Uncovering the Functional Link Between SHANK3 Deletions and Deficiency in Neurodevelopment Using iPSC-Derived Human Neurons

SHANK3 mutations, including de novo deletions, have been associated with autism spectrum disorders (ASD). However, the effects of SHANK3 loss of function on neurodevelopment remain poorly understood. Here we generated human induced pluripotent stem cells (iPSC) in vitro, followed by neuro-differentiation and lentivirus-mediated shRNA expression to evaluate how SHANK3 knockdown affects the in vitro neurodevelopmental process at multiple time points (up to 4 weeks). We found that SHANK3 knockdown impaired both early stage of neuronal development and mature neuronal function, as demonstrated by a reduction in neuronal soma size, growth cone area, neurite length and branch numbers. Notably, electrophysiology analyses showed defects in excitatory and inhibitory synaptic transmission. Furthermore, transcriptome analyses revealed that multiple biological pathways related to neuron projection, motility and regulation of neurogenesis were disrupted in cells with SHANK3 knockdown. In conclusion, utilizing a human iPSC-based neural induction model, this study presented combined morphological, electrophysiological and transcription evidence that support that SHANK3 as an intrinsic, cell autonomous factor that controls cellular function development in human neurons

A Hybrid Approach to Failure Analysis Using Stochastic Petri Nets and Ranking Generalized Fuzzy Numbers

We present a novel failure analysis approach combining structural properties of stochastic Petri Nets and flexibility of fuzzy logic. Firstly, we develop a powerful fuzzy ranking technique. We analyze major drawbacks of existing ranking techniques. Then we demonstrate the capabilities of the presented algorithm to overcome such drawbacks. The approach considers weight, spread, and difference of coordinate of the center of gravity (COG) point of each fuzzy number and is able to deal with a wide variety of fuzzy numbers. Using this technique, we utilize isomorphism between stochastic Petri Nets and their corresponding Markov chains and present a failure analysis algorithm incorporating some critical factors. This algorithm can be implemented in diverse industrial applications

Graph-based semi-supervised learning with genomic data integration using condition-responsive genes applied to phenotype classification.

ObjectiveData integration methods that combine data from different molecular levels such as genome, epigenome, transcriptome, etc., have received a great deal of interest in the past few years. It has been demonstrated that the synergistic effects of different biological data types can boost learning capabilities and lead to a better understanding of the underlying interactions among molecular levels.MethodsIn this paper we present a graph-based semi-supervised classification algorithm that incorporates latent biological knowledge in the form of biological pathways with gene expression and DNA methylation data. The process of graph construction from biological pathways is based on detecting condition-responsive genes, where 3 sets of genes are finally extracted: all condition responsive genes, high-frequency condition-responsive genes, and P-value-filtered genes.ResultsThe proposed approach is applied to ovarian cancer data downloaded from the Human Genome Atlas. Extensive numerical experiments demonstrate superior performance of the proposed approach compared to other state-of-the-art algorithms, including the latest graph-based classification techniques.ConclusionsSimulation results demonstrate that integrating various data types enhances classification performance and leads to a better understanding of interrelations between diverse omics data types. The proposed approach outperforms many of the state-of-the-art data integration algorithms

Transcriptional network analysis on brains reveals a potential regulatory role of PPP1R3F in autism spectrum disorders

Abstract Objective This study aims at identifying master regulators of transcriptional networks in autism spectrum disorders (ASDs). Results With two sets of independent RNA-Seq data generated on cerebellum from patients with ASDs and control subjects (N = 39 and 45 for set 1, N = 24 and 38 for set 2, respectively), we carried out a network deconvolution of transcriptomic data, followed by virtual protein activity analysis. We identified PPP1R3F (Protein Phosphatase 1 Regulatory Subunit 3F) as a candidate master regulator affecting a large body of downstream genes that are associated with the disease phenotype. Pathway analysis on the identified targets of PPP1R3F in both datasets indicated alteration of endocytosis pathway. Despite a limited sample size, our study represents one of the first applications of network deconvolution approach to brain transcriptomic data to generate hypotheses that may be further validated by large-scale studies

Sequential Failure Analysis Using Novel Algorithms in Sequence Determination of Petri Nets Firing

Failure occurrence in industrial systems can be a result of a sequence of failures leading to a total system failure. Up to now, several methods to determine failure sequences and to calculate probability of such failures have been proposed. These methods primarily focus on modeling aspects of the problem and do not present a certain framework to determine potential failure sequences. In this paper, a novel approach based on Petri net modeling of the systems is proposed and several heuristic algorithms are developed. Determination of potential failures in sample industrial problems and comparing the results with other existing methods demonstrates that the presented algorithms are much more efficient in dealing with complex Petri net models while existing methods are not capable of handling such complicated models

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Additional file 2. The constructed networks from the Parikshak et al. dataset [19]

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Additional file 1. Detailed explanation of the methods being used in this study

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Additional file 5. The list of the combined set of target genes of PPP1R3F