Gene Expression Patterns最新文献

Walnuts (including those covered with a pellicle) are loved for their rich nutritional value. And the popular varieties of walnut cultivation are Juglans sigillata L. The pellicle (seed coat) of these walnut cultivars has different colors and has an indispensable influence on the walnut quality formation. However, there are few reports on the pellicle color and quality formation in different developmental stages of walnut (Juglans sigillata L.). Therefore, in this study, three walnut cultivars (F, Q, and T) with different pellicle colors were selected for transcriptome sequencing and physiological index analysis of the color and quality formation mechanisms at different development stages. The results showed that with the development of walnut fruit, the starch sucrose metabolism pathway in the pellicle was activated and promoted starch hydrolysis. Meanwhile, the expression levels of genes related to the alpha-linolenic acid metabolism pathway were significantly increased during walnut maturation, especially in F2. Some physiological indicators related to lipid oxidation were also detected and analyzed in this study, such as MDA, CAT, POD and DPPH. These results were similar to the expression patterns of corresponding regulatory genes in the RNA-Seq profile. In addition, lignin synthesis genes were up-regulated in the phenylpropanoid metabolic pathway, while key genes enriched in the flavonoid and anthocyanin synthesis pathways were down-regulated. The results were consistent with the results of total anthocyanins and flavonoid content detection during walnut development. Therefore, this experiment suggested that with the maturation of walnut pellicle, the gene expression in the phenylpropanoid metabolic pathway flowed to the branch of lignin synthesis, especially in the Q variety, resulting in lower flavonoid and anthocyanin content at the maturity stage than immature. This is also the main reason for the pale pellicle of the three walnut varieties after mature. The findings of this study showed that changes in the expression levels of regulating genes for lipid, starch, sugar, and flavonoid synthesis during walnut development influenced the accumulation of the related metabolite for walnut quality formation and pellicle color. The results of this experiment provided the molecular basis and reference for the breeding of high nutritional quality walnut varieties.

Handwritten character recognition has continually been a fascinating field of study in pattern recognition due to its numerous real-life applications, such as the reading tools for blind people and the reading tools for handwritten bank cheques. Therefore, the proper and accurate conversion of handwriting into organized digital files that can be easily recognized and processed by computer algorithms is required for various applications and systems. This paper proposes an accurate and precise autonomous structure for handwriting recognition using a ShuffleNet convolutional neural network to produce a multi-class recognition for the offline handwritten characters and numbers. The developed system utilizes the transfer learning of the powerful ShuffleNet CNN to train, validate, recognize, and categorize the handwritten character/digit images dataset into 26 classes for the English characters and ten categories for the digit characters. The experimental outcomes exhibited that the proposed recognition system achieves extraordinary overall recognition accuracy peaking at 99.50% outperforming other contrasted character recognition systems reported in the state-of-art. Besides, a low computational cost has been observed for the proposed model recording an average of 2.7 (ms) for the single sample inferencing.

For (k, n)-threshold secret image sharing (SIS) scheme, only k or more than k complete parts can recover the secret information, and the correct image cannot be obtained if the count of shadow images is not enough or the shadow images are damaged. The existing schemes are weak in resisting large-area shadow image tampering. In this paper, we propose a robust secret image sharing scheme resisting to maliciously tampered shadow images by Absolute Moment Block Truncation Coding (AMBTC) and quantization (RSIS-AQ). The secret image is successively compressed in two ways: AMBTC and quantization. The sharing shadow images contain the sharing results of both compressed image from different parts, so that even the shadow images are faced with large-scale area of malicious tampering, the secret image can be recovered with acceptable visual quality. Compared with related works, our scheme can resist larger area of tampering and yield better recovered image visual quality. The experimental results prove the effectiveness of our scheme.

In recent years, progressive application of convolutional neural networks in image processing has successfully filtered into medical diagnosis. As a prerequisite for images detection and classification, object segmentation in medical images has attracted a great deal of attention. This study is based on the fact that most of the analysis of pathological diagnoses requires nuclei detection as the starting phase for obtaining an insight into the underlying biological process and further diagnosis. In this paper, we introduce an embedded attention model in multi-bridge Wnet (AMB-Wnet) to achieve suppression of irrelevant background areas and obtain good features for learning image semantics and modality to automatically segment nuclei, inspired by the 2018 Data Science Bowl. The proposed architecture, consisting of the redesigned down sample group, up-sample group, and middle block (a new multiple-scale convolutional layers block), is designed to extract different level features. In addition, a connection group is proposed instead of skip-connection to transfer semantic information among different levels. In addition, the attention model is well embedded in the connection group, and the performance of the model is improved without increasing the amount of calculation. To validate the model's performance, we evaluated it using the BBBC038V1 data sets for nuclei segmentation. Our proposed model achieves 85.83% F1-score, 97.81% accuracy, 86.12% recall, and 83.52% intersection over union. The proposed AMB-Wnet exhibits superior results compared to the original U-Net, MultiResUNet, and recent Attention U-Net architecture.

Although being a really active area of research, television super-resolution remains a difficult problem. Additionally, it is noted that the blur motion and computational crisis hinder the enhancement. As a result, the goal of this research is to present a brand-new smart SR framework for the camera shot. To create High Resolution (HR) videos, first frames in RGB format are converted to HSV and then the V-channel is enhanced. In order to create enriched video frames, a high - dimension grid with enhanced pixel intensity is then created. This paper introduces a particular progression to enable this: Motion estimation, Cubic Spline Interpolation, and Deblurring or Sharpening are the three methods. By carefully adjusting the parameters, the cubic spline interpolation is improved during operation. A brand-new hybrid technique dubbed Lion with Particle Swarm Velocity Update (LPSO-VU), which combines the principles of the Lion Algorithm (LA) and Particle Swarm Optimization (PSO) algorithms, is presented for this optimal tuning purpose. Finally, using the BRISQUE, SDME, and ESSIM metrics, the adequacy of the method is contrasted to other traditional models, and its superiority is demonstrated. Accordingly, the analysis shows that the suggested LPSO-VU model for video frame 1 is 16.6%, 25.56%, 26.2%, 26.2%, and 27.2% superior to the previous systems like PSO, GWO, WOA, ROA, MF-ROA, and LA, respectively, in terms of BRISQUE.

Ultrasound (US) is an inexpensive and non-invasive technique for capturing the image of the thyroid gland and nearby tissue. The classification and detection of thyroid disorders is still in its infant stage. This study aims to present a new thyroid diagnosis approach, which consists of three phases like “(i) feature extraction, (ii) feature dimensionality reduction, and (iii) classification”. Initially, the thyroid images as well as its related data are given as input. From the input image, the features such as“ Grey Level Co-occurrence Matrix(GLCM), Grey level Run Length Matrix(GLRM), proposed Local Binary Pattern(LBP), and Local Tetra Patterns (LTrP)” are extracted. Meanwhile, from the input data, the higher-order statistical features like skewness, kurtosis, entropy, as well as moment get retrieved. Consequently, the Linear Discriminant Analysis (LDA) based dimensionality reduction is processed to resolve the problem of “curse of dimensionality”. Finally, the classification is carried out via two phases: Image features are classified using an ensemble classifier that includes Support Vector Machine (SVM)& Neural Network(NN) models. The data features are subjected to Recurrent Neural Network(RNN) based classification, which is optimized by an Adaptive Elephant Herding Algorithm (AEHO) via tuning the optimal weight. At last, the performance of the adopted scheme is compared to the extant models in terms of various measures. Especially, the mean value of the suggested RNN + AEHO model is 4.35%, 3.54%, 6.07%, 3.8%, 1.69%, 2.85%, 2.07%, 2.54%, 0.13%, 0.035%, and 8.53% better than the existing CNN, NB, RF, KNN, Levenberg, RNN + EHO, RNN + FF, RNN + WOA, WF-CS, FU-SLnO and HFBO methods respectively.

Spiders represent widely used model organisms for chelicerate and even arthropod development and evolution. Wnt genes are important and evolutionary conserved factors that control and regulate numerous developmental processes. Recent studies comprehensively investigated the complement and expression of spider Wnt genes revealing conserved as well as diverged aspects of their expression and thus (likely) function among different groups of spiders representing Mygalomorphae (tarantulas), and both main groups of Araneae (true spiders) (Haplogynae/Synspermiata and Entelegynae). The allegedly most modern/derived group of entelegyne spiders is represented by the RTA-clade of which no comprehensive data on Wnt expression were available prior to this study. Here, we investigated the embryonic expression of all Wnt genes of the RTA-clade spider Cupiennius salei. We found that most of the Wnt expression patterns are conserved between Cupiennius and other spiders, especially more basally branching species. Surprisingly, most differences in Wnt gene expression are seen in the common model spider Parasteatoda tepidariorum (a non-RTA clade entelegyne species). These results show that data and conclusions drawn from research on one member of a group of animals (or any other organism) cannot necessarily be extrapolated to the group as a whole, and instead highlight the need for comprehensive taxon sampling.

Background

Mammalian inner ear cells are produced during embryonic development. NIN1/RPN12 binding protein 1 homolog (Nob1), a ribosome assembly factor, is believed to be involved in transcriptional regulation and may play an important role in mediating certain physiological and pathological functions.

Methods

We investigated the expression levels and distribution patterns of Nob1 in rat cochlea at different developmental stages using reverse transcription-polymerase chain reaction (RT-PCR), Western blot analysis, and immunofluorescence microscopy (P0, P3, P7, P14, and P30, n = 11 per age group).

Results

The expression of Nob1 was gradually upregulated from Postnatal day (P) 0 to P14 and stabilized at P30. Our results analyzing Nob1 expression at P0 and P3 in the rat cochleae revealed expression in the greater epithelial ridge cells, lesser epithelial ridge cells, and spiral ganglion neurons (SGNs), whereas low levels of Nob1 expression were observed in the stria vascularis cells. During the development of the cochlea, the organ of Corti undergoes dramatic architectural remodeling. Nob1 expression was stably detected in supporting cells and hair cells from P7 to P30, and its expression gradually increased in the SGNs.

Conclusions

Thus, the present study revealed that upregulation of Nob1 might explain the maturation of the cytoarchitecture in the cochlea during the postnatal period, and a complete understanding of its function in the mammalian inner ear may help to develop strategies to resolve auditory hair cell loss.

The wiring of neuronal networks is far from understood. One outstanding question is how neurons of different types link up to form subnetworks within the greater context. Cadherins have been suggested to create an inclusion code where interconnected neurons express the same subtypes. Here, we have used a CRISPR/Cas9 knock-in approach to generate a transgenic zebrafish reporter line for protocadherin 9 (pcdh9), which is predominantly expressed within the central nervous system. Expression of eGFP was detected in subsets of neurons in the cerebellum, retina and spinal cord, in both larvae and juveniles. A closer characterization of the spinal locomotor network revealed that a portion of distinct classes of both excitatory and inhibitory interneurons, as well as motor neurons, expressed pcdh9. This transgenic line could thus be used to test the cadherin network hypothesis, through electrophysiological characterization of eGFP positive cells, to show if these are synaptically connected and form a discrete network within the spinal cord.