Environmental sex determination (ESD) occurs in divergent, phylogenetically unrelated taxa, and in some species, co-occurs with genetic sex determination (GSD) mechanisms. Although epigenetic ...regulation in response to environmental effects has long been proposed to be associated with ESD, a systemic analysis on epigenetic regulation of ESD is still lacking. Using half-smooth tongue sole (Cynoglossus semilaevis) as a model-a marine fish that has both ZW chromosomal GSD and temperature-dependent ESD-we investigated the role of DNA methylation in transition from GSD to ESD. Comparative analysis of the gonadal DNA methylomes of pseudomale, female, and normal male fish revealed that genes in the sex determination pathways are the major targets of substantial methylation modification during sexual reversal. Methylation modification in pseudomales is globally inherited in their ZW offspring, which can naturally develop into pseudomales without temperature incubation. Transcriptome analysis revealed that dosage compensation occurs in a restricted, methylated cytosine enriched Z chromosomal region in pseudomale testes, achieving equal expression level in normal male testes. In contrast, female-specific W chromosomal genes are suppressed in pseudomales by methylation regulation. We conclude that epigenetic regulation plays multiple crucial roles in sexual reversal of tongue sole fish. We also offer the first clues on the mechanisms behind gene dosage balancing in an organism that undergoes sexual reversal. Finally, we suggest a causal link between the bias sex chromosome assortment in the offspring of a pseudomale family and the transgenerational epigenetic inheritance of sexual reversal in tongue sole fish.
The current study integrated the Extended Technology Acceptance Model (TAM) and included information system quality (ISQ), user interface (UI), knowledge sharing motivation (KSM), the expectation ...confirmation model (ECM), safety management practices (SMP), interface aesthetics (IA), and perceived value (PV) to evaluate the logistics couriers’ experience while using an Online logistics platform. This research examines the relationships of KSM, SMP, and ISQ on the TAM’s, perceived usefulness (PU), and perceived ease of use (PEOU). In addition, it explores the relationship between UI on PEOU. Furthermore, to explore the impact of ECM, it examines the impact of confirmation (CON) on PU and satisfaction (SAT). Finally, this research explores the impact of logistics couriers’ SAT on continuous intention (CI). According to the findings of this study, UI did not have a significant association with PEOU. Furthermore, KSM was found to significantly impact PEOU, while having no significant effect on PU. Moreover, SMP was found to have no significance on PEOU, however, SMP was discovered to be in a significant association with PU. In addition, ISQ was found to significantly impact PEOU, PU, and, PV. Moreover, CON was in a significant relationship with PU, while not having a significant impact on SAT. Furthermore, IA did not significantly impact PV. Also, SAT was significantly impacted by PU, while not having any significant impact from PEOU, and PV. Besides, PEOU was discovered to significantly impact PU. Finally, SAT was found to be in a significant relationship with CI.
The activation functions play increasingly important roles in deep convolutional neural networks. The traditional activation functions have some problems such as gradient disappearance, neuron death ...and output offset, and so on. To solve these problems, we propose a new activation function in this paper, Fast Exponentially Linear Unit (FELU), aiming to speed up exponential linear calculations and reduce the time of network running. FELU has the advantages of Rectified Linear Unit (RELU) and Exponential Linear Unit (ELU), leading to have better classification accuracy and faster calculation speed. We test five traditional activation functions such as ReLU, ELU, SLU, MPELU, TReLU, and our new activation function on the cifar10, cifar100 and GTSRB data sets. Experiments show that the proposed activation function FELU not only improves the speed of the exponential calculation, reducing the time of convolutional neural network running, but also effectively enhances the noise robustness of network to improve the accuracy of classification.
The Pacific oyster Crassostrea gigas belongs to one of the most species-rich but genomically poorly explored phyla, the Mollusca. Here we report the sequencing and assembly of the oyster genome using ...short reads and a fosmid-pooling strategy, along with transcriptomes of development and stress response and the proteome of the shell. The oyster genome is highly polymorphic and rich in repetitive sequences, with some transposable elements still actively shaping variation. Transcriptome studies reveal an extensive set of genes responding to environmental stress. The expansion of genes coding for heat shock protein 70 and inhibitors of apoptosis is probably central to the oyster's adaptation to sessile life in the highly stressful intertidal zone. Our analyses also show that shell formation in molluscs is more complex than currently understood and involves extensive participation of cells and their exosomes. The oyster genome sequence fills a void in our understanding of the Lophotrochozoa.
Real-time identification of gas-liquid two-phase flow can help fluid systems maintain safe operating conditions. A flow pattern identification method based on a convolutional neural network (CNN) ...algorithm (after this referred to as liqnet) is proposed in this paper to realize automatic detection and real-time identification of two-phase flow patterns. This paper mainly focuses on solving two problems of CNN algorithm flow pattern identification (1): the experimental samples for two-phase flow classification are few, and (2): the existing methods do not fully consider the real-time nature of two-phase flow identification. Therefore, this paper constructs a two-phase flow database containing 6242 images using data enhancement, proposes a lightweight network liqnet, and compares it with six mainstream CNN models. The results show that liqnet can achieve the highest accuracy (98.65%), has the least amount of parameters (1.3708 M), and can achieve the purpose of real-time prediction (32.11FPS).
•A two-phase flow image enhancement method is proposed.•A two-phase flow recognition network liqnet is designed.•The feasibility of this method is evaluated through comparative experiments.
The discrete wavelet transform (DWT) is unable to represent the directional features of an image. Similarly, a fixed embedding strength is not able to establish an ideal balance between ...imperceptibility and robustness of a watermarked image. In this work, we propose an adaptive embedding strength watermarking algorithm based on shearlets’ capture directional features (S-AES). We improve the watermarking algorithm in the domain of DWT using non-subsampled shearlet transform (NSST). The improvement is made in terms of coping with anti-geometric attacks. The embedding strength is optimized by artificial bee colony (ABC) to achieve higher robustness under the premise of satisfying imperceptibility. The principle components (PC) of the watermark are embedded into the host image to overcome the false positive problem. The simulation results show that the proposed algorithm has better imperceptibility and strong robustness against multi-attacks, especially those of high intensity.
In this study, Lactococcus lactis lactis subspecies 1.2472, Streptococcus thermophilus 1.2718, and thermostable Lactobacillus rhamnosus HCUL 1.1901–1912 were used to ferment rice flour for preparing ...rice bread. The characteristics of fermented rice bread were studied to elucidate the mechanism by which fermentation improves the anti-staling ability of rice bread. The amylose content of rice flour increased after fermentation. The peak viscosity, attenuation value, final viscosity, recovery value, and gelatinization temperature decreased. Amylopectin was partially hydrolyzed, and the amylose content decreased. The crystallinity of starch decreased, and the minimum crystallinity of Lactococcus lactis subsp. lactis fermented rice starch (LRS) was 11.64%. The thermal characteristics of fermented rice starch, including To, Tp, Tc, and ΔH, were lower than RS (rice starch), and the △H of LRS was the lowest. Meanwhile, LRS exhibited the best anti-staling ability, and with a staling degree of 43.22%. The T22 of the LRF rice flour dough was lower, and its moisture fluidity was the weakest, indicating that moisture was more closely combined with other components. The texture characteristics of fermented rice bread were improved; among these, LRF was the best: the hardness change value was 1.421 times, the elasticity decrease was 2.35%, and the chewability change was 47.07%. There, it provides a theoretical basis for improving the shelf life of bread.
There are two mainstream approaches currently used for self-supervised monocular depth estimation. One option is to utilize a complete convolution method to construct the encoder and decoder; ...however, the local linear operation and the pooling method result in the loss of pixel information in each layer of the feature map, limiting the performance. Another way is to use the transformer and other methods for feature extraction on the encoder side, which are processed at a constant resolution at each stage and have a global receptive field. Therefore, more subtle depth features can be captured, and higher accuracy can be obtained. Unfortunately, the computational cost of self-attention is too large, which increases the memory overhead. With the comprehensive analysis of the advantages and disadvantages of the above two methods, this paper employs a combination of decomposed large kernel convolution and a multilayer perceptron (MLP) to design a new framework — CSMHNet (a hybrid of a Convolution, self-attention, and an MLP network). It cannot only compensate for the disadvantages of convolution static weights and locality but also significantly reduce the memory overhead compared to the transformer architecture while obtaining a more accurate and consistent depth. Experiments on the KITTI dataset demonstrate the effectiveness of our method, which significantly improves the depth prediction accuracy compared with other self-supervised methods.
Depth estimation is crucial for scene understanding and downstream tasks, especially the self-supervised training methods showing great potential. The overall structure and local details of the scene ...are essential for improving the quality of depth estimation. The proposal of Monodepth2 has led to significant progress in self-supervised monocular depth estimation. However, Monodepth2 uses the most basic encoder–decoder architecture. The limited data flow information of the network leads to a large semantic gap between the encoder and the decoder, which reduces the accuracy of the network for fine-grained feature recognition. Monodepth2 adopts Resnet18 pre-trained on the Imagenet dataset as the encoder. This traditional convolutional pooling structure results in a loss of pixel information in the network at every scale. In order to solve this problem, this paper proposes an improved DepthNet. The network adopts Hrnet in semantic segmentation as the base encoder, which adopts an advanced multi-scale fusion method in the whole process, thus avoiding the loss of pixel information. An additional densely connected U-Net is employed at the decoder side to provide more information flow. Furthermore, the semantic gap between the encoder and decoder is reduced by adding different numbers of residual connections and channel attention on each layer. The network structure can be regarded as a collection of fully convolutional networks. Since the deep features of the network have a higher correlation with the vertical position, we add a spatial location attention module to the deep-level network to reduce this semantic gap. The approach performs significantly well on the KITTI dataset benchmark, with several performance criteria comparable to supervised monocular depth inference methods.
•This work is a deep estimation network for scene reconstruction and scene understanding. This network redesigns the self-supervised monocular depth framework from an entirely new perspective. The network uses HrNet from the field of semantic segmentation as the base encoder, which employs a progressive multi-scale fusion approach throughout, thus avoiding the loss of pixel information.•An additional densely connected U-Net is used at the decoder side to provide further information flow. To reduce the semantic gap between codecs, we add a different number of residual connections and channel attention on each layer. The network is not trained with the help of other auxiliary networks, and the performance of the depth estimation is improved only by stimulating the network’s potential.•This work achieves best-in-class accuracy in monocular depth estimation. When the model in this paper is used for 3D scene reconstruction, it can perform a complete recovery of the scene structure.
PDF
