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

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

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

Morphological, Physiological and Molecular Classification of Mouse Retinal Ganglion Cells

Visual information is conveyed from the retina to the brain through axons of retinal ganglion cells (RGCs). There are >20 different subtypes of RGCs, each of which detects specific features. Classification of RGC subtypes is thus essential for us to understand how visual information is processed and delivered to the brain. Here I reported my efforts in classifying different subtypes of RGCs, using morphological, physiological and molecular criteria. A combination of these criteria allowed me to successfully identify subtypes from alpha RGCs, Foxp2-positive RGCs (F-RGCs) and RGCs labeled in a transgenic mouse line W3. First, I presented studies of classifying subtypes of alpha RGCs. Cell attached recording followed by morphology reconstruction revealed four subtypes of alpha-like RGCs: Off-sustained, Off-transient, On-sustained, On-transient subtypes, each of which has distinct morphological properties. In addition, we found osteopontin (OPN) as a molecular marker for all alpha RGCs. Following this discovery, we studied the role of OPN in alpha RGCs, Analysis showed that alpha RGCs preferentially survive and regenerate compared with other RGCs, leading us to test whether OPN can promote axon regeneration. Indeed, by combining OPN with growth factors, we were able to promote axon regenerations of RGCs. Second, I presented work in classifying subtypes of F-RGCs, which are recognized by expressing a transcription factor, Foxp2. Combinatory expression of Foxp2 with other transcriptional factors divides F-RGCs into four subtypes, which form two pairs differing in their dendritic field sizes. Cell attached recording showed that one pair, F-minion and F-minioff RGCs, are direction-selective, while the other pair, F-midion and F-midioff RGCs, are not. Thus, we identified four new subtypes of RGCs labeled by transcriptional factor Foxp2. Third, I described initial efforts in classifying subtypes of RGCs labeled in the transgenic mouse line W3. W3 RGCs can be separated into two group based on their expression levels of fluorescent proteins, with the dimly labeled RGCs (W3D) remained uncharacterized. Initial analysis showed W3D RGCs include at least five subtypes of RGCs, which are different in their structures and physiological properties. Lastly, I described my work in developing a molecular tool for mapping electrical synaptic connections from genetically defined neurons or neuronal subtypes, making use of a dipeptide transporter, Pept2. Cells expressing Pept2 (in a Cre-dependent way) take up a gap junction permeable fluorescent dipeptide, which then diffuses and labels the coupled cells. We tested this method in cultured cells and validated it in mouse retina using AAV carrying Cre-dependent Pept2. I applied this method to one subtype of RGCs, J-RGCs, to label their coupling partners.Biology, Molecular and Cellula

Multilayer perceptron network optimization for chaotic time series modeling

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

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

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