118 research outputs found
Analysis of Co-Associated Transcription Factors via Ordered Adjacency Differences on Motif Distribution
Transcription factors (TFs) binding to specific DNA sequences or motifs, are elementary to the regulation of transcription. The gene is regulated by a combination of TFs in close proximity. Analysis of co-TFs is an important problem in understanding the mechanism of transcriptional regulation. Recently, ChIP-seq in mapping TF provides a large amount of experimental data to analyze co-TFs. Several studies show that if two TFs are co-associated, the relative distance between TFs exhibits a peak-like distribution. In order to analyze co-TFs, we develop a novel method to evaluate the associated situation between TFs. We design an adjacency score based on ordered differences, which can illustrate co-TF binding affinities for motif analysis. For all candidate motifs, we calculate corresponding adjacency scores, and then list descending-order motifs. From these lists, we can find co-TFs for candidate motifs. On ChIP-seq datasets, our method obtains best AUC results on five datasets, 0.9432 for NMYC, 0.9109 for KLF4, 0.9006 for ZFX, 0.8892 for ESRRB, 0.8920 for E2F1. Our method has great stability on large sample datasets. AUC results of our method on all datasets are above 0.8
Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions
Though deep learning-based object detection methods have achieved promising
results on the conventional datasets, it is still challenging to locate objects
from the low-quality images captured in adverse weather conditions. The
existing methods either have difficulties in balancing the tasks of image
enhancement and object detection, or often ignore the latent information
beneficial for detection. To alleviate this problem, we propose a novel
Image-Adaptive YOLO (IA-YOLO) framework, where each image can be adaptively
enhanced for better detection performance. Specifically, a differentiable image
processing (DIP) module is presented to take into account the adverse weather
conditions for YOLO detector, whose parameters are predicted by a small
convolutional neural net-work (CNN-PP). We learn CNN-PP and YOLOv3 jointly in
an end-to-end fashion, which ensures that CNN-PP can learn an appropriate DIP
to enhance the image for detection in a weakly supervised manner. Our proposed
IA-YOLO approach can adaptively process images in both normal and adverse
weather conditions. The experimental results are very encouraging,
demonstrating the effectiveness of our proposed IA-YOLO method in both foggy
and low-light scenarios.Comment: AAAI 2022, Preprint version with Appendi
Improvement of Phylogenetic Method to Analyze Compositional Heterogeneity
Background: Phylogenetic analysis is a key way to understand current research in the biological processes and detect theory in evolution of natural selection. The evolutionary relationship between species is generally reflected in the form of phylogenetic trees. Many methods for constructing phylogenetic trees, are based on the optimization criteria. We extract the biological data via modeling features, and then compare these characteristics to study the biological evolution between species.
Results: Here, we use maximum likelihood and Bayesian inference method to establish phylogenetic trees; multi-chain Markov chain Monte Carlo sampling method can be used to select optimal phylogenetic tree, resolving local optimum problem. The correlation model of phylogenetic analysis assumes that phylogenetic trees are built on homogeneous data, however there exists a large deviation in the presence of heterogeneous data. We use conscious detection to solve compositional heterogeneity. Our method is evaluated on two sets of experimental data, a group of bacterial 16S ribosomal RNA gene data, and a group of genetic data with five homologous species.
Conclusions: Our method can obtain accurate phylogenetic trees on the homologous data, and also detect the compositional heterogeneity of experimental data. We provide an efficient method to enhance the accuracy of generated phylogenetic tre
A Novel Peptide Binding Prediction Approach for HLA-DR Molecule Based on Sequence and Structural Information
MHC molecule plays a key role in immunology, and the molecule binding reaction with peptide is an important prerequisite for T cell immunity induced. MHC II molecules do not have conserved residues, so they appear as open grooves. As a consequence, this will increase the difficulty in predicting MHC II molecules binding peptides. In this paper, we aim to propose a novel prediction method for MHC II molecules binding peptides. First, we calculate sequence similarity and structural similarity between different MHC II molecules. Then, we reorder pseudosequences according to descending similarity values and use a weight calculation formula to calculate new pocket profiles. Finally, we use three scoring functions to predict binding cores and evaluate the accuracy of prediction to judge performance of each scoring function. In the experiment, we set a parameter in the weight formula. By changing value, we can observe different performances of each scoring function. We compare our method with the best function to some popular prediction methods and ultimately find that our method outperforms them in identifying binding cores of HLA-DR molecules
Indeterminate pulmonary subsolid nodules in patients with no history of cancer: growing prediction, CT pattern, and pathological diagnosis
PURPOSEWe aimed to evaluate and compare the growth patterns among pathological types of inde- terminate subsolid nodules in patients without a history of cancer as observed on computed tomography (CT).METHODSThis retrospective study included 77 consecutive patients with 80 indeterminate subsolid nod- ules on unenhanced thin-section CT. Subsolid nodules were classified into 2 growth pattern groups based on volume: growth (n = 35) and non-growth (n = 42). According to the pathologi- cal diagnosis, subsolid nodules were further subdivided into 3 groups: adenocarcinoma in situ (growth, n = 8 vs. non-growth, n = 22), minimally invasive adenocarcinoma (n = 14 vs. n = 15), and invasive adenocarcinoma (n=13 vs. n=5). Kaplan–Meier and Cox proportional hazards regres- sion analyses were performed to identify the risk factors for subsolid nodules growth. The CT findings of the 35 subsolid nodules in the growth group were compared among the 3 pathologi- cal groups.RESULTSIn the growth group, the overall mean volume doubling time and mass doubling time (MDT) were 811.5 days and 616.5 days, respectively. Patient’s age (odds ratio=1.041, P=.045) and CT subtype of non-solid nodule and part-solid nodule (odds ratio=3.430, P=.002) could predict subsolid nodule growth. The baseline volume, mass, and mean CT value were larger in the inva- sive adenocarcinoma group than in the adenocarcinoma in situ group (all P < .01). The shortest volume doubling time was observed in the invasive adenocarcinoma group, followed by the minimally invasive adenocarcinoma group and the adenocarcinoma in situ group. A shorter mass doubling time was observed in the minimally invasive adenocarcinoma group than in the adenocarcinoma in situ group (all P < .02).CONCLUSIONAs age increases, the risk of pulmonary subsolid nodule growth increases by 4% each year, and part-solid nodules have a 3 times higher risk of growth compared to non-solid nodules in patients with no history of cancer. Subsolid nodules with more aggressive pathological charac- teristics grow at a faster rate
PSR J1926-0652: A Pulsar with Interesting Emission Properties Discovered at FAST
We describe PSR J1926-0652, a pulsar recently discovered with the
Five-hundred-meter Aperture Spherical radio Telescope (FAST). Using sensitive
single-pulse detections from FAST and long-term timing observations from the
Parkes 64-m radio telescope, we probed phenomena on both long and short time
scales. The FAST observations covered a wide frequency range from 270 to 800
MHz, enabling individual pulses to be studied in detail. The pulsar exhibits at
least four profile components, short-term nulling lasting from 4 to 450 pulses,
complex subpulse drifting behaviours and intermittency on scales of tens of
minutes. While the average band spacing P3 is relatively constant across
different bursts and components, significant variations in the separation of
adjacent bands are seen, especially near the beginning and end of a burst. Band
shapes and slopes are quite variable, especially for the trailing components
and for the shorter bursts. We show that for each burst the last detectable
pulse prior to emission ceasing has different properties compared to other
pulses. These complexities pose challenges for the classic carousel-type
models.Comment: 13pages with 12 figure
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