pwc-archive/papers-with-abstracts huggingface dataset:
Probabilistic modeling reveals coordinated social interaction states and their multisensory bases
Authors: Sarah Josephine Stednitz, Andrew Lesak, Adeline L Fecker, Peregrine Painter, Phil Washbourne, Luca Mazzucato, Ethan K Scott
Date: 2024-08-03 00:00:00

Social behavior across animal species ranges from simple pairwise interactions to thousands of individuals coordinating goal-directed movements. Regardless of the scale, these interactions are governed by the interplay between multimodal sensory information and the internal state of each animal. Here, we investigate how animals use multiple sensory modalities to guide social behavior in the highly social zebrafish (Danio rerio) and uncover the complex features of pairwise interactions early in development. To identify distinct behaviors and understand how they vary over time, we developed a new hidden Markov model with constrained linear-model emissions to automatically classify states of coordinated interaction, using the movements of one animal to predict those of another. We discovered that social behaviors alternate between two interaction states within a single experimental session, distinguished by unique movements and timescales. Long-range interactions, akin to shoaling, rely on vision, while mechanosensation underlies rapid synchronized movements and parallel swimming, precursors of schooling. Altogether, we observe spontaneous interactions in pairs of fish, develop novel hidden Markov modeling to reveal two fundamental interaction modes, and identify the sensory systems involved in each. Our modeling approach to pairwise social interactions has broad applicability to a wide variety of naturalistic behaviors and species and solves the challenge of detecting transient couplings between quasi-periodic time series.
Human foraging strategies flexibly adapt to resource distribution and time constraints
Authors: Valeria Simonelli, Davide Nuzzi, Gian Luca Lancia, Giovanni Pezzulo
Date: 2024-08-02 00:00:00
Tasks: Memorization, Navigate

Foraging is a crucial activity, yet the extent to which humans employ flexible versus rigid strategies remains unclear. This study investigates how individuals adapt their foraging strategies in response to resource distribution and foraging time constraints. For this, we designed a video-game-like foraging task that requires participants to navigate a four-areas environment to collect coins from treasure boxes within a limited time. This task engages multiple cognitive abilities, such as navigation, learning, and memorization of treasure box locations. Findings indicate that participants adjust their foraging strategies -- encompassing both stay-or-leave decisions, such as the number of boxes opened in initial areas and behavioral aspects, such as the time to navigate from box to box -- depending on both resource distribution and foraging time. Additionally, they improved their performance over time as an effect of both enhanced navigation skills and adaptation of foraging strategies. Finally, participants' performance was initially distant from the reward-maximizing performance of optimal agents due to the learning process humans undergo; however, it approximated the optimal agent's performance towards the end of the task, without fully reaching it. These results highlight the flexibility of human foraging behavior and underscore the importance of employing optimality models and ecologically rich scenarios to study foraging.
Mean-field approximation for networks with synchrony-driven adaptive coupling
Authors: Niamh Fennelly, Alannah Neff, Renaud Lambiotte, Andrew Keane, Áine Byrne
Date: 2024-07-31 00:00:00

Synaptic plasticity is a key component of neuronal dynamics, describing the process by which the connections between neurons change in response to experiences. In this study, we extend a network model of $\theta$-neuron oscillators to include a realistic form of adaptive plasticity. In place of the less tractable spike-timing-dependent plasticity, we employ recently validated phase-difference-dependent plasticity rules, which adjust coupling strengths based on the relative phases of $\theta$-neuron oscillators. We investigate two approaches for implementing this plasticity: pairwise coupling strength updates and global coupling strength updates. A mean-field approximation of the system is derived and we investigate its validity through comparison with the $\theta$-neuron simulations across various stability states. The synchrony of the system is examined using the Kuramoto order parameter. A bifurcation analysis, by means of numerical continuation and the calculation of maximal Lyapunov exponents, reveals interesting phenomena, including bistability and evidence of period-doubling and boundary crisis routes to chaos, that would otherwise not exist in the absence of adaptive coupling.
QEEGNet: Quantum Machine Learning for Enhanced Electroencephalography Encoding
Authors: Chi-Sheng Chen, Samuel Yen-Chi Chen, Aidan Hung-Wen Tsai, Chun-Shu Wei
Date: 2024-07-27 00:00:00
Tasks: EEG, Quantum Machine Learning

Electroencephalography (EEG) is a critical tool in neuroscience and clinical practice for monitoring and analyzing brain activity. Traditional neural network models, such as EEGNet, have achieved considerable success in decoding EEG signals but often struggle with the complexity and high dimensionality of the data. Recent advances in quantum computing present new opportunities to enhance machine learning models through quantum machine learning (QML) techniques. In this paper, we introduce Quantum-EEGNet (QEEGNet), a novel hybrid neural network that integrates quantum computing with the classical EEGNet architecture to improve EEG encoding and analysis, as a forward-looking approach, acknowledging that the results might not always surpass traditional methods but it shows its potential. QEEGNet incorporates quantum layers within the neural network, allowing it to capture more intricate patterns in EEG data and potentially offering computational advantages. We evaluate QEEGNet on a benchmark EEG dataset, BCI Competition IV 2a, demonstrating that it consistently outperforms traditional EEGNet on most of the subjects and other robustness to noise. Our results highlight the significant potential of quantum-enhanced neural networks in EEG analysis, suggesting new directions for both research and practical applications in the field.
Phase transformation and synchrony for a network of coupled Izhikevich neurons
Authors: Áine Byrne
Date: 2024-07-29 00:00:00
Tasks: Articles, valid

A number of recent articles have employed the Lorentz ansatz to reduce a network of Izhikevich neurons to a tractable mean-field description. In this letter, we construct an equivalent phase model for the Izhikevich model and apply the Ott-Antonsen ansatz, to derive the mean field dynamics in terms of the Kuramoto order parameter. In addition, we show that by defining an appropriate order parameter in the voltage-firing rate framework, the conformal mapping of Montbri\'o et al., which relates the two mean-field descriptions, remains valid.
Dynamic transitions of blind spots in the Hermann grid illusion
Authors: Yutaka Nishiyama
Date: 2024-07-17 00:00:00

Hermann discovered the grid illusion in 1870, but its cause has remained a mystery for more than 150 years. In 1960, Baumgartner proposed a hypothesis for the illusion based on neural receptive fields, but Geier presented a counterexample in 2008. In 1994, Spillmann devised the scintillating grid illusion, an improvement on the Hermann grid illusion. I propose that a hypothesis involving blind spots (optic discs) can significantly contribute to unraveling the mystery of the grid illusion.
Linton Stereo Illusion
Authors: Paul Linton
Date: 2024-07-15 00:00:00
Tasks: 3D geometry

We present a new illusion that challenges our understanding of stereo vision. The illusion consists of a larger circle at 50cm, and smaller circle in front of it at 40cm, with constant angular sizes throughout. We move the larger circle forward by 10cm (to 40cm) and then back again (to 50cm). The question is, what distance should we move the smaller circle forward and back to maintain a constant perceived separation in depth between the circles? Constant physical distance (10cm) or constant retinal disparity (6.7cm)? Observers choose constant disparity. We therefore argue the 'Linton Stereo Illusion' appears to suggest that perceived stereo depth reflects retinal disparities rather than 3D geometry.
Markov Processes and Brain Network Hubs
Authors: M. Ram Murty, A. Narayan Prasad
Date: 2024-07-09 00:00:00

Current concepts of neural networks have emerged over two centuries of progress beginning with the neural doctrine to the idea of neural cell assemblies. Presently the model of neural networks involves distributed neural circuits of nodes, hubs, and connections that are dynamic in different states of brain function. Advances in neurophysiology, neuroimaging and the field of connectomics have given impetus to the application of mathematical concepts of graph theory. Current approaches do carry limitations and inconsistency in results achieved. We model the neural network of the brain as a directed graph and attach a matrix (called the Markov matrix) of transition probabilities (determined by the synaptic strengths) to every pair of distinct nodes giving rise to a (continuous) Markov process. We postulate that the network hubs are the nodes with the highest probabilities given by the stationary distribution of Markov theory. We also derive a new upper bound for the diameter of a graph in terms of the eigenvalues of the Markov matrix.
Dareplane: A modular open-source software platform for BCI research with application in closed-loop deep brain stimulation
Authors: Matthias Dold, Joana Pereira, Bastian Sajonz, Volker A. Coenen, Jordy Thielen, Marcus L. F. Janssen, Michael Tangermann
Date: 2024-08-02 00:00:00
Tasks: Brain Computer Interface

Objective - This work introduces Dareplane, a modular and broad technology-agnostic open source software platform for brain-computer interface research with an application focus on adaptive deep brain stimulation (aDBS). One difficulty for investigating control approaches for aDBS resides with the complex setups required for aDBS experiments, a challenge Dareplane tries to address. Approach - The key features of the platform are presented and the composition of modules into a full experimental setup is discussed in the context of a Python-based orchestration module. The performance of a typical experimental setup on Dareplane for aDBS is evaluated in three benchtop experiments, covering (a) an easy-to-replicate setup using an Arduino microcontroller, (b) a setup with hardware of an implantable pulse generator, and (c) a setup using an established and CE certified external neurostimulator. The full technical feasibility of the platform in the aDBS context is demonstrated in a first closed-loop session with externalized leads on a patient with Parkinson's disease receiving DBS treatment and further in a non-invasive BCI speller application using code-modulated visual evoked responses (c-VEP). Main results - The platform is implemented and open-source accessible on https://github.com/bsdlab/Dareplane. Benchtop results show that performance of the platform is sufficient for current aDBS latencies, and the platform could successfully be used in the aDBS experiment. The timing-critical c-VEP speller could be successfully implemented on the platform achieving expected information transfer rates. Significance - The Dareplane platform supports aDBS setups, and more generally the research on neurotechnological systems such as brain-computer interfaces. It provides a modular, technology-agnostic, and easy-to-implement software platform to make experimental setups more resilient and replicable.
Foundations of a Compositional Systems Biology
Authors: Eran Agmon
Date: 2024-08-01 00:00:00

Composition is a powerful principle for systems biology, focused on the interfaces, interconnections, and orchestration of distributed processes to enable integrative multiscale simulations. Whereas traditional models focus on the structure or dynamics of specific subsystems in controlled conditions, compositional systems biology aims to connect these models, asking critical questions about the space between models: What variables should a submodel expose through its interface? How do coupled models connect and translate across scales? How do domain-specific models connect across biological and physical disciplines to drive the synthesis of new knowledge? This approach requires robust software to integrate diverse datasets and submodels, providing researchers with tools to flexibly recombine, iteratively refine, and collaboratively expand their models. This article offers a comprehensive framework to support this vision, including: a conceptual and graphical framework to define interfaces and composition patterns; standardized schemas that facilitate modular data and model assembly; biological templates that integrate detailed submodels that connect molecular processes to the emergence of the cellular interface; and user-friendly software interfaces that empower research communities to construct and improve multiscale models of cellular systems. By addressing these needs, compositional systems biology will foster a unified and scalable approach to understanding complex cellular systems.
BERT and LLMs-Based avGFP Brightness Prediction and Mutation Design
Authors: X. Guo, W. Che
Date: 2024-07-30 00:00:00
Tasks: Protein Design

This study aims to utilize Transformer models and large language models (such as GPT and Claude) to predict the brightness of Aequorea victoria green fluorescent protein (avGFP) and design mutants with higher brightness. Considering the time and cost associated with traditional experimental screening methods, this study employs machine learning techniques to enhance research efficiency. We first read and preprocess a proprietary dataset containing approximately 140,000 protein sequences, including about 30,000 avGFP sequences. Subsequently, we constructed and trained a Transformer-based prediction model to screen and design new avGFP mutants that are expected to exhibit higher brightness. Our methodology consists of two primary stages: first, the construction of a scoring model using BERT, and second, the screening and generation of mutants using mutation site statistics and large language models. Through the analysis of predictive results, we designed and screened 10 new high-brightness avGFP sequences. This study not only demonstrates the potential of deep learning in protein design but also provides new perspectives and methodologies for future research by integrating prior knowledge from large language models.
Modelling vitamin D food fortification among Aboriginal and Torres Strait Islander peoples in Australia
Authors: Belinda Neo, Noel Nannup, Dale Tilbrook, Eleanor Dunlop, John Jacky, Carol Michie, Cindy Prior, Brad Farrant, Carrington C. J. Shepherd, Lucinda J. Black
Date: 2024-07-29 00:00:00

Background: Low vitamin D intake and high prevalence of vitamin D deficiency (serum 25-hydroxyvitamin D concentration < 50 nmol/L) among Aboriginal and Torres Strait Islander peoples highlight a need for public health strategies to improve vitamin D status. As few foods contain naturally occurring vitamin D, fortification strategies may be needed to improve vitamin D intake and status among Aboriginal and Torres Strait Islander peoples. Objective: We aimed to model vitamin D food fortification scenarios among Aboriginal and Torres Strait Islander peoples. Methods: We used nationally representative food consumption data (n=4,109) and vitamin D food composition data to model four food fortification scenarios. The modelling for Scenario 1 included foods and maximum vitamin D concentrations permitted for fortification in Australia: i) dairy products and alternatives, ii) butter/margarine/oil spreads, iii) formulated beverages, and iv) selected ready-to-eat breakfast cereals. The modelling for Scenarios 2a-c included some vitamin D concentrations higher than permitted in Australia; Scenario 2c included bread, which is not permitted for vitamin D fortification in Australia. Scenario 2a: i) dairy products and alternatives, ii) butter/margarine/oil spreads, iii) formulated beverages. Scenario 2b: as per Scenario 2a plus selected ready-to-eat breakfast cereals. Scenario 2c: as per Scenario 2b plus bread. Results: Vitamin D fortification of a range of staple foods could potentially increase vitamin D intake among Aboriginal and Torres Strait Islander peoples by ~ 3-6 {\mu}g/day. Scenario 2c showed the highest potential median vitamin D intake increase to ~ 8 {\mu}g/day. Across all modelled scenarios, none of the participants had vitamin D intake above the Australian upper level of intake of 80 {\mu}g/day.
Comparative Evaluation of the Proximate and Cytogenotoxicity of Ash and Rice Chips Used as Mango Fruit Artificial Ripening Agents in Birnin Kebbi, Nigeria
Authors: CD Obadiah, TO Yahaya, AA Aliero, M Abdulkareem
Date: 2024-06-29 00:00:00

The high demand for mango (Mangifera indica L.) fruits has led sellers to employ ripening agents. However, concerns are growing regarding the potential toxicities of induced ripening, emphasizing the need for scientific investigation. Samples of artificially and naturally ripened mangoes were analyzed for proximate composition using standard protocols. Cytogenotoxicity was then assessed using the Allium cepa L. toxicity test. Twenty (20)A. cepa (onion) bulbs were used, with 5 ripened naturally, 5 with wood ash, 5 with herbaceous ash, and 5 with rice chips, all grown over tap water for five days. The root tips of the bulbs were assayed and examined for chromosomal aberrations. The results revealed a significant (P<0.05) increase in moisture, protein, and ash content of mangoes as ripening agents were introduced. Mangoes ripened with wood ash exhibited the highest moisture content (81%), while those ripened with rice chips had the highest protein (0.5%) and ash content (1.5%). Naturally ripened mangoes displayed the highest fat (0.0095%) and fiber (11.46%) contents. The A. cepa toxicity test indicated significant (p<0.05) differences in the root growth of mangoes ripened with various agents. Wood ash resulted in the highest root growth (2.62cm), while herbaceous ash had the least (2.18%). Chromosomal aberrations, including sticky, vagrant, and laggard abnormalities, were observed in all agents, with herbaceous ash exhibiting the highest and rice chips the least. The obtained results suggest that induced ripening of the fruits could induce toxicities, highlighting the necessity for public awareness regarding the potential dangers posed by these agents.
A consistent least-squares criterion for calibrating edge lengths in phylogenetic networks
Authors: Jingcheng Xu, Cécile Ané
Date: 2024-07-27 00:00:00

In phylogenetic networks, it is desirable to estimate edge lengths in substitutions per site or calendar time. Yet, there is a lack of scalable methods that provide such estimates. Here we consider the problem of obtaining edge length estimates from genetic distances, in the presence of rate variation across genes and lineages, when the network topology is known. We propose a novel criterion based on least-squares that is both consistent and computationally tractable. The crux of our approach is to decompose the genetic distances into two parts, one of which is invariant across displayed trees of the network. The scaled genetic distances are then fitted to the invariant part, while the average scaled genetic distances are fitted to the non-invariant part. We show that this criterion is consistent provided that there exists a tree path between some pair of tips in the network, and that edge lengths in the network are identifiable from average distances. We also provide a constrained variant of this criterion assuming a molecular clock, which can be used to obtain relative edge lengths in calendar time.
APOGeT: Automated Phylogeny over Geological Time-scales
Authors: Kevin Godin-Dubois, Sylvain Cussat-Blanc, Yves Duthen
Date: 2024-07-31 00:00:00

To tackle the challenge of producing tractable phylogenetic trees in contexts where complete information is available, we introduce APOGeT: an online, pluggable, clustering algorithm for a stream of genomes. It is designed to run alongside a given experimental protocol with minimal interactions and integration effort. From the genomic flow, it extracts and displays species' boundaries and dynamics. Starting with a light introduction to the core idea of this classification we discuss the requirements on the genomes and the underlying processes of building species' identities and managing hybridism. Though stemming from an ALife experimental setting, APOGeT ought not be limited to this field but could be used by (and benefit from) a broader audience.
Large-scale Epidemiological modeling: Scanning for Mosquito-Borne Diseases Spatio-temporal Patterns in Brazil
Authors: Eduardo C. Araujo, Claudia T. Codeço, Sandro Loch, Luã B. Vacaro, Laís P. Freitas, Raquel M. Lana, Leonardo S. Bastos, Iasmim F. de Almeida, Fernanda Valente, Luiz M. Carvalho, Flávio C. Coelho
Date: 2024-07-31 00:00:00

The influence of climate on mosquito-borne diseases like dengue and chikungunya is well-established, but comprehensively tracking long-term spatial and temporal trends across large areas has been hindered by fragmented data and limited analysis tools. This study presents an unprecedented analysis, in terms of breadth, estimating the SIR transmission parameters from incidence data in all 5,570 municipalities in Brazil over 14 years (2010-2023) for both dengue and chikungunya. We describe the Episcanner computational pipeline, developed to estimate these parameters, producing a reusable dataset describing all dengue and chikungunya epidemics that have taken place in this period, in Brazil. The analysis reveals new insights into the climate-epidemic nexus: We identify distinct geographical and temporal patterns of arbovirus disease incidence across Brazil, highlighting how climatic factors like temperature and precipitation influence the timing and intensity of dengue and chikungunya epidemics. The innovative Episcanner tool empowers researchers and public health officials to explore these patterns in detail, facilitating targeted interventions and risk assessments. This research offers a new perspective on the long-term dynamics of climate-driven mosquito-borne diseases and their geographical specificities linked to the effects of global temperature fluctuations such as those captured by the ENSO index.