Profiling transcription factor activity dynamics using intronic reads in time-series transcriptome data

Activities of transcription factors (TFs) are temporally modulated to regulate dynamic cellular processes, including development, homeostasis, and disease. Recent developments of bioinformatic tools have enabled the analysis of TF activities using transcriptome data. However, because these methods typically use exon-based target expression levels, the estimated TF activities have limited temporal accuracy. To address this, we proposed a TF activity measure based on intron-level information in time-series RNA-seq data, and implemented it to decode the temporal control of TF activities during dynamic processes. We showed that TF activities inferred from intronic reads can better recapitulate instantaneous TF activities compared to the exon-based measure. By analyzing public and our own time-series transcriptome data, we found that intron-based TF activities improve the characterization of temporal phasing of cycling TFs during circadian rhythm, and facilitate the discovery of two temporally opposing TF modules during T cell activation. Collectively, we anticipate that the proposed approach would be broadly applicable for decoding global transcriptional architecture during dynamic processes.

Single Image Re ection Removal via Deep Feature Contrast

Removing undesired re ection from a single image is in demand for computational photography. Re ection removal methods are gradually effective because of the fast development of deep neural networks. However, current results of re ection removal methods usually leave salient re ection residues due to the challenge of recognizing diverse re ection patterns. In this paper, we present a one-stage re ection removal framework with an end-to-end manner that considers both low-level information correlation and efficient feature separation. Our approach employs the criss-cross attention mechanism to extract low-level features and to efficiently enhance contextual correlation. To thoroughly remove re ection residues in the background image, we punish the similar texture feature by contrasting the parallel feature separa- tion networks, and thus unrelated textures in the background image could be progressively separated during model training. Experiments on both real-world and synthetic datasets manifest our approach can reach the state-of-the-art effect quantitatively and qualitatively.

B-FGC-Net: A Building Extraction Network from High Resolution Remote Sensing Imagery

Deep learning (DL) shows remarkable performance in extracting buildings from high resolution remote sensing images. However, how to improve the performance of DL based methods, especially the perception of spatial information, is worth further study. For this purpose, we proposed a building extraction network with feature highlighting, global awareness, and cross level information fusion (B-FGC-Net). The residual learning and spatial attention unit are introduced in the encoder of the B-FGC-Net, which simplifies the training of deep convolutional neural networks and highlights the spatial information representation of features. The global feature information awareness module is added to capture multiscale contextual information and integrate the global semantic information. The cross level feature recalibration module is used to bridge the semantic gap between low and high level features to complete the effective fusion of cross level information. The performance of the proposed method was tested on two public building datasets and compared with classical methods, such as UNet, LinkNet, and SegNet. Experimental results demonstrate that B-FGC-Net exhibits improved profitability of accurate extraction and information integration for both small and large scale buildings. The IoU scores of B-FGC-Net on WHU and INRIA Building datasets are 90.04% and 79.31%, respectively. B-FGC-Net is an effective and recommended method for extracting buildings from high resolution remote sensing images.

Cascaded Cross-Layer Fusion Network for Pedestrian Detection

The detection method based on anchor-free not only reduces the training cost of object detection, but also avoids the imbalance problem caused by an excessive number of anchors. However, these methods only pay attention to the impact of the detection head on the detection performance, thus ignoring the impact of feature fusion on the detection performance. In this article, we take pedestrian detection as an example and propose a one-stage network Cascaded Cross-layer Fusion Network (CCFNet) based on anchor-free. It consists of Cascaded Cross-layer Fusion module (CCF) and novel detection head. Among them, CCF fully considers the distribution of high-level information and low-level information of feature maps under different stages in the network. First, the deep network is used to remove a large amount of noise in the shallow features, and finally, the high-level features are reused to obtain a more complete feature representation. Secondly, for the pedestrian detection task, a novel detection head is designed, which uses the global smooth map (GSMap) to provide global information for the center map to obtain a more accurate center map. Finally, we verified the feasibility of CCFNet on the Caltech and CityPersons datasets.

Breast Cancer Histopathological Image Classification using Stochastic Dilated Residual Ghost Model

A new deep learning-based classification model called the Stochastic Dilated Residual Ghost (SDRG) was proposed in this work for categorizing histopathology images of breast cancer. The SDRG model used the proposed Multiscale Stochastic Dilated Convolution (MSDC) model, a ghost unit, stochastic upsampling, and downsampling units to categorize breast cancer accurately. This study addresses four primary issues: first, strain normalization was used to manage color divergence, data augmentation with several factors was used to handle the overfitting. The second challenge is extracting and enhancing tiny and low-level information such as edge, contour, and color accuracy; it is done by the proposed multiscale stochastic and dilation unit. The third contribution is to remove redundant or similar information from the convolution neural network using a ghost unit. According to the assessment findings, the SDRG model scored overall 95.65 percent accuracy rates in categorizing images with a precision of 99.17 percent, superior to state-of-the-art approaches.

Multistage Polymerization Network for Multiperson Pose Estimation

Multiperson pose estimation is an important and complex problem in computer vision. It is regarded as the problem of human skeleton joint detection and solved by the joint heat map regression network in recent years. The key of achieving accurate pose estimation is to learn robust and discriminative feature maps. Although the current methods have made significant progress through interlayer fusion and intralevel fusion of feature maps, few works pay attention to the combination of the two methods. In this paper, we propose a multistage polymerization network (MPN) for multiperson pose estimation. The MPN continuously learns rich underlying spatial information by fusing features within the layers. The MPN also adds hierarchical connections between feature maps at the same resolution for interlayer fusion, so as to reuse low-level spatial information and refine high-level semantic information to obtain accurate keypoint representation. In addition, we observe a lack of connection between the output low-level information and the high-level information. To solve this problem, an effective shuffled attention mechanism (SAM) is proposed. The shuffle aims to promote the cross-channel information exchange between pyramid feature maps, while attention makes a trade-off between the low-level and high-level representations of the output features. As a result, the relationship between the space and the channel of the feature map is further enhanced. Evaluation of the proposed method is carried out on public datasets, and experimental results show that our method has better performance than current methods.

Accumulation and maintenance of information in evolution

Selection accumulates information in the genome - it guides stochastically evolving populations towards states (genotype frequencies) that would be unlikely under neutrality. This can be quantified as the Kullback-Leibler (KL) divergence between the actual distribution of genotype frequencies and the corresponding neutral distribution. First, we show that this population-level information sets an upper bound on the information at the level of genotype and phenotype, limiting how precisely they can be specified by selection. Next, we study how the accumulation and maintenance of information is limited by the cost of selection, measured as the genetic load or the relative fitness variance, both of which we connect to the control-theoretic KL cost of control. The information accumulation rate is upper bounded by the population size times the cost of selection. This bound is very general, and applies across models (Wright-Fisher, Moran, diffusion) and to arbitrary forms of selection, mutation and recombination. Finally, the cost of maintaining information depends on how it is encoded: specifying a single allele out of two is expensive, but one bit encoded among many weakly specified loci (as in a polygenic trait) is cheap.