A Model of High-Dimensional Feature Reduction Based on Variable Precision Rough Set and Genetic Algorithm in Medical Image

Aiming at the shortcomings of high feature reduction using traditional rough sets, such as insensitivity with noise data and easy loss of potentially useful information, combining with genetic algorithm, in this paper, a VPRS-GA (Variable Precision Rough Set--Genetic Algorithm) model for high-dimensional feature reduction of medical image is proposed. Firstly, rigid inclusion of the lower approximation is extended to partial inclusion by classification error rate β in the traditional rough set model, and the ability dealing with noise data is improved. Secondly, some factors of feature reduction are considered, such as attribute dependency, attributes reduction length, and gene coding weight. A general framework of fitness function is put forward, and different fitness functions are constructed by using different factors such as weight and classification error rate β. Finally, 98 dimensional features of PET/CT lung tumor ROI are extracted to build decision information table of lung tumor patients. Three kinds of experiments in high-dimensional feature reduction are carried out, using support vector machine to verify the influence of recognition accuracy in different fitness function parameters and classification error rate. Experimental results show that classification accuracy is affected deeply by different weight values under the invariable classification error rate condition and by increasing classification error rate under the invariable weigh value condition. Hence, in order to achieve better recognition accuracy, different problems use suitable parameter combination

Quantitative estimates of collective geo-tagged human activities in response to typhoon Hato using location-aware big data

Location-aware big data from social media have been widely used to quantitatively characterize natural disasters and disaster-induced losses. It is not clear how human activities collectively respond to a disaster. In this study, we examined the collective human activities in response to Typhoon Hato at multi spatial scales using aggregated location request data. We proposed a Multilevel Abrupt Changes Detection (MACD) methodological framework to detect and characterize the abrupt changes in location requests in response to Typhoon Hato. Results show that, at the grid level, most anomaly grids were located within a radius of 53 km around the typhoon trajectory. At the city level, there are significant spatial difference in terms of the human activity recovery duration (230 h on average). At the subnational level, the absolute magnitude of abrupt location request changes is strongly correlated with the typhoon-induced economic losses and the population affected

Quantitative Association between Nighttime Lights and Geo-Tagged Human Activity Dynamics during Typhoon Mangkhut

The daily nighttime lights (NTL) and the amount of location-service requests (NLR) data have been widely used as a proxy for measures of disaster-induced power outages and geo-tagged human activity dynamics. However, the association between the two datasets is not well understood. In this study, we investigated how the NTL signals and geo-tagged human activities changed in response to Typhoon Mangkhut. The confusion matrix is constructed to quantify the changes of the NLR in response to Typhoon Mangkhut, as well as the changes of the NTL signals at the grid level. Geographically-weighted regression and quantile regression were used to examine the associations between the changes of the NTL and the NLR at both grid and county levels. The quantile regressions were also used to quantify the relationships between the dimmed NTL signals and the change of the NLR in disaster damage estimates at the county level. Results show that the percent of the grids with anomalous human activities is significantly correlated with the nearby air pressure and wind speed. Geo-tagged human activities varied in response to the evolution of Mangkhut with significant areal differentiation. Over 69.3% of the grids with significant human activity change is also characterized by declined NTL brightness, which is closely associated with abnormal human activities. Significant log-linear and moderate positive correlations were found between the changes of the NTL and NLR at both the grid and county levels, as well as between the county-level changes of NLR/NTL and the damage estimates. This study shows the geo-tagged human activities are closely associated with the changes of the daily NTL signals in response to Typhoon Mangkhut. The two datasets are complimentary in sensing the typhoon-induced losses and damages

Text to realistic image generation with attentional concatenation generative adversarial networks

In this paper, we propose an Attentional Concatenation Generative Adversarial Network (ACGAN) aiming at generating 1024 × 1024 high-resolution images. First, we propose a multilevel cascade structure, for text-to-image synthesis. During training progress, we gradually add new layers and, at the same time, use the results and word vectors from the previous layer as inputs to the next layer to generate high-resolution images with photo-realistic details. Second, the deep attentional multimodal similarity model is introduced into the network, and we match word vectors with images in a common semantic space to compute a fine-grained matching loss for training the generator. In this way, we can pay attention to the fine-grained information of the word level in the semantics. Finally, the measure of diversity is added to the discriminator, which enables the generator to obtain more diverse gradient directions and improve the diversity of generated samples. The experimental results show that the inception scores of the proposed model on the CUB and Oxford-102 datasets have reached 4.48 and 4.16, improved by 2.75% and 6.42% compared to Attentional Generative Adversarial Networks (AttenGAN). The ACGAN model has a better effect on text-generated images, and the resulting image is closer to the real image

Transcriptome-wide Dynamics of m6A mRNA Methylation During Porcine Spermatogenesis

Spermatogenesis is a continual process that occurs in the testes, in which diploid spermatogonial stem cells (SSCs) differentiate and generate haploid spermatozoa. This highly efficient and intricate process is orchestrated at multiple levels. N6-methyladenosine (m6A), an epigenetic modification prevalent in mRNAs, is implicated in the transcriptional regulation during spermatogenesis. However, the dynamics of m6A modification in non-rodent mammalian species remains unclear. Here, we systematically investigated the profile and role of m6A during spermatogenesis in pigs. By analyzing the transcriptomic distribution of m6A in spermatogonia, spermatocytes, and round spermatids, we identified a globally conserved m6A pattern between porcine and murine genes with spermatogenic function. We found that m6A was enriched in a group of genes that specifically encode the metabolic enzymes and regulators. In addition, transcriptomes in porcine male germ cells could be subjected to the m6A modification. Our data show that m6A plays the regulatory roles during spermatogenesis in pigs, which is similar to that in mice. Illustrations of this point are three genes (SETDB1, FOXO1, and FOXO3) that are crucial to the determination of the fate of SSCs. To the best of our knowledge, this study for the first time uncovers the expression profile and role of m6A during spermatogenesis in large animals and provides insights into the intricate transcriptional regulation underlying the lifelong male fertility in non-rodent mammalian species