Gene Expression Patterns最新文献

Inka box actin regulator 1 (Inka1) is a novel protein identified in Xenopus and is found in vertebrates. While Inka1 is required for facial skeletal development in Xenopus and zebrafish, it is dispensable in mice despite its conserved expression in the cranial neural crest, indicating that Inka1 function in facial skeletal development is not conserved among vertebrates. Zebrafish bears two paralogs of inka1 (inka1a and inka1b) in the genome, with the biological roles of inka1b barely known. Here, we analyzed the expression and function of inka1b during facial skeletal development in zebrafish. inka1b was expressed sequentially in the head mesoderm adjacent to the pharyngeal pouches essential for facial skeletal development at the stage of arch segmentation. However, a loss-of-function mutation in inka1b displayed normal head development, including the pouches and facial cartilages. The normal head of inka1b mutant fish was unlikely a result of the genetic redundancy of inka1b with inka1a, given the distinct expression of inka1a and inka1b in the cranial neural crest and head mesoderm, respectively, during craniofacial development. Our findings suggest that the inka1b expression in the head mesoderm might not be essential for head development in zebrafish.

A recommendation system is an imaginative resolution for managing the restrictions in e-commerce services with item details and user details. Also, it is used to determine the user preferences to recommend the items they expected to buy. Several conventional collaborative filtering techniques are devised in the recommender model, but it has some complexities. Hence, an innovative optimization-driven deep residual network is devised in this paper for a product recommendation system. Here, the product of images is used for extracting features where the Convolutional neural network (CNN) features are computed, and then it is given as input to the deep residual network aimed at product recommendation. The deep residual network is trained using developed Elephant Herding Feedback Artificial Optimization (EHFAO), which is obtained by integrating Elephant Herding optimization (EHO) into the Feedback Artificial Tree (FAT). Here, the item grouping is carried out on input data based on K-means clustering. After item grouping, Cosine similarity is used to perform matching of groups, where the best group is acquired among all the available groups. Extraction of list of visitors is done from the best group. Then, the list of items is obtained from the sequence of best visitor. Next, the corresponding binary sequence is obtained for the applicable sequence of visitor. From this sequence of best visitor, the recommended product is acquired. Then, the recommended product is subjected to the sentiment analysis for which the score is determined. Here, the sentiment analysis helps to decide whether the product is recommended or not recommended. If the score is positive, then the same product is recommended; otherwise, the new product is recommended. The proposed EHFAO-based deep residual network attained better performance in comparison to the other techniques with a maximal F-measure at 84.061%, 84.061% precision, 87.845% recall along with minimal Mean Squared Error (MSE) of 0.216.

Nel is a multimeric extracellular glycoprotein which predominantly expressed in the nervous system and play an important role in neural development and functions. There are three nel paralogues included nell2a, nell2b, and nell3 in zebrafish, while systematic expression analysis of the nel family is still lacking. In this study, we performed a phylogenetic analysis on 7 species, in different species the nell2a are highly conserved, as is nell2b. Then, the expression profiles of nell2a, nell2b and nell3 were detected by in situ hybridization in zebrafish embryo, and the result showed that nel genes highly enriched in the central nervous system, but distributed in different regions of the brain. In addition, nell2a is also expressed in the olfactory pit, spinal cord, otic vesicle and retina (ganglion cell layer), nell2b was detected to express in gill arches, olfactory epithelium, olfactory pit, spinal cord, photoreceptor and retina (ganglion cell layer), it should be noted that the expression of nell3 is special, was only detected at 96 hpf in the brain and spinal cord of zebrafish. Overall, our results indicate that nell2a and nell2b genes are expressed in the nervous system and eyes of zebrafish embryo, while nell3 is expressed in different regions in the nervous system. The phylogenetic analysis also shows that nell3 sequences are significantly different from nell2a and nell2b. This study provides new evidence to better understand the role of nel in zebrafish embryo development.

With the achievements of deep learning, applications of deep convolutional neural networks for the image denoising problem have been widely studied. However, these methods are typically limited by GPU in terms of network layers and other aspects. This paper proposes a multi-level network that can efficiently utilize GPU memory, named Double Enhanced Residual Network (DERNet), for biological-image denoising. The network consists of two sub-networks, and U-Net inspires the basic structure. For each sub-network, the encoder-decoder hierarchical structure is used for down-scaling and up-scaling feature maps so that GPU can yield large receptive fields. In the encoder process, the convolution layers are used for down-sampling to obtain image information, and residual blocks are superimposed for preliminary feature extraction. In the operation of the decoder, transposed convolution layers have the capability to up-sampling and combine with the Residual Dense Instance Normalization (RDIN) block that we propose, extract deep features and restore image details. Finally, both qualitative experiments and visual effects demonstrate the effectiveness of our proposed algorithm.

Classification of brain tumor in Magnetic Resonance Imaging (MRI) images is highly popular in treatment planning, early diagnosis, and outcome evaluation. It is very difficult for classifying and diagnosing tumors from several images. Thus, an automatic prediction strategy is essential in classifying brain tumors as malignant, core, edema, or benign. In this research, a novel approach using Salp Water Optimization-based Deep Belief network (SWO-based DBN) is introduced to classify brain tumor. At the initial stage, the input image is pre-processed to eradicate the artifacts present in input image. Following pre-processing, the segmentation is executed by SegNet, where the SegNet is trained using the proposed SWO. Moreover, the Convolutional Neural Network (CNN) features are employed to mine the features for future processing. At last, the introduced SWO-based DBN technique efficiently categorizes the brain tumor with respect to the extracted features. Thereafter, the produced output of the introduced SegNet + SWO-based DBN is made use of in brain tumor segmentation and classification. The developed technique produced better results with highest values of accuracy at 0.933, specificity at 0.880, and sensitivity at 0.938 using BRATS, 2018 datasets and accuracy at 0.921, specificity at 0.853, and sensitivity at 0.928 for BRATS, 2020 dataset.

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.

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.

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.