1,002 research outputs found

    Factorized linear discriminant analysis for phenotype-guided representation learning of neuronal gene expression data

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    A central goal in neurobiology is to relate the expression of genes to the structural and functional properties of neuronal types, collectively called their phenotypes. Single-cell RNA sequencing can measure the expression of thousands of genes in thousands of neurons. How to interpret the data in the context of neuronal phenotypes? We propose a supervised learning approach that factorizes the gene expression data into components corresponding to individual phenotypic characteristics and their interactions. This new method, which we call factorized linear discriminant analysis (FLDA), seeks a linear transformation of gene expressions that varies highly with only one phenotypic factor and minimally with the others. We further leverage our approach with a sparsity-based regularization algorithm, which selects a few genes important to a specific phenotypic feature or feature combination. We applied this approach to a single-cell RNA-Seq dataset of Drosophila T4/T5 neurons, focusing on their dendritic and axonal phenotypes. The analysis confirms results obtained by conventional methods but also points to new genes related to the phenotypes and an intriguing hierarchy in the genetic organization of these cells

    StackInsights: Cognitive Learning for Hybrid Cloud Readiness

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    Hybrid cloud is an integrated cloud computing environment utilizing a mix of public cloud, private cloud, and on-premise traditional IT infrastructures. Workload awareness, defined as a detailed full range understanding of each individual workload, is essential in implementing the hybrid cloud. While it is critical to perform an accurate analysis to determine which workloads are appropriate for on-premise deployment versus which workloads can be migrated to a cloud off-premise, the assessment is mainly performed by rule or policy based approaches. In this paper, we introduce StackInsights, a novel cognitive system to automatically analyze and predict the cloud readiness of workloads for an enterprise. Our system harnesses the critical metrics across the entire stack: 1) infrastructure metrics, 2) data relevance metrics, and 3) application taxonomy, to identify workloads that have characteristics of a) low sensitivity with respect to business security, criticality and compliance, and b) low response time requirements and access patterns. Since the capture of the data relevance metrics involves an intrusive and in-depth scanning of the content of storage objects, a machine learning model is applied to perform the business relevance classification by learning from the meta level metrics harnessed across stack. In contrast to traditional methods, StackInsights significantly reduces the total time for hybrid cloud readiness assessment by orders of magnitude

    Multilayer perceptron network optimization for chaotic time series modeling

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    Chaotic time series are widely present in practice, but due to their characteristics—such as internal randomness, nonlinearity, and long-term unpredictability—it is difficult to achieve high-precision intermediate or long-term predictions. Multi-layer perceptron (MLP) networks are an effective tool for chaotic time series modeling. Focusing on chaotic time series modeling, this paper presents a generalized degree of freedom approximation method of MLP. We then obtain its Akachi information criterion, which is designed as the loss function for training, hence developing an overall framework for chaotic time series analysis, including phase space reconstruction, model training, and model selection. To verify the effectiveness of the proposed method, it is applied to two artificial chaotic time series and two real-world chaotic time series. The numerical results show that the proposed optimized method is effective to obtain the best model from a group of candidates. Moreover, the optimized models perform very well in multi-step prediction tasks.This research was funded in part by the NSFC grant numbers 61972174 and 62272192, the Science-Technology Development Plan Project of Jilin Province grant number 20210201080GX, the Jilin Province Development and Reform Commission grant number 2021C044-1, the Guangdong Universities’ Innovation Team grant number 2021KCXTD015, and Key Disciplines Projects grant number 2021ZDJS138

    2-[(2,6-Diethyl­phen­yl)imino­meth­yl]-N-(2-methoxy­phen­yl)aniline

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    The title anilide–imine compound, C24H26N2O, features an intra­molecular N—H⋯N hydrogen bond involving the imine and anilide groups to generate an S(6) ring motif. The mol­ecule displays an E configuration about the imine C=N double bond, with the dihedral angle between the two benzene rings being 86.5°. The packing is stabilized by three different C—H⋯π inter­actions

    Mapping Transgene Insertion Sites Reveals Complex Interactions Between Mouse Transgenes And Neighboring Endogenous Genes

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    Transgenic mouse lines are routinely employed to label and manipulate distinct cell types. The transgene generally comprises cell-type specific regulatory elements linked to a cDNA encoding a reporter or other protein. However, off-target expression seemingly unrelated to the regulatory elements in the transgene is often observed, it is sometimes suspected to reflect influences related to the site of transgene integration in the genome. To test this hypothesis, we used a proximity ligation-based method, Targeted Locus Amplification (TLA), to map the insertion sites of three well-characterized transgenes that appeared to exhibit insertion site-dependent expression in retina. The nearest endogenous genes to transgenes HB9-GFP, Mito-P, and TYW3 are Cdh6, Fat4 and Khdrbs2, respectively. For two lines, we demonstrate that expression reflects that of the closest endogenous gene (Fat4 and Cdh6), even though the distance between transgene and endogenous gene is 550 and 680 kb, respectively. In all three lines, the transgenes decrease expression of the neighboring endogenous genes. In each case, the affected endogenous gene was expressed in at least some of the cell types that the transgenic line has been used to mark and study. These results provide insights into the effects of transgenes and endogenous genes on each other’s expression, demonstrate that mapping insertion site is valuable for interpreting results obtained with transgenic lines, and indicate that TLA is a reliable method for integration site discovery
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