Diversification and Molecular Evolution of ATOH8, a Gene Encoding a bHLH Transcription Factor

ATOH8 is a bHLH domain transcription factor implicated in the development of the nervous system, kidney, pancreas, retina and muscle. In the present study, we collected sequence of ATOH8 orthologues from 18 vertebrate species and 24 invertebrate species. The reconstruction of ATOH8 phylogeny and sequence analysis showed that this gene underwent notable divergences during evolution. For those vertebrate species investigated, we analyzed the gene structure and regulatory elements of ATOH8. We found that the bHLH domain of vertebrate ATOH8 was highly conserved. Mammals retained some specific amino acids in contrast to the non-mammalian orthologues. Mammals also developed another potential isoform, verified by a human expressed sequence tag (EST). Comparative genomic analyses of the regulatory elements revealed a replacement of the ancestral TATA box by CpG-islands in the eutherian mammals and an evolutionary tendency for TATA box reduction in vertebrates in general. We furthermore identified the region of the effective promoter of human ATOH8 which could drive the expression of EGFP reporter in the chicken embryo. In the opossum, both the coding region and regulatory elements of ATOH8 have some special features, such as the unique extended C-terminus encoded by the third exon and absence of both CpG islands and TATA elements in the regulatory region. Our gene mapping data showed that in human, ATOH8 was hosted in one chromosome which is a fusion product of two orthologous chromosomes in non-human primates. This unique chromosomal environment of human ATOH8 probably subjects its expression to the regulation at chromosomal level. We deduce that the great interspecific differences found in both ATOH8 gene sequence and its regulatory elements might be significant for the fine regulation of its spatiotemporal expression and roles of ATOH8, thus orchestrating its function in different tissues and organisms

An Ontology-Based Framework for Complex Urban Object Recognition through Integrating Visual Features and Interpretable Semantics

Although previous works have proposed sophisticatedly probabilistic models that has strong capability of extracting features from remote sensing data (e.g., convolutional neural networks, CNN), the efforts that focus on exploring the human’s semantics on the object to be recognized are required more explorations. Moreover, interpretability of feature extraction becomes a major disadvantage of the state-of-the-art CNN. Especially for the complex urban objects, which varies in geometrical shapes, functional structures, environmental contexts, etc, due to the heterogeneity between low-level data features and high-level semantics, the features derived from remote sensing data alone are limited to facilitate an accurate recognition. In this paper, we present an ontology-based methodology framework for enabling object recognition through rules extracted from the high-level semantics, rather than unexplainable features extracted from a CNN. Firstly, we semantically organize the descriptions and definitions of the object as semantics (RDF-triple rules) through our developed domain ontology. Secondly, we exploit semantic web rule language to propose an encoder model for decomposing the RDF-triple rules based on a multilayer strategy. Then, we map the low-level data features, which are defined from optical satellite image and LiDAR height, to the decomposed parts of RDF-triple rules. Eventually, we apply a probabilistic belief network (PBN) to probabilistically represent the relationships between low-level data features and high-level semantics, as well as a modified TanH function is used to optimize the recognition result. The experimental results on lacking of the training process based on data samples show that our proposed approach can reach an accurate recognition with high-level semantics. This work is conducive to the development of complex urban object recognition toward the fields including multilayer learning algorithms and knowledge graph-based relational reinforcement learning

Landslide Susceptibility Assessment Model Construction Using Typical Machine Learning for the Three Gorges Reservoir Area in China

The Three Gorges Reservoir region in China is the Yangtze River Economic Zone’s natural treasure trove. Its natural environment has an important role in development. The unique and fragile ecosystem in the Yangtze River’s Three Gorges Reservoir region is prone to natural disasters, including soil erosion, landslides, debris flows, landslides, and earthquakes. Therefore, to better alleviate these threats, an accurate and comprehensive assessment of the susceptibility of this area is required. In this study, based on the collection of relevant data and existing research results, we applied machine learning models, including logistic regression (LR), the random forest model (RF), and the support vector machine (SVM) model, to analyze landslide susceptibility in the Yangtze River’s Three Gorges Reservoir region to analyze landslide events in the whole study region. The models identified five categories (i.e., topographic, geological, ecological, meteorological, and human engineering activities), with nine independent variables, influencing landslide susceptibility. The accuracy of landslide susceptibility derived from different models and raster cells was then verified by the accuracy, recall, F1-score, ROC curve, and AUC of each model. The results illustrate that the accuracy of different machine learning algorithms is ranked as SVM > RF > LR. The LR model has the lowest generalization ability. The SVM model performs well in all regions of the study area, with an AUC value of 0.9708 for the entire Three Gorges Reservoir area, indicating that the SVM model possesses a strong spatial generalization ability as well as the highest robustness and can be adapted as a real-time model for assessing regional landslide susceptibility

Crop classification for UAV visible imagery using deep semantic segmentation methods

Unmanned aerial vehicle (UAV) has become a mainstream data collection platform in precision agriculture. For more accessible UAV–visible imagery, the high spatial resolution brings the rich geometric texture features triggered large differences in same crop image's features. We proposed an encoder–decoder's fully convolutional neural network combined with a visible band difference vegetation index (VDVI) to perform deep semantic segmentation of crop image features. This model ensures the accuracy and the generalization ability, while reducing parameters and the operation cost. A case study of crop classification was conducted in Chengdu, China, where classified four crops, namely, maize, rice, balsam pear, and Loropetalum chinese, it was shown more effective results. In addition, this study explores a fine crop classification method based on visible light features, which is feasible with low equipment cost, and has a prospect of application in crop survey based on UAV low altitude remote sensing

Diversification and molecular evolution of ATOH8\it ATOH8, a gene encoding a bHLH transcription factor

$\it ATOH8$ is a bHLH domain transcription factor implicated in the development of the nervous system, kidney, pancreas, retina and muscle. In the present study, we collected sequence of $\it ATOH8$ orthologues from 18 vertebrate species and 24 invertebrate species. The reconstruction of $\it ATOH8$ phylogeny and sequence analysis showed that this gene underwent notable divergences during evolution. For those vertebrate species investigated, we analyzed the gene structure and regulatory elements of $\it ATOH8$. We found that the bHLH domain of vertebrate ATOH8 was highly conserved. Mammals retained some specific amino acids in contrast to the non-mammalian orthologues. Mammals also developed another potential isoform, verified by a human expressed sequence tag (EST). Comparative genomic analyses of the regulatory elements revealed a replacement of the ancestral TATA box by CpG-islands in the eutherian mammals and an evolutionary tendency for TATA box reduction in vertebrates in general. We furthermore identified the region of the effective promoter of human $\it ATOH8$ which could drive the expression of EGFP reporter in the chicken embryo. In the opossum, both the coding region and regulatory elements of $\it ATOH8$ have some special features, such as the unique extended C-terminus encoded by the third exon and absence of both CpG islands and TATA elements in the regulatory region. Our gene mapping data showed that in human, $\it ATOH8$ was hosted in one chromosome which is a fusion product of two orthologous chromosomes in non-human primates. This unique chromosomal environment of human $\it ATOH8$ probably subjects its expression to the regulation at chromosomal level. We deduce that the great interspecific differences found in both $\it ATOH8$ gene sequence and its regulatory elements might be significant for the fine regulation of its spatiotemporal expression and roles of $\it ATOH8$, thus orchestrating its function in different tissues and organisms