deviation of <0.7° between the two processes. Throughout the real-time adjustments controlled by the experimental platform, cargo angles consistently remained stable. The proposed two-dimensional conveyance platform, compared to existing methods, exhibits simplicity, accurate recognition, enhanced flexibility, and wide applicability.

Non-fragile guaranteed cost control of microbial fuel cells.

A microbial fuel cell (MFC), which is a new type of energy source, utilises electrogenic bacteria in sewage or soil to convert chemical energy into electrical energy. MFCs typically require an external controller to provide a stable output voltage to the external load. This study develops a non-fragile guaranteed cost (NFGC) controller to suppress the interference of the controller of an MFC and ensure that the quadratic cost function of the system satisfies certain performance indexes. First, for the convenience of controller design, a Takagi-Sugeno fuzzy model is established to approximate a single-chamber single-population MFC model. Subsequently, the linear matrix inequality method is used to design the NFGC controller. This control scheme can reduce the influence of controller disturbances on the system and ensure asymptotic stability of the closed-loop system under the specified upper bound of the provided cost function. The simulation results demonstrate that the developed control method has a shorter adjustment time and smaller steady-state error than traditional control methods such as sliding mode control (SMC), backstepping control, and fuzzy SMC.

Efficient sky-blue cesium lead bromide light-emitting diodes with enhanced stability via synergistic interfacial induction and polymer scaffold inhibition.

All inorganic CsPbX3 perovskite has aroused broad interests in building efficient light-emitting devices with wide color gamut and flexible fabrication process. So far, the realization of high-performance blue perovskite light-emitting devices (PeLEDs) is still a critical challenge. Herein, we propose an interfacial induction strategy to generate low-dimensional CsPbBr3 with sky blue emission by employing γ-aminobutyric acid (GABA) modified poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS). The interaction between GABA and Pb2+ inhibited the formation of bulk CsPbBr3 phase. Further assisted by the polymer networks, the sky-blue CsPbBr3 film exhibited much improved stability under both photoluminescence and electrical excitation. This can be ascribed to the scaffold effect and the passivation function of the polymer. Consequently, the obtained sky-blue PeLEDs exhibited an average external quantum efficiency (EQE) of 5.67% (maximum of 7.21%) with a maximum brightness of 3308 cd/m2 and a working lifespan reaching 0.41 h. The strategy in this work provides a new opportunity for exploitation the full potential of blue PeLEDs towards application in lighting and display devices.

Corrigendum: Improved real-time semantic segmentation network model for crop vision navigation line detection.

[This corrects the article DOI: 10.3389/fpls.2022.898131.].

Simultaneously improving the quality factor and outcoupling efficiency of organic light-emitting field-effect transistors with planar microcavity.

Organic light-emitting field-effect transistors (OLEFETs) are regarded as an ideal device platform to achieve electrically pumped organic semiconductor lasers (OSLs). However, the incorporation of a high-quality resonator into OLEFETs is still challenging since the process usually induces irreparable deterioration to the electric-related emission performance of the device. We here propose a dual distributed Bragg reflector (DBR)-based planar microcavity, which is verified to be highly compatible with the OLEFETs. The dual DBR planar microcavity shows the great advantage of simultaneously promoting the quality (Q) factor and outcoupling efficiency of the device due to the reduced optical loss. As a result, a moderately high Q factor of ∼160, corresponding to EL spectrum linewidth as narrow as 3.2 nm, concomitantly with high outcoupling efficiency (∼7.1%) has been successfully obtained. Our results manifest that the dual DBR-based planar microcavity is a promising type of resonator, which might find potential applications in improving the spectra and efficiency performance of OLEFETs as well as in OLEFET-based electrically pumped OSLs.

Candidate Genes and Gene Networks Change with Age in Japanese Black Cattle by Blood Transcriptome Analysis.

Age is an important physiological factor that affects the metabolism and immune function of beef cattle. While there have been many studies using the blood transcriptome to study the effects of age on gene expression, few have been reported on beef cattle. To this end, we used the blood transcriptomes of Japanese black cattle at different ages as the study subjects and screened 1055, 345, and 1058 differential expressed genes (DEGs) in the calf vs. adult, adult vs. old, and calf vs. old comparison groups, respectively. The weighted co-expression network consisted of 1731 genes. Finally, blue, brown, and yellow age-specific modules were obtained, in which genes were enriched in signaling pathways related to growth and development and immune metabolic dysfunction, respectively. Protein-protein interaction (PPI) analysis showed gene interactions in each specific module, and 20 of the highest connectivity genes were chosen as potential hub genes. Finally, we identified 495, 244, and 1007 genes by exon-wide selection signature (EWSS) analysis of different comparison groups. Combining the results of hub genes, we found that VWF, PARVB, PRKCA, and TGFB1I1 could be used as candidate genes for growth and development stages of beef cattle. CORO2B and SDK1 could be used as candidate marker genes associated with aging. In conclusion, by comparing the blood transcriptome of calves, adult cattle, and old cattle, the candidate genes related to immunity and metabolism affected by age were identified, and the gene co-expression network of different age stages was constructed. It provides a data basis for exploring the growth, development, and aging of beef cattle.

Evolution trend of soil fertility in tobacco-planting area of Chenzhou, Hunan Province, China.

In this study, the data of fertility indicators of soil samples (0-20 cm) in 1980s, 2000 and 2015 in Chenzhou city were used, and the soil integrated fertility index (IFI) was calculated. The results showed that the soil pH was decreased, total nitrogen (TN), organic matter (OM), available phosphorus (AP) and potassium (AK), exchangeable calcium (Ca2+), magnesium (Mg2+) and available copper (Cu) contents were increased, total phosphorus (TP), available sulfur (S) and water-soluble chlorine (Cl-) contents were decreased, total potassium (TK), available boron (B), iron (Fe), manganese (Mn) and zinc (Zn) were decreased first and then increased. In 2015, most of the fields were higher in pH, OM, TN, AN, AK, Ca2+, Mg2+, S, Fe, Mn, Cu and Zn, suitable in B, but lower in TP, AP, TK, available molybdenum (Mo) and Cl-. Most of the fields were in the middle grade of IFI in 2000 and 2015, and the mean IFI increased from 0.492 to 0.556 from 2000 to 2015. Thus, for soil improvement, more attention should be paid to adjust soil pH, reduce the application of organic, nitrogen and calcium fertilizers, while increase the fertilizer application of other nutrients.

A 3D Hand Attitude Estimation Method for Fixed Hand Posture Based on Dual-View RGB Images.

This work provides a 3D hand attitude estimation approach for fixed hand posture based on a CNN and LightGBM for dual-view RGB images to facilitate the application of hand posture teleoperation. First, using dual-view cameras and an IMU sensor, we provide a simple method for building 3D hand posture datasets. This method can quickly acquire dual-view 2D hand image sets and automatically append the appropriate three-axis attitude angle labels. Then, combining ensemble learning, which has strong regression fitting capabilities, with deep learning, which has excellent automatic feature extraction capabilities, we present an integrated hand attitude CNN regression model. This model uses a Bayesian optimization based LightGBM in the ensemble learning algorithm to produce 3D hand attitude regression and two CNNs to extract dual-view hand image features. Finally, a mapping from dual-view 2D images to 3D hand attitude angles is established using a training approach for feature integration, and a comparative experiment is run on the test set. The results of the experiments demonstrate that the suggested method may successfully solve the hand self-occlusion issue and accomplish 3D hand attitude estimation using only two normal RGB cameras.

Calibration of Ultrasonic Transducer Based on Ultrasonic Logging Instrument for Shaft Sinking.

High-precision logging equipment is critical for measuring the borehole diameter and drilling offset in coal mining and petroleum drilling. We propose a module composition and positioning principle for an ultrasonic transducer based on an ultrasonic logging instrument for shaft sinking by drilling (ULISSD) for calculating the reflection distance. The logging distance, which is the primary performance index of a logging system, is determined by using the self-reception sensitivity and error of the ultrasonic transducer in a downhole system. To measure the error between the piezoelectric element of the transducer and the rubber seal of the borehole logging system, we developed an ultrasonic-transducer error-calibration device and a calibration method for a central-air-return-shaft-drilling project. This calibration device can eliminate the inherent error of the transducer and calculate the rate of propagation with high accuracy. The measurement error is reduced by approximately 1.5 mm; thus, the ULISSD measurement accuracy can be effectively improved in central-air-return-shaft drilling.

Distributed Averaging Problems of Agriculture Picking Multi-Robot Systems via Sampled Control.

Distributed control of agriculture picking multi-robot systems has been widely used in the field of smart agriculture, this paper aims to explore the distributed averaging problems of agriculture picking multi-robot systems under directed communication topologies by taking advantage of the sampled data. With the algebraic graph theory concepts and the matrix theory, a distributed protocol is proposed based on the nearest sampled neighbor information. It is shown that under the proposed protocol, the states of all agents can be guaranteed to reach average consensus whose value is the averaging of the initial states of all agents. Besides, when considering time-delay, the other distributed protocol is constructed, in which a time margin of the time-delay can be determined simultaneously. The necessary and sufficient consensus results can be developed even though the time delay exists. Simulation results are given to demonstrate the effectiveness of our developed consensus results.

Improved Real-Time Semantic Segmentation Network Model for Crop Vision Navigation Line Detection.

Field crops are generally planted in rows to improve planting efficiency and facilitate field management. Therefore, automatic detection of crop planting rows is of great significance for achieving autonomous navigation and precise spraying in intelligent agricultural machinery and is an important part of smart agricultural management. To study the visual navigation line extraction technology of unmanned aerial vehicles (UAVs) in farmland environments and realize real-time precise farmland UAV operations, we propose an improved ENet semantic segmentation network model to perform row segmentation of farmland images. Considering the lightweight and low complexity requirements of the network for crop row detection, the traditional network is compressed and replaced by convolution. Based on the residual network, we designed a network structure of the shunting process, in which low-dimensional boundary information in the feature extraction process is passed backward using the residual stream, allowing efficient extraction of low-dimensional information and significantly improving the accuracy of boundary locations and row-to-row segmentation of farmland crops. According to the characteristics of the segmented image, an improved random sampling consensus algorithm is proposed to extract the navigation line, define a new model-scoring index, find the best point set, and use the least-squares method to fit the navigation line. The experimental results showed that the proposed algorithm allows accurate and efficient extraction of farmland navigation lines, and it has the technical advantages of strong robustness and high applicability. The algorithm can provide technical support for the subsequent quasi-flight of agricultural UAVs in farmland operations.

Face-to-Face Assembly of Ag Nanoplates on Filter Papers for Pesticide Detection by Surface-Enhanced Raman Spectroscopy.

Surface-enhanced Raman spectroscopy (SERS) technology has been regarded as a most efficient and sensitive strategy for the detection of pollutants at ultra-low concentrations. Fabrication of SERS substrates is of key importance in obtaining the homogeneous and sensitive SERS signals. Cellulose filter papers loaded with plasmonic metal NPs are well known as cost-effective and efficient paper-based SERS substrates. In this manuscript, face-to-face assembly of silver nanoplates via solvent-evaporation strategies on the cellulose filter papers has been developed for the SERS substrates. Furthermore, these developed paper-based SERS substrates are utilized for the ultra-sensitive detection of the rhodamine 6G dye and thiram pesticides. Our theoretical studies reveal the creation of high density hotspots, with a huge localized and enhanced electromagnetic field, near the corners of the assembled structures, which justifies the ultrasensitive SERS signal in the fabricated paper-based SERS platform. This work provides an excellent paper-based SERS substrate for practical applications, and one which can also be beneficial to human health and environmental safety.

2D/2D FeNi-layered double hydroxide/bimetal-MOFs nanosheets for enhanced photo-Fenton degradation of antibiotics: Performance and synergetic degradation mechanism.

The photo-Fenton system exhibits great potential in environmental remediation. However, photo-Fenton process suffers from slow reaction kinetics, which is caused by the low yield of available charge carriers and active radicals. In this work, the 2D/2D FeNi-layered double hydroxide/bimetal-organic frameworks nanosheets (FeNi-LDH/BMNSs) photocatalyst was fabricated via an in-situ semi-sacrificial template strategy. The optimized FeNi-LDH/BMNSs + H2O2+Vis system showed excellent tetracycline hydrochloride (TC-HCl) removal rate of 95.76% in 60 min. Besides, the high TC-HCl degradation rates (above 80%) are obtained in a wide pH range and the total organic carbon (TOC) removal rate of 48.98% was remained after four cycles. Experiments and characterizations identified the fast catalysis process were ascribed to the synergetic effect between 2D/2D heterojunctions and Lewis acid sites with mixed-valence (Fe (III)/Ni (II)) in FeNi-LDH/BMNSs. As a result, the catalysis of H2O2 and the reduction of O2 was accelerated by the continuous generation of Fe (II) and available photogenerated electrons, respectively, producing abundant active radicals including OH and O2-. Finally, this photo-Fenton system exhibited high removal rate to oxycycline, levofloxacin, norfloxacin and doxycycline and showed excellent performance for TC-HCl removal in different composed wastewater. The findings provide a new strategy towards creating 2D/2D active heterogeneous catalysts for photo-Fenton catalytic application.

Investigation of the effective aperture: towards high-resolution Fresnel incoherent correlation holography.

Fresnel incoherent correlation holography (FINCH) shows great advantages of coherent-light-source-free, high lateral resolution, no scanning, and easy integration, and has exhibited great potential in recording three-dimensional information of objects. Despite the rapid advances in the resolution of the FINCH system, little attention has been paid to the influence of the effective aperture of the system. Here, the effective aperture of the point spread function (PSF) has been investigated both theoretically and experimentally. It is found that the effective aperture is mainly restricted by the aperture of the charge-coupled device (CCD), the pixel size of the CCD, and the actual aperture of the PSF at different recording distances. It is also found that the optimal spatial resolution exists only for a small range of recording distance, while this range would become smaller as the imaging wavelength gets longer, leading to the result that the optimal spatial resolution is solely determined by the actual aperture of the PSF. By further combining the FINCH system with a microscopy system and optimizing the recording distance, a spatial resolution as high as 0.78 µm at the wavelength of 633 nm has been obtained, enabling a much higher quality imaging of unstained living biological cells compared to the commercial optical microscope. The results of this work may provide some helpful insights into the design of high-resolution FINCH systems and pave the way for their application in biomedical imaging.