Gene Expression Patterns最新文献

Background

Expression level of EMX2 plays an important role in the development of nervous system and cancers. CNE2.04, a conserved enhancer downstream of emx2, drives fluorescent protein expression in the similar pattern of emx2.

Methods

CNE2.04 truncated or motif-mutated transgenic reporter plasmids were constructed and injected into the zebrafish fertilized egg with Tol2 mRNA at the unicellular stage of zebrafish eggs. The green fluorescence expression patterns were observed at 24, 48, and 72 hpf, and the fluorescence rates of different tissues were counted at 48 hpf.

Results

Compared to CNE2.04, CNE2.04-R400 had comparable enhancer activity, while the tissue specificity of CNE2.04-L400 was obviously changed. Motif CCCCTC mutation obviously changed the enhancer activity, while motif CCGCTC mutations also changed it.

Conclusion

Due to their correlation with tissue specificity, CNE2.04-R400 is associated with the tissue-specificity of CNE2.04, and motif CCCCTC plays an important role in the enhancer activity of CNE2.04.

The family of novel transmembrane proteins (TMEM) 132 have been associated with multiple neurological disorders and cancers in humans, but have hardly been studied in vivo. Here we report the expression patterns of the five Tmem132 genes (a, b, c, d and e) in developing mouse nervous system with RNA in situ hybridization in wholemount embryos and tissue sections. Our results reveal differential and partially overlapping expression of multiple Tmem132 family members in both the central and peripheral nervous system, suggesting potential partial redundancy among them.

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.

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.

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.