54 research outputs found
SALIENCY DETECTION VIA GLOBAL-OBJECT-SEED-GUIDED CELLULAR AUTOMATA
No full textImage saliency detection has attracted much attention in recent years, while several challenging problems are still unsolved, such as inaccurate saliency detection in complex scenes and suppressing salient objects near image borders. In this paper, a novel algorithm is proposed to solve these problems. Firstly, we collect background seeds from image borders by boundary information and construct a background based saliency map via low level features. Then, a novel propagation mechanism named global-object-seed-guided Cellular Automata model is builded. Cellular Automata exploits the intrinsic relevance of similar regions through interactions with neighbors, and global object seeds reduce the difference between dissimilar adjacent regions in the whole salient object. Experimental results on public benchmark datasets demonstrate the superiority of the proposed algorithm over ten state-of-the-art saliency models.CPCI-S(ISTP)[email protected]; [email protected]; [email protected]
Abstract 1540: Creating faithful genetic zebrafish models of pediatric high grade gliomas and MPNSTs
No full textAbstract
Pediatric high-grade gliomas (HGGs) are the leading cause of cancer-related death in children. Despite a slight improvement of patient prognosis over the past decades pediatric HGGs remain largely incurable. Thus, new experimental models are needed to understand the mechanisms of the disease and find more effective treatment options. We previously reported a model of HGGs and malignant peripheral nerve sheath tumors (MPNSTs) which is based on the combined deficiencies in the tumor suppressor genes tp53 and nf1. However, HGG penetrance is very low in this line and most fish develop MPNSTs starting at about 3 months of age. On top of the existing model we used CRISPR/Cas9 to incorporate knock-out mutations in the tumor suppressor genes atrx or suz12 which are described to be involved in pediatric HGG biology. Heterozygous atrx loss-of-function (lof) did not impact tumor onset or penetrance of neither HGGs nor MPNSTs. Since a total loss of atrx was lethal in development, we re-injected effective atrx targeting gRNAs and Cas9 mRNA into the atrx+/- line to create a mosaic atrx-/- genotype. Surprisingly, despite a high mutation efficiency of the remaining atrx allele the re-injection strategy still did not alter tumor onset and penetrance in that model. This suggests that loss of atrx is only effective in HGGs in cooperation with additional hits other than tp53 and nf1. In contrast, loss of suz12 cooperated well with the tp53/nf1-deficient background. As nf1, suz12 is duplicated in zebrafish (suz12a and suz12b) resulting in 4 alleles of each gene per cell. When at least 2 out of 4 alleles of either suz12a or suz12b were lost, MPNST onset was accelerated. This effect was much stronger in tp53-/-, nf1a+/-, nf1b-/- fish (5-7 weeks) compared to tp53-/-, nf1a+/+, nf1b-/- siblings (3-4 weeks). This indicates that the tumor supporting effect of suz12 lof increases the more nf1 levels decrease. However, HGG onset still remained unchanged. We hypothesize that efficient onset of HGGs in our model requires the presence of an activated oncogene. Specific missense mutations in H3F3A are reported to be implicated in HGG progression in children and young adults. Thus, we overexpressed zebrafish h3f3a-K27M or -G34R mutant sequences in the tp53/nf1/atrx/suz12-deficient line and are currently investigating possible changes in tumor biology. Our zebrafish models of pediatric HGGs and MPNSTs will be useful to dissect the mechanisms underlying the cooperation among driver mutations and for small molecule screens to identify specific inhibitors of cell growth and survival in these malignancies.
Citation Format: Felix Oppel, Ting Tao, Shuning He, Mark W. Zimmerman, Dong H. Ki, Nina Weichert, A Thomas Look. Creating faithful genetic zebrafish models of pediatric high grade gliomas and MPNSTs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1540. doi:10.1158/1538-7445.AM2017-1540</jats:p
Remediation of Copper Contaminated Kaolin by Electrokinetics Coupled with Permeable Reactive Barrier
No full textAbstractElectrokinetics is an in situ soil remediation technique by which the flow direction of the pollutants can be controlled and the soil with low permeability can be treated. In this study, the remediation of copper contaminated kaolin by electrokinetic process coupled with activated carbon permeable reactive barrier (PRB) was investigated. The experimental results showed that the integration of PRB with electrokinetics successfully removed copper from kaolin with pH control of the catholyte. The average removal rate reached the highest of 96.60% when the initial Cu2+ concentration was 2000mg/kg. Compared to the electrokinetic process without PRB, the application of the coupled system could reduce the pollution of the electrolyte
Post-processing Procedures for Passive GPS based Travel Survey
No full textAbstractA challenge in posteriori data processing for passive GPS based travel survey, which constitute the heart of this paper, is to develop a series of methods to automatically restore the sequences of data points, both in space and time. It means the trips and activities occurred in the survey time should be identifiable chronologically and those identified by the program should respect this definition convention. Reference to the research results of our colleagues, and by combining the experiences of other French travel survey and personal mobility survey at Lille, a series of methods has been developed and put into application. The data outcome is ready for further applications
Identification of Tool-Wear State Using Information Fusion and SSA–BP Neural Network
No full textIn modern manufacturing, cutting tools are essential for cutting processes, and their wear state directly affects the processing accuracy, production efficiency, and product quality. Identification of the tool-wear state using a single sensor is insufficient to satisfy the requirements of high-precision, high-efficiency machining. To address this problem, this paper proposes a novel approach to identify the tool-wear state using information fusion technology and the sparrow search algorithm (SSA)–backpropagation (BP) neural network framework. This method uses a principal component analysis (PCA) to fuse multi-domain features extracted from three-way vibration signals, power signals, and temperature signals. Subsequently, the optimal initial threshold and weight of the BP neural network are optimized using the SSA to prevent the network from falling into the local optimum and accelerate the convergence of the algorithm. Lastly, a tool-wear-state identification model based on the SSA–BP neural network is constructed. Experimental results show that the proposed method has an identification accuracy of 98.33%, precision rate of 98.81%, recall rate of 97.96%, and F1 score of 98.36%
Performance optimization of latent heat storage by structural parameters and operating conditions using Al-based alloy as phase change material
No full textAn Improved Coordinate Update Method for the Identification of Adaptive Hinging Hyperplanes Model
No full textDatasets for replicating the paper "Raman Spectrum Matching with Contrastive Representation Learning"
No full textDatasets Mineral and Organic for replicating the paper "Raman spectrum matching with contrastive learning".
The detailed instruction about how to use the dataset, please visit our Github Repository.
To use these two datasets, please cite:
@inproceedings{Lafuente2016ThePO,
title={The power of databases: The RRUFF project},
author={B. Lafuente and R. Downs and Hexiong Yang and N. Stone},
booktitle = {Highlights in Mineralogical Crystallography},
year={2016}
}
@article{organic_dataset,
author = {Zhang, Rui and Xie, Huimin and Cai, Shuning and Hu, Yong and Liu, Guo-kun and Hong, Wenjing and Tian, Zhong-qun},
title = {Transfer-learning-based Raman spectra identification},
journal = {Journal of Raman Spectroscopy},
volume = {51},
number = {1},
pages = {176-186},
keywords = {deep learning, Raman spectroscopy, transfer learning},
year = {2020}
}
@Article{D2AN00403H,
author ="Li, Bo and Schmidt, Mikkel N. and Alstrøm, Tommy S.",
title ="Raman spectrum matching with contrastive representation learning",
journal ="Analyst",
year ="2022",
volume ="147",
issue ="10",
pages ="2238-2246",
publisher ="The Royal Society of Chemistry",
doi ="https://doi.org/10.1039/d2an00403h",
url ="http://dx.doi.org/10.1039/D2AN00403H",
}</p
- …
