Our previous study on the ovarian transcriptomic analysis in Leizhou black duck revealed that the ESR2 gene was involved in hormone regulation in reproduction and the estrogen signaling pathway related to reproductive performance was enriched. This suggested that ESR2 may have a functional role in the reproductive performance of the Leizhou black duck. Thus, this study aimed at evaluating the polymorphism of the ESR2 gene and its association with egg-laying traits and the distribution pattern of ESR2 mRNA in laying and non-laying Leizhou black ducks. In this study, genomic DNA was extracted from blood samples of 101 Leizhou black ducks to identify single nucleotide polymorphisms ( SNPs ) of the ESR2 gene to elucidate molecular markers highly associated with egg-laying traits. Four each of laying and non-laying Leizhou black ducks were selected to collect different tissues to analyze the ESR2 gene expression. A total of 23 SNPs were identified and association analysis of the single SNP sites showed that SNPs g.56805646 T>C and exon 3-20G>A were significantly ( P < 0.05) associated with egg weight. Ducks with CT and AG genotypes had significantly higher (P < 0.05) egg weights than their respective other genotypes. Haplotype association analysis of g.56805646 T>C and exon 3-20G>A showed that the haplotypes were significantly associated with egg weight. Higher egg weight was seen in individuals with H3H4 haplotypes. In the hypothalamus-pituitary-gonadal ( HPG ) axis, the results of qRT/PCR showed that ESR2 mRNA was significantly ( P < 0.05) expressed in the ovaries of both duck groups than in the hypothalamus and pituitary. In the oviduct, ESR2 was significantly ( P < 0.05) higher in the infundibulum and magnum of laying and non-laying ducks respectively. This study provides a molecular marker for selecting Leizhou black ducks for egg production. In addition, it offers theoretical knowledge for studying the related biological functions of the ESR2 gene at the cellular level.

When the robot grabs the object, the force information detection of the object is the basis for the smooth grabbing process. The force information of the object can be fully reflected by detecting the force in the three-dimensional direction. In this paper, with polydimethylsiloxane (PDMS) as the substrate and embedded into the sensitive unit prepared by conductive rubber, a new flexible sensor which can detect three-dimensional force is designed. Firstly, based on the piezoresistive effect of conductive rubber, COMSOL Multiphysics software was used to carry out multi-physical field simulation experiment for the sensor. Furthermore, based on the nonlinear approximation ability of BP neural network and the resistance of simulation output, the training sample set and the test sample set were constructed by using the 5-fold cross validation method, and the BP neural network model was constructed to achieve the accurate prediction of three-dimensional force. Finally, the number of hidden layer neurons was adjusted to optimize the BP network model. The results of cross-validation experiments show that the sensor designed in this paper can effectively detect the three-dimensional force information, and the optimized BP neural network can significantly improve the accuracy of the three-dimensional force prediction.

To measure three-dimensional (3D) forces efficiently and improve the sensitivity of tactile sensors, a novel piezoelectric tactile sensor with a “sandwich” structure is proposed in this paper. An array of circular truncated cone-shaped sensitive units made of polyvinylidene fluoride (PVDF) is sandwiched between two flexible substrates of polydimethylsiloxane (PDMS). Based on the piezoelectric properties of the PVD F sensitive units, finite element modelling and analysis are carried out for the sensor. The relation between the force and the voltage of the sensitive unit is obtained, and a tactile perception model is established. The model can distinguish the sliding direction and identify the material of the slider loaded on the sensor. A backpropagation neural network (BPNN) algorithm is built to predict the 3D forces applied on the tactile sensor model, and the 3D forces are decoupled from the voltages of the sensitive units. The BPNN is further optimized by a genetic algorithm (GA) to improve the accuracy of the 3D force prediction, and fairly good prediction results are obtained. The experimental results show that the novel tactile sensor model can effectively predict the 3D forces, and the BPNN model optimized by the GA can predict the 3D forces with much higher precision, which also improves the intelligence of the sensor. All the prediction results indicate that the BPNN algorithm has very efficient performance in 3D force prediction for the piezoelectric tactile sensor.

Haptic force feedback is an important perception method for humans to understand the surrounding environment. It can estimate tactile force in real time and provide appropriate feedback. It has important research value for robot-assisted minimally invasive surgery, interactive tactile robots, and other application fields. However, most of the existing noncontact visual power estimation methods are implemented using traditional machine learning or 2D/3D CNN combined with LSTM. Such methods are difficult to fully extract the contextual spatiotemporal interaction semantic information of consecutive multiple frames of images, and their performance is limited. To this end, this paper proposes a time-sensitive dual-resolution learning network-based force estimation model to achieve accurate noncontact visual force prediction. First, we perform continuous frame normalization processing on the robot running the video captured by the camera and use the hybrid data augmentation to improve the data diversity; secondly, a deep semantic interaction model is constructed based on the time-sensitive dual-resolution learning network, which is used to automatically extract the deep spatiotemporal semantic interaction information of continuous multiframe images; finally, we construct a simplified prediction model to realize the efficient estimation of interaction force. The results based on the large-scale robot hand interaction dataset show that our method can estimate the interaction force of the robot hand more accurately and faster. The average prediction MSE reaches 0.0009 N, R 2 reaches 0.9833, and the average inference time for a single image is 6.5532 ms; in addition, our method has good prediction generalization performance under different environments and parameter settings.

BackgroundOur previous study on the ovarian transcriptomic analysis in Leizhou black duck revealed that the ESR2 gene was involved in hormone regulation in reproduction and the estrogen signaling pathway related to reproductive performance was enriched. This suggested that ESR2 may have a functional role in the reproductive performance of the Leizhou black duck. Thus, this study aimed at evaluating the polymorphism of the ESR2 gene and its association with egg-laying traits and the distribution pattern of ESR2 mRNA in laying and non-laying Leizhou black ducks. MethodIn this study, genomic DNA was extracted from blood samples of 101 Leizhou black ducks to identify single nucleotide polymorphisms (SNPs) of the ESR2 gene to elucidate molecular markers highly associated with egg-laying traits. Four (4) each of laying and non-laying Leizhou black ducks were selected to collect different tissues to analyze the ESR2 gene expression. ResultsA total of 23 SNPs were identified and association analysis of the single SNP sites showed that SNPs g.56805646 T>C and exon 3-20G>A were significantly (P < 0.05) associated with egg weight. Ducks with CT and AG genotypes had significantly higher (P < 0.05) egg weights than their respective other genotypes. Haplotype association analysis of g.56805646 T>C and exon 320G>A showed that the haplotypes were significantly associated with egg weight where higher egg weight was seen in individuals with H3H4 haplotypes. In the hypothalamus-pituitary-gonadal (HPG) axis, the results of qRT/PCR showed that ESR2 mRNA was significantly (P < 0.05) expressed in the ovaries of both duck groups than in the hypothalamus and pituitary. In the oviduct, ESR2 was significantly (P < 0.05) higher in the infundibulum and magnum of laying and nonlaying ducks respectively. ConclusionThis study provides molecular marker for selecting Leizhou black ducks for egg production and provides theoretical knowledge for the study of the related biological functions of the ESR2 gene at the cellular level.

BackgroundEstrogen receptor 2 (ESR2) plays significant biological roles in the reproductive system and ovarian follicle development. This study, therefore, aimed to reveal the expression pattern and cell-specific localization of ESR2 in the ovarian follicles of Leizhou black ducks. MethodFour laying Leizhou black ducks at 43 weeks old were annihilated and different grade-sized follicles were collected for immunohistochemistry and expression profile study. The follicles were grouped into seven (7) as small white follicles (SWF), large white follicles (LWF), small yellow follicles (SYF), large yellow follicles (LYF), follicle 5 (F5), follicle 2 (F2), and follicle 1 (F1). ResultsThe qRT/PCR results displayed that ESR2 mRNA was expressed in all follicles with the highest (P < 0.05) level of expression found in F1 compared to other follicles. Immunohistochemistry analysis of the cell-specific localization of ESR2 protein revealed that ESR2 was distributed in both granulosa and theca cells region in all the follicles examined. There was a significantly higher localization of ESR2 protein in the granulosa cells than the theca cells of SWF, SYF, LYF, F2, and F1. Comparatively, ESR2 was highly expressed in the granulosa cells of LYF than in all the other follicles. ConclusionThese results provide theoretical knowledge for the in-depth study of the related biological functions of the ESR2 gene and its application at the cellular level.

Background Ovary is an important reproductive organ for poultry. MicroRNA (miRNA) is a highly conserved class of small non-coding RNA that function in a specific manner to post-transcriptionally regulate gene expression in organisms. Currently, miRNA has been studied extensively but research on duck ovarian tissue is relatively rare. Thus, in this study, we performed the first miRNA analysis of ovarian tissues in Leizhou black ducks with low and high rates of egg production. Method: Using high-throughput sequencing technology, miRNA library was constructed to obtain miRNA expression profile; differentially expressed miRNAs were screened, to predict miRNA target genes. Results A total of 29 differentially expressed miRNAs were obtained from the miRNA library, of which 7 were up-regulated and 22 were down-regulated. Verification of 12 randomly selected miRNAs, using RT-qPCR technology showed the results are consistent with the sequencing data, indicating that the sequencing results are reliable. The GO enrichment and KEGG pathway enrichment analysis of differential miRNAs showed that target genes were significantly enriched in signal pathways related to ovarian development, such as the oxytocin signaling pathway, GnRH signaling pathway, progesterone-mediated oocyte maturation, and AMPK signal pathway. Conclusion The research results enrich the data resources of duck miRNAs, and provide a theoretical basis for further research on the laying performance of poultry and molecular-assisted breeding of poultry in the future.

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