24 papers · ranked by Valyu relevance
Niklas Lück, Robert Lohmayer, Stefan Solbrig, Stefan Schrod + 9 more
Advances in omics technologies have allowed spatially resolved molecular profiling of single cells, providing a window not only into the diversity and distribution of cell types within a tissue, but also into the effects of interactions between cells in shaping the transcriptional landscape. Cells send chemical and…
Sarah Schöttler, Yalong Yang, Hanspeter Pfister, Benjamin Bach
This paper surveys visualization and interaction techniques for geospatial networks from a total of 95 papers. Geospatial networks are graphs where nodes and links can be associated with geographic locations. Examples can include social networks, trade and migration, as well as traffic and transport networks.…
Simon K. Dahlberg, David Fernández Bonet, Lovisa Franzén, Patrik L. Ståhl + 1 more
Spatial transcriptomics technologies aim to localize gene expression in tissues and typically use capture surfaces that have undergone spatial indexing or optical decoding to relate surface sequences to spatial positions. Slide-tags is a recently developed method by Russell et al. that inverts this principle, instead…
Andrew Liu, Mason A. Porter
For many networks, it is useful to think of their nodes as being embedded in a latent space, and such embeddings can affect the probabilities for nodes to be adjacent to each other. In this paper, we extend existing models of synthetic networks to spatial network models by first embedding nodes in Euclidean space and…
Thibault Rolland, Fabrizio De Vico Fallani, Praveen Kumar Donta
In many fields of science and technology we are confronted with complex networks. Making sense of these networks often require the ability to visualize and explore their intermingled structure consisting of nodes and links. To facilitate the identification of significant connectivity patterns, many methods have been…
Zheng Zhang, Sirui Li, Jingcheng Zhou, Junxiang Wang + 3 more
'Abhinav Angirekula' 'Allen Zhang' 'Liang Zhao'] Spatial networks are networks whose graph topology is constrained by their embedded spatial space. Understanding the coupled spatial-graph properties is crucial for extracting powerful representations from spatial networks. Therefore, merely combining individual spatial…
Marc Wiedermann, Jonathan F. Donges, Jürgen Kurths, Reik V. Donner
Networks with nodes embedded in a metric space have gained increasing interest in recent years. The effects of spatial embedding on the networks' structural characteristics, however, are rarely taken into account when studying their macroscopic properties. Here, we propose a hierarchy of null models to generate random…
Federica Cerina, Vincenzo De Leo, Marc Barthelemy, Alessandro Chessa + 1 more
'Sergio Gómez'] Community detection is an important tool for exploring and classifying the properties of large complex networks and should be of great help for spatial networks. Indeed, in addition to their location, nodes in spatial networks can have attributes such as the language for individuals, or any other…
Simon Scheider, Tom de Jong
Spatial network analysis is a collection of methods for measuring accessibility potentials as well as for analyzing flows over transport networks. Though it has been part of the practice of geographic information systems for a long time, designing network analytical workflows still requires a considerable amount of…
Felichism Kabo, Umberto Baresi
Site selection is a complex, multicriteria process and a key if poorly understood contributor to the success of new scientific facilities. Typical site selection factors include the budget, sustainability, accessibility, utility and infrastructure costs, and environmental factors, e.g., air quality. Ideally, site…
Yu Tian, Chinmayi Subramanya, Carl D. Modes
Spatially embedded networks are central to many physical and biological systems, where geometry and connectivity jointly shape structure and function. Examples abound across the scales of biological organization, from network-like membrane-bound organelles in the cell to mesoscale tissue organization of multiple…
Ling Wu, Qiong Peng, Michael Lemke, Tao Hu + 1 more
A restless and dynamic intellectual landscape has taken hold in the field of spatial social network studies, given the increasingly attention towards fine-scale human dynamics in this urbanizing and mobile world. The measuring parameters of such dramatic growth of the literature include scientific outputs, domain…
David Fernandez Bonet, Johanna I. Blumenthal, Shuai Lang, Simon K Dahlberg + 1 more
Sequencing-based microscopy is a novel, optics-free method for imaging molecules in biological samples using molecular DNA barcodes, spatial networks, and sequencing technologies. Despite its promise, the principles determining how these networks preserve spatial information are not fully understood. Current validation…
Gustave Ronteix, Valentin Bonnet, Sebastien Sart, Jeremie Sobel + 2 more
Microscopy techniques and image segmentation algorithms have improved dramatically this decade, leading to an ever increasing amount of biological images and a greater reliance on imaging to investigate biological questions. This has created a need for methods to extract the relevant information on the behaviors of…
Wei Luo, Peifeng Yin, Qian Di, Frank Hardisty + 2 more
'Bin Jiang'] The world has become a complex set of geo-social systems interconnected by networks, including transportation networks, telecommunications, and the internet. Understanding the interactions between spatial and social relationships within such geo-social systems is a challenge. This research aims to address…
Satwik Acharyya, Jian Kang, Veerabhadran Baladandayuthapani
Modern spatial transcriptomic profiling techniques facilitate spatially resolved, high-dimensional assessment of cellular gene transcription across the tumor domain. The characterization of spatially varying gene networks enables the discovery of heterogeneous regulatory patterns and biological mechanisms underlying…
Steven M. Radil, Olivier Walther
The spatial metaphor of the network along with its accompanying abstractions, such as flow, movement, and connectivity, have been central themes throughout the relational turn in human geography. However, to date networks in geography have been primarily explored either through actor-network theory or assemblage…
Matthew Bailey, Mark Wilson
One of the critical tools of persistent homology is the persistence diagram. We demonstrate the applicability of a persistence diagram showing the existence of topological features (here rings in a 2D network) generated over time instead of space as a tool to analyse trajectories of biological networks. We show how the…
Authors not listed
Gibbs’ paradox—the apparent discontinuity in mixing entropy for gases of varying similarity and the seeming reversibility of mixing-separation cycles—has resisted fully satisfactory resolution for 150 years. We present a solution based on categorical state theory, which posits that physical con f igurations are…
Benjamin Ries, Richard J Gowers, James RB Eastwood, Irfan Alibay + 4 more
Alchemical free energy campaigns can be planned using graph theory by building up networks that contain nodes representing molecules that are connected by possible transformations as edges. We introduce Konnektor, an open-source Python package, for systematically planning, modifying, and analyzing free energy…
Dong Hyeon Mok, Jongseung Kim, Seoin Back
To realize renewable and sustainable energy cycle, there has been a lot of effort put into discovering catalysts with desired properties from a large chemical space. To achieve this goal, several screening strategies have been proposed, most of which require validation of thermodynamic stability and synthesizability of…
Jana Weber, Pietro Lio’, Alexei Lapkin
Networks of chemical reactions represent relationships between molecules within chemical supply chains and promise to enhance planning of multi-step synthesis routes from bio-renewable feedstocks. This study aims to identify strategic molecules in chemical reaction networks that may potentially play a significant role…
Authors not listed
Real-world datasets in chemical engineering and bioengineering processes--such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials--can often be unlabelled or disorganized, rendering the training of existing supervised learning models ineffective at learning the…
Hang Hu, Jyothsna Padmakumar Bindu, Julia Laskin
Mass spectrometry imaging (MSI) is widely used for the label-free molecular mapping of biological samples. The identification of co-localized molecules in MSI data is crucial to the understanding of biochemical pathways. However, complex MSI data are too large for manual annotation but too small for training deep…