Article
Computer Science, Software Engineering
Tomas Polasek, David Hrusa, Bedrich Benes, Martin Cadik
Summary: The study focuses on evaluating the visual realism of virtual trees through user studies and neural network predictors. It reveals that branching angles, branch lengths, and widths are crucial for perceived realism in trees.
ACM TRANSACTIONS ON GRAPHICS
(2021)
Article
Biochemical Research Methods
Xiaoke Ma, Wei Zhao, Wenming Wu
Summary: Multi-layer networks provide an effective tool to model complex systems with multiple interactions. Graph clustering in multi-layer networks is challenging due to the difficulty in balancing cluster connectivity and layer connections. To address this, a novel algorithm called LSNMF is proposed to identify layer-specific modules in multi-layer networks. LSNMF first extracts vertex features using NMF and decomposes them into common and specific components, with orthogonality constraint imposed on the specific components. Extensive experiments show that LSNMF outperforms existing baselines and efficiently extracts stage-specific modules associated with known functions and survival time of patients.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Biochemistry & Molecular Biology
Kazuhiro Maeda, Hiroyuki Kurata
Summary: This article presents a new approach called KinModGPT that generates kinetic models directly from natural language text. KinModGPT utilizes GPT as a natural language interpreter and Tellurium as an SBML generator. The effectiveness of KinModGPT in creating SBML kinetic models from complex natural language descriptions is demonstrated, including metabolic pathways, protein-protein interaction networks, and heat shock response. This article showcases the potential of KinModGPT in kinetic modeling automation.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Multidisciplinary Sciences
Pablo Venegas, Francisco Calderon, Daniel Riofrio, Diego Benitez, Giovani Ramon, Diego Cisneros-Heredia, Miguel Coimbra, Jose Luis Rojo-Alvarez, Noel Perez
Summary: This study proposes an automatic detector for ladybird beetles in images, achieving a 92% accuracy rate using image processing modules and a deep convolutional neural network-based classifier on a validation dataset.
Article
Mathematics, Applied
Yushuang Luo, Xiantao Li, Wenrui Hao
Summary: In this paper, a data-driven modeling approach with latent variables for dynamics problems is introduced. The proposed model includes artificial latent variables in addition to observed variables and allows for easy enforcement of stability of the coupled dynamics. The model is implemented using recurrent cells and trained with backpropagation through time, demonstrating stability and efficiency through numerical examples. Two fluid-structure interaction problems are used as applications to illustrate the accuracy and predictive capability of the model.
Article
Plant Sciences
Yan Zhang, Shiyun Wa, Longxiang Zhang, Chunli Lv
Summary: The detection of plant diseases is crucial in agricultural production. Traditional deep learning algorithms have limitations in this area, and this study proposes a Tranvolution detection network with GAN modules to improve detection performance. The method combines generative models, modified Transformers, and CNNs. Experimental results show satisfactory performance in terms of precision, recall, and mAP.
FRONTIERS IN PLANT SCIENCE
(2022)
Article
Construction & Building Technology
Ruihua Liang, Weifeng Liu, Sakdirat Kaewunruen, Hougui Zhang, Zongzhen Wu
Summary: In this study, advanced hybrid models of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network were built to accurately and efficiently classify and evaluate the impact of multiple external vibration sources on sensitive buildings such as laboratories and heritage buildings. The proposed optimal model achieved an accuracy of over 97% for identifying external vibration sources by utilizing extensive data recorded in Beijing. A real-world case study was conducted, demonstrating the necessity and feasibility of this study for engineering applications.
STRUCTURAL CONTROL & HEALTH MONITORING
(2023)
Article
Computer Science, Interdisciplinary Applications
Julian Koellermeier, Hannes Vandecasteele
Summary: This paper introduces a hierarchical micro-macro acceleration based on moment models, which combines the speed of macroscopic models and the accuracy of microscopic models. Several new micro-macro methods are derived and compared to existing methods. In 1D and 2D test cases, the new methods achieve high accuracy and significant speedup.
JOURNAL OF COMPUTATIONAL PHYSICS
(2023)
Article
Engineering, Chemical
Yafeng Xing, Yachao Dong, Christos Goergakis, Yu Zhuang, Lei Zhang, Jian Du, Qingwei Meng
Summary: This article proposes a hybrid-modeling framework that combines data-driven and knowledge-driven approaches for studying homogeneous synthesis reactions. By using constrained enumeration, dynamic response surface methodology, target factor analysis, and mass balance, the framework can accurately identify stoichiometries and obtain accurate kinetic models.
Article
Computer Science, Information Systems
Merim Dzaferagic, Nicola Marchetti, Irene Macaluso
Summary: In this study, a distributed and a centralized approach to minimize signaling and latency related to user mobility in cellular networks were demonstrated, showing significant reductions in signaling between core and RAN compared to traditional methods. These solutions adapt quickly to changes in user movement patterns by utilizing locally available information.
IEEE SYSTEMS JOURNAL
(2021)
Article
Engineering, Electrical & Electronic
Fuxin Zhang, Chunbo Luo, Jialang Xu, Yang Luo, Fu-Chun Zheng
Summary: Automatic modulation recognition (AMR) using deep learning (DL) shows high recognition accuracy and low false alarms, but faces challenges in complexity and explainability, which hinders practical deployment in wireless communication systems.
DIGITAL SIGNAL PROCESSING
(2022)
Editorial Material
Engineering, Civil
Saeid Mehdizadeh, Farshad Fathian, Mir Jafar Sadegh Safari, Jan Adamowski
Summary: In this response, we address the comments made by Ebtehaj et al. (2020) and provide additional details on various aspects of our study.
JOURNAL OF HYDROLOGY
(2021)
Article
Chemistry, Multidisciplinary
Peter J. Skrdla
Summary: Simple methodologies for predicting the particle size distribution of nanoparticle preparations based on kinetics can guide the development of new synthetic strategies. Dispersive kinetic models possess a unique advantage in linking evolving specific rate to an underlying distribution of activation energies, and can readily predict the PSD in burst nucleation scenarios. The derivation of the discussed DKMs in this work provides connections to classical mechanics and fractal dynamics from a geometrical perspective.
CRYSTAL GROWTH & DESIGN
(2021)
Article
Chemistry, Multidisciplinary
Ivan Kuric, Ivana Klackova, Kseniia Domnina, Vladimir Stenchlak, Milan Saga Jr
Summary: This article provides a detailed description of using predictive models of NAR neural networks to predict the course of certain quantities related to industrial machines. It presents an algorithm to automatically find the settings of these models to achieve the desired accuracy. The algorithm was tested on simulated data collected using the M5StickC microcontroller device.
APPLIED SCIENCES-BASEL
(2022)
Review
Physics, Multidisciplinary
Madhumita Srinivasan, Robert Clarke, Pavel Kraikivski
Summary: This review provides an overview of the progress made by computational and systems biologists in characterizing different cell death regulatory mechanisms and emphasizes the importance and complexity of the cell death network. It also highlights the need for mathematical modeling and system-oriented approaches in understanding the dynamic behavior of this complex regulatory mechanism.
Article
Multidisciplinary Sciences
Daniel Dimitrov, Denes Tuerei, Martin Garrido-Rodriguez, Paul L. Burmedi, James S. Nagai, Charlotte Boys, Ricardo O. Ramirez Flores, Hyojin Kim, Bence Szalai, Ivan G. Costa, Alberto Valdeolivas, Aurelien Dugourd, Julio Saez-Rodriguez
Summary: Multiple methods and resources for inferring cell-cell communication are compared in this study, and an interface called LIANA is developed to facilitate the use and comparison of these approaches. The impact of choice of resource and method on the predicted intercellular interactions is shown, and the predictions are found to be coherent with other data modalities.
NATURE COMMUNICATIONS
(2022)
Article
Multidisciplinary Sciences
Kai Markus Schneider, Antje Mohs, Wenfang Gui, Eric J. C. Galvez, Lena Susanna Candels, Lisa Hoenicke, Uthayakumar Muthukumarasamy, Christian H. Holland, Carsten Elfers, Konrad Kilic, Carolin Victoria Schneider, Robert Schierwagen, Pavel Strnad, Theresa H. Wirtz, Hanns-Ulrich Marschall, Eicke Latz, Benjamin Lelouvier, Julio Saez-Rodriguez, Willem de Vos, Till Strowig, Jonel Trebicka, Christian Trautwein
Summary: Intestinal dysbiosis affects liver disease progression towards cancer by inducing hepatic monocytic myeloid-derived suppressor cells (mMDSC) expansion and T-cell suppression. The dysbiosis phenotype can be transmitted through fecal microbiota transfer and reversed by antibiotic treatment. Loss of Akkermansia muciniphila correlates with increased mMDSC abundance, but its reintroduction reduces liver inflammation and fibrosis. Patients with cirrhosis display increased bacterial abundance in hepatic tissue, leading to transcriptional changes and activation of fibro-inflammatory pathways as well as cancer immunosuppression.
NATURE COMMUNICATIONS
(2022)
Article
Biochemistry & Molecular Biology
Abel Sousa, Aurelien Dugourd, Danish Memon, Borgthor Petursson, Evangelia Petsalaki, Julio Saez-Rodriguez, Pedro Beltrao
Summary: Genetic alterations in cancer cells lead to dysregulation of kinase and transcription factor activities, contributing to oncogenic transformation. We analyzed genomics and (phospho)proteomics data from a large cohort of tumors and cell lines to estimate activity changes in hundreds of kinases and transcription factors. We found co-regulation of kinase and TF activities, revealing known regulatory relationships and dissecting genetic drivers of signaling changes in cancer. Our study also identified differentially regulated activities in cancer subtypes and their association with patient survival, providing insights into the dysregulation of protein activities and its impact on disease severity in cancer.
MOLECULAR SYSTEMS BIOLOGY
(2023)
Editorial Material
Biochemistry & Molecular Biology
Maria Polychronidou, Jingyi Hou, M. Madan Babu, Prisca Liberali, Ido Amit, Bart Deplancke, Galit Lahav, Shalev Itzkovitz, Matthias Mann, Julio Saez-Rodriguez, Fabian Theis, Roland Eils
Summary: In this Editorial, our Chief Editor and members of our Advisory Editorial Board discuss the recent breakthroughs, current challenges, and emerging opportunities in single-cell biology, and share their vision for the future of the field.
MOLECULAR SYSTEMS BIOLOGY
(2023)
Letter
Biotechnology & Applied Microbiology
Sebastian Lobentanzer, Patrick Aloy, Jan Baumbach, Balazs Bohar, Vincent J. Carey, Pornpimol Charoentong, Katharina Danhauser, Tunca Dogan, Johann Dreo, Ian Dunham, Elias Farr, Adria Fernandez-Torras, Benjamin M. Gyori, Michael Hartung, Charles Tapley Hoyt, Christoph Klein, Tamas Korcsmaros, Andreas Maier, Matthias Mann, David Ochoa, Elena Pareja-Lorente, Ferdinand Popp, Martin Preusse, Niklas Probul, Benno Schwikowski, Buenyamin Sen, Maximilian T. Strauss, Denes Turei, Erva Ulusoy, Dagmar Waltemath, Judith A. H. Wodke, Julio Saez-Rodriguez
NATURE BIOTECHNOLOGY
(2023)
Review
Genetics & Heredity
Lukas Heumos, Anna C. Schaar, Christopher Lance, Anastasia Litinetskaya, Felix Drost, Luke Zappia, Malte Luecken, Daniel Strobl, Juan Henao, Fabiola B. Curion, Herbert Schiller, Fabian Theis
Summary: Recent advances in single-cell technologies have allowed for high-throughput molecular profiling of cells across different modalities and locations. This article presents a summary of benchmarking studies and provides comprehensive best-practice workflows for single-cell (multi-)omic analysis. The article serves as a guide for both novice and advanced users in the field.
NATURE REVIEWS GENETICS
(2023)
Article
Oncology
Barbora Salovska, Erli Gao, Sophia Mueller-Dott, Li Wenxue, Carlos Chacon Cordon, Wang Shisheng, Aurelien Dugourd, George Rosenberger, Julio Saez-Rodriguez, Liu Yansheng
Summary: This study found that long-term use of the oral antidiabetic drug metformin can significantly alter the signaling network of colorectal cancer cells. Through network analysis and metabolite labeling, the study also identified candidate drugs with potential anticancer effects. This research provides an important resource for understanding the mechanism of metformin-induced cell signaling.
CLINICAL AND TRANSLATIONAL MEDICINE
(2023)
Article
Medicine, General & Internal
Jan D. Lanzer, Alberto Valdeolivas, Mark Pepin, Hauke Hund, Johannes Backs, Norbert Frey, Hans-Christoph Friederich, Jobst-Hendrik Schultz, Julio Saez-Rodriguez, Rebecca T. Levinson
Summary: Using systems medicine approaches, we analyzed comorbidity profiles of heart failure patients and identified distinct profiles for HFpEF and HFrEF. This can improve diagnosis and treatment for HFpEF patients.
Article
Urology & Nephrology
Masaomi Nangaku, A. Richard Kitching, Peter Boor, Alessia Fornoni, Jurgen Floege, P. Toby Coates, Jonathan Himmelfarb, Rachel Lennon, Hans-Joachim Anders, Benjamin D. Humphreys, Fergus J. Caskey, Agnes B. Fogo
Summary: The International Society of Nephrology organized the TRANSFORM meeting to provide guidance on translational animal studies for new drug development in kidney disease. The meeting covered various themes such as disease model selection, pharmacokinetics, interventions, choice of animal, statistical power, organoids and organ-on-a-chip models, and reporting of results. These recommendations aim to accelerate the development of new drugs for efficacious diseases.
KIDNEY INTERNATIONAL
(2023)
Review
Biochemistry & Molecular Biology
Theodore Alexandrov, Julio Saez-Rodriguez, Sinem K. Saka
Summary: Spatial omics is a rapidly growing field that integrates imaging and omics to obtain spatially resolved information. It has opened up new opportunities and challenges for method developers and provides a new window into spatial biology.
MOLECULAR SYSTEMS BIOLOGY
(2023)
Review
Genetics & Heredity
Pau Badia-i-Mompel, Lorna Wessels, Sophia Mueller-Dott, Remi Trimbour, Ricardo Ramirez O. Flores, Ricard Argelaguet, Julio Saez-Rodriguez
Summary: Gene regulatory networks (GRNs) are complex regulatory circuits involving chromatin, transcription factors, and genes. Studying GRNs helps understand the establishment, maintenance, and disruption of cellular identity in disease. GRNs can be inferred from experimental or literature data, and the latest computational methods utilize single-cell multi-omics data to achieve unprecedented resolution in GRN inference.
NATURE REVIEWS GENETICS
(2023)
Article
Biochemistry & Molecular Biology
Sophia Mueller-Dott, Eirini Tsirvouli, Miguel Vazquez, Ricardo O. Ramirez Flores, Pau Badia-i-Mompel, Robin Fallegger, Denes Tuerei, Astrid Laegreid, Julio Saez-Rodriguez
Summary: Gene regulation plays a crucial role in cellular processes related to human health and disease. This study presents and evaluates a collection of reliable and comprehensive TF regulons created using the CollecTRI meta-resource. These regulons accurately estimate TF activities and help interpret transcriptomics data.
NUCLEIC ACIDS RESEARCH
(2023)
Article
Immunology
Patricia Sole, Jun Yamanouchi, Josep Garnica, Muhammad Myn Uddin, Robert Clarke, Joel Moro, Nahir Garabatos, Shari Thiessen, Mireia Ortega, Santiswarup Singha, Debajyoti Mondal, Cesar Fandos, Julio Saez-Rodriguez, Yang Yang, Pau Serra, Pere Santamaria
Summary: Chronic antigenic stimulation can induce the differentiation of CD4(+) T cells into T regulatory type 1 (TR1) cells, which produce interleukin-10 and do not express FOXP3. The progenitors and transcriptional regulators of this T-cell subset are still unknown.
CELLULAR & MOLECULAR IMMUNOLOGY
(2023)
Article
Biotechnology & Applied Microbiology
Jovan Tanevski, Ricardo Omar Ramirez Flores, Attila Gabor, Denis Schapiro, Julio Saez-Rodriguez
Summary: The paper presents MISTy, a machine learning framework that can extract relationships from any spatial omics data. The framework is flexible, scalable, and capable of dissecting different effects through the construction of multiple views.
Article
Endocrinology & Metabolism
Nicola Wanner, Geoffroy Andrieux, Pau Badia-I-Mompel, Carolin Edler, Susanne Pfefferle, Maja T. Lindenmeyer, Christian Schmidt-Lauber, Jan Czogalla, Milagros N. Wong, Yusuke Okabayashi, Fabian Braun, Marc Luetgehetmann, Elisabeth Meister, Shun Lu, Maria L. M. Noriega, Thomas Guenther, Adam Grundhoff, Nicole Fischer, Hanna Braeuninger, Diana Lindner, Dirk Westermann, Fabian Haas, Kevin Roedl, Stefan Kluge, Marylyn M. Addo, Samuel Huber, Ansgar W. Lohse, Jochen Reiser, Benjamin Ondruschka, Jan P. Sperhake, Julio Saez-Rodriguez, Melanie Boerries, Salim S. Hayek, Martin Aepfelbacher, Pietro Scaturro, Victor G. Puelles, Tobias B. Huber
Summary: The study provides evidence for the hepatic tropism of SARS-CoV-2 and its impact on liver injury in COVID-19 patients. The researchers found viral RNA in autopsy liver specimens and successfully isolated infectious SARS-CoV-2 from liver tissue postmortem. The study also reveals similarities between the liver signatures of COVID-19 and other viral infections.