network motif
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2022 ◽  
Author(s):  
Stella M. Sanchez ◽  
Helmut Schmidt ◽  
Guillermo Gallardo ◽  
Alfred Anwander ◽  
Jens Brauer ◽  
...  

Individual differences in the ability to deal with language have long been discussed. The neural basis of these, however, is yet unknown. Here we investigated the relationship between long-range white matter connectivity of the brain, as revealed by diffusion tractography, and the ability to process syntactically complex sentences in the participants' native language as well as the improvement thereof by multi-day training. We identified specific network motifs that indeed related white matter tractography to individual language processing performance. First, for two such motifs, one in the left and one in the right hemisphere, their individual prevalence significantly predicted the individual language performance suggesting a predisposition for the individual ability to process syntactically complex sentences, which manifests itself in the white matter brain structure. Both motifs comprise a number of cortical regions, but seem to be dominated by areas known for the involvement in working memory rather than the classical language network itself. Second, we identified another left hemispheric network motif, whose change of prevalence over the training period significantly correlated with the individual change in performance, thus reflecting training induced white matter plasticity. This motif comprises diverse cortical areas including regions known for their involvement in language processing, working memory and motor functions. The present findings suggest that individual differences in language processing and learning can be explained, in part, by individual differences in the brain's white matter structure. Brain structure may be a crucial factor to be considered when discussing variations in human cognitive performance, more generally.


2022 ◽  
Vol 8 ◽  
Author(s):  
Yinshuang Xiao ◽  
Zhenghui Sha

Abstract Seasonal effects can significantly impact the robustness of socio-technical systems (STS) to demand fluctuations. There is an increasing need to develop novel design approaches that can support capacity planning decisions for enhancing the robustness of STS against seasonal effects. This paper proposes a new network motif-based approach to supporting capacity planning in STS for an improved seasonal robustness. Network motifs are underlying nonrandom subgraphs within a complex network. In this approach, we introduce three motif-based metrics for system performance evaluation and capacity planning decision-making. The first one is the imbalance score of a motif (e.g., a local service network), the second one is the measurement of a motif’s seasonal robustness, and the third one is a capacity planning decision criterion. Based on these three metrics, we validate that the sensitivity of STS performance against seasonal effects is highly correlated with the imbalanced capacity between service nodes in an STS. Correspondingly, we formulate a design optimisation problem to improve the robustness of STS by rebalancing the resources at critical service nodes. To demonstrate the utility of the approach, a case study on Divvy bike-sharing system in Chicago is conducted. With a focus on the size-3 motifs (a subgraph consisting three docked stations), we find that there is a significant correlation between the difference of the number of docks among the stations in a motif and the return/rental performance of such a motif against seasonal changes. Guided by this finding, our design approach can successfully balance out the number of docks between those stations that have caused the most severe seasonal perturbations. The results also imply that the network motifs can be an effective local structural representation in support of STS robust design. Our approach can be generally applied in other STS where the system performances are significantly impacted by seasonal changes, for example, supply chain networks, transportation systems and power grids.


2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Shumin Huang ◽  
Yin Huang ◽  
Xiaofan Zhang ◽  
Sishi Sheng ◽  
Lisha Mao ◽  
...  

The study uses python software to crawl O-D big data on the freight information platform and construct a frequency matrix based on freight connections between cities, then forming a freight network. There are 31 cities in the middle reaches of the Yangtze river that form the subject of the research. The study adopts the methods of the node degree, community analysis, network motif analysis, and multielement regression analysis to assess the differences of the spatio-temporal evolution and factors influencing the freight network in 2014 and 2018. The following conclusions can be drawn: (1) the freight network has experienced a change in pattern from “island” to “radial,” and the tightness of the freight network is strengthened. (2) The circulation accumulation of elements causes the change of node degree to have a high tendency of agglomeration of capital and central cities. (3) The phenomenon of “enclave freight” and “freight union” exists in the inner-city group, but the “freight alliance” formed by the “enclave” is relatively loose. (4) With the increase in the scale of the freight network, the module characteristics are gradually simplified. (5) Science and technology run through the entire process of the formation and development of the urban freight network.


2021 ◽  
Author(s):  
Doris Voina ◽  
Eric Shea-Brown ◽  
Stefan Mihalas

Humans and other animals navigate different landscapes and environments with ease, a feat that requires the brain's ability to rapidly and accurately adapt to different visual domains, generalizing across contexts/backgrounds. Despite recent progress in deep learning applied to classification and detection in the presence of multiple confounds including contextual ones, there remain important challenges to address regarding how networks can perform context-dependent computations and how contextually-invariant visual concepts are formed. For instance, recent studies have shown artificial networks that repeatedly misclassified familiar objects set on new backgrounds, e.g. incorrectly labeling known animals when they appeared in a different setting. Here, we show how a bio-inspired network motif can explicitly address this issue. We do this using a novel dataset which can be used as a benchmark for future studies probing invariance to backgrounds. The dataset consists of MNIST digits of varying transparency, set on one of two backgrounds with different statistics: a Gaussian noise or a more naturalistic background from the CIFAR-10 dataset. We use this dataset to learn digit classification when contexts are shown sequentially, and find that both shallow and deep networks have sharply decreased performance when returning to the first background after experience learning the second -- the catastrophic forgetting phenomenon in continual learning. To overcome this, we propose an architecture with additional ``switching'' units that are activated in the presence of a new background. We find that the switching network can learn the new context even with very few switching units, while maintaining the performance in the previous context -- but that they must be recurrently connected to network layers. When the task is difficult due to high transparency, the switching network trained on both contexts outperforms networks without switching trained on only one context. The switching mechanism leads to sparser activation patterns, and we provide intuition for why this helps to solve the task. We compare our architecture with other prominent learning methods, and find that elastic weight consolidation is not successful in our setting, while progressive nets are more complex but less effective. Our study therefore shows how a bio-inspired architectural motif can contribute to task generalization across context.


2021 ◽  
Author(s):  
Juan Camilo Arboleda Rivera ◽  
Gloria Machado Rodriguez ◽  
Boris Anghelo Rodriguez Rey ◽  
Jayson Gutierrez Betancur

Background: A central problem in developmental and synthetic biology is understanding the mechanisms by which cells in a tissue or a Petri dish process external cues and transform such information into a coherent response, e.g., a terminal differentiation state. It was long believed that this type of positional information could be entirely attributed to a gradient of concentration of a specific signaling molecule (i.e., a morphogen). However, advances in experimental methodologies and computer modeling have demonstrated the crucial role of the dynamics of a cell's gene regulatory network (GRN) in decoding the information carried by the morphogen, which is eventually translated into a spatial pattern. This morphogen interpretation mechanism has gained much attention in systems biology as a tractable system to investigate the emergent properties of complex genotype-phenotype maps. Methods: In this study, we apply a Markov chain Monte Carlo (MCMC)-like algorithm to probe the design space of three-node GRNs with the ability to generate a band-like expression pattern (target phenotype) in the middle of an arrangement of 30 cells, which resemble a simple (1-D) morphogenetic field in a developing embryo. Unlike most modeling studies published so far, here we explore the space of GRN topologies with nodes having the potential to perceive the same input signal differently. This allows for a lot more flexibility during the search space process, and thus enables us to identify a larger set of potentially interesting and realizable morphogen interpretation mechanisms. Results: Out of 2061 GRNs selected using the search space algorithm, we found 714 classes of network topologies that could correctly interpret the morphogen. Notably, the main network motif that generated the target phenotype in response to the input signal was the type 3 Incoherent Feed-Forward Loop (I3-FFL), which agrees with previous theoretical expectations and experimental observations. Particularly, compared to a previously reported pattern forming GRN topologies, we have uncovered a great variety of novel network designs, some of which might be worth inquiring through synthetic biology methodologies to test for the ability of network design with minimal regulatory complexity to interpret a developmental cue robustly.


2021 ◽  
Vol 8 ◽  
Author(s):  
Xu Zheng ◽  
Huimin Su ◽  
Liping Wang ◽  
Ruiyuan Yao ◽  
Yuze Ma ◽  
...  

In addition to serving as the building blocks for protein synthesis, amino acids serve as critical signaling molecules in cells. However, the mechanism through which amino acid signals are sensed in cells is not yet fully understood. This study examined differences in the phosphorylation levels of proteins in response to amino acid signals in Cashmere goat fetal fibroblasts (GFb). Amino acid deficiency was found to induce autophagy and attenuate mammalian/mechanistic target of rapamycin complex (mTORC1)/Unc-51-like autophagy activating kinase 1 (ULK1) signaling in GFb cells. A total of 144 phosphosites on 102 proteins positively associated with amino acid signaling were screened using phosphorylation-based proteomics analysis. The mitogen-activated protein kinase (MAPK) signaling pathway was found to play a potentially important role in the interaction network involved in the response to amino acid signals, according to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and MAPK1/3 may serve as a central hub for the entire network. Motif analysis identified three master motifs, xxx_S_Pxx, xxx_S_xxE, and xxx_S_xDx, which were centered on those phosphosites at which phosphorylation was positively regulated by amino acid signaling. Additionally, the phosphorylation levels of three membrane proteins, the zinc transporter SLC39A7, the sodium-dependent neutral amino acid transporters SLC1A5 and SLC38A7, and three translation initiation factors, eukaryotic initiation factor (eIF)5B, eIF4G, and eIF3C, were positively regulated by amino acid signals. These pivotal proteins were added to currently known signaling pathways to generate a novel model of the network pathways associated with amino acid signals. Finally, the phosphorylation levels of threonine 203 and tyrosine 205 on MAPK3 in response to amino acid signals were examined by western blot analysis, and the results were consistent with the data from the phosphoproteomics analysis. The findings of this study provide new evidence and insights into the precise mechanism through which amino acid signals are sensed and conducted in Cashmere goat fetal fibroblasts.


2021 ◽  
Author(s):  
Alexander Hoffmann ◽  
Paul Loriaux ◽  
Ying Tang

The identification of prognostic biomarkers fuels personalized medicine. Here we tested two underlying, but often overlooked assumptions: 1) measurements at the steady state are sufficient for predicting the response to drug action, and 2) specifically, measurements of molecule abundances are sufficient. It is not clear that these are justified, as 1) the response results from non-linear molecular relationships, and 2) the steady state is defined by both abundance and orthogonal flux information. An experimentally validated mathematical model of the cellular response to the anti-cancer agent TRAIL was our test case. We developed a mathematical representation in which abundances and fluxes (static and kinetic network features) are largely independent, and simulated heterogeneous drug responses. Machine learning revealed predictive power, but that kinetic, not static network features were most informative. Analytical treatment of the underlying network motif identified kinetic buffering as the relevant circuit design principle. Our work suggests that network topology considerations ought to guide biomarker discovery efforts.


2021 ◽  
pp. 004728752110247
Author(s):  
Sangwon Park ◽  
Ren Ridge Zhong

Urban tourism is considered a complex system. Tourists who visit cities have diverse purposes, leading to multifaceted travel behaviors. Understanding travel movement patterns is crucial in developing sustainable planning for urban tourism. Built on network science, this article discusses 12 key topologies of travel patterns/flow occurring in a city network by applying network motif analytics. The 12 significant types of travel mobility can account for approximately 50% of the total movement patterns. In addition, this study presents variations in travel movement patterns depending on not only different lengths of stay in topological structures of travel mobility, but also relative proportions of each type. As a result, this article suggests an interdisciplinary approach that adopts the network science method to better understand city travel behaviors. Important methodological and practical implications that could be useful for city destination planners are suggested.


2021 ◽  
Vol 101 ◽  
pp. 102734
Author(s):  
Esra Ruzgar Ateskan ◽  
Kayhan Erciyes ◽  
Mehmet Emin Dalkilic

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