Search Results (1,473)

DNA methylation is crucial in gene expression regulation and tissue differentiation in livestock. However, genome-wide methylation patterns among tissues remain underexplored in cattle, one of the world’s most important farm animals. This study investigates sex- and tissue-specific DNA methylation in cattle using CpG site methylation data generated by an Infinium DNA Methylation array (HorvathMammalMethyl-Chip40) across seven tissues. Our analysis revealed significant tissue-specific methylation differences, with reproductive tissues/cells, such as the sperm, exhibiting distinct profiles compared to somatic tissues like hair and blood. Principal component analysis (PCA) highlighted tissue differentiation as the primary driver of methylation variability. We also identified 222 CpG sites with significant sex-based methylation differences, particularly on the X chromosome, suggesting the potential epigenetic regulation of sex-specific traits. The Gene Ontology (GO) enrichment analysis indicated that these methylation patterns may influence biological processes such as epithelial cell proliferation and blood vessel remodeling. Overall, this study provides important insights into sex- and tissue-specific epigenetic regulation in cattle, with implications for improving livestock breeding strategies through integrating epigenetic data. Full article

Convolutional Neural Network (CNN)-based Synthetic Aperture Radar (SAR) target detection eliminates manual feature engineering and improves robustness but suffers from high computational costs, hindering on-satellite deployment. To address this, we propose HE-BiDet, an ultra-lightweight Binary Neural Network (BNN) framework co-designed with hardware acceleration. First, we develop an ultra-lightweight SAR ship detection model. Second, we design a BNN accelerator leveraging four-directions of parallelism and an on-chip data buffer with optimized addressing to feed the computing array efficiently. To accelerate post-processing, we introduce a hardware-based threshold filter to eliminate redundant anchor boxes early and a dedicated Non-Maximum Suppression (NMS) unit. Evaluated on SAR-Ship, AirSAR-Ship 2.0, and SSDD, our model achieves 91.3%, 71.0%, and 92.7% accuracy, respectively. Implemented on a Xilinx Virtex-XC7VX690T FPGA, the system achieves $189.3$ FPS, demonstrating real-time capability for spaceborne deployment. Full article

Liangshan Black pigs are a new Chinese indigenous breed discovered during the Third National Survey of Livestock and Plant Genetic Resources. To uncover genetic diversity, population structure, and potential exotic introgression in this breed, we sampled 191 Liangshan Black pigs from the conservation population and genotyped these individuals using the “Zhongxin-I” porcine chip, then conducted in-depth population genetic analyses in the context of pigs from five introduced breeds. The results revealed that the tested individuals exhibited significant genetic diversity, displayed uneven kinship relationships, and were assigned to nine families according to their clustering patterns in the phylogenetic tree. Further relationship analyses with the five introduced breeds demonstrated that Liangshan Black pigs were clustered separately from the introduced breeds, had larger evolutionary distances with the introduced breeds, and possessed certain genetic components of the introduced breeds, especially those of Duroc. These findings demonstrate that Liangshan Black pigs are generally an indigenous breed independent of the introduced breeds but are slightly affected by the introduced breeds. In summary, the results of our study not only contribute to an in-depth understanding of the population genetic characteristics of Liangshan Black pigs but also provide the necessary data for the implementation of conservation programs. Full article

Many vision and graphics applications involve the efficient solving of various linear systems, which has been a popular topic for decades. With mobile devices arising and becoming popularized, designing a high-performance solver tailored for them, to ensure the smooth migration of various applications from PC to mobile devices, has become urgent. However, the unique features of mobile devices present new challenges. Mainstream mobile devices are equipped with so-called heterogeneous multiprocessor systems-on-chips (MPSoCs), which consist of processors with different architectures and performances. Designing algorithms to push the limits of of MPSoCs is attractive yet difficult. Different cores are suitable for different tasks. Further, data sharing among different cores can easily neutralize performance gains. Fortunately, the comparable performance of CPUs and GPUs on MPSoCs make the heterogeneous systems promising, compared to their counterparts on PCs. This paper is devoted to a high-performance mobile linear solver for a sparse system with a tailored asynchronous algorithm, to fully exploit the computing power of heterogeneous processors on mobile devices while alleviating the data-sharing overhead. Comprehensive evaluations are performed, with in-depth discussion to shed light on the future design of other numerical solvers. Full article

Polarization navigation is an emerging navigation technology, that exhibits significant advantages, including strong anti-interference capability and non-cumulative errors over time, making it highly promising for applications in aerospace, autonomous driving, and robotics. To address the requirements of high integration and low power consumption for tri-directional polarization navigation sensors, this study proposes a system-on-chip (SoC) design solution. The system employs the ZYNQ MPSoC (Xilinx Inc., San Jose, CA, USA) as its core, leveraging hardware acceleration on the Programmable Logic (PL) side for three-angle polarization image data acquisition, image preprocessing, and edge detection. Simultaneously, the Processing System (PS) side orchestrates task coordination, performs polarization angle resolution, and extracts the solar meridian via Hough transform. Experimental results demonstrate that the system achieves an average heading angle output time interval of 9.43 milliseconds (ms) with a mean error of 0.50°, fulfilling the real-time processing demands of mobile devices. Full article

Tree crown detection of high-resolution UAV forest remote sensing images using computer technology has been widely performed in the last ten years. In forest resource inventory management based on remote sensing data, crown detection is the most important and essential part. Deep learning technology has achieved good results in tree crown segmentation and species classification, but relying on high-performance computing platforms, edge calculation, and real-time processing cannot be realized. In this thesis, the UAV images of coniferous Pinus tabuliformis and broad-leaved Salix matsudana collected by Jingyue Ecological Forest Farm in Changping District, Beijing, are used as datasets, and a lightweight neural network U-Net-Light based on U-Net and VGG16 is designed and trained. At the same time, the IP core and SoC architecture of the neural network accelerator are designed and implemented on the Xilinx ZYNQ 7100 SoC platform. The results show that U-Net-light only uses 1.56 MB parameters to classify and segment the crown images of double tree species, and the accuracy rate reaches 85%. The designed SoC architecture and accelerator IP core achieved 31 times the speedup of the ZYNQ hard core, and 1.3 times the speedup compared with the high-end CPU (Intel CoreTM i9-10900K). The hardware resource overhead is less than 20% of the total deployment platform, and the total on-chip power consumption is 2.127 W. Shorter prediction time and higher energy consumption ratio prove the effectiveness and rationality of architecture design and IP development. This work departs from conventional canopy segmentation methods that rely heavily on ground-based high-performance computing. Instead, it proposes a lightweight neural network model deployed on FPGA for real-time inference on unmanned aerial vehicles (UAVs), thereby significantly lowering both latency and system resource consumption. The proposed approach demonstrates a certain degree of innovation and provides meaningful references for the automation and intelligent development of forest resource monitoring and precision agriculture. Full article

Bandwidth-limited transmitters have become a severe issue with the rapid growth of bandwidth-hungry services. We investigate the impact of an on-chip optical pre-emphasizer on a bandwidth-limited transmitter and quantitatively analyze the results of bandwidth extension. Improvements in eye diagram performance are discussed. The 3 dB electro-optical bandwidth of the transmission system is effectively extended from 18 GHz to 40 GHz. The extinction ratio of the on–off keying (OOK) signal at data rates of 20 to 50 Gbps is improved by 0.64–3.2 dB. Additionally, the Q factor of the eye diagram increases by 0.78–4.36 at data rates ranging from 20 to 50 Gbps. Full article

Treatment of cancer patients with autologous T-cells expressing a chimeric antigen receptor (CAR) is one of the most promising therapeutic modalities for hematological malignancy treatment. For this treatment, primary T-cell expansion is needed. Microfluidic technologies can be used to better understand T-cell activation and proliferation. Microfluidics have had a meaningful impact in the way experimental biology and biomedical research are approached in general. Furthermore, microfluidic technology allows the generation of large amounts of data and enables the use of image processing for analysis. However, one of the major technical hurdles involved in growing suspension cells under microfluidic conditions is their immobilization, to avoid washing them out of the microfluidic chip during medium renewal. In this work, we use a multilevel microfluidic chip to successfully capture and immobilize suspension cells. Jurkat cells and T-cells are isolated through traps to microscopically track their development and proliferation after activation over a period of 8 days. The T-cell area of four independent microchannels was compared and there is no statistically significant difference between them (ANOVA p-value = 0.976). These multilevel microfluidic chips provide a new method of studying T-cell activation. Full article

Runs of homozygosity (ROH) are key elements of the genetic structure of populations, reflecting inbreeding levels, selection history, and potential associations with phenotypic traits. This study proposes a novel approach to ROH analysis through visualization and classification using convolutional neural networks (CNNs). Genetic data from Large White (n = 568) and Duroc (n = 600) pigs were used to construct ROH maps, where each homozygous segment was classified by length and visualized as a color-coded image. The analysis was conducted in two stages: (1) classification of animals by breed based on ROH maps and (2) identification of the presence or absence of a phenotypic trait (limb defects). Genotyping was performed using the GeneSeek^® GGP SNP80x1_XT chip (Illumina Inc., San Diego, CA, USA), and ROH segments were identified using the software tool PLINK v1.9. To visualize individual maps, we utilized a modified function from the HandyCNV package. The results showed that the CNN model achieved 100% accuracy, sensitivity, and specificity in classifying pig breeds based on ROH maps. When analyzing the binary trait (presence or absence of limb defects), the model demonstrated an accuracy of 78.57%. Despite the moderate accuracy in predicting the phenotypic trait, the high negative predictive value (84.62%) indicates the model’s reliability in identifying healthy animals. This method can be applied not only in animal breeding research but also in medicine to study the association between ROH and hereditary diseases. Future plans include expanding the method to other types of genetic data and developing mechanisms to improve the interpretability of deep learning models. Full article

Patients with end-stage renal disease (ESRD) are at increased risk of cardiovascular disease (CVD), such as myocardial infarction (MI). Uremic toxins and endothelial dysfunction are central to this process. In this exploratory study, we used the Affymetrix GeneChip microarray to investigate the gene expression profile in uremic serum-induced human coronary arterial endothelial cells (HCAECs) from ESRD patients with and without MI (UWI and UWOI groups) as an approach to its underlying mechanism. We also explored which pathways are involved in this process. We found 100 differentially expressed genes (DEGs) among the conditions of interest by supervised principal component analysis and hierarchical cluster analysis. The expressions of four major DEGs were validated by quantitative RT-PCR. Pathway analysis and molecular network were used to analyze the interaction and expression patterns. Ten pathways were identified as the main enriched metabolic pathways according to the transcriptome profiling analysis, which were, among others, positive regulation of inflammatory response, positive regulation of extracellular signal-regulated kinases 1 and 2 (ERK1/2) cascade, cardiac muscle cell development, highlighting positive regulation of mitogen-activated protein kinase (MAPK) activity (p = 0.00016). Up- and down-regulation of genes from HCAECs exposed to uremic serum could contribute to increased endothelial dysfunction and CVD in ESRD patients. Our study suggests that inflammation and the ERK-MAPK pathway are highly enriched in kidney disease patients with MI, suggesting their role in ESRD pathology. Further studies and approaches based on MAPK pathway interfering strategies are needed to confirm these data. Full article

Named data networking (NDN) is a content-centric network architecture that requires efficient name lookup to forward packets based on hierarchical content names. Whereas IP lookup operates on fixed-length addresses, NDN name lookup must identify the longest matching prefix (LMP) from a variable-length name space, making it computationally challenging. Hash table-based approaches provide O (1) search complexity but often require multiple accesses due to the unknown LMP length. To mitigate excessive off-chip memory accesses, Bloom filter-assisted pre-checking is commonly employed. However, conventional Bloom filter-based approaches can only perform membership tests and do not provide information about the next search range, which limits their ability to effectively reduce Bloom filter accesses. This paper proposes a distance-encoded Bloom filter to improve name lookup efficiency. It encodes two distance values into the Bloom filter, enabling a more refined search range compared to binary search-based methods. By utilizing these encoded distances, the proposed scheme not only reduces the number of Bloom filter queries but also ensures that only prefix nodes need to be stored in the hash table. This helps reduce hash collisions and minimize off-chip memory accesses. Experimental evaluation using large-scale FIB datasets shows that the proposed approach reduces both Bloom filter accesses and hash table lookups, which contributes to improving overall lookup performance. Full article

Congenital developmental abnormalities in piglets, such as intersex and aproctia, adversely affect survival rates, growth performance, and genetic breeding efficiency in pig populations. To elucidate their genetic basis, we performed a genome-wide association study (GWAS) on 1030 Large White pigs. We combined 50 K SNP chip data with SWIM-based genotype imputation to enhance the resolution of genetic variation detection, followed by MLM analysis. Our results identified 53 significant SNPs, with 52 associated with intersex and 1 with aproctia. Key candidate genes included MAD1L1, ID4, EFNA5, and PPP1R16B for intersex and ARNT2 for aproctia. Functional enrichment analysis highlighted pathways related to gonadal development (e.g., progesterone-mediated oocyte maturation) and embryonic morphogenesis. Collectively, the identification of these SNPs and candidate genes advances our understanding of the genetic architecture of intersex and aproctia in piglets. These findings provide actionable insights for optimizing genetic breeding strategies and improving health management in Large White pig production, with potential implications for reducing economic losses caused by congenital disorders. Full article

In the age of hardware accelerators, increasing pressure is applied on computer architects and hardware engineers to improve the balance between the cost and benefits of specialized computing units, in contrast to more general-purpose architectures. The first part of this study presents the embedded Streaming Hardware Accelerator (eSAC) architecture. This architecture can reduce the idle time of specialized logic. The remainder of this paper explores the integration of an eSAC into a Central Processing Unit (CPU) core embedded inside a System-on-Chip (SoC) design, using the AXI-Stream protocol specification. The three evaluated architectures are the Tightly Coupled Streaming, Protocol Adapter FIFO, and Direct Memory Access (DMA) Streaming architectures. When comparing the tightly coupled architecture with the one including the DMA, the experiments in this paper show an almost 3× decrease in frame latency when using the DMA. Nevertheless, this comes at the price of an increase in FPGA resource utilization as follows: LUT (2.5×), LUTRAM (3×), FF (3.4×), and BRAM (1.2×). Four different test scenarios were run for the DMA architecture, showcasing the best and worst practices for data organization. The evaluation results highlight that poor data organization can lead to a more than 7× increase in latency. The CPU model was selected as the newly released MicroBlaze-V softcore processor. The designs presented herein successfully operate on a popular low-cost Field-Programmable Gate Array (FPGA) development board at 100 MHz. Block diagrams, FPGA resource utilization, and latency metrics are presented. Finally, based on the evaluation results, possible improvements are discussed. Full article

The large-diameter slurry tunnel boring machine (TBM) is widely used in the construction of tunnels across rivers and seas. However, cutter wear has become a critical issue that severely limits the tunnelling efficiency. Taking the Qingdao Jiaozhou Bay Second Subsea Tunnel Project as the background, the wear patterns of disc cutters on the atmospheric cutterhead of a large-diameter slurry TBM under complex geological conditions were analyzed. The flat wear of disc cutters induced by factors such as rock chip accumulation in front of the cutterhead, the jump trajectory when changing disc cutters, alloy-insert disc cutter mismatch, cutter barrel clogging, and severe wear of scrapers is discussed. Furthermore, the impacts of measures such as slurry circulation to remove rock chips during TBM stoppage, clay dispersant injection into the slurry chamber, cutter barrel flushing, and the wear resistance optimization of cutters and cutter barrels on reducing cutter wear were investigated. Based on numerical simulations and field data, a methodology for determining the optimal timing for cutter replacement is proposed. The results indicate the following: The circulation system effectively reduces accumulation, minimizing secondary wear of the disc cutters and lowering the risk of clogging in the cutter barrel. Adopting measures such as shield shutdown, a circulation system to carry away the slag, cutter barrel flushing, and soaking in 2% dispersant for 8 h can effectively reduce the accumulation of rock chips and mud cakes on the cutterhead, which in turn reduces the flat wear of the disc cutter. Measures such as making the cutter body and cutter ring rotate together and adding wear-resistant plates to the cutter barrel greatly improve the life of the cutter. The sharp increase in composite parameters can serve as an effective marker for assessing cutter conditions. The findings of this study can provide valuable insights into reducing cutter wear in similar projects. Full article

Drivers’ facial expression recognition systems play a pivotal role in Advanced Driver Assistance Systems (ADASs) by monitoring emotional states and detecting fatigue or distractions in real time. However, deploying such systems in resource-constrained environments like vehicles requires lightweight architectures to ensure real-time performance, efficient model updates, and compatibility with embedded hardware. Smaller models significantly reduce communication overhead in distributed training. For autonomous vehicles, lightweight architectures also minimize the data transfer required for over-the-air updates. Moreover, they are crucial for their deployability on hardware with limited on-chip memory. In this work, we propose a novel Dual Attention Lightweight Deep Learning (DALDL) approach for drivers’ facial expression recognition. The proposed approach combines the SqueezeNext architecture with a Dual Attention Convolution (DAC) block. Our DAC block integrates Hybrid Channel Attention (HCA) and Coordinate Space Attention (CSA) to enhance feature extraction efficiency while maintaining minimal parameter overhead. To evaluate the effectiveness of our architecture, we compare it against two baselines: (a) Vanilla SqueezeNet and (b) AlexNet. Compared with SqueezeNet, DALDL improves accuracy by 7.96% and F1-score by 7.95% on the KMU-FED dataset. On the CK+ dataset, it achieves 8.51% higher accuracy and 8.40% higher F1-score. Against AlexNet, DALDL improves accuracy by 4.34% and F1-score by 4.17% on KMU-FED. Lastly, on CK+, it provides a 5.36% boost in accuracy and a 7.24% increase in F1-score. These results demonstrate that DALDL is a promising solution for efficient and accurate emotion recognition in real-world automotive applications. Full article