scholarly journals The optimal odor-receptor interaction network is sparse in olfactory systems: Compressed sensing by nonlinear neurons with a finite dynamic range

2018 ◽  
Author(s):  
Shanshan Qin ◽  
Qianyi Li ◽  
Chao Tang ◽  
Yuhai Tu
Keyword(s):  
Compressed Sensing ◽  
Dynamic Range ◽  
Interaction Network ◽  
Receptor Interaction ◽  
Odor Receptor ◽  
Basal Activity ◽  
Learning Tasks ◽  
Olfactory Systems ◽  
Odor Mixtures ◽  
Nonlinear Sensors

There are numerous different odorant molecules in nature but only a relatively small number of olfactory receptor neurons (ORNs) in brains. This “compressed sensing” challenge is compounded by the constraint that ORNs are nonlinear sensors with a finite dynamic range. Here, we investigate possible optimal olfactory coding strategies by maximizing mutual information between odor mixtures and ORNs’ responses with respect to the bipartite odor-receptor interaction network (ORIN) characterized by sensitivities between all odorant-ORN pairs. We find that the optimal ORIN is sparse – a finite fraction of sensitives are zero, and the nonzero sensitivities follow a broad distribution that depends on the odor statistics. We show that the optimal ORIN enhances performances of downstream learning tasks (reconstruction and classification). For ORNs with a finite basal activity, we find that having a basal-activity-dependent fraction of inhibitory odor-receptor interactions increases the coding capacity. All our theoretical findings are consistent with existing experiments and predictions are made to further test our theory. The optimal coding model provides a unifying framework to understand the peripheral olfactory systems across different organisms.

scholarly journals Optimal compressed sensing strategies for an array of nonlinear olfactory receptor neurons with and without spontaneous activity

2019 ◽  
Vol 116 (41) ◽  
pp. 20286-20295 ◽  
Author(s):  
Shanshan Qin ◽  
Qianyi Li ◽  
Chao Tang ◽  
Yuhai Tu
Keyword(s):  
Compressed Sensing ◽  
Olfactory Receptor ◽  
Dynamic Range ◽  
Olfactory Receptor Neurons ◽  
Receptor Interaction ◽  
Trade Off ◽  
Odor Receptor ◽  
Basal Activity ◽  
Receptor Neurons ◽  
Coding Capacity

There are numerous different odorant molecules in nature but only a relatively small number of olfactory receptor neurons (ORNs) in brains. This “compressed sensing” challenge is compounded by the constraint that ORNs are nonlinear sensors with a finite dynamic range. Here, we investigate possible optimal olfactory coding strategies by maximizing mutual information between odor mixtures and ORNs’ responses with respect to the bipartite odor-receptor interaction network (ORIN) characterized by sensitivities between all odorant–ORN pairs. For ORNs without spontaneous (basal) activity, we find that the optimal ORIN is sparse—a finite fraction of sensitives are zero, and the nonzero sensitivities follow a broad distribution that depends on the odor statistics. We show analytically that sparsity in the optimal ORIN originates from a trade-off between the broad tuning of ORNs and possible interference. Furthermore, we show that the optimal ORIN enhances performances of downstream learning tasks (reconstruction and classification). For ORNs with a finite basal activity, we find that having inhibitory odor–receptor interactions increases the coding capacity and the fraction of inhibitory interactions increases with the ORN basal activity. We argue that basal activities in sensory receptors in different organisms are due to the trade-off between the increase in coding capacity and the cost of maintaining the spontaneous basal activity. Our theoretical findings are consistent with existing experiments and predictions are made to further test our theory. The optimal coding model provides a unifying framework to understand the peripheral olfactory systems across different organisms.

ODOR MIXTURES AND CHEMOSENSORY ADAPTATION IN GAS SENSOR ARRAYS

2003 ◽  
Vol 12 (01) ◽  
pp. 1-16 ◽  
Author(s):  
RICARDO GUTIERREZ-OSUNA ◽  
NILESH U. POWAR
Keyword(s):  
Chemical Sensor ◽  
Sensor Arrays ◽  
Ternary Mixtures ◽  
Discriminant Functions ◽  
Linear Discriminant ◽  
Chemical Sensor Array ◽  
Binary And Ternary Mixtures ◽  
Olfactory Systems ◽  
Odor Mixtures ◽  
Input Adaptation

Inspired by the process of olfactory adaptation in biological olfactory systems, this article presents two algorithms that allow a chemical sensor array to reduce its sensitivity to odors previously detected in the environment. The first algorithm is based on a committee machine of linear discriminant functions that operate on multiple subsets of the overall sensory input. Adaptation occurs by depressing the voting strength of discriminant functions that display higher sensitivity to previously detected odors. The second algorithm is based on a topology-preserving linear projection derived from Fisher's class separability criteria. In this case, the process of adaptation is implemented through a reformulation of the between-to-within-class scatter eigenvalue problem. The proposed algorithms are validated on two datasets of binary and ternary mixtures of organic solvents using an array of temperature-modulated metal-oxide chemoresistors.

scholarly journals Whole Transcriptome Analysis of Hypertension Induced Cardiac Injury Using Deep Sequencing

2016 ◽  
Vol 38 (2) ◽  
pp. 670-682 ◽  
Author(s):  
Tao-Tao Li ◽  
Xiao-Yan Li ◽  
Li-Xin Jia ◽  
Jing Zhang ◽  
Wen-Mei Zhang ◽  
...  
Keyword(s):  
Transcriptome Analysis ◽  
Cell Activation ◽  
Critical Role ◽  
Cardiac Injury ◽  
Interaction Network ◽  
Receptor Interaction ◽  
Rna Seq ◽  
Cardiac Inflammation ◽  
Molecular Events ◽  
Focal Adhesion Protein

Background/Aims: Hypertension plays a critical role in the cardiac inflammation and injury. However, the mechanism of how hypertension causes the cardiac injury at a molecular level remains to be elucidated. Methods: RNA-Seq has been demonstrated to be an effective approach for transcriptome analysis, which is essential to reveal the molecular constituents of cells and tissues. In this study, we investigated the global molecular events associated with the mechanism of hypertension induced cardiac injury using RNA-Seq analysis. Results: Our results showed that totally 1,801 genes with different expression variations were identified after Ang II infusion at 1, 3 and 7 days. Go analysis showed that the top 5 high enrichment Go terms were response to stress, response to wounding, cellular component organization, cell activation and defense response. KEGG pathway analysis revealed the top 5 significantly overrepresented pathways were associated with ECM-receptor interaction, focal adhesion, protein digestion and absorption, phagosome and asthma. Moreover, protein-protein interaction network analysis indicated that ubiquitin C may play a key role in the processes of hypertension-induced cardiac injury. Conclusion: Our study provides a comprehensive understanding of the transcriptome events in hypertension-induced cardiac pathology.

scholarly journals Positive transcriptional response on inflammation and joint remodelling influenced by physical exercise in proteoglycan-induced arthritis: An animal study

2020 ◽  
Vol 9 (1) ◽  
pp. 36-48
Author(s):  
Susana A. González-Chávez ◽  
César Pacheco-Tena ◽  
Celia M. Quiñonez-Flores ◽  
Gerardo P. Espino-Solis ◽  
Jessica I. Burrola-De Anda ◽  
...  
Keyword(s):  
Physical Exercise ◽  
Bone Mineralization ◽  
Transcriptional Response ◽  
Interaction Network ◽  
Bioinformatic Analysis ◽  
Janus Kinase ◽  
Signalling Pathways ◽  
Receptor Interaction ◽  
Chemokine Ligand ◽  
Cartilage Erosion

Aims To assess the effect of physical exercise (PE) on the histological and transcriptional characteristics of proteoglycan-induced arthritis (PGIA) in BALB/c mice. Methods Following PGIA, mice were subjected to treadmill PE for ten weeks. The tarsal joints were used for histological and genetic analysis through microarray technology. The genes differentially expressed by PE in the arthritic mice were obtained from the microarray experiments. Bioinformatic analysis in the DAVID, STRING, and Cytoscape bioinformatic resources allowed the association of these genes in biological processes and signalling pathways. Results Arthritic mice improved their physical fitness by 42.5% after PE intervention; it induced the differential expression of 2,554 genes. The bioinformatic analysis showed that the downregulated genes (n = 1,371) were significantly associated with cellular processes that mediate the inflammation, including Janus kinase-signal transducer and activator of transcription proteins (JAK-STAT), Notch, and cytokine receptor interaction signalling pathways. Moreover, the protein interaction network showed that the downregulated inflammatory mediators interleukin (IL) 4, IL5, IL2 receptor alpha (IL2rα), IL2 receptor beta (IL2rβ), chemokine ligand (CXCL) 9, and CXCL12 were interacting in several pathways associated with the pathogenesis of arthritis. The upregulated genes (n = 1,183) were associated with processes involved in the remodelling of the extracellular matrix and bone mineralization, as well as with the processes of aerobic metabolism. At the histological level, PE attenuated joint inflammatory infiltrate and cartilage erosion. Conclusion Physical exercise influences parameters intimately linked to inflammatory arthropathies. Research on the effect of PE on the pathogenesis process of arthritis is still necessary for animal and human models. Cite this article: Bone Joint Res. 2020;9(1):36–48.

scholarly journals Insights Into the Polerovirus–Plant Interactome Revealed by Coimmunoprecipitation and Mass Spectrometry

2015 ◽  
Vol 28 (4) ◽  
pp. 467-481 ◽  
Author(s):  
Stacy L. DeBlasio ◽  
Richard Johnson ◽  
Jaclyn Mahoney ◽  
Alexander Karasev ◽  
Stewart M. Gray ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Carbon Fixation ◽  
Dynamic Range ◽  
High Resolution Mass Spectrometry ◽  
Protein Complexes ◽  
Interaction Network ◽  
Host Protein ◽  
Host Proteins ◽  
Leafroll Virus ◽  
Readthrough Protein

Identification of host proteins interacting with the aphidborne Potato leafroll virus (PLRV) from the genus Polerovirus, family Luteoviridae, is a critical step toward understanding how PLRV and related viruses infect plants. However, the tight spatial distribution of PLRV to phloem tissues poses challenges. A polyclonal antibody raised against purified PLRV virions was used to coimmunoprecipitate virus-host protein complexes from Nicotiana benthamiana tissue inoculated with an infectious PLRV cDNA clone using Agrobacterium tumefaciens. A. tumefaciens-mediated delivery of PLRV enabled infection and production of assembled, insect-transmissible virus in most leaf cells, overcoming the dynamic range constraint posed by a systemically infected host. Isolated protein complexes were characterized using high-resolution mass spectrometry and consisted of host proteins interacting directly or indirectly with virions, as well as the nonincorporated readthrough protein (RTP) and three phosphorylated positional isomers of the RTP. A bioinformatics analysis using ClueGO and STRING showed that plant proteins in the PLRV protein interaction network regulate key biochemical processes, including carbon fixation, amino acid biosynthesis, ion transport, protein folding, and trafficking.

scholarly journals High dynamic range coherent imaging using compressed sensing

2015 ◽  
Vol 23 (24) ◽  
pp. 30904 ◽  
Author(s):  
Kuan He ◽  
Manoj Kumar Sharma ◽  
Oliver Cossairt
Keyword(s):  
Compressed Sensing ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
Coherent Imaging ◽  
High Dynamic

scholarly journals Effects of ADC Nonlinearity on the Spurious Dynamic Range Performance of Compressed Sensing

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Rongzong Kang ◽  
Pengwu Tian ◽  
Hongyi Yu
Keyword(s):  
Compressed Sensing ◽  
Dynamic Range ◽  
Dynamic Performance ◽  
Digital Converter ◽  
Analog To Digital ◽  
Sensing System ◽  
Spurious Free Dynamic Range ◽  
Simulation Results ◽  
The Impact ◽  
Free Dynamic

Analog-to-information converter (AIC) plays an important role in the compressed sensing system; it has the potential to significantly extend the capabilities of conventional analog-to-digital converter. This paper evaluates the impact of AIC nonlinearity on the dynamic performance in practical compressed sensing system, which included the nonlinearity introduced by quantization as well as the circuit non-ideality. It presents intuitive yet quantitative insights into the harmonics of quantization output of AIC, and the effect of other AIC nonlinearity on the spurious dynamic range (SFDR) performance is also analyzed. The analysis and simulation results demonstrated that, compared with conventional ADC-based system, the measurement process decorrelates the input signal and the quantization error and alleviate the effect of other decorrelates of AIC, which results in a dramatic increase in spurious free dynamic range (SFDR).

scholarly journals Identification of Latent Diagnostic Biomarkers and Biological Pathways in Dermatomyositis Based on WGCNA

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Shaxi Ouyang ◽  
Yifang Liu ◽  
Changjuan Xiao ◽  
Qinghua Zeng ◽  
Xun Luo ◽  
...  
Keyword(s):  
Cell Lines ◽  
Differentially Expressed Gene ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Receptor Interaction ◽  
Interferon Α ◽  
Pathway Enrichment Analysis ◽  
Oligoadenylate Synthetase

Introduction. Dermatomyositis (DM) is a chronic autoimmune disease of predominantly lymphocytic infiltration mainly involving the transverse muscle. Its pathogenesis is remaining unknown. This research is designed to probe the latent pathogenesis of dermatomyositis, identify potential biomarkers, and reveal the pathogenesis of dermatomyositis through information biology analysis of gene chips. Methods. In this study, we utilised the GSE14287 and GSE11971 datasets rooted in the Gene Expression Omnibus (GEO) databank, which included a total of 62 DM samples and 9 normal samples. The datasets were combined, and the differentially expressed gene sets were subjected to weighted gene coexpression network analysis, and the hub gene was screened using a protein interaction network from genes in modules highly correlated with dermatomyositis progression. Results. A total of 3 key genes—myxovirus resistance-2 (MX2), oligoadenylate synthetase 1 (OAS1), and oligoadenylate synthetase 2 (OAS2)—were identified in combination with cell line samples, and the expressions of the 3 genes were verified separately. The results showed that MX2, OAS1, and OAS2 were highly expressed in LPS-treated cell lines compared to normal cell lines. The results of pathway enrichment analysis of the genes indicated that all 3 genes were enriched in the cytosolic DNA signalling and cytokine and cytokine receptor interaction signalling pathways; the results of functional enrichment analysis showed that all 3 were enriched in interferon-α response and interferon-γ response functions. Conclusions. This is important for the study of the pathogenesis and objective treatment of dermatomyositis and provides important reference information for the targeted therapy of dermatomyositis.

scholarly journals Identification of Potent Novel Biomarkers of Uterine Leiomyoma Through DEGs Screening and Networking.

2021 ◽  
Author(s):  
Sonal UPADHYAY ◽  
Anjali RANI ◽  
Pawan K. DUBEY ◽  
Ravi BHUSHAN
Keyword(s):  
Uterine Leiomyoma ◽  
Benign Lesion ◽  
Interaction Network ◽  
Principal Component ◽  
Uterine Leiomyomas ◽  
Receptor Interaction ◽  
Pathway Enrichment Analysis ◽  
Novel Biomarkers

Abstract Background: Uterine leiomyomas is a benign lesion arising in myometrium of the uterus. Various risk factors like stress, obesity, hormonal imbalance are involved in the progression of the uterine leiomyomas. Despite the significant research, the potential biomarkers related to uterine leiomyomas are yet to be discovered. Methods: The present study deals with searching the common potent markers of Uterine Leiomyoma (ULM) that was responsible for their pathogenicity. The microarray dataset (GSEID: GSE30673) was fetched through Gene Expression Omnibus database. Comparing with normal myometrium samples, Principal Component Analysis (PCA) and heat map were constructed to obtain differential expressed genes (DEGs) for ULM. Common DEGs were obtained through Venny software v 2.1.0. Significant enriched pathways and ontological study for DEGs were also performed through online Database for Annotation, Visualization and Integrated Discovery tool. Based upon STRING v 10.5, protein- protein interaction network was constructed in order to predict functional interactions among proteins. Results: 176 of total DEGs with 101 overexpressed and 75 under expressed genes were screened out with their official gene symbol. 2 DEGs were found as common genes in OMIM and Gene Cards.Only 9 DEGs were found to have combined score > 0.4 and hence included in interaction network. The present study revealed EGF, FYN, VCAN, TRIP13, FBXW7 as up-regulated genes and GATA2, JAG1, TLR3, APOL1 as down-regulated genes found to be expressed in samples with ULM disease. KEGG pathway enrichment analysis for DEGs revealed Focal adhesion, ECM-receptor interaction, long-term depression and Retinol metabolism are major pathways which have been enriched for these DEGs. Conclusion: We conclude that TRIP13 and TLR3 might be the novel biomarkers related to ULM disease which were revealed through our findings. The present study provides us a new perspective to detect the potent biomarkers responsible for ULM and further in vitro and in vivo experiments needed to be performed to verify the results.

scholarly journals The Influence of Let-7b-5p Target Gene in Cholangiocarcinoma and its Bioinformatic Analysis

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
Xibing Zhang ◽  
Jianghua Ran ◽  
Fang Liu ◽  
Yingpeng Zhao ◽  
Zongqiang Hu
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Network ◽  
Target Gene ◽  
Interaction Network ◽  
Receptor Interaction ◽  
Pathway Enrichment Analysis ◽  
Signal Pathways ◽  
Biological Functions ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Objective: To analyze the biological functions and its clinical significance of let-7b-5p target gene in cholangiocarcinoma utilizing bioinformatics. Methods: The paper focuses on the let-7b-5p target gene, and predicts its biological functions as well as related signal pathways through GO biological function and KEGG signal pathway enrichment analysis. The STRING database and Cytoscape are used to construct a protein-protein interaction network to screen core genes. Results: The results of GO analysis showed that let-7b-5p target gene was mainly enriched in biological processes such as Small GTPase binding, Rho GTPase binding, and Rac GTPase binding. The results of KEGG analysis showed that let-7b-5p target gene was significantly enriched in key signaling pathways including Focal adhesion and ECM-receptor interaction. Through protein-protein interaction network and module analysis, CXCL8 and SDC2 were screened as the core site. Conclusion: let-7b-5p can participate in the regulation of biological functions of tumor cells in cholangiocarcinoma, suggesting that it may play an important role as a tumor suppressor gene and biomarker in the occurrence and development of cholangiocarcinoma, which provides new ideas for its diagnosis and treatment.

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