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Metabolic Pathways Enhancement Confers Poor Prognosis in p53 Exon Mutant Hepatocellular Carcinoma.
RNA-Sequencing (RNA-Seq), the most commonly used sequencing application tool, is not only a method for measuring gene expression but also an excellent media to detect important structural variants such as single nucleotide variants (SNVs), insertion/deletion (Indels), or fusion transcripts. The Cancer Genome Atlas (TCGA) contains genomic data from a variety of cancer types and also provides the raw data generated by TCGA consortium. p53 is among the top 10 somatic mutations associated with hepatocellular carcinoma (HCC). The aim of the present study was to analyze concordant different gene profiles and the priori defined set of genes based on p53 mutation status in HCC using RNA-Seq data. In the study, expression profile of 11 799 genes on 42 paired tumor and adjacent normal tissues was collected, processed, and further stratified by the mutated versus normal p53 expression. Furthermore, we used a knowledge-based approach Gene Set Enrichment Analysis (GSEA) to compare between normal and p53 mutation gene expression profiles. The statistical significance (nominal P value) of the enrichment score (ES) genes was calculated. The ranked gene list that reflects differential expression between p53 wild-type and mutant genotypes was then mapped to metabolic process by KEGG, an encyclopedia of genes and genomes to assign functional meanings. These approaches enable us to identify pathways and potential target gene/pathways that are highly expressed in p53 mutated HCC. Our analysis revealed 2 genes, the hexokinase 2 (HK2) and Enolase 1 (ENO1), were conspicuous of red pixel in the heatmap. To further explore the role of these genes in HCC, the overall survival plots by Kaplan-Meier method were performed for HK2 and ENO1 that revealed high HK2 and ENO1 expression in patients with HCC have poor prognosis. These results suggested that these glycolysis genes are associated with mutated-p53 in HCC that may contribute to poor prognosis. In this proof-of-concept study, we proposed an approach for identifying novel potential therapeutic targets in human HCC with mutated p53. These approaches can take advantage of the massive next-generation sequencing (NGS) data generated worldwide and make more out of it by exploring new potential therapeutic targets
FOLT: Fast Multiple Object Tracking from UAV-captured Videos Based on Optical Flow
Multiple object tracking (MOT) has been successfully investigated in computer
vision.
However, MOT for the videos captured by unmanned aerial vehicles (UAV) is
still challenging due to small object size, blurred object appearance, and very
large and/or irregular motion in both ground objects and UAV platforms.
In this paper, we propose FOLT to mitigate these problems and reach fast and
accurate MOT in UAV view.
Aiming at speed-accuracy trade-off, FOLT adopts a modern detector and
light-weight optical flow extractor to extract object detection features and
motion features at a minimum cost.
Given the extracted flow, the flow-guided feature augmentation is designed to
augment the object detection feature based on its optical flow, which improves
the detection of small objects.
Then the flow-guided motion prediction is also proposed to predict the
object's position in the next frame, which improves the tracking performance of
objects with very large displacements between adjacent frames.
Finally, the tracker matches the detected objects and predicted objects using
a spatially matching scheme to generate tracks for every object.
Experiments on Visdrone and UAVDT datasets show that our proposed model can
successfully track small objects with large and irregular motion and outperform
existing state-of-the-art methods in UAV-MOT tasks.Comment: Accepted by ACM Multi-Media 202
Template-Based Structure Prediction and Classification of Transcription Factors in \u3ci\u3eArabidopsis thaliana\u3c/i\u3e
Transcription factors (TFs) play important roles in plants. However, there is no systematic study of their structures and functions of most TFs in plants. Here, we performed template-based structure prediction for all TFs in Arabidopsis thaliana, with their full-length sequences as well as C-terminal and N-terminal regions. A total of 2,918 model structures were obtained with a high confidence score. We find that TF families employ only a smaller number of templates for DNA-binding domains (DBD) but a diverse number of templates for transcription regulatory domains (TRD). Although TF families are classified according to DBD, their sizes have a significant correlation with the number of unique non-DNA-binding templates employed in the family (Pearson correlation coefficient of 0.74). That is, the size of TF family is related to its functional diversity. Network analysis reveals new connections between TF families based on shared TRD or DBD templates; 81% TF families share DBD and 67% share TRD templates. Two large fully connected family clusters in this network are observed along with 69 island families. In addition, 25 genes with unknown functions are found to be DNA-binding and/or TF factors according to predicted structures. This work provides a global view of the classification of TFs based on their DBD or TRD templates, and hence, a deeper understanding of DNA-binding and regulatory functions from structural perspective. All structural models of TFs are deposited in the online database for public usage at http://sysbio.unl.edu/AthTF
Template-Based Structure Prediction and Classification of Transcription Factors in \u3ci\u3eArabidopsis thaliana\u3c/i\u3e
Transcription factors (TFs) play important roles in plants. However, there is no systematic study of their structures and functions of most TFs in plants. Here, we performed template-based structure prediction for all TFs in Arabidopsis thaliana, with their full-length sequences as well as C-terminal and N-terminal regions. A total of 2,918 model structures were obtained with a high confidence score. We find that TF families employ only a smaller number of templates for DNA-binding domains (DBD) but a diverse number of templates for transcription regulatory domains (TRD). Although TF families are classified according to DBD, their sizes have a significant correlation with the number of unique non-DNA-binding templates employed in the family (Pearson correlation coefficient of 0.74). That is, the size of TF family is related to its functional diversity. Network analysis reveals new connections between TF families based on shared TRD or DBD templates; 81% TF families share DBD and 67% share TRD templates. Two large fully connected family clusters in this network are observed along with 69 island families. In addition, 25 genes with unknown functions are found to be DNA-binding and/or TF factors according to predicted structures. This work provides a global view of the classification of TFs based on their DBD or TRD templates, and hence, a deeper understanding of DNA-binding and regulatory functions from structural perspective. All structural models of TFs are deposited in the online database for public usage at http://sysbio.unl.edu/AthTF
Weight-dependent Gates for Differentiable Neural Network Pruning
In this paper, we propose a simple and effective network pruning framework,
which introduces novel weight-dependent gates to prune filter adaptively. We
argue that the pruning decision should depend on the convolutional weights, in
other words, it should be a learnable function of filter weights. We thus
construct the weight-dependent gates (W-Gates) to learn the information from
filter weights and obtain binary filter gates to prune or keep the filters
automatically. To prune the network under hardware constraint, we train a
Latency Predict Net (LPNet) to estimate the hardware latency of candidate
pruned networks. Based on the proposed LPNet, we can optimize W-Gates and the
pruning ratio of each layer under latency constraint. The whole framework is
differentiable and can be optimized by gradient-based method to achieve a
compact network with better trade-off between accuracy and efficiency. We have
demonstrated the effectiveness of our method on Resnet34, Resnet50 and
MobileNet V2, achieving up to 1.33/1.28/1.1 higher Top-1 accuracy with lower
hardware latency on ImageNet. Compared with state-of-the-art pruning methods,
our method achieves superior performance.Comment: ECCV worksho
Dirac-Surface-State Modulated Spin Dynamics in a Ferrimagnetic Insulator at Room Temperature
This work demonstrates dramatically modified spin dynamics of magnetic
insulator (MI) by the spin-momentum locked Dirac surface states of the adjacent
topological insulator (TI) which can be harnessed for spintronic applications.
As the Bi-concentration x is systematically tuned in 5 nm thick (BixSb1-x)2Te3
TI film, the weight of the surface relative to bulk states peaks at x = 0.32
when the chemical potential approaches the Dirac point. At this concentration,
the Gilbert damping constant of the precessing magnetization in 10 nm thick
Y3Fe5O12 MI film in the MI/TI heterostructures is enhanced by an order of
magnitude, the largest among all concentrations. In addition, the MI acquires
additional strong magnetic anisotropy that favors the in-plane orientation with
similar Bi-concentration dependence. These extraordinary effects of the Dirac
surface states distinguish TI from other materials such as heavy metals in
modulating spin dynamics of the neighboring magnetic layer
Decoupled Contrastive Learning
Contrastive learning (CL) is one of the most successful paradigms for
self-supervised learning (SSL). In a principled way, it considers two augmented
"views" of the same image as positive to be pulled closer, and all other images
as negative to be pushed further apart. However, behind the impressive success
of CL-based techniques, their formulation often relies on heavy-computation
settings, including large sample batches, extensive training epochs, etc. We
are thus motivated to tackle these issues and establish a simple, efficient,
yet competitive baseline of contrastive learning. Specifically, we identify,
from theoretical and empirical studies, a noticeable negative-positive-coupling
(NPC) effect in the widely used InfoNCE loss, leading to unsuitable learning
efficiency concerning the batch size. By removing the NPC effect, we propose
decoupled contrastive learning (DCL) loss, which removes the positive term from
the denominator and significantly improves the learning efficiency. DCL
achieves competitive performance with less sensitivity to sub-optimal
hyperparameters, requiring neither large batches in SimCLR, momentum encoding
in MoCo, or large epochs. We demonstrate with various benchmarks while
manifesting robustness as much less sensitive to suboptimal hyperparameters.
Notably, SimCLR with DCL achieves 68.2% ImageNet-1K top-1 accuracy using batch
size 256 within 200 epochs pre-training, outperforming its SimCLR baseline by
6.4%. Further, DCL can be combined with the SOTA contrastive learning method,
NNCLR, to achieve 72.3% ImageNet-1K top-1 accuracy with 512 batch size in 400
epochs, which represents a new SOTA in contrastive learning. We believe DCL
provides a valuable baseline for future contrastive SSL studies.Comment: Accepted by ECCV202
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