Article
Psychology, Biological
Arthur Prat-Carrabin, Michael Woodford
Summary: Humans weight different stimuli in averaging tasks, possibly indicating encoding bias. In a study, participants were asked to compare the averages of two series of numbers with varying prior distributions. It was found that participants encoded numbers with bias and noise, with more noise for infrequently occurring numbers.
NATURE HUMAN BEHAVIOUR
(2022)
Article
Optics
Ignacio Perito, Guido Bellomo, Daniel Galicer, Santiago Figueira, Augusto J. Roncaglia, Ariel Bendersky
Summary: The study characterizes the set of nonsignaling correlations using a two-dimensional representation involving the maximal value of a Bell functional and the mutual information between the parties. By investigating the frontier between quantum and postquantum subsets of nonsignaling correlations, the analysis reveals a trade-off between classical correlations and violations of Bell inequalities. The Tsirelson bound is identified as a singular point of this trade-off, independent of quantum mechanics.
Article
Biochemical Research Methods
Lior I. I. Shachaf, Elijah Roberts, Patrick Cahan, Jie Xiao
Summary: In this study, a new method for gene regulatory network reconstruction is proposed, which combines CMIA and the KSG-MI estimator. The results show that this method achieves an improvement of 20-35% in precision-recall measures compared to the current gold standard. This new method will help researchers discover new gene interactions or better choose gene candidates for experimental validations.
BMC BIOINFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Xiaolong Fan, Maoguo Gong, Yue Wu, Hao Li
Summary: This paper proposes a method for graph representation learning by maximizing mutual information between feature and topology views. The method constructs a feature graph and uses a cross-view representation learning module to capture graph information. Experimental results demonstrate the effectiveness of integrating feature and topology views.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Engineering, Electrical & Electronic
Madeline Navarro, Santiago Segarra
Summary: In this study, we address the problem of estimating the topology of multiple networks from nodal observations. Using a graphon model, we can infer the joint structure of graphs with different sizes and imprecise alignment. Our approach combines maximum likelihood penalty with graphon estimation schemes to enhance network inference. The proposed method is validated through comparisons with competing methods on synthetic and real-world datasets.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2022)
Article
Computer Science, Information Systems
Yaqun Liu, Changyou Xing, Guomin Zhang, Lihua Song, Hongxiu Lin
Summary: This article presents a proactive deception-based network anti-reconnaissance method called AntiTomo to defend against adversarial tomography-based topology inference. By providing attackers with obfuscated path measurement metrics, AntiTomo guides them to form a fake network topology view, thus hiding the key elements of the physical network.
COMPUTERS & SECURITY
(2022)
Article
Mathematics
Manish Kumar Shukla, Minyi Huang, Indranil Chakrabarty, Junde Wu
Summary: With the development of quantum technologies, quantum networks have become a vital research field. Recent progress has been made in understanding the correlations in quantum networks. This article investigates cloning as a potential method to generate three-party quantum networks, enabling the creation of larger networks. Various quantum network topologies created using cloning transformations are analyzed, which is especially useful when entangled pairs are limited. Moreover, a focus is placed on distinguishing cloning-created networks from those formed by independently generated entangled pairs. The article proposes an extension to the existing Finner inequality for triangle networks by increasing the number of observers to four or six, depending on the network topology, to account for additional correlations in cloned networks. Finally, tripartite mutual information is utilized to differentiate cloned networks from networks created by independent sources, and squashed entanglement is used to quantify the dependence in cloned networks.
Article
Physics, Multidisciplinary
Damian G. Hernandez, Ines Samengo
Summary: This paper proposes a general framework for selecting priors in order to infer the value of a property of a large stochastic system. By using a maximum entropy approach, a linear combination of indexed priors is obtained to select the relevant components for the Bayesian estimator, avoiding the need for handcrafted priors. Experimental results show that this method performs well compared to other methods proposed in the literature. The method also highlights the connection between Bayesian inference and equilibrium statistical mechanics.
Article
Computer Science, Artificial Intelligence
Yadong Zhou, Zhihao Ding, Xiaoming Liu, Chao Shen, Lingling Tong, Xiaohong Guan
Summary: This paper proposes an attribute inference model based on Adversarial VAE (Infer-AVAE) to address the issues of overfitting and oversmoothing in attribute inference. The model combines multi-layer perceptron (MLP) and graph neural networks (GNNs) in the encoder to learn positive and negative latent representations, and reduces noise through adversarial training. Additionally, a mutual information constraint is introduced as a regularizer for the decoder to improve output quality. Experimental results demonstrate that the model outperforms baselines in terms of accuracy.
Article
Telecommunications
Qiang Wang, Hao Jiang, Ying Jiang, Shuwen Yi, Qi Nie, Geng Zhang
Summary: This paper proposes an unsupervised embedding framework to represent information of multiple layers into a unified embedding space. Experimental results demonstrate that the method achieves competitive performance on both node-related and edge-related tasks.
DIGITAL COMMUNICATIONS AND NETWORKS
(2023)
Article
Computer Science, Artificial Intelligence
Martin Hofmann, Patrick Maeder
Summary: Nature has inspired scientists to develop new methods based on observations, with recent advances allowing insights into biological neural processes. Homeostatic plasticity, particularly synaptic scaling, has been identified as a mature and applicable theory to enhance learning capabilities of neural networks. Analyzing mutual information affected by synaptic scaling, the proposed approach outperforms previous regularization techniques in regression and classification tasks across various network topologies and data sets.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Physics, Multidisciplinary
Chris Jones, Karoline Wiesner
Summary: The article explores the relationship between degree distribution entropy and network robustness, finding that degree distribution entropy only sets a lower bound to robustness for randomly configured networks. It is shown that degree distribution entropy does not indicate robustness for networks with the same form of degree distribution, while remaining degree entropy and robustness have a positive monotonic relationship.
Article
Mathematics, Applied
Chris G. Antonopoulos
Summary: This paper introduces a method that combines information-theoretical and statistical approaches to infer connectivity in complex networks using time-series data. The method is based on estimations of the Mutual Information Rate for pairs of time-series and on statistical significance tests for connectivity acceptance using the false discovery rate method for multiple hypothesis testing. The method shows promising results in various scenarios, including correlated normal-variates data, coupled circle and logistic maps, coupled Lorenz systems, and coupled stochastic Kuramoto phase oscillators. It is able to accurately infer the number and pairs of connected nodes, even in the presence of noise, and can recover the initial connectivity matrices for different network structures. The proposed methodology has the advantage of relying solely on the recorded datasets to infer underlying network connectivity.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2023)
Article
Computer Science, Artificial Intelligence
Dafeng Wang, Hongbo Liu, Naiyao Wang, Yiyang Wang, Hua Wang, Sean McLoone
Summary: In this paper, a novel Sequence Entropy Energy-based Model (SEEM) is proposed to address the limitations of current trajectory prediction models. SEEM achieves diversity in candidate trajectory generation by optimizing sequence entropy, and improves accuracy and stability through probability distribution clipping mechanism and energy network. Experimental results demonstrate that SEEM outperforms the state-of-the-art approaches in terms of diversity, accuracy, and stability of pedestrian trajectory prediction.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Physics, Multidisciplinary
Piotr Nyczka, Marc-Thorsten Huett, Annick Lesne
Summary: The article discusses the core issue of network-based data analysis, examining whether a given pattern on a network is randomly distributed or systematically generated. It introduces generic 'pattern generators' based on an Eden growth model, evaluates the ability of different pattern measures to infer generator parameters, and finds that the best inference measures depend on the global topology of the network.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2021)
Article
Geriatrics & Gerontology
Francesca Di Cesare, Claudio Luchinat, Leonardo Tenori, Edoardo Saccenti
Summary: This study investigated the variations in concentrations, correlations, and ratios of 202 free circulating blood metabolites and lipids with age. The results showed that certain metabolites and lipids were associated with age in a sex-dependent manner, with linoleic acid, alpha-linoleic acid, and carnitine being associated with age in women, and monoacylglycerols and lysophosphatidylcholines being associated with age in men. Furthermore, correlations among phosphatidylcholines tended to have a positive trend with age in women, while correlations among monoacylglycerols and lysophosphatidylcholines tended to have a negative trend with age in men. The ratios between molecular features also showed age-dependent changes, with the decanoyl-l-carnitine/lysophosphatidylcholine ratio decreasing with age in women, and the l-carnitine/phosphatidylcholine and l-acetylcarnitine/phosphatidylcholine ratios increasing with age in men.
JOURNALS OF GERONTOLOGY SERIES A-BIOLOGICAL SCIENCES AND MEDICAL SCIENCES
(2022)
Article
Biotechnology & Applied Microbiology
Sara Moreno-Paz, Joep Schmitz, Vitor A. P. Martins dos Santos, Maria Suarez-Diez
Summary: This study assessed the accuracy and predictive power of genome-scale, constraint-based models in predicting metabolic changes in response to operational conditions in a bioreactor and intracellular, active reactions. The findings showed that the enzyme-constrained version of the model outperformed the original model in all simulations. The combination of this model with dynamic FBA allowed for the prediction of yields and productivities of different strains and production processes. Additionally, constraining protein availability improved the accuracy of the metabolic state description under dynamic conditions. These findings have important implications for the design of industrially relevant cell-based processes.
MICROBIAL BIOTECHNOLOGY
(2022)
Article
Agriculture, Multidisciplinary
Lenny Ferrer, Melanie Mindt, Maria Suarez-Diez, Tatjana Jilg, Maja Zagorscak, Jin-Ho Lee, Kristina Gruden, Volker F. Wendisch, Katarina Cankar
Summary: Indole, a compound with a characteristic odor, is produced by various organisms in nature and plays a role in food flavor and fragrance industry. Researchers discovered a bacterial enzyme that can synthesize indole, leading to an increase in indole production.
JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY
(2022)
Article
Biotechnology & Applied Microbiology
Melanie Mindt, Arman Beyraghdar Kashkooli, Maria Suarez-Diez, Lenny Ferrer, Tatjana Jilg, Dirk Bosch, Vitor Martins dos Santos, Volker F. Wendisch, Katarina Cankar
Summary: The biotechnological production process established in this study provides an attractive route for sustainable indole production from tryptophan in C. glutamicum. Industrially relevant indole titers were achieved within 24 hours and indole was concentrated in the organic layer as a pure product after fermentation.
MICROBIAL CELL FACTORIES
(2022)
Article
Biotechnology & Applied Microbiology
Sara Benito-Vaquerizo, Ivette Parera Olm, Thijs de Vroet, Peter J. Schaap, Diana Z. Sousa, Vitor A. P. Martins dos Santos, Maria Suarez-Diez
Summary: This study used computational and experimental methods to study the metabolism of Anaerotignum neopropionicum, a propionate-producing bacterium. The construction of the genome-scale metabolic model iANEO_SB607 provided insight into the acrylate pathway for fermentation of ethanol into propionate, shedding light on the energetic aspects of the cell.
MICROBIAL CELL FACTORIES
(2022)
Article
Multidisciplinary Sciences
Wasin Poncheewin, Anne D. van Diepeningen, Theo A. J. van der Lee, Maria Suarez-Diez, Peter J. Schaap
Summary: This study used genome properties and machine learning to establish the relationship between Pseudomonas strains isolated from the rhizosphere and phyllosphere and their plant-associated lifestyle. It identified 28 discriminating features and demonstrated the potential of genome properties annotation as a computational tool for classifying plant-associated lifestyles.
SCIENTIFIC REPORTS
(2022)
Article
Biochemical Research Methods
Maksim Zakhartsev, Filip Rotnes, Marie Gulla, Ove Oyas, Jesse C. J. van Dam, Maria Suarez-Diez, Fabian Grammes, Robert Anton Hafthorsson, Wout van Helvoirt, Jasper J. Koehorst, Peter J. Schaap, Yang Jin, Liv Torunn Mydland, Arne B. Gjuvsland, Simen R. Sandve, Vitor A. P. Martins dos Santos, Jon Olav Vik
Summary: SALARECON is a model that links the genome of Atlantic salmon to metabolic fluxes and growth, allowing for the simulation of metabolism and growth. It can be used to explain Atlantic salmon physiology and address key challenges in aquaculture.
PLOS COMPUTATIONAL BIOLOGY
(2022)
Article
Biotechnology & Applied Microbiology
Zeynep Efsun Duman-Ozdamar, Vitor A. P. Martins Dos Santos, Jeroen Hugenholtz, Maria Suarez-Diez
Summary: Replacing palm oil with oils produced by microbes through the conversion of sustainable feedstocks is a promising alternative, but major technical challenges must be overcome. In this study, the authors cultivated two yeasts and determined the optimal C/N ratio and temperature for maximum oil production. They also demonstrated the impact of C/N ratio and temperature on lipid accumulation and fatty acid composition, highlighting the potential for tailored fatty acid production.
MICROBIAL CELL FACTORIES
(2022)
Article
Multidisciplinary Sciences
Iva Budimir, Enrico Giampieri, Edoardo Saccenti, Maria Suarez-Diez, Martina Tarozzi, Daniele Dall'Olio, Alessandra Merlotti, Nico Curti, Daniel Remondini, Gastone Castellani, Claudia Sala
Summary: The detection and characterization of bacteria in biological samples is important for monitoring infections and epidemics, as well as studying human health and its relationship with commensal microorganisms. In this study, a new reference-free approach based on protein domains was proposed to define the phylogenetic distance between bacteria. By extracting protein domain profiles from bacterial genomes and modeling their relative species abundance distribution, a new measurement of phylogenetic distance was derived. The model-based distance was shown to detect differences between bacteria in cases where the 16S rRNA-based method failed, providing a potentially complementary approach for analyzing bacterial populations measured by shotgun sequencing.
SCIENTIFIC REPORTS
(2022)
Article
Immunology
Pieter M. Dekker, Meghan B. Azad, Sjef Boeren, Piushkumar J. Mandhane, Theo J. Moraes, Elinor Simons, Padmaja Subbarao, Stuart E. Turvey, Edoardo Saccenti, Kasper A. Hettinga
Summary: This study explores the relationship between the human milk proteome and allergy in both mothers and breastfed infants. The results suggest that milk for infants who develop allergies contains higher levels of immunoglobulin chains, regardless of the mother's allergy status. Furthermore, network analysis reveals increased connectivity of proteins in the milk of allergic mothers and infants who develop allergies, particularly in proteins involved in protein translation machinery. These findings provide new insights into the complex interaction between the mother, milk, and infant in relation to allergy.
FRONTIERS IN IMMUNOLOGY
(2022)
Review
Microbiology
Juan Jose Gonzalez-Plaza, Cristina Furlan, Tomaz Rijavec, Ales Lapanje, Rocio Barros, Juan Antonio Tamayo-Ramos, Maria Suarez-Diez
Summary: The study of microbial cells interacting with natural and synthetic interfaces has been enriched by the development of advanced omics technologies, allowing for the isolation and analysis of nucleic acids, proteins, and metabolites from complex samples. This review discusses the challenges in analyzing microbial cells at genomic, transcriptomic, proteomic, and metabolomic levels, and describes both experimental and computational approaches to address them. The integration of multi omics datasets is presented as a means to achieve a systems level understanding of these complex interactions.
FRONTIERS IN MICROBIOLOGY
(2022)
Article
Biochemistry & Molecular Biology
Toshihiko Katoh, Chihaya Yamada, Michael D. Wallace, Ayako Yoshida, Aina Gotoh, Moe Arai, Takako Maeshibu, Toma Kashima, Arno Hagenbeek, Miriam N. Ojima, Hiromi Takada, Mikiyasu Sakanaka, Hidenori Shimizu, Keita Nishiyama, Hisashi Ashida, Junko Hirose, Maria Suarez-Diez, Makoto Nishiyama, Ikuo Kimura, Keith A. Stubbs, Shinya Fushinobu, Takane Katayama
Summary: The BbhII enzyme from Bifidobacterium bifidum is involved in the breakdown of mucin O-glycans, modulating host-microbiota symbiosis and dysbiosis.
NATURE CHEMICAL BIOLOGY
(2023)
Article
Biochemistry & Molecular Biology
Francesca Di Cesare, Alessia Vignoli, Claudio Luchinat, Leonardo Tenori, Edoardo Saccenti
Summary: This study analyzed the association networks of serum metabolites and found that the metabolite-metabolite association network of colorectal cancer (CRC) patients is distinct from that of patients with polyposis and healthy controls. Energy metabolism-related nodes play a crucial role in the CRC network.
Article
Biochemical Research Methods
William T. R. Scott Jr, Sara Benito-Vaquerizo, Johannes R. Zimmermann, Djordje Bajic, Almut R. Heinken, Maria Suarez-Diez, Peter R. Schaap
Summary: This study examined and evaluated 24 constraint-based modeling tools for genome-scale metabolic modeling of microbial consortia. It found that newer, more accessible, and well-documented tools generally outperformed older tools, but some older tools had tradeoffs in terms of accuracy or flexibility. The study provides recommendations for researchers in choosing the most suitable tools and opportunities for improvement in metabolic modeling of multi-species microbial consortia.
PLOS COMPUTATIONAL BIOLOGY
(2023)
