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Salt stress is a key environmental factor altering rice plant growth. Strigolactones (GR24) play a vital role in responding to various abiotic stresses and regulating plant growth. However, the regulatory mechanisms of SLs on rice seedlings under salt stress have not yet been clarified. A pot experiment was undertaken to evaluate the effects of GR24 soaking on the rice variety 'Huanghuazhan' (salt-sensitive) seedling growth, antioxidant metabolism, and endogenous hormones under NaCl stress. Results showed that NaCl stress significantly inhibited rice growth; disrupted antioxidant enzymes activity; and increased the content of soluble proteins (SPs), proline (Pro), malondialdehyde (MDA) and hydrogen–peroxide (H2O2). GR24 significantly improved photosynthetic pigments and antioxidant–enzyme activities, including superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), and ascorbate–peroxidase (APX); increased SP, ascorbic acid (AsA); and reduced glutathione (GSH) content and MDA, H2O2, and Pro content, resulting in the mitigation of oxidative injury caused by NaCl stress. Moreover, GR24 significantly increased the content of strigolactones (SLs), cytokinin (CTK), auxin (IAA), Gibberellin A3 (GA3), and IAA/ABA and CTK/ABA ratios and decreased the abscisic acid (ABA). Findings indicated that GR24 alleviated oxidative damage caused by NaCl stress by increasing photosynthetic and antioxidant capacity and maintaining the balance of endogenous hormones, thus improving the salt tolerance of rice seedlings. [ABSTRACT FROM AUTHOR]

Cytochromes P450 (P450s) are one of the largest enzymatic protein families and play critical roles in the synthesis and metabolism of plant secondary metabolites. Astragaloside IV (AS-IV) is one of the primary active components in Astragalus herbs, exhibiting diverse biological activities and pharmacological effects. However, P450s involved in the astragaloside biosynthesis have not been systematically analyzed in Astragalus mongholicus (A. mongholicus). In this study, we identified 209 P450 genes from the genome of A. mongholicus (AmP450s), which were classified into nine clans and 47 families and performed a systematic overview of their physical and chemical properties, phylogeny, gene structures and conserved motifs. Weighted gene co-expression network analysis (WGCNA) revealed that AmP450s are critical in the astragaloside biosynthesis pathway. The expression levels of these AmP450s were verified by quantitative real-time PCR (qRT-PCR) analysis in the root, stem and leaf, showing that most AmP450s are abundant in the root. Additionally, the correlation analysis between gene expressions and AS-IV content showed that twelve AmP450s, especially CYP71A28, CYP71D16 and CYP72A69, may have significant potential in the biosynthesis of astragaloside. This study systematically investigates the P450s of A. mongholicus and offers valuable insights into further exploring the functions of CYP450s in the astragaloside biosynthesis pathway. [ABSTRACT FROM AUTHOR]

Domain adaptive object detection (DAOD) aims to leverage labeled source domain data to train object detection models that can generalize well to unlabeled target domains. Recently, many researchers have considered implementing fine-grained pixel-level domain adaptation using graph representations. Existing methods construct semantically complete graphs and align them across domains via graph matching. This work introduced an auxiliary node classification task before domain alignment through graph matching, which utilizes the inherent information of graph nodes to classify them, in order to avoid suboptimal graph matching results caused by node class confusion. However, previous methods neglected the contextual information of graph nodes, leading to biased node classification and suboptimal graph matching. To solve this issue, we propose a novel patch-based auxiliary node classification method for DAOD. Unlike existing methods that use only the inherent information of nodes for node classification, our method exploits the local region information of nodes and employs multi-layer convolutional neural networks to learn the local region feature representation of nodes, enriching the node context information. Thus, accurate and robust node classification results are produced and the risk of class confusion is reduced. Moreover, we propose a progressive strategy to fuse the inherent features and the learned local region features of nodes, which ensures that the network can stably and reliably utilize local region features for accurate node classification. In this paper, we conduct abundant experiments on various DAOD scenarios and demonstrate that our proposed model outperforms existing works. [ABSTRACT FROM AUTHOR]

With the rapid advancement of ecological civilization construction, prioritizing green stormwater infrastructure to address urban stormwater management issues has become an important strategy for ecological priority and green development in sustainable urban development. Green stormwater infrastructure, as a major facility in the construction of sponge cities, can reduce the generation and external discharge of runoff and play a purification role. However, there are various types of green stormwater infrastructure, each with different control effects and applicable conditions. Therefore, to facilitate the planning, design, acceptance, assessment, and monitoring evaluation of sponge city green stormwater infrastructure, this study proposes the "sponge equivalent" method. By comparing the control effects of different facilities with bioretention facilities, the method standardizes the effects, making them easier to understand and apply. Taking a typical area of Beijing and its urban roads as examples, the study analyzed and applied planning and design control strategies. The results show that for a residential area of 1 km2, to achieve the annual runoff total control rate target of 85%, the method of converting runoff volume control equivalents, using bioretention pools as a benchmark, allows for the calculation of various combinations of areas of different types of green stormwater infrastructure, such as sunken green spaces, permeable paving bricks, green roofs, and water storage tanks. This optimizes the planning index of Beijing, which mandates stormwater detention facilities for new projects with a hardened surface area of 2000 m2 or more. The sponge equivalent method can optimize the planning and design control strategy of green stormwater infrastructure, allowing for rapid assessment and application of the design scale of green stormwater infrastructure in areas during the planning and design stage, providing theoretical and technical support for ecological and green urban stormwater management. The application of this research method helps promote green development and ecological priority in urban sustainable development strategies, and the conclusions provide valuable references for decision-makers and practitioners in related fields. [ABSTRACT FROM AUTHOR]

With the rapid growth of trajectory big data, there is a need for more efficient methods to extract, analyze, and visualize these data. However, existing research on trajectory big data visualization mainly focuses on displaying trajectories for a specific period or showing spatial distribution characteristics of trajectory points in a single time slice using clustering, filtering, and other techniques. Therefore, this paper proposes a vector field visualization model for trajectory big data, aiming to effectively represent the inherent movement trends in the data and provide a more intuitive visualization of urban traffic congestion trends. The model utilizes the motion information of vehicles to create a travel vector grid and employs WebGL technology for vector field visualization rendering. The vector field effects are effectively displayed by generating many particles and simulating their movements. Furthermore, this research also designs and implements congestion trend point identification and hotspot congestion analysis, thus validating the practicality and effectiveness of trajectory big data vector field visualization. The results indicate that compared to traditional visualization methods, the vector field visualization method can demonstrate the direction and density changes in traffic flow and predict future traffic congestion. This work provides valuable data references and decision support for urban traffic management and planning. [ABSTRACT FROM AUTHOR]

The proliferation of the Internet and the widespread adoption of mobile devices have given rise to an immense volume of real-time trajectory big data. However, a single computer and conventional databases with limited scalability struggle to manage this data effectively. During the process of visual rendering, issues such as page stuttering and subpar visual outcomes often arise. This paper, founded on a distributed architecture, introduces a multi-level time encoding method using "minutes", "hours", and "days" as fundamental units, achieving a storage model for trajectory data at multi-scale time. Furthermore, building upon an improved DBSCAN clustering algorithm and integrating it with the K-means clustering algorithm, a novel density-based partitioning clustering algorithm has been introduced, which incorporates road coefficients to circumvent architectural obstacles, successfully resolving page stuttering issues and significantly enhancing the quality of visualization. The results indicate the following: (1) when data is extracted using the units of "minutes", "hours", and "days", the retrieval efficiency of this model is 6.206 times, 12.475 times, and 18.634 times higher, respectively, compared to the retrieval efficiency of the original storage model. As the volume of retrieved data increases, the retrieval efficiency of the proposed storage model becomes increasingly superior to that of the original storage model. Under identical experimental conditions, this model's retrieval efficiency also outperforms the space–time-coded storage model; (2) Under a consistent rendering level, the clustered trajectory data, when compared to the unclustered raw data, has shown a 40% improvement in the loading speed of generating heat maps. There is an absence of page stuttering. Furthermore, the heat kernel phenomenon in the heat map was also resolved while enhancing the visualization rendering speed. [ABSTRACT FROM AUTHOR]

The fashion industry has a critical need for fashion compatibility. Modeling compatibility is a challenging task that involves extracting (in)compatible features of pairs, obtaining compatible relationships between matching items, and applying them to personalized recommendation tasks. Measuring compatibility is a complex and subjective concept in general. The complexity is reflected in the fact that relationships between fashion items are determined by multiple matching rules, such as color, shape, and material. Each personal aesthetic style and fashion preference differs, adding subjectivity to the compatibility concept. As a result, personalized factors must be considered. Previous works mainly utilize a convolutional neural network to measure compatibility by extracting general features, but they ignore fine-grained compatibility features and only model overall compatibility. We propose a novel neural network framework called the Attention-based Personalized Compatibility Embedding Network (PCE-Net). It comprises two components: attention-based compatibility embedding modeling and attention-based personal preference modeling. In the second part, we utilize matrix factorization and content-based features to obtain user preferences. Both pieces are jointly trained using the BPR framework in an end-to-end method. Extensive experiments on the IQON3000 dataset demonstrate that PCE-Net significantly outperforms most baseline methods. [ABSTRACT FROM AUTHOR]

Lane detection is a crucial task in the field of autonomous driving, as it enables vehicles to safely navigate on the road by interpreting the high-level semantics of traffic signs. Unfortunately, lane detection is a challenging problem due to factors such as low-light conditions, occlusions, and lane line blurring. These factors increase the perplexity and indeterminacy of the lane features, making them hard to distinguish and segment. To tackle these challenges, we propose a method called low-light enhancement fast lane detection (LLFLD) that integrates the automatic low-light scene enhancement network (ALLE) with the lane detection network to improve lane detection performance under low-light conditions. Specifically, we first utilize the ALLE network to enhance the input image's brightness and contrast while reducing excessive noise and color distortion. Then, we introduce symmetric feature flipping module (SFFM) and channel fusion self-attention mechanism (CFSAT) to the model, which refine the low-level features and utilize more abundant global contextual information, respectively. Moreover, we devise a novel structural loss function that leverages the inherent prior geometric constraints of lanes to optimize the detection results. We evaluate our method on the CULane dataset, a public benchmark for lane detection in various lighting conditions. Our experiments show that our approach surpasses other state of the arts in both daytime and nighttime settings, especially in low-light scenarios. [ABSTRACT FROM AUTHOR]

Trajectory big data is suitable for distributed storage retrieval due to its fast update speed and huge data volume, but currently there are problems such as hot data writing, storage skew, high I/O overhead and slow retrieval speed. In order to solve the above problems, this paper proposes a trajectory big data model that incorporates data partitioning and spatio-temporal multi-perspective hierarchical organization. At the spatial level, the model partitions the trajectory data based on the Hilbert curve and combines the pre-partitioning mechanism to solve the problems of hot writing and storage skewing of the distributed database HBase; at the temporal level, the model takes days as the organizational unit, finely encodes them into a minute system and then fuses the data partitioning to build spatio-temporal hybrid encoding to hierarchically organize the trajectory data and solve the problems of efficient storage and retrieval of trajectory data. The experimental results show that the model can effectively improve the storage and retrieval speed of trajectory big data under different orders of magnitude, while ensuring relatively stable writing and query speed, which can provide an efficient data model for trajectory big data mining and analysis. [ABSTRACT FROM AUTHOR]

The quality of poultry products depends on genotype, rearing system, and environment. The aim of this study was to investigate the effects of different rearing systems on meat quality, amino acid composition, and breast muscle transcriptome from Lueyang black-bone chickens. Lueyang black-bone chickens (n = 900) were randomly divided into three groups (cage, flat-net, and free-range groups), with three replicates per group (100 chickens per replicate). At 16 weeks, a total of 36 healthy chickens (six males and six females per group) were collected, and their breast muscles were sampled to detect meat quality parameters, amino acid composition, and fatty acid contents. Furthermore, breast muscles from six random hens in each group were used for RNA-seq analysis. The results revealed that the values of pH, shear force, inosine monophosphate (IMP), palmitic acid, and linoleic acid in the free-range group were significantly higher than those in the caged group (p < 0.05). Fat content in the free-range group was significantly lower than in the caged and flat-net groups (p < 0.05). Glutamate (Glu) levels, the amino acid crucial for the umami taste, was significantly higher in the free-range group than in the caged group (p < 0.05). Meanwhile, there was no significant difference between the free-range and flat-net groups (p > 0.05). The breast muscle transcriptome results showed that there were 291, 131, and 387 differently expressed genes (DEGs) among the three comparison groups (caged vs. free-range, flat-net vs. caged, and flat-net vs. free-range, respectively) that were mainly related to muscle development and amino acid metabolism pathways. To validate the accuracy of the transcriptome data, eight genes (GOS2, ASNS, NMRK2, GADL1, SMTNL2, SLC7A5, AMPD1, and GLUL) which relate to fat deposition, skeletal muscle function, and flavor formation were selected for Real-time Quantitative PCR (RT-qPCR) verification. In conclusion, these results suggested that rearing systems significantly influenced the meat quality and gene expression of Lueyang black-bone chickens. All the data proved that free-range and flat-net systems may provide better flavor to consumers by affecting the deposition of flavor substances and the expression of related genes. These findings will provide a valuable theoretical basis for the rearing system selection in the poultry industry. [ABSTRACT FROM AUTHOR]

Image style transfer is a challenging problem in computer vision which aims at rendering an image into different styles. A lot of progress has been made to transfer the style of one painting of a representative artist in real time, whereas less attention has been focused on transferring an artist's style from a collection of his paintings. This task requests capturing the artist's precise style from his painting collection. Existing methods did not pay more attention on the possible disruption of original content details and image structures by texture elements and noises, which leads to the structure deformation or edge blurring of the generated images. To address this problem, we propose IFFMStyle, a high-quality image style transfer framework. Specifically, we introduce invalid feature filtering modules (IFFM) to the encoder–decoder architecture to filter the content-independent features in the original image and the generated image. Then, the content-consistency constraint is used to enhance the model's content-preserving capability. We also introduce style perception consistency loss to jointly train a network with content loss and adversarial loss to maintain the distinction of different semantic content in the generated image. Additionally, we have no requirement for paired content image and style image. The experimental results show that the stylized image generated by the proposed method significantly improves the quality of the generated images, and can realize the style transfer based on the semantic information of the content image. Compared with the advanced method, our method is more favored by users. [ABSTRACT FROM AUTHOR]

The prediction and control of passenger flow in scenic spots is very important to the traffic management and safety of scenic spots. This study aims to predict the passenger flow of a scenic spot based on the passenger flow of the bus and subway stations around the scenic spots. We propose a passenger flow prediction model based on graph convolutional network–recurrent neural network (GCN–RNN). First, a "graph" is constructed according to the geographical relationship between the scenic spot and the surrounding bus and subway stations. Then, characteristics of surrounding areas of bus and subway stations are constructed based on the crowd behavior analysis, and these are then used as the node-information of the "graph". Last, the GCN–RNN model is used to extract the temporal and spatial characteristics of the passenger flow data of the scenic spot to realize the prediction. The experimental results show that the proposed model is effective in passenger flow prediction in scenic spots. [ABSTRACT FROM AUTHOR]

Gynostemma pentaphyllum (GP), known as "southern ginseng", can reduce the blood pressure and blood lipid levels. In this study, 300 layer chicks of one day old were divided randomly into three groups (control group (base diet), high addition group (base diet with 1% GP), and low addition group (base diet with 0.5% GP)). After 29 weeks, the growth performance, egg quality, and serum index were determined. Additionally, liver mRNA was identified using RNA-seq to investigate the molecular mechanisms. The results indicated that the serum total cholesterol and triglycerides decreased significantly in the GP addition group. The addition of GP increased the egg weight, Haugh unit and redness (a*) of the egg yolk color, and reduced the yolk cholesterol concentration. Moreover, 95 differentially expressed genes (DEGs) were screened between the control and GP addition group. GO and the KEGG analysis showed that the PPAR pathway was significantly enriched. Five fatty acid metabolism-related genes (FABP3, CYP7A1, ANKRD22, SCD1, and PCK1) were validated by qRT-PCR analysis, which confirmed the tendency of the expression. These DEGs in the PPAR pathway may be the key factors of GP affecting fatty acid metabolism. These results may provide a theoretical basis for further research and new insights into GP as a feed additive. [ABSTRACT FROM AUTHOR]

Simple Summary: Skeletal muscle is an important tissue and its development is strictly regulated by genes. In this study, in order to understand the muscle-related gene expression in Pekin duck, RNA-seq was performed to analyze and compare skeletal muscle at different growth stages. Alternative splicing, single nucleotide polymorphisms and insertion–deletions were detected, and 299 novel genes were discovered. MYL4, IGF2BP1, CSRP3, SPP1, KLHL31, LAMB2, LAMA2, ITGB1 and OPN played crucial roles in skeletal muscle development. Oxidative phosphorylation, ECM-receptor interaction, focal adhesion, carbon metabolism, and biosynthesis of amino acids participated in the regulation of skeletal muscle development in Pekin duck. This study provides an important reference for revealing the developmental mechanisms of pectoral and leg muscles in duck. Skeletal muscle, accounting for approximately 50% of body weight, is the largest and most important tissue. In this study, the gene expression profiles and pathways in skeletal muscle of Pekin duck were investigated and compared at embryonic day 17, 21, and 27 and postnatally at 6 months of age. An average of 49,555,936 reads in each sample was obtained from the transcriptome libraries. Over 70.0% of alternative splicing (AS) in each sample was mainly alternative 5′ first exon (transcription start site)—the first exon splicing (TSS) and alternative 3′ last exon (transcription terminal site)—the last exon splicing (TTS), indicating that TSS and TTS were the most common AS event in Pekin ducks, and these AS events were closely related to the regulation of muscle development at different growth stages. The results provided a valuable genomic resource for selective breeding and functional studies of genes. A total of 299 novel genes with ≥2 exons were obtained. There were 294 to 2806 differentially expressed genes (DEGs) in each pairwise comparison of Pekin duck. Notably, 90 DEGs in breast muscle and 9 DEGs in leg muscle were co-expressed at all developmental points. DEGs were validated by qPCR analysis, which confirmed the tendency of the expression. DEGs related to muscle development were involved in biological processes such as "endodermal cell differentiation", "muscle cell cellular homeostasis", "skeletal muscle tissue growth" and "skeletal muscle cell differentiation", and were involved in pathways such as oxidative phosphorylation, ECM-receptor (extracellular matrix receptor) interaction, focal adhesion, carbon metabolism, and biosynthesis of amino acids. Some DEGs, including MYL4, IGF2BP1, CSRP3, SPP1 and KLHL31, as well as LAMB2, LAMA2, ITGB1 and OPN, played crucial roles in muscle growth and development. This study provides valuable information about the expression profile of mRNAs and pathways from duck skeletal muscle at different growth stages, and further functional study of these mRNAs and pathways could provide new ideas for studying the molecular networks of growth and development in duck skeletal muscle. [ABSTRACT FROM AUTHOR]

As one of the most important poultry worldwide, ducks (Anas platyrhynchos) are raised mainly for meat and egg products, and muscle development in ducks is important for meat production. Therefore, an investigation of gene expression in duck skeletal muscle would significantly contribute to our understanding of muscle development. In this study, twenty-four cDNA libraries were constructed from breast and leg muscles of Hanzhong Ma ducks at day 17, 21, 27 of the embryo and postnatal at 6-month-old. High-throughput sequencing and bioinformatics were used to determine the abundances and characteristics of transcripts. A total of 632,172,628 (average 52,681,052) and 637,213,938 (average 53,101,162) reads were obtained from the sequencing data of breast and leg muscles, respectively. Over 71.63% and 77.36% of the reads could be mapped to the Anas platyrhynchos genome. In the skeletal muscle of Hanzhong duck, intron variant (INTRON), synonymous variant (SYNONYMOUS_CODING), and prime 3′ UTR variant (UTR_3_PRIME) were the main single nucleotide polymorphisms (SNP) annotation information, and "INTRON", "UTR_3_PRIME", and downstream-gene variant (DOWNSTREAM) were the main insertion-deletion (InDel) annotation information. The predicted number of alternative splicing (AS) in all samples were mainly alternative 5′ first exon (transcription start site)-the first exon splicing (TSS) and alternative 3′ last exon (transcription terminal site)-the last exon splicing (TTS). Besides, there were 292 to 2801 annotated differentially expressed genes (DEGs) in breast muscle and 304 to 1950 annotated DEGs in leg muscle from different databases. It is worth noting that 75 DEGs in breast muscle and 49 DEGs in leg muscle were co-expressed at all developmental points of comparison, respectively. The RNA-Seq data were confirmed to be reliable by qPCR. The identified DEGs, such as CREBL2, RHEB, GDF6, SHISA2, MYLK2, ACTN3, RYR3, and STMN1, were specially highlighted, indicating their strong associations with muscle development in the Hanzhong Ma duck. KEGG pathway analysis suggested that regulation of actin cytoskeleton, oxidative phosphorylation, and focal adhesion were involved in the development of skeletal muscle. The findings from this study can contribute to future investigations of the growth and development mechanism in duck skeletal muscle. [ABSTRACT FROM AUTHOR]

The egg-laying rate is an important indicator for evaluating fertility of poultry. In order to better understand the laying mechanism of Muscovy ducks, gene expression profiles and pathways of ovarian tissues in high- and low-laying black (BH and BL) and white Muscovy ducks (WH and WL) during the peak production period were performed by using RNA-seq. The total number of reads produced for each ovarian sample ranged from 44,344,070 to 47,963,328. A total of 113, 619 and 87 differentially expressed genes (DEGs) were identified in BH-vs-WH, BL-vs-BH and BL-vs-WL, respectively. Among them, 54, 356 and 49 genes were up regulated and 59, 263 and 38 genes were down regulated. In addition, there were only 10 up-regulated genes in WL-vs-WH. In the comparison of DEGs in black and white Muscovy ducks, two co-expressed DEG genes were detected between BH-vs-WH and BL-vs-WL and seven DEGs were co-expressed between BL-vs-BH and WL-vs-WH. The RNA-Seq data were confirmed to be reliable by qPCR. Numerous DEGs known to be involved in ovarian development were identified, including TGFβ2, NGFR, CEBPD, CPEB2, POSTN, SMOC1, FGF18, EFNA5 and SDC4. Gene Ontology (GO) annotations indicated that DEGs related to ovarian development were mainly enriched in biological processes of "circadian sleep/wake cycle process," "negative regulation of transforming growth factor-β secretion," "positive regulation of calcium ion transport" in BH-vs-WH and "cell surface receptor signaling pathway," "Notch signaling pathway" and "calcium ion transport" in BL-vs-BH. Besides, "steroid biosynthetic process," "granulosa cell development" and "egg coat formation" were mainly enriched in BL-vs-WL and "reproduction," "MAPK cascade" and "mitotic cell cycle" were mainly enriched in WL-vs-WH. KEGG pathway analysis showed that the PI3K-Akt signaling pathway and ovarian steroidogenesis were the most enriched in Muscovy duck ovary transcriptome data. This work highlights potential genes and pathways that may affect ovarian development in Muscovy duck. [ABSTRACT FROM AUTHOR]

Simple Summary: In the intensive and scale poultry industry, the level of heat stress (HS) directly affects the growth, development, and production performance of poultry. To alleviate the adverse effects of stress in broilers, microRNA (miRNA) was regarded as a potential regulator of immune cytokines. In this study, through the sequencing analysis of spleens after cold/heat stress, we found that 33 and 37 miRNA were differentially expressed in the heat stress group compared with the normal (NS) group and cold stress (CS) group, respectively. The differential miRNA were mainly involved in biological processes such as the cytokine–cytokine receptor interaction. To further understand the miRNA-mediated effect of heat stress on the immune level of chickens, we selected miR-34a and miR-449c as the research objects, predicted and verified that interleukin 2 (IL-2) and interleukin 12α (IL-12α) were the target genes of miR-34a and miR-449c. Coupled with the analysis of the expression of other cytokines, we found that miRNA could change the expression of immune cytokines directly or indirectly. This discovery provides a new insight into the mediation of miRNA for immune cytokines in acute cold/heat stressed broiler chicken. An increasing amount of evidence has revealed that microRNAs (miRNAs) participated in immune regulation and reaction to acute cold and heat stresses. As a new type of post-transcriptional regulatory factor, miRNA has received widespread attention; However, the specific mechanism used for this regulation still needs to be determined. In this study, thirty broilers at the same growth period were divided into three groups and treated with different temperature and humidity of CS (10–15 °C and 90% Relative Humidity (RH)), HS (39 °C and 90% RH), and NS (26 °C and 50–60% RH) respectively. After 6 h, splenic tissues were collected from all study groups. miRNA sequencing was performed to identify the differentially expressed miRNAs (DEMs) between HS, CS, and NS. We found 33, 37, and 7 DEMs in the HS-NS, HS-CS, CS-NS group. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that DEMs were significantly enriched in cytokine–cytokine receptor interaction and functioned as the cellular responders to stress. We chose two miRNA, miR-34a and miR-449c, from the same family and differential expressed in HS-CS and HS-NS group, as the research objects to predict and verify the target genes. The dual-luciferase reporter assay and quantitative real-time PCR (qRT-PCR) confirmed that two cytokines, IL-2 and IL-12α, were the direct target genes of miR-34a and miR-449c. To further understand the mediation mechanism of miRNAs in acute cold/heat-stressed broiler chicken, a splenic cytokines profile was constructed. The results showed that IL-1β was strongly related to acute heat stress in broiler chicken, and from this we predicted that the increased expression of IL-1β might promote the expression of miR-34a, inducing the upregulation of interferon-γ (INF-γ) and IL-17. Our finds have laid a theoretical foundation for the breeding of poultry resistance and alleviation of the adverse effects of stress. [ABSTRACT FROM AUTHOR]

Origin-destination (OD) flow pattern mining is an important research method of urban dynamics, in which OD flow clustering analysis discovers the activity patterns of urban residents and mine the coupling relationship of urban subspace and dynamic causes. The existing flow clustering methods are limited by the spatial constraints of OD points, rely on the spatial similarity of geographical points, and lack in-depth analysis of high-dimensional flow characteristics, and therefore it is difficult to find irregular flow clusters. In this paper, we propose an OD flow clustering method based on vector constraints (ODFCVC), which defines OD flow event point and OD flow vector to express the spatial location relationship and geometric flow behavior characteristics of OD flow. First, the OD flow vector coordinate system is normalized by the Euclidean distance-based OD flow event point spatial clustering, and then the OD flow clusters with similar flow patterns are mined using adjusted cosine similarity-based OD flow vector feature clustering. The transformation of OD data from point set space to vector space is realized by constraining the vector coordinate system and vector similarity through two-step clustering, which simplifies the calculation of high-dimensional similarity of OD flow and helps mining representative OD flow clusters in flow space. Due to the OD flow cluster property, the k-means algorithm is selected as the basic clustering logic in the two-step clustering method, and a sum of squared error perceptually important points algorithm considering silhouette coefficients (SSEPIP) is adopted to automatically extract the optimal cluster number without defining any parameters. Tested by origin-destination flow data in Beijing, China, new traffic flow communities based on traffic hubs are obtained by using the ODFCVC method, and irregular traffic flow clusters (including cluster mode, divergence mode, and convergence mode) with representative travel trends are found. [ABSTRACT FROM AUTHOR]

The egg-laying rate is an important indicator for evaluating fertility of poultry. In order to better understand the laying mechanism of Muscovy ducks, gene expression profiles and pathways of ovarian tissues in high- and low-laying black (BH and BL) and white Muscovy ducks (WH and WL) during the peak production period were performed by using RNA-seq. The total number of reads produced for each ovarian sample ranged from 44,344,070 to 47,963,328. A total of 113, 619 and 87 differentially expressed genes (DEGs) were identified in BH-vs-WH, BL-vs-BH and BL-vs-WL, respectively. Among them, 54, 356 and 49 genes were up regulated and 59, 263 and 38 genes were down regulated. In addition, there were only 10 up-regulated genes in WL-vs-WH. In the comparison of DEGs in black and white Muscovy ducks, two co-expressed DEG genes were detected between BH-vs-WH and BL-vs-WL and seven DEGs were co-expressed between BL-vs-BH and WL-vs-WH. The RNA-Seq data were confirmed to be reliable by qPCR. Numerous DEGs known to be involved in ovarian development were identified, including TGFβ 2, NGFR , CEBPD , CPEB 2, POSTN , SMOC 1, FGF 18, EFNA 5 and SDC 4. Gene Ontology (GO) annotations indicated that DEGs related to ovarian development were mainly enriched in biological processes of "circadian sleep/wake cycle process," "negative regulation of transforming growth factor-β secretion," "positive regulation of calcium ion transport" in BH-vs-WH and "cell surface receptor signaling pathway," "Notch signaling pathway" and "calcium ion transport" in BL-vs-BH. Besides, "steroid biosynthetic process," "granulosa cell development" and "egg coat formation" were mainly enriched in BL-vs-WL and "reproduction," "MAPK cascade" and "mitotic cell cycle" were mainly enriched in WL-vs-WH. KEGG pathway analysis showed that the PI3K-Akt signaling pathway and ovarian steroidogenesis were the most enriched in Muscovy duck ovary transcriptome data. This work highlights potential genes and pathways that may affect ovarian development in Muscovy duck.