Article
Multidisciplinary Sciences
Nicholas C. Lammers, Avi I. Flamholz, Hernan G. Garcia
Summary: Gene regulation is crucial for cellular function, but we lack quantitative models to predict how it emerges from molecular interactions. Thermodynamic models have been successful for bacterial systems, but they may not capture how eukaryotic gene circuits respond to transcription factors due to energy dissipation. By using simple kinetic models, we found that energy input can enhance the rate of gene information transmission, but the regulatory mechanisms depend on the interference from noncognate activator binding.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2023)
Article
Physics, Fluids & Plasmas
Nahuel Freitas, Karel Proesmans, Massimiliano Esposito
Summary: We investigate the relationship between reliability and entropy production in a realistic model of electronic memory, and derive an explicit expression bounding the error rate of the memory. Our results go beyond the classical instanton theory and are confirmed by comparison with stochastic simulations.
Article
Business, Finance
Catalin Dragomirescu-Gaina, Dionisis Philippas, Mike G. Tsionas
Summary: Active fund managers may improve market timing performance by making quick portfolio adjustments backed by rough estimates, but oversimplification could lead to inability to profit in calm markets. Selecting accuracy levels upfront through different data-filtering techniques can expose a trade-off between prediction accuracy and reaction speed across hedge funds' investment styles. Less accurate predictions may speed up reactions to unexpected changes in uncertainty and risk measures, as shown in empirical analysis and complemented by simulation exercises.
INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS
(2021)
Article
Multidisciplinary Sciences
Benjamin James Dyson
Summary: The paper examines the relationship between decision-making speed and quality, finding that slowing down decision-making can increase the likelihood of successful performance. Self-imposed reductions in processing time following losses were identified as causal factors in determining behavior quality during competitive decision-making, with adjustment responses after losses being less flexible than after wins.
SCIENTIFIC REPORTS
(2021)
Article
Multidisciplinary Sciences
Javier G. Orlandi, Mohammad Abdolrahmani, Ryo Aoki, Dmitry R. Lyamzin, Andrea Benucci
Summary: The authors discovered that choice signals in the posterior cortex of the mouse brain are distinct from sensory and movement-related signals, and their variability is influenced by task features and cognitive demands. They found that choice information is represented in multi-area brain networks that also incorporate sensory, motor, and cognitive variables. These findings suggest the presence of an independent, multi-area decision variable in the posterior cortex that is influenced by task features and cognitive demands.
NATURE COMMUNICATIONS
(2023)
Article
Physics, Fluids & Plasmas
Sarah E. Harvey, Subhaneil Lahiri, Surya Ganguli
Summary: We use stochastic thermodynamics, large deviation theory, and information theory to establish fundamental limits on the accuracy of single cell receptors in estimating external concentrations. While an ideal observer of receptor states cannot be outperformed by any nonequilibrium receptor, we find that a simple observer measuring the fraction of time the receptor is bound has a fundamental limit on the accuracy of non-equilibrium receptors. We also derive explicit formulas to numerically estimate the tradeoff between accuracy and energy consumption in nonequilibrium receptors.
Article
Biology
Kyra Schapiro, Kresimir Josic, Zachary P. Kilpatrick, Joshua Gold
Summary: This study used human psychophysics to examine the impact of working-memory limitations on the accuracy of continuous decision variables. The results suggest that the degradation of the decision variable depends on the strategy used to form it, either as a single value or as multiple values stored in memory.
Article
Management
Tahir Ekin, Tevfik Aktekin
Summary: This paper proposes decision analysis methods for determining the optimal number of agents in a service system, using Bayesian inference and simulation-based optimization techniques. The novelty of the approach lies in utilizing dependent system rates to determine optimal staffing in constrained settings for stochastic service systems, with implications of ignoring dependencies and uncertainties demonstrated on simulated data for general service systems, and applications in call center operations showcased.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Pediatrics
Maa-Ohui Quarmyne, Diana Ross, Cynthia Sinha, Nitya Bakshi, Jeanne Boudreaux, Lakshmanan Krishnamurti
Summary: This study explored the knowledge and decision-making of patients/families with transfusion-dependent thalassemia (TDT) regarding gene therapy (GT). The results showed that the participants desired a "cure" from thalassemia, including transfusion independence, reduced chelation therapy, and improved quality of life. However, they had insufficient knowledge and expressed concerns about the process, long-term outcomes, safety, side effects, and the potential risks of death/failure associated with GT. Despite the acceptance of transfusion frequency reduction, there was a divided preference between continuing transfusions which were familiar and GT which had an uncertain outcome. None of the participants had a matched sibling donor, making alternate donor HSCT the least preferred option in this group.
Article
Computer Science, Artificial Intelligence
Andrey Zhitnikov, Vadim Indelman
Summary: This paper proposes a simplification framework to address the increased complexity of decision-making algorithms, with a focus on risk-aware objectives. The framework includes novel stochastic bounds on the return and considers the correlation between returns. The paper also presents a risk averse objective and a tool called the probabilistic loss (PLoss) to characterize the simplification impact. Extensive simulations demonstrate the advantages of the approach.
ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Raiz Ali, Shakoor Muhammad, Ricardo H. C. Takahashi
Summary: This paper investigates the reuse of leftover pipes in construction, proposing a model and heuristic approach. A genetic-based decision support system is applied to validate the effectiveness of the solution, successfully minimizing the amount of leftovers.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Article
Behavioral Sciences
Sarah M. Lane, Mark Briffa
Summary: Skilful fighting involves the individual's ability to accurately and efficiently perform agonistic behaviors and choose the most appropriate behavior or tactic based on available information. The efficacy of different tactics may depend on the opponent's behavior and the individual's internal state, and the ability to choose the right tactic may be constrained by cognitive ability.
Article
Environmental Sciences
Jose Pulgar, Patricio H. Manriquez, Stephen Widdicombe, Roberto Garcia-Huidobro, Pedro A. Quijon, Mauricio Carter, Marcela Aldana, Diego Quintanilla-Ahumada, Cristian Duarte
Summary: Artificial Light at Night (ALAN) affects the behavior of rockfish by altering day and night cycles. Exposure to ALAN influenced their decision-making abilities, as fish spent more time in a non-decision area rather than choosing dark conditions, which are safer for smaller fish facing threats. This suggests that ALAN exposure disorients or reduces the ability of rockfish to make appropriate choices.
MARINE POLLUTION BULLETIN
(2023)
Article
Engineering, Electrical & Electronic
Xi Wang, Tianpeng Xin, Hongwei Wang, Li Zhu, Dongliang Cui
Summary: This paper proposes a novel learning-based framework that combines deep learning technology with distributed tracking control methods to solve the decision-making problem in autonomous driving of high-speed trains. It addresses the issue of insufficient training data by introducing a generative adversarial network-based data augmentation scheme and constructing a hybrid learning network. Furthermore, the model predictive control scheme and dual decomposition technique are employed to optimize distributed tracking control and ensure safe distance headway.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Green & Sustainable Science & Technology
Ayman M. Mansour, Abdulaziz Almutairi, Saeed Alyami, Mohammad A. Obeidat, Dhafer Almkahles, Jagabar Sathik
Summary: The paper introduces an innovative method for assessing wind speed in different locations and utilizes machine learning software to support data processing and analysis, to assist electric grid operators and investors in making better decisions.
Article
Clinical Neurology
Matteo Saponati, Jordi Garcia-Ojalvo, Enrico Cataldo, Alberto Mazzoni
Summary: In a feedforward network between the thalamus and primary sensory cortex, thalamic oscillations in the alpha range do not entrain cortical activity due to weaker oscillations in neurons projecting to the cortex and gamma resonance dynamics in the cortical networks hampering alpha range oscillations. This highlights the importance of corticothalamic feedback in sustaining alpha range oscillations for an integrated understanding of sensory stream transmission.
Article
Biochemistry & Molecular Biology
Kwang-Tao Chou, Dong-Yeon D. Lee, Jian-Geng Chiou, Leticia Galera-Laporta, San Ly, Jordi Garcia-Ojalvo, Gurol M. Suel
Summary: This study reveals a ring-like pattern in gene expression underlying the nitrogen stress response of a developing Bacillus subtilis biofilm. Mathematical modeling and experiments show that this pattern is generated by a clock and wavefront mechanism. The study also confirms that this mechanism is driven by cell-autonomous oscillations and is responsible for spatial patterning of sporulation within the biofilm.
Article
Computer Science, Artificial Intelligence
Yuliya Tsybina, Innokentiy Kastalskiy, Mikhail Krivonosov, Alexey Zaikin, Victor Kazantsev, Alexander N. Gorban, Susanna Gordleeva
Summary: Modeling the neuronal processes underlying short-term working memory in neuroscience remains a major focus, and this paper proposes a mathematical model of a spiking neural network that demonstrates how information fragments are maintained and disappear through the activation of astrocytes. The astrocytes exhibit calcium transients on a scale of seconds, modulating synaptic transmission efficiency and neuronal firing rates. The study illustrates how these transients encode neuronal discharge frequencies and provide robust short-term storage of information.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Multidisciplinary Sciences
Ronghui Zhu, Jesus M. del Rio-Salgado, Jordi Garcia-Ojalvo, Michael B. Elowitz
Summary: This study explores synthetic circuits that generate multiple stable states, providing insights into natural cell fate control circuit structures and enabling engineering of multicellular programs requiring interactions among distinct cell types.
Editorial Material
Biochemistry & Molecular Biology
Jordi Garcia-Ojalvo
Summary: This is a snapshot of the peer review process for a study on a synthetic gene circuit for imaging-free detection of signaling pulses (Ravindran et al., 2022).
Article
Biochemistry & Molecular Biology
Xuejie Wang, Casilda Olveira, Rosa Giron, Marta Garcia-Clemente, Luis Maiz, Oriol Sibila, Rafael Golpe, Rosario Menendez, Juan Rodriguez-Lopez, Concepcion Prados, Miguel Angel Martinez-Garcia, Juan Luis Rodriguez, David de la Rosa, Liyun Qin, Xavier Duran, Jordi Garcia-Ojalvo, Esther Barreiro
Summary: This study investigated different phenotypic characteristics in bronchiectasis patients based on neutrophil counts through biostatistics analysis, identifying two distinct clinical phenotypes. Patients with neutrophil counts above a certain threshold exhibited more severe lung function impairment, poorer nutritional status, and higher systemic inflammation compared to those below the threshold. Cluster analysis of combined systemic and respiratory variables defined well-distinguished phenotypic profiles in bronchiectasis patients.
Article
Multidisciplinary Sciences
Kaito Kikuchi, Leticia Galera-Laporta, Colleen Weatherwax, Jamie Y. Lam, Eun Chae Moon, Emmanuel A. Theodorakis, Jordi Garcia-Ojalvo, Gurol M. Suel
Summary: Despite their dormant state, bacterial spores are able to sense environmental signals through a preexisting electrochemical potential and integrate these signals over time, affecting their decision to exit dormancy.
Article
Biochemistry & Molecular Biology
Sheng Wang, Jordi Garcia-Ojalvo, Michael B. Elowitz
Summary: This article demonstrates that a single morphogen is sufficient to generate stable spatial patterns in multicellular development and identifies key factors for robust pattern formation.
Editorial Material
Neurosciences
Jinyu Li, Alexey Zaikin, Xiaochun Zhang, Shangbin Chen
FRONTIERS IN SYSTEMS NEUROSCIENCE
(2022)
Article
Medicine, Research & Experimental
Nuno R. R. Nene, Alexander Ney, Tatiana Nazarenko, Oleg Blyuss, Harvey E. E. Johnston, Harry J. J. Whitwell, Eva Sedlak, Aleksandra Gentry-Maharaj, Sophia Apostolidou, Eithne Costello, William Greenhalf, Ian Jacobs, Usha Menon, Justin Hsuan, Stephen P. P. Pereira, Alexey Zaikin, John F. F. Timms
Summary: Nene et al. developed machine learning models using serum protein biomarker data to detect pancreatic ductal adenocarcinoma. Their ensemble modelling approach outperformed existing biomarker combinations.
COMMUNICATIONS MEDICINE
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Nikita Sushentsev, Leonardo Rundo, Luis Abrego, Zonglun Li, Tatiana Nazarenko, Anne Y. Warren, Vincent J. Gnanapragasam, Evis Sala, Alexey Zaikin, Tristan Barrett, Oleg Blyuss
Summary: In this study, a time series radiomics predictive model was developed using a long short-term memory recurrent neural network. The model analyzed longitudinal changes in tumor-derived radiomic features and serial PSA density to predict histopathological tumor progression in prostate cancer patients on active surveillance. The model outperformed conventional models and achieved comparable performance to expert-performed serial MRI analysis.
EUROPEAN RADIOLOGY
(2023)
Article
Computer Science, Cybernetics
Pau Clusella, Elif Koksal-Ersoz, Jordi Garcia-Ojalvo, Giulio Ruffini
Summary: Neural mass models (NMMs) aim to replicate the collective dynamics of neuronal populations. A common framework assumes that the firing rate of a population can be described by a static nonlinear transfer function. However, a recent theory challenges this view by showing that the firing rate follows nonlinear differential equations. This study analyzes and compares these two descriptions in the presence of second-order synaptic dynamics.
BIOLOGICAL CYBERNETICS
(2023)
Article
Physics, Multidisciplinary
Alex Zhao, Anastasia Ermolaeva, Ekkehard Ullner, Juergen Kurths, Susanna Gordleeva, Alexey Zaikin
Summary: This article investigates the information processing of a neuron-astrocyte network model under the influence of stochastic effects and astrocytes. It is found that astrocytes play a crucial role not only in memory and cognitive processing, but also in the generation of artificial intelligence functions.
PHYSICAL REVIEW RESEARCH
(2022)
Article
Clinical Neurology
Ruben Molina-Fernandez, Pol Picon-Pages, Alejandro Barranco-Almohalla, Giulia Crepin, Victor Herrera-Fernandez, Anna Garcia-Elias, Hugo Fanlo-Ucar, Xavier Fernandez-Busquets, Jordi Garcia-Ojalvo, Baldomero Oliva, Francisco J. Munoz
Summary: Alzheimer's disease and Type 2 diabetes are linked to aging, and there is a connection between Alzheimer's disease and insulin resistance. The aggregation of amyloid beta-peptide into beta-sheets is the main characteristic of Alzheimer's disease. In this study, it was found that monomeric amyloid beta-peptide 1-40 shares a similar structure to insulin and can activate insulin receptor. However, the accumulation and oligomerization of amyloid beta-peptide can block the insulin receptor, leading to insulin resistance and compromising neuronal metabolism and protective pathways.
BRAIN COMMUNICATIONS
(2022)
Article
Biochemistry & Molecular Biology
Xuejie Wang, Carmen Villa, Yadira Dobarganes, Casilda Olveira, Rosa Giron, Marta Garcia-Clemente, Luis Maiz, Oriol Sibila, Rafael Golpe, Rosario Menendez, Juan Rodriguez-Lopez, Concepcion Prados, Miguel Angel Martinez-Garcia, Juan Luis Rodriguez, David de la Rosa, Xavier Duran, Jordi Garcia-Ojalvo, Esther Barreiro
Summary: Differential phenotypic clusters were identified in a large cohort of bronchiectasis patients using data mining approaches. The three clusters were classified as mild, moderate, and severe based on systemic biomarkers, with severe cluster exhibiting worse disease severity and poorer outcomes. Clustering analysis proved to be a powerful tool in characterizing the complexity and heterogeneity of bronchiectasis patients.
