Transcriptome analysis of orange-spotted grouper (Epinephelus coioides) spleen in response to Singapore grouper iridovirus

<p>Abstract</p> <p>Background</p> <p>Orange-spotted grouper (<it>Epinephelus coioides</it>) is an economically important marine fish cultured in China and Southeast Asian countries. The emergence of infectious viral diseases, including iridovirus and betanodavirus, have severely affected food products based on this species, causing heavy economic losses. Limited available information on the genomics of <it>E. coioides </it>has hampered the understanding of the molecular mechanisms that underlie host-virus interactions. In this study, we used a 454 pyrosequencing method to investigate differentially-expressed genes in the spleen of the <it>E. coioides </it>infected with Singapore grouper iridovirus (SGIV).</p> <p>Results</p> <p>Using 454 pyrosequencing, we obtained abundant high-quality ESTs from two spleen-complementary DNA libraries which were constructed from SGIV-infected (V) and PBS-injected fish (used as a control: C). A total of 407,027 and 421,141 ESTs were produced in control and SGIV infected libraries, respectively. Among the assembled ESTs, 9,616 (C) and 10,426 (V) ESTs were successfully matched against known genes in the NCBI non-redundant (nr) database with a cut-off E-value above 10^-5. Gene ontology (GO) analysis indicated that "cell part", "cellular process" and "binding" represented the largest category. Among the 25 clusters of orthologous group (COG) categories, the cluster for "translation, ribosomal structure and biogenesis" represented the largest group in the control (185 ESTs) and infected (172 ESTs) libraries. Further KEGG analysis revealed that pathways, including cellular metabolism and intracellular immune signaling, existed in the control and infected libraries. Comparative expression analysis indicated that certain genes associated with mitogen-activated protein kinase (MAPK), chemokine, toll-like receptor and RIG-I signaling pathway were alternated in response to SGIV infection. Moreover, changes in the pattern of gene expression were validated by qRT-PCR, including cytokines, cytokine receptors, and transcription factors, apoptosis-associated genes, and interferon related genes.</p> <p>Conclusion</p> <p>This study provided abundant ESTs that could contribute greatly to disclosing novel genes in marine fish. Furthermore, the alterations of predicted gene expression patterns reflected possible responses of these fish to the virus infection. Taken together, our data not only provided new information for identification of novel genes from marine vertebrates, but also shed new light on the understanding of defense mechanisms of marine fish to viral pathogens.</p

Dataflow Optimization through Exploring Single-Layer and Inter-Layer Data Reuse in Memory-Constrained Accelerators

Off-chip memory access has become the performance and energy bottleneck in memory-constrained neural network accelerators. To provide a solution for the energy efficient processing of various neural network models, this paper proposes a dataflow optimization method for modern neural networks by exploring the opportunity of single-layer and inter-layer data reuse to minimize the amount of off-chip memory access in memory-constrained accelerators. A mathematical analysis of three inter-layer data reuse methods is first presented. Then, a comprehensive exploration to determine the optimal data reuse strategy from single-layer and inter-layer data reuse approaches is proposed. The result shows that when compared to the existing single-layer-based exploration method, SmartShuttle, the proposed approach can achieve up to 20.5% and 32.5% of off-chip memory access reduction for ResNeXt-50 and DenseNet-121, respectively

Dataflow Optimization through Exploring Single-Layer and Inter-Layer Data Reuse in Memory-Constrained Accelerators

Off-chip memory access has become the performance and energy bottleneck in memory-constrained neural network accelerators. To provide a solution for the energy efficient processing of various neural network models, this paper proposes a dataflow optimization method for modern neural networks by exploring the opportunity of single-layer and inter-layer data reuse to minimize the amount of off-chip memory access in memory-constrained accelerators. A mathematical analysis of three inter-layer data reuse methods is first presented. Then, a comprehensive exploration to determine the optimal data reuse strategy from single-layer and inter-layer data reuse approaches is proposed. The result shows that when compared to the existing single-layer-based exploration method, SmartShuttle, the proposed approach can achieve up to 20.5% and 32.5% of off-chip memory access reduction for ResNeXt-50 and DenseNet-121, respectively

Scalable Hardware Efficient Architecture for Parallel FIR Filters with Symmetric Coefficients

Symmetric convolutions can be utilized for potential hardware resource reduction. However, they have not been realized in state-of-the-art transposed block FIR designs. Therefore, we explore the feasibility of symmetric convolution in transposed parallel FIRs and propose a scalable hardware efficient parallel architecture. The proposed design inserts delay elements after multipliers for temporal reuse of intermediate tap products. By doing this, the number of required multipliers can be reduced by half. As a result, we can achieve up to 3.2× and 1.64× area efficiency improvements over the modern transposed block method on reconfigurable and fixed designs, respectively. These results confirm the effectiveness of the proposed STB-FIR architecture for hardware-efficient, high-speed signal processing

Construction of a high-density genetic map: genotyping by sequencing (GBS) to map purple seed coat color (Psc) in hulless barley

Abstract Background Colored hulless barley are more suitable in food processing compared to normal (yellow) varieties because it is rich in bioactive compounds and produces higher extraction pearling fractions. Therefore, seed coat color is an important agronomic trait for the breeding and study of hulless barley. Results Genotyping-by-sequencing single-nucleotide polymorphism (GBS-SNP) analysis of a doubled haploid (DH) mapping population (Nierumuzha × Kunlun10) was conducted to map the purple seed coat color genes (Psc). A high-density genetic map of hulless barley was constructed, which contains 3662 efficient SNP markers with 1129 bin markers. Seven linkage groups were resolved, which had a total length of 645.56 cM. Chromosome length ranged from 60.21 cM to 127.21 cM, with average marker density of 0.57 cM. A total of five loci accounting for 3.79% to 23.86% of the observed phenotypic variation for Psc were detected using this high-density map. Five structural candidate genes (F3’M, HID, UF3GT, UFGT and 5MAT) and one regulatory factor (Ant1) related to flavonoid or anthocyanin biosynthesis were identified.. Conclusions Five structural candidate genes and one regulatory factor related to flavonoid or anthocyanin biosynthesis have been identified using a high-density genetic map of hulless barley. This study lays the foundation for map-based cloning of Psc but provides a valuable tool for studying marker-trait associations and its application to marker-assisted breeding of hulless barley

Chemical-microbial effects of acetic acid, oxalic acid and citric acid on arsenic transformation and migration in the rhizosphere of paddy soil

Low-molecular-weight organic acids (LMWOAs) are essential components of rice roots exudates and an important source of soil organic carbon. The chemical-microbial pathway by which LMWOA affects arsenic (As) cycling in the rhizosphere of paddy soils is still unclear. In this study, three typical LMWOAs (acetic acid (AA), oxalic acid (OA), and citric acid (CA)) in rice root exudates were added to As-contaminated soil at a concentration of 10 mM, mimicking the rhizosphere environment. The results showed that the addition of AA and OA inhibited the mobilization of As in the rhizosphere soil. After 14 days of incubation, the content of As in the porewater of AA and OA decreased by 40% and 22%, respectively, compared with the control. AA hindered the mobilization of As in soil via promoting the formation of secondary minerals. The addition of OA inhibits the mobilization of As via increasing the proportion of As (V) in porewater and promoting the formation of secondary minerals in soil. In addition, OA addition not only significantly increased the aioA gene abundance but also notably enriched the microorganisms containing As (III) methylation functional genes (arsM). The addition of CA greatly expedited the release of As from the soil solid phase through the solubilization of Fe/Mn minerals via the effects of both soil chemistry and microbial action. Furthermore, linear discriminant analysis effect size (LEfSe) revealed the possibility that bacteria such as Burkholderia, Magnetospirillum, and Mycobacterium were involved in the reduction or methylation of As in the rhizosphere of paddy soil. This study revealed the internal causes of LMWOAs regulating As transformation and mobilization in flooded paddy soil and provided theoretical support for reducing As accumulation in rice by breeding rice varieties with high AA and OA secretions

Deep Learning Based Detector YOLOv5 for Identifying Insect Pests

Insect pests are a major element influencing agricultural production. According to the Food and Agriculture Organization (FAO), an estimated 20–40% of pest damage occurs each year, which reduces global production and becomes a major challenge to crop production. These insect pests cause sooty mold disease by sucking the sap from the crop’s organs, especially leaves, fruits, stems, and roots. To control these pests, pesticides are frequently used because they are fast-acting and scalable. Due to environmental pollution and health awareness, less use of pesticides is recommended. One of the salient approaches could be to reduce the wide use of pesticides by spraying on demand. To perform spot spraying, the location of the pest must first be determined. Therefore, the growing population and increasing food demand emphasize the development of novel methods and systems for agricultural production to address environmental concerns and ensure efficiency and sustainability. To accurately identify these insect pests at an early stage, insect pest detection and classification have recently become in high demand. Thus, this study aims to develop an object recognition system for the detection of crops damaging insect pests and their classification. The current work proposes an automatic system in the form of a smartphone IP- camera to detect insect pests from digital images/videos to reduce farmers’ reliance on pesticides. The proposed approach is based on YOLO object detection architectures including YOLOv5 (n, s, m, l, and x), YOLOv3, YOLO-Lite, and YOLOR. For this purpose, we collected 7046 images in the wild under different illumination and background conditions to train the underlying object detection approaches. We trained and test the object recognition system with different parameters from scratch. The eight models are compared and analyzed. The experimental results show that the average precision ([email protected]) of the eight models including YOLO-Lite, YOLOv3, YOLOR, and YOLOv5 with five different scales (n, s, m, l, and x) reach 51.7%, 97.6%, 96.80%, 83.85%, 94.61%, 97.18%, 97.04%, and 98.3% respectively. The larger the model, the higher the average accuracy of the detection validation results. We observed that the YOLOv5x model is fully functional and can correctly identify the twenty-three species of insect pests at 40.5 milliseconds (ms). The developed model YOLOv5x performs the state-of-the-art model with an average precision value of ([email protected]) 98.3%, ([email protected]:0.95) value of 79.8%, precision of 94.5% and a recall of 97.8%, and F1-score with 96% on our IP-23 dataset. The results show that the system works efficiently and was able to correctly detect and identify insect pests, which can be employed for realistic application while farming

Deep Learning Based Detector YOLOv5 for Identifying Insect Pests

Insect pests are a major element influencing agricultural production. According to the Food and Agriculture Organization (FAO), an estimated 20&ndash;40% of pest damage occurs each year, which reduces global production and becomes a major challenge to crop production. These insect pests cause sooty mold disease by sucking the sap from the crop&rsquo;s organs, especially leaves, fruits, stems, and roots. To control these pests, pesticides are frequently used because they are fast-acting and scalable. Due to environmental pollution and health awareness, less use of pesticides is recommended. One of the salient approaches could be to reduce the wide use of pesticides by spraying on demand. To perform spot spraying, the location of the pest must first be determined. Therefore, the growing population and increasing food demand emphasize the development of novel methods and systems for agricultural production to address environmental concerns and ensure efficiency and sustainability. To accurately identify these insect pests at an early stage, insect pest detection and classification have recently become in high demand. Thus, this study aims to develop an object recognition system for the detection of crops damaging insect pests and their classification. The current work proposes an automatic system in the form of a smartphone IP- camera to detect insect pests from digital images/videos to reduce farmers&rsquo; reliance on pesticides. The proposed approach is based on YOLO object detection architectures including YOLOv5 (n, s, m, l, and x), YOLOv3, YOLO-Lite, and YOLOR. For this purpose, we collected 7046 images in the wild under different illumination and background conditions to train the underlying object detection approaches. We trained and test the object recognition system with different parameters from scratch. The eight models are compared and analyzed. The experimental results show that the average precision ([email protected]) of the eight models including YOLO-Lite, YOLOv3, YOLOR, and YOLOv5 with five different scales (n, s, m, l, and x) reach 51.7%, 97.6%, 96.80%, 83.85%, 94.61%, 97.18%, 97.04%, and 98.3% respectively. The larger the model, the higher the average accuracy of the detection validation results. We observed that the YOLOv5x model is fully functional and can correctly identify the twenty-three species of insect pests at 40.5 milliseconds (ms). The developed model YOLOv5x performs the state-of-the-art model with an average precision value of ([email protected]) 98.3%, ([email protected]:0.95) value of 79.8%, precision of 94.5% and a recall of 97.8%, and F1-score with 96% on our IP-23 dataset. The results show that the system works efficiently and was able to correctly detect and identify insect pests, which can be employed for realistic application while farming

Agronomic Management and Rice Varieties Controlling Cd Bioaccumulation in Rice

Selection of rice varieties and application of amendments are effective measures to ensure food safety. Here we report that in the non-Cd area, the grain quality of all rice varieties met the Chinese National Grain Safety Standards (CNGSS). In the high-Cd area, rice varieties showed significant different bioaccumulation of Cd with lower rice yields than those in non-Cd area with the average decrease of 31.1%. There was a negative correlation between grain Cd content and yields. A total of 19 rice varieties were selected as low Cd accumulating rice varieties and their Cd content met CNGSS in the low-Cd area. Six of them met CNGSS in the high-Cd area. The application of amendments significantly reduced Cd content in rice grains by 1.0&ndash;84.7% with an average of 52.6% and 13 of varieties met CNGSS. The amendments reduced available Cd content in soils by 1.1&ndash;75.8% but had no significant effects on rice yields. Therefore, the current study implied that proper agronomic management with selection of rice varieties and soil amendments was essential in controlling Cd accumulation in rice grains