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Multi-omics with dynamic network biomarker algorithm prefigures organ-specific metastasis of lung adenocarcinoma

Efficacious strategies for early detection of lung cancer metastasis are of significance for improving the survival of lung cancer patients. Utilizing two clinical cohorts of four major types of lung cancer distant metastases, with single-cell RNA sequencing (scRNA-seq) of primary lesions and liquid chromatography mass spectrometry data of sera, we identified the marker genes and serum secretome foreshadowing the lung cancer site-specific metastasis through dynamic network biomarker (DNB) algorithm. Also, we located the intermediate status of cancer cells, along with its gene signatures, in each metastatic state trajectory that cancer cells at this stage still had no specific organotropism. Furthermore, an integrated neural network model based on the filtered scRNA-seq data was successfully constructed and validated to predict the metastatic state trajectory of cancer cells. Overall, our study provided a new insight to locate the pre-metastasis status of lung cancer and primarily examined its clinical application value, contributing to the early detection of lung cancer metastasis in a more feasible and efficacious way.

Ren et al. (2024), Integrated Risk Management for Cascading Reservoirs Under Uncertainty using Networked Modelling

Description of Research Data and Code This repository contains the data and code associated with the research paper: Ren et al. (2024), Integrated Risk Management for Cascading Reservoirs Under Uncertainty using Networked Modelling, currently under review at Water Resources Research. Overview To investigate the risk interdependencies arising from hydraulic interactions in cascading reservoir systems, we developed a risk propagation model using Bayesian networks (see file: Risk_propagation_model). Building on this model, we employed EMODPS to create a robust operational model for the reservoirs (see file: Robust_operation_model). Our goal was to minimize the joint risks of insufficient hydropower output and ecological water shortages while formulating robust operating policies to mitigate system performance degradation in the face of uncertain future runoffs (generated from our runoff simulations, see file: runoff simulation). Additionally, we analyzed the relationship between overall risk and risk at individual reservoir sites using a scenario discovery algorithm to pinpoint scenarios that reveal vulnerabilities (see file: python_project_scenariodiscovery). Acknowledgments This project builds upon the code developed by Giuliani et al. (2016) M3O-Multi-Objective-Optimal-Operations (https://mxgiuliani00.github.io/M3O-Multi-Objective-Optimal-Operations/), Hadka and Reed (2013) BORG MOEA (http://borgmoea.org/), and Kevin Patrick Murphy et al. (2007) Bayesian Network Toolbox (https://www.ipcc.ch/report/ar6/wg1/#InteractiveAtlas). We are grateful to the original authors for their contributions. While we have made modifications and extensions to the original code, we have not altered its license. Users should refer to the original repositories for more details and ensure compliance with the terms of the original authors' licenses.

Transferability of Atom-Based Neural Networks Datasets

This repository contains the datasets used in our article "Transferability of Atom-Based Neural Networks". The datasets are in the extxyz format, which can be used to train NequIP models.  The extended xyz files contain all necessary information for the training of the models. The first four columns contain the xyz representation (Ångstrom), while the last column contains the atomic energies (kcal/mol), computed using either the AO-based EDA or MO-based IBO/IAO decomposition scheme. The total atomization energy (kcal/mol) is in the line below the number of atoms. The files are named as {dataset}_{decomp}.xyz.

Eletronic Companion - A Novel MILP Formulation for Optimal Allocation and Coordination of Protective and Switching Devices in Active Distribution Networks

This release is associated with a paper entitled "A Novel MILP Formulation for Optimal Allocation and Coordination of Protective and Switching Devices in Active Distribution Networks". The attached document provides the data adopted to obtain the results presented in the paper.

Lotus2 on Mycorrhizal Fungi in the Galaxy Training Network - Example Run Output

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opencc-by-4.0Apr 2024View details →

D2.7 Report on practices and innovations from networks and research M16

Deliverable 2.7 reports the results of a desk research where we screened for European research programmes potentially generating useful innovations and practices in beekeeping. The document is an update of Deliverable 2.6, where a database of research projects and networks yielding practices and innovations was presented in M7. In Deliverable 2.7, the database was updated, and the Technology Readiness Level (TRL) of the results of the projects screened were analysed.

Pan-Cancer T cell atlas from "The combined use of scRNA-seq and network propagation highlights key features of pan-cancer Tumor-Infiltrating T cells" (https://doi.org/10.1371/journal.pone.0315980)

The scRNA-seq data were collected from previously published datasets (GSE140228, GSE139555, GSE155698, GSE121636, and GSE139324), adhering to the following selection criteria: 1) presence of T cells, 2) treatment-naïve patients, 3) solid tumors, and 4) inclusion of at least tumor and blood samples. Each scRNA-seq dataset underwent separate preprocessing in R (v4.0.2). We filtered out cells from the original count matrices that had fewer than 200 genes detected or more than 10% mitochondrial UMI counts and we only kept genes detected in at least 3 cells. Then, we applied Seurat (v4.0.5) with default parameters for count data normalization and scaling. Each cell was assigned a cell cycle score using the CellCycleScoring function and we computed the difference between the G2M and S phase scores. This approach allows for the separation of non-cycling from cycling cells while minimizing the differences in cell cycle phase among proliferating cells. The SelectIntegrationFeatures function was ran with the nfeatures parameter set to 3,000 before merging all samples from each dataset. These integration features were then used for Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP). Clustering was performed using the Louvain algorithm with the resolution parameter set to 2.0 for all datasets. Finally, T cells were isolated based on CD3D and CD3G genes expression (CD3D or CD3G expression level > 0). To integrate heterogeneous data from different sources, a two-step procedure was applied. We first concatenated all datasets together and ran the scaling and PCA steps based on the top 3,000 highly variable genes identified by the FindVariableFeatures function with the “vst” method. Harmony was applied for batch effect correction then UMAP and clustering using the Louvain algorithm with the resolution parameter set to 2.0 were performed on the harmony reduction. Examining the result from the first clustering run, we identified contamination clusters and clusters that arose from unwanted factors: we removed the contamination clusters including low quality cells highly expressing marker genes associated with apoptosis and tissue dissociation operation, pancreatic acinar cells (expressing PRSS1, CLPS, PNLIP and CTRB1 among others), myeloid cells (expressing CD68) and B cells (expressing CD79A). Then, we performed the second run of integration and clustering excluding immunoglobulin, ribosome-protein-coding, and T cell receptor (TCR) genes (gene symbol with string pattern "^IGK|^IGH|^IGL|^IGJ|^IGS|^IGD|IGFN1", "^RP([0–9]+-|[LS])", and "^TRA|^TRB|^TRG" respectively) from the top 3,000 highly variable genes and regressing out the cell cycle difference effect as well as the percentage of mitochondrial UMI counts. Harmony (v0.1.0) was applied again for batch effect correction and UMAP was performed on the harmony reduction. T cell subtypes identification and annotation was performed by clustering cells using the Louvain algorithm with the resolution parameter set to 4.1 after iterative testing from 3.5 to 5.0 by 0.1 (more granular than default), computing clusters signatures based on differential gene expression using the FindAllMarkers function with the “MAST” method and interrogating known gene markers expression. A resolution value of 4.1 was notably found to be the lowest resolution value enabling the correct separation of proliferating CD4+ T cells from proliferating CD8+ T cells.

The Petrinja 2020 earthquake sequence - Croatian Seismological Network

The data set contains waveforms of the 2020 MW6.4 Petrinja earthquake mainshock and six additional M>4 earthquakes from the same earthquake sequence, recorded on the Croatian Seismological Network. The operation of the VINV, STON, RUJC, RABC, NVLJ, and CERK seismic stations are/were part of the semi-permanent seismic network operated by the Department of Geophysics at the Faculty of Science of the University of Zagreb that is/was financially supported by various seismological projects.

Graph neural network emulator for modeling of ice dynamics and calving in the Pine Island Glacier, Antarctica

These files include the following codes and datasets for developing graph neural network (GNN) emulators for the Ice-sheet and Sea-level System Model (ISSM) for modeling ice sheet dynamics and calving in the Pine Island Glacier, Antarctica <ul> <li>ISSM_DGL_PIG2.py: Python file for training GNN models (*single.py: code for single GPU environment)</li> <li>ISSM_CNN_PIG.py: Python file for training convolutional neural network (CNN) models</li> <li>*.mat: Datasets of the ISSM transient simulation results (graphs for GNNs)</li> <li>*.pkl: Datasets of the ISSM transient simulation results (grids for CNNs)</li> </ul>

Map of valley networks in the northwestern Hellas region, Mars

This dataset provides a representation of the valley networks in the northwestern Hellas region. We mapped the valley networks as vector-based polylines within ArcGIS software, using the ~5 m/pixel CTX mosaics with THEMIS daytime IR mosaics as the regional background. Additionally, we used the 200 m/pixel MOLA-HRSC blended DEM for detailed identification, mapping, and derivation of topographic and morphologic parameters of valley networks. The dataset includes the whole valley networks within the study region, the mainstreams of the valley networks, single valleys, and extracted junction angles. Attribute tables are associated with the dataset, providing useful information and morphologic parameters of the valley networks such as length, sinuosity, stream order for each valley networks.

opencc-by-4.0Dec 2023View details →

Trans-CAUNet: A Swin Transformer-Based Network for Automated Extracting Mountain Glaciers with Multisource Remote Sensing Imagery

Trans-CAUNet: A Swin Transformer-Based <a>Network</a> for Automated Extracting Mountain Glaciers with Multisource Remote Sensing Imagery Original remote sensing images <div> </div>

DMGAT: Predicting ncRNA-Drug resistance associations based on diffusion map and heterogeneous graph attention network

Dataset for the paper: DMGAT: Predicting ncRNA-Drug resistance associations based on diffusion map and heterogeneous graph attention network

opencc-by-4.0May 2024View details →

Exponential Synchronization of BAM Neural Networks with Delay on Arbitrary Time Domains

Code to reproduce the plots presented in our manuscript ``Exponential Synchronization of BAM Neural Networks with Delay on Arbitrary Time Domains''.

Communities in global tourism network and related data

1995-2021 Communities within Global Tourism Network and Population, Urban population, GDP, GDP per capita, Exports of goods and services, Air passengers carried, Number of world heritage, Number of representative intangible cultural heritage, War and conflict, Political violence and protest events, Geographical proximity.

Improving the use of social networks to disseminate information aboutreal estate in the Republic of Kazakhstan

In the modern era, digital platforms, particularly social networks, have revolutionized the way governments interact with citizens. Social media has emerged as a critical tool for disseminating information quickly and efficiently, especially in areas where public awareness is essential, such as taxes, land registration, and real estate laws. In his 2024 Address to the Nation, the President of Kazakhstan underscored the importance of digitizing public services to increase transparency, improve access to information, and simplify interactions between citizens and government bodies. This aligns with the country's long-term goal of modernizing the e-government system, emphasizing the need for effective communication channels that foster civic engagement and compliance.

Ids of Tweets including ZeroWaste from Twitter for A Case of Marketing Segmentation tool in Social Networks Research

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Battery Pack Temperature Change Prediction for Running BLDC 1500 W Motor using Artificial Neural Network

The study explores the prediction of battery temperature using an artificial neural network (ANN) model, trained with experimental data from a brushless DC (BLDC) motor setup. The ANN model, with a 15-14-1 architecture, successfully predicted battery temperature change based on various input parameters, including RPM, load and voltage change of thirteen series of battery. The ANN predictions aligned closely with experimental results, demonstrating the model's effectiveness in capturing the nonlinear behavior of battery temperature changes. These findings highlight the potential of deep learning techniques to improve real-time thermal management in BMS, offering a promising approach for extending battery life and optimizing performance in electric vehicles and energy storage systems.

Data for "Learning Collective Cell Migratory Dynamics from a Static Snapshot with Graph Neural Networks"

This dataset contains snapshots of cell monolayers, represented as graphs, along with their corresponding average displacement measurements.

Information diffusion assumptions can distort our understanding of social network dynamics (code and data)

This repository contains the data (<code>cascade_reconstruction.tar.gz</code>) and code (<code>code_repository.tar.gz</code>) for a paper titled "Information diffusion assumptions can distort our understanding of social network dynamics" by <a href="https://www.matthewdeverna.com/">Matthew R. DeVerna</a>, <a href="https://pierri.faculty.polimi.it/">Francesco Pierri</a>, <a href="https://rachithaiyappa.github.io/">Rachith Aiyappa</a>, <a href="https://diogofpacheco.github.io/">Diogo Pachecho</a>, <a href="https://jbryden.co.uk/home/">John Bryden</a>, and <a href="https://cnets.indiana.edu/fil">Filippo Menczer</a> . Please see the README.md file for important details! If you would like to report issues with the code, you can do so through an associated GitHub repository that houses the project's code, which can be found <a href="https://github.com/osome-iu/cascade_reconstruction">here</a>.

Impact of Multimodal Freight Network on Private Sector Global Distribution

Data for FERSC Year 1 Project 4: Impact of Multimodal Freight Network on Private Sector Global Distribution