Features of Methylation and Gene Expression in the Promoter-Associated CpG Islands Using Human Methylome Data

CpG islands are typically located in the 5′ end of genes and considered as gene markers because they play important roles in gene regulation via epigenetic change. In this study, we compared the features of CpG islands identified by several major algorithms by setting the parameter cutoff values in order to obtain a similar number of CpG islands in a genome. This approach allows us to systematically compare the methylation and gene expression patterns in the identified CpG islands. We found that Takai and Jones' algorithm tends to identify longer CpG islands but with weaker CpG island features (e.g., lower GC content and lower ratio of the observed over expected CpGs) and higher methylation level. Conversely, the CpG clusters identified by Hackenberg et al.'s algorithm using stringent criteria are shorter and have stronger features and lower methylation level. In addition, we used the genome-wide base-resolution methylation profile in two cell lines to show that genes with a lower methylation level at the promoter-associated CpG islands tend to express in more tissues and have stronger expression. Our results validated that the DNA methylation of promoter-associated CpG islands suppresses gene expression at the genome level

DeepCL: Deep Change Feature Learning on Remote Sensing Images in the Metric Space

Change detection (CD) is an important yet challenging task in the Earth observation field for monitoring Earth surface dynamics. The advent of deep learning techniques has recently propelled automatic CD into a technological revolution. Nevertheless, deep learning-based CD methods are still plagued by two primary issues: 1) insufficient temporal relationship modeling and 2) pseudo-change misclassification. To address these issues, we complement the strong temporal modeling ability of metric learning with the prominent fitting ability of segmentation and propose a deep change feature learning (DeepCL) framework for robust and explainable CD. Firstly, we designed a hard sample-aware contrastive loss, which reweights the importance of hard and simple samples. This loss allows for explicit modeling of the temporal correlation between bi-temporal remote sensing images. Furthermore, the modeled temporal relations are utilized as knowledge prior to guide the segmentation process for detecting change regions. The DeepCL framework is thoroughly evaluated both theoretically and experimentally, demonstrating its superior feature discriminability, resilience against pseudo changes, and adaptability to a variety of CD algorithms. Extensive comparative experiments substantiate the quantitative and qualitative superiority of DeepCL over state-of-the-art CD approaches.Comment: 12 pages,7 figures, submitted to IEEE Transactions on Image Processin

Quantum multipartite maskers vs quantum error-correcting codes

Since masking of quantum information was introduced by Modi et al. in [PRL 120, 230501 (2018)], many discussions on this topic have been published. In this paper, we consider relationship between quantum multipartite maskers (QMMs) and quantum error-correcting codes (QECCs). We say that a subset $Q$ of pure states of a system $K$ can be masked by an operator $S$ into a multipartite system \H^{(n)} if all of the image states $S|\psi\>$ of states $|\psi\>$ in $Q$ have the same marginal states on each subsystem. We call such an $S$ a QMM of $Q$. By establishing an expression of a QMM, we obtain a relationship between QMMs and QECCs, which reads that an isometry is a QMM of all pure states of a system if and only if its range is a QECC of any one-erasure channel. As an application, we prove that there is no an isometric universal masker from \C^2 into \C^2\otimes\C^2\otimes\C^2 and then the states of \C^3 can not be masked isometrically into \C^2\otimes\C^2\otimes\C^2. This gives a consummation to a main result and leads to a negative answer to an open question in [PRA 98, 062306 (2018)]. Another application is that arbitrary quantum states of \C^d can be completely hidden in correlations between any two subsystems of the tripartite system \C^{d+1}\otimes\C^{d+1}\otimes\C^{d+1}, while arbitrary quantum states cannot be completely hidden in the correlations between subsystems of a bipartite system [PRL 98, 080502 (2007)].Comment: This is a revision about arXiv:2004.14540. In the present version, $k$ and $j$ old Eq. (2.2) have been exchanged and the followed three equations have been correcte

Fast Multi-Task SCCA Learning with Feature Selection for Multi-Modal Brain Imaging Genetics

Brain imaging genetics studies the genetic basis of brain structures and functions via integrating both genotypic data such as single nucleotide polymorphism (SNP) and imaging quantitative traits (QTs). In this area, both multi-task learning (MTL) and sparse canonical correlation analysis (SCCA) methods are widely used since they are superior to those independent and pairwise univariate analyses. MTL methods generally incorporate a few of QTs and are not designed for feature selection from a large number of QTs; while existing SCCA methods typically employ only one modality of QTs to study its association with SNPs. Both MTL and SCCA encounter computational challenges as the number of SNPs increases. In this paper, combining the merits of MTL and SCCA, we propose a novel multi-task SCCA (MTSCCA) learning framework to identify bi-multivariate associations between SNPs and multi-modal imaging QTs. MTSCCA could make use of the complementary information carried by different imaging modalities. Using the G2,1-norm regularization, MTSCCA treats all SNPs in the same group together to enforce sparsity at the group level. The l2,1-norm penalty is used to jointly select features across multiple tasks for SNPs, and across multiple modalities for QTs. A fast optimization algorithm is proposed using the grouping information of SNPs. Compared with conventional SCCA methods, MTSCCA obtains improved performance regarding both correlation coefficients and canonical weights patterns. In addition, our method runs very fast and is easy-to-implement, and thus could provide a powerful tool for genome-wide brain-wide imaging genetic studies

Detecting extreme-mass-ratio inspirals for space-borne detectors with deep learning

One of the primary objectives for space-borne gravitational wave detectors is the detection of extreme-mass-ratio inspirals (EMRIs). This undertaking poses a substantial challenge because of the complex and long EMRI signals, further complicated by their inherently faint signal. In this research, we introduce a 2-layer Convolutional Neural Network (CNN) approach to detect EMRI signals for space-borne detectors. Our method employs the Q-transform for data preprocessing, effectively preserving EMRI signal characteristics while minimizing data size. By harnessing the robust capabilities of CNNs, we can reliably distinguish EMRI signals from noise, particularly when the signal-to-noise~(SNR) ratio reaches 50, a benchmark considered a ``golden'' EMRI. At the meantime, we incorporate time-delay interferometry (TDI) to ensure practical utility. We assess our model's performance using a 0.5-year dataset, achieving a true positive rate~(TPR) of 94.2\% at a 1\% false positive rate~(FPR) across various signal-to-noise ratio form 50-100, with 91\% TPR and 1\% FPR at an SNR of 50. This study underscores the promise of incorporating deep learning methods to advance EMRI data analysis, potentially leading to rapid EMRI signal detection.Comment: 12 pages, 8 figures, 2 table

The detection, extraction and parameter estimation of extreme-mass-ratio inspirals with deep learning

One of the primary goals of space-borne gravitational wave detectors is to detect and analyze extreme-mass-ratio inspirals (EMRIs). This endeavor presents a significant challenge due to the complex and lengthy EMRI signals, further compounded by their inherently faint nature. In this letter, we introduce a 2-layer Convolutional Neural Network (CNN) approach to detect EMRI signals for space-borne detectors, achieving a true positive rate (TPR) of 96.9 % at a 1 % false positive rate (FPR) for signal-to-noise ratio (SNR) from 50 to 100. Especially, the key intrinsic parameters of EMRIs such as mass and spin of the supermassive black hole (SMBH) and the initial eccentricity of the orbit can be inferred directly by employing a VGG network. The mass and spin of the SMBH can be determined at 99 % and 92 % respectively. This will greatly reduce the parameter spaces and computing cost for the following Bayesian parameter estimation. Our model also has a low dependency on the accuracy of the waveform model. This study underscores the potential of deep learning methods in EMRI data analysis, enabling the rapid detection of EMRI signals and efficient parameter estimation .Comment: 6 pages, 5 figure

Performance, microbial community and fluorescent characteristic of microbial products in a solid-phase denitrification biofilm reactor for WWTP effluent treatment

© 2018 Microbial products, i.e. extracellular polymeric substance (EPS) and soluble microbial product (SMP), have a significant correlation with microbial activity of biologically based systems. In present study, the spectral characteristics of two kinds of microbial products were comprehensively evaluated in a solid-phase denitrification biofilm reactor for WWTP effluent treatment by using poly (butylene succinate) (PBS) as carbon source. After the achievement of PBS-biofilm, nitrate and total nitrogen removal efficiencies were high of 97.39 ± 1.24% and 96.38 ± 1.1%, respectively. The contents of protein and polysaccharide were changed different degrees in both LB-EPS and TB-EPS. Excitation-emission matrix (EEM) implied that protein-like substances played a significant role in the formation of PBS-biofilm. High-throughput sequencing result implied that the proportion of denitrifying bacteria, including Simplicispira, Dechloromonas, Diaphorobacter, Desulfovibrio, increased to 9.2%, 7.4%, 4.8% and 3.6% in PBS-biofilm system, respectively. According to EEM-PARAFAC, two components were identified from SMP samples, including protein-like substances for component 1 and humic-like and fulvic acid-like substances for component 2, respectively. Moreover, the fluorescent scores of two components expressed significant different trends to reaction time. Gas chromatography-mass spectrometer (GC-MS) implied that some new organic matters were produced in the effluent of SP-DBR due to biopolymer degradation and denitrification processes. The results could provide a new insight about the formation and stability of solid-phase denitrification PBS-biofilm via the point of microbial products

High-throughput cell-based screening reveals a role for ZNF131 as a repressor of ERalpha signaling

<p>Abstract</p> <p>Background</p> <p>Estrogen receptor α (ERα) is a transcription factor whose activity is affected by multiple regulatory cofactors. In an effort to identify the human genes involved in the regulation of ERα, we constructed a high-throughput, cell-based, functional screening platform by linking a response element (ERE) with a reporter gene. This allowed the cellular activity of ERα, in cells cotransfected with the candidate gene, to be quantified in the presence or absence of its cognate ligand E2.</p> <p>Results</p> <p>From a library of 570 human cDNA clones, we identified zinc finger protein 131 (ZNF131) as a repressor of ERα mediated transactivation. ZNF131 is a typical member of the BTB/POZ family of transcription factors, and shows both ubiquitous expression and a high degree of sequence conservation. The luciferase reporter gene assay revealed that ZNF131 inhibits ligand-dependent transactivation by ERα in a dose-dependent manner. Electrophoretic mobility shift assay clearly demonstrated that the interaction between ZNF131 and ERα interrupts or prevents ERα binding to the estrogen response element (ERE). In addition, ZNF131 was able to suppress the expression of pS2, an ERα target gene.</p> <p>Conclusion</p> <p>We suggest that the functional screening platform we constructed can be applied for high-throughput genomic screening candidate ERα-related genes. This in turn may provide new insights into the underlying molecular mechanisms of ERα regulation in mammalian cells.</p