pwc-archive/papers-with-abstracts huggingface dataset:
An Update to the SBML Human-Readable Antimony Language
Authors: Lucian Smith, Herbert M Sauro
Date: 2024-05-23 00:00:00

Antimony is a high-level, human-readable text-based language designed for defining and sharing models in the systems biology community. It enables scientists to describe biochemical networks and systems using a simple and intuitive syntax. It allows users to easily create, modify, and distribute reproducible computational models. By allowing the concise representation of complex biological processes, Antimony enhances collaborative efforts, improves reproducibility, and accelerates the iterative development of models in systems biology. This paper provides an update to the Antimony language since it was introduced in 2009. In particular, we highlight new annotation features, support for flux balance analysis, a new rateOf method, support for probability distributions and uncertainty, named stochiometries, and algebraic rules. Antimony is also now distributed as a C/C++ library, together with python and Julia bindings, as well as a JavaScript version for use within a web browser. Availability: https://github.com/sys-bio/antimony.
Bifurcation enhances temporal information encoding in the olfactory periphery
Authors: Kiri Choi, Will Rosenbluth, Isabella R. Graf, Nirag Kadakia, Thierry Emonet
Date: 2024-05-30 00:00:00

Living systems continually respond to signals from the surrounding environment. Survival requires that their responses adapt quickly and robustly to the changes in the environment. One particularly challenging example is olfactory navigation in turbulent plumes, where animals experience highly intermittent odor signals while odor concentration varies over many length- and timescales. Here, we show theoretically that Drosophila olfactory receptor neurons (ORNs) can exploit proximity to a bifurcation point of their firing dynamics to reliably extract information about the timing and intensity of fluctuations in the odor signal, which have been shown to be critical for odor-guided navigation. Close to the bifurcation, the system is intrinsically invariant to signal variance, and information about the timing, duration, and intensity of odor fluctuations is transferred efficiently. Importantly, we find that proximity to the bifurcation is maintained by mean adaptation alone and therefore does not require any additional feedback mechanism or fine-tuning. Using a biophysical model with calcium-based feedback, we demonstrate that this mechanism can explain the measured adaptation characteristics of Drosophila ORNs.
Precision microfluidic control of neuronal ensembles in cultured cortical networks
Authors: Hakuba Murota, Hideaki Yamamoto, Nobuaki Monma, Shigeo Sato, Ayumi Hirano-Iwata
Date: 2024-05-29 00:00:00

In vitro neuronal culture is an important research platform in cellular and network neuroscience. However, neurons cultured on a homogeneous scaffold form dense, randomly connected networks and display excessively synchronized activity; this phenomenon has limited their applications in network-level studies, such as studies of neuronal ensembles, or coordinated activity by a group of neurons. Herein, we develop polydimethylsiloxane-based microfluidic devices to create small neuronal networks exhibiting a hierarchically modular structure resembling the connectivity observed in the mammalian cortex. The strength of intermodular coupling was manipulated by varying the width and height of the microchannels that connect the modules. Using fluorescent calcium imaging, we observe that the spontaneous activity in networks with smaller microchannels (2.2$-$5.5 $\mu$m$^2$) had lower synchrony and exhibit a threefold variety of neuronal ensembles. Optogenetic stimulation demonstrates that a reduction in intermodular coupling enriches evoked neuronal activity patterns and that repeated stimulation induces plasticity in neuronal ensembles in these networks. These findings suggest that cell engineering technologies based on microfluidic devices enable in vitro reconstruction of the intricate dynamics of neuronal ensembles, thus providing a robust platform for studying neuronal ensembles in a well-defined physicochemical environment.
TVB C++: A Fast and Flexible Back-End for The Virtual Brain
Authors: Ignacio Martín, Gorka Zamora, Jan Fousek, Michael Schirner, Petra Ritter, Viktor Jirsa, Gustavo Deco, Gustavo Patow
Date: 2024-05-29 00:00:00

This paper introduces TVB C++, a streamlined and fast C++ Back-End for The Virtual Brain (TVB), a renowned platform and a benchmark tool for full-brain simulation. TVB C++ is engineered with speed as a primary focus while retaining the flexibility and ease of use characteristic of the original TVB platform. Positioned as a complementary tool, TVB serves as a prototyping platform, whereas TVB C++ becomes indispensable when performance is paramount, particularly for large-scale simulations and leveraging advanced computation facilities like supercomputers. Developed as a TVB-compatible Back-End, TVB C++ seamlessly integrates with the original TVB implementation, facilitating effortless usage. Users can easily configure TVB C++ to execute the same code as in TVB but with enhanced performance and parallelism capabilities.
Shaping the distribution of neural responses with interneurons in a recurrent circuit model
Authors: David Lipshutz, Eero P. Simoncelli
Date: 2024-05-28 00:00:00

Efficient coding theory posits that sensory circuits transform natural signals into neural representations that maximize information transmission subject to resource constraints. Local interneurons are thought to play an important role in these transformations, dynamically shaping patterns of local circuit activity to facilitate and direct information flow. However, the relationship between these coordinated, nonlinear, circuit-level transformations and the properties of interneurons (e.g., connectivity, activation functions, response dynamics) remains unknown. Here, we propose a normative computational model that establishes such a relationship. Our model is derived from an optimal transport objective that conceptualizes the circuit's input-response function as transforming the inputs to achieve an efficient target response distribution. The circuit, which is comprised of primary neurons that are recurrently connected to a set of local interneurons, continuously optimizes this objective by dynamically adjusting both the synaptic connections between neurons as well as the interneuron activation functions. In an example application motivated by redundancy reduction, we construct a circuit that learns a dynamical nonlinear transformation that maps natural image data to a spherical Gaussian, significantly reducing statistical dependencies in neural responses. Overall, our results provide a framework in which the distribution of circuit responses is systematically and nonlinearly controlled by adjustment of interneuron connectivity and activation functions.
Exploring the Enigma of Neural Dynamics Through A Scattering-Transform Mixer Landscape for Riemannian Manifold
Authors: Tingting Dan, Ziquan Wei, Won Hwa Kim, Guorong Wu
Date: 2024-05-25 00:00:00
Tasks: Anatomy

The human brain is a complex inter-wired system that emerges spontaneous functional fluctuations. In spite of tremendous success in the experimental neuroscience field, a system-level understanding of how brain anatomy supports various neural activities remains elusive. Capitalizing on the unprecedented amount of neuroimaging data, we present a physics-informed deep model to uncover the coupling mechanism between brain structure and function through the lens of data geometry that is rooted in the widespread wiring topology of connections between distant brain regions. Since deciphering the puzzle of self-organized patterns in functional fluctuations is the gateway to understanding the emergence of cognition and behavior, we devise a geometric deep model to uncover manifold mapping functions that characterize the intrinsic feature representations of evolving functional fluctuations on the Riemannian manifold. In lieu of learning unconstrained mapping functions, we introduce a set of graph-harmonic scattering transforms to impose the brain-wide geometry on top of manifold mapping functions, which allows us to cast the manifold-based deep learning into a reminiscent of MLP-Mixer architecture (in computer vision) for Riemannian manifold. As a proof-of-concept approach, we explore a neural-manifold perspective to understand the relationship between (static) brain structure and (dynamic) function, challenging the prevailing notion in cognitive neuroscience by proposing that neural activities are essentially excited by brain-wide oscillation waves living on the geometry of human connectomes, instead of being confined to focal areas.
Tangent space functional reconfigurations in individuals at risk for alcohol use disorder
Authors: Mahdi Moghaddam, Mario Dzemidzic, Daniel Guerrero, Mintao Liu, Jonathan Alessi, Martin H. Plawecki, Jaroslaw Harezlak, David Kareken, Joaquín Goñi
Date: 2024-05-24 00:00:00

Human brain function dynamically adjusts to ever-changing stimuli from the external environment. Studies characterizing brain functional reconfiguration are nevertheless scarce. Here we present a principled mathematical framework to quantify brain functional reconfiguration when engaging and disengaging from a stop signal task (SST). We apply tangent space projection (a Riemannian geometry mapping technique) to transform functional connectomes (FCs) of 54 participants and quantify functional reconfiguration using the correlation distance of the resulting tangent-FCs. Our goal was to compare functional reconfigurations in individuals at risk for alcohol use disorder (AUD). We hypothesized that functional reconfigurations when transitioning to/from a task would be influenced by family history of alcohol use disorder (FHA) and other AUD risk factors. Multilinear regression models showed that engaging and disengaging functional reconfiguration were associated with FHA and recent drinking. When engaging in the SST after a rest condition, functional reconfiguration was negatively associated with recent drinking, while functional reconfiguration when disengaging from the SST was negatively associated with FHA. In both models, several other factors contributed to the functional reconfiguration. This study 1demonstrates that tangent-FCs can characterize task-induced functional reconfiguration, and that it is related to AUD risk.
Pseudo Channel: Time Embedding for Motor Imagery Decoding
Authors: Zhengqing Miao, Meirong Zhao
Date: 2024-05-21 00:00:00
Tasks: EEG, Motor Imagery

Motor imagery (MI) based EEG represents a frontier in enabling direct neural control of external devices and advancing neural rehabilitation. This study introduces a novel time embedding technique, termed traveling-wave based time embedding, utilized as a pseudo channel to enhance the decoding accuracy of MI-EEG signals across various neural network architectures. Unlike traditional neural network methods that fail to account for the temporal dynamics in MI-EEG in individual difference, our approach captures time-related changes for different participants based on a priori knowledge. Through extensive experimentation with multiple participants, we demonstrate that this method not only improves classification accuracy but also exhibits greater adaptability to individual differences compared to position encoding used in Transformer architecture. Significantly, our results reveal that traveling-wave based time embedding crucially enhances decoding accuracy, particularly for participants typically considered "EEG-illiteracy". As a novel direction in EEG research, the traveling-wave based time embedding not only offers fresh insights for neural network decoding strategies but also expands new avenues for research into attention mechanisms in neuroscience and a deeper understanding of EEG signals.
Peripheral Nervous System Responses to Food Stimuli: Analysis Using Data Science Approaches
Authors: Maelle Moranges, Marc Plantevit, Moustafa Bensafi
Date: 2024-05-21 00:00:00
Tasks: Descriptive, Subgroup Discovery

In the field of food, as in other fields, the measurement of emotional responses to food and their sensory properties is a major challenge. In the present protocol, we propose a step-by-step procedure that allows a physiological description of odors, aromas, and their hedonic properties. The method rooted in subgroup discovery belongs to the field of data science and especially data mining. It is still little used in the field of food and is based on a descriptive modeling of emotions on the basis of human physiological responses.
DSAM: A Deep Learning Framework for Analyzing Temporal and Spatial Dynamics in Brain Networks
Authors: Bishal Thapaliya, Robyn Miller, Jiayu Chen, Yu-Ping Wang, Esra Akbas, Ram Sapkota, Bhaskar Ray, Pranav Suresh, Santosh Ghimire, Vince Calhoun, Jingyu Liu
Date: 2024-05-19 00:00:00
Tasks: Functional Connectivity, Graph Neural Network

Resting-state functional magnetic resonance imaging (rs-fMRI) is a noninvasive technique pivotal for understanding human neural mechanisms of intricate cognitive processes. Most rs-fMRI studies compute a single static functional connectivity matrix across brain regions of interest, or dynamic functional connectivity matrices with a sliding window approach. These approaches are at risk of oversimplifying brain dynamics and lack proper consideration of the goal at hand. While deep learning has gained substantial popularity for modeling complex relational data, its application to uncovering the spatiotemporal dynamics of the brain is still limited. We propose a novel interpretable deep learning framework that learns goal-specific functional connectivity matrix directly from time series and employs a specialized graph neural network for the final classification. Our model, DSAM, leverages temporal causal convolutional networks to capture the temporal dynamics in both low- and high-level feature representations, a temporal attention unit to identify important time points, a self-attention unit to construct the goal-specific connectivity matrix, and a novel variant of graph neural network to capture the spatial dynamics for downstream classification. To validate our approach, we conducted experiments on the Human Connectome Project dataset with 1075 samples to build and interpret the model for the classification of sex group, and the Adolescent Brain Cognitive Development Dataset with 8520 samples for independent testing. Compared our proposed framework with other state-of-art models, results suggested this novel approach goes beyond the assumption of a fixed connectivity matrix and provides evidence of goal-specific brain connectivity patterns, which opens up the potential to gain deeper insights into how the human brain adapts its functional connectivity specific to the task at hand.
Free-ranging dogs quickly learn to recognize a rewarding person
Authors: Srijaya Nandi, Mousumi Chakraborty, Aesha Lahiri, Hindolii Gope, Sujata Khan Bhaduri, Anindita Bhadra
Date: 2024-05-30 00:00:00

Individual human recognition is important for species that live in close proximity to humans. Numerous studies on domesticated species and urban-adapted birds have highlighted this ability. One such species which is heavily reliant on humans is the free-ranging dog. Very little knowledge exists on the amount of time taken by free-ranging dogs to learn and remember individual humans. Due to their territorial nature, they have a high probability of encountering the same people multiple times on the streets. Being able to distinguish individual humans might be helpful in making decisions regarding people from whom to beg for food or social reward. We investigated if free-ranging dogs are capable of identifying the person rewarding them and the amount of time required for them to learn it. We conducted field trials on randomly selected adult free-ranging dogs in West Bengal, India. On Day 1, a choice test was conducted. The experimenter chosen did not provide reward while the other experimenter provided a piece of boiled chicken followed by petting. The person giving reward on Day 1 served as the correct choice on four subsequent days of training. Day 6 was the test day when none of the experimenters had a reward. We analyzed the choice made by the dogs, the time taken to approach during the choice tests, and the socialization index, which was calculated based on the intensity of affiliative behaviour shown towards the experimenters. The dogs made correct choices at a significantly higher rate on the fifth and sixth days, as compared to Day 2, suggesting learning. This is the first study aiming to understand the time taken for individual human recognition in free-ranging dogs and can serve as the scaffold for future studies to understand the dog-human relationship in open environments, like urban ecosystems.
Biodiversity data standards for the organization and dissemination of complex research projects and digital twins: a guide
Authors: Carrie Andrew, Sharif Islam, Claus Weiland, Dag Endresen
Date: 2024-05-30 00:00:00
Tasks: Data Integration

Biodiversity data are substantially increasing, spurred by technological advances and community (citizen) science initiatives. To integrate data is, likewise, becoming more commonplace. Open science promotes open sharing and data usage. Data standardization is an instrument for the organization and integration of biodiversity data, which is required for complex research projects and digital twins. However, just like with an actual instrument, there is a learning curve to understanding the data standards field. Here we provide a guide, for data providers and data users, on the logistics of compiling and utilizing biodiversity data. We emphasize data standards, because they are integral to data integration. Three primary avenues for compiling biodiversity data are compared, explaining the importance of research infrastructures for coordinated long-term data aggregation. We exemplify the Biodiversity Digital Twin (BioDT) as a case study. Four approaches to data standardization are presented in terms of the balance between practical constraints and the advancement of the data standards field. We aim for this paper to guide and raise awareness of the existing issues related to data standardization, and especially how data standards are key to data interoperability, i.e., machine accessibility. The future is promising for computational biodiversity advancements, such as with the BioDT project, but it rests upon the shoulders of machine actionability and readability, and that requires data standards for computational communication.
Large deviation principles and evolutionary multiple structure alignment of non-coding RNA
Authors: Brandon Legried
Date: 2024-05-22 00:00:00

Non-coding RNA are functional molecules that are not translated into proteins. Their function comes as important regulators of biological function. Because they are not translated, they need not be as stable as other types of RNA. The TKF91 Structure Tree from Holmes 2004 is a probability model that effectively describes correlated substitution, insertion, and deletion of base pairs, and found to have some worth in understanding dynamic folding patterns. In this paper, we provide a new probabilistic analysis of the TKF91 Structure Tree. Large deviation principles on stem lengths, helix lengths, and tree size are proved. Additionally, we give a new alignment procedure that constructs accurate sequence and structural alignments for sequences with low identity for a dense enough phylogeny.
Investigation of P. Vivax Elimination via Mass Drug Administration
Authors: Md Nurul Anwar, James M. McCaw, Alexander E. Zarebski, Roslyn I. Hickson, Jennifer A. Flegg
Date: 2024-05-31 00:00:00

Plasmodium vivax is the most geographically widespread malaria parasite due to its ability to remain dormant (as a hypnozoite) in the human liver and subsequently reactivate. Given the majority of P. vivax infections are due to hypnozoite reactivation, targeting the hypnozoite reservoir with a radical cure is crucial for achieving P. vivax elimination. Stochastic effects can strongly influence dynamics when disease prevalence is low or when the population size is small. Hence, it is important to account for this when modelling malaria elimination.cWe use a stochastic multiscale model of P. vivax transmission to study the impacts of multiple rounds of mass drug administration (MDA) with a radical cure, accounting for superinfection and hypnozoite dynamics. Our results indicate multiple rounds of MDA with a high-efficacy drug are needed to achieve a substantial probability of elimination. This work has the potential to help guide P. vivax elimination strategies by quantifying elimination probabilities for an MDA approach.
Life history shapes variation in egg composition in the blue tit Cyanistes caeruleus
Authors: Cristina-Maria Valcu, Richard A. Scheltema, Ralf M. Schweiggert, Mihai Valcu, Kim Teltscher, Dirk M. Walther, Reinhold Carle, Bart Kempenaers
Date: 2024-05-30 00:00:00

Maternal investment directly shapes early developmental conditions and therefore has longterm fitness consequences for the offspring. In oviparous species prenatal maternal investment is fixed at the time of laying. To ensure the best survival chances for most of their offspring, females must equip their eggs with the resources required to perform well under various circumstances, yet the actual mechanisms remain unknown. Here we describe the blue tit egg albumen and yolk proteomes and evaluate their potential to mediate maternal effects. We show that variation in egg composition (proteins, lipids, carotenoids) primarily depends on laying order and female age. Egg proteomic profiles are mainly driven by laying order, and investment in the egg proteome is functionally biased among eggs. Our results suggest that maternal effects on egg composition result from both passive and active (partly compensatory) mechanisms, and that variation in egg composition creates diverse biochemical environments for embryonic development.
Antigenic Cooperation in Viral Populations: Redistribution of Loads Among Altruistic Viruses and Maximal Load per Altruist
Authors: Leonid Bunimovich, Athulya Ram
Date: 2024-05-28 00:00:00

The paper continues the study of the phenomenon of local immunodeficiency (LI) in viral cross-immunoreactivity networks, with a focus on the roles and interactions between altruistic and persistent viral variants. As always, only the state of stable (i.e. observable) LI is analysed. First, we show that a single altruistic viral variant has an upper limit for the number of persistent viral variants that it can support. Our findings reveal that in viral cross-immunoreactivity networks, altruistic viruses act essentially autonomously from each other. Namely, connections between altruistic viruses do not change neither their qualitative roles, nor the quantitative values of the strengths of their connections in the CRNs. In other words, each altruistic virus does exactly the same actions and with the same strengths with or without presence of other altruistic viruses. However, having more altruistic viruses allows to keep sizes of populations of persistent viruses at the higher levels. Likewise, the strength of the immune response against any altruistic virus remains at the same constant level regardless of how many persistent viruses this altruistic virus supports, i.e. shields from the immune response of the host's immune system. It is also shown that viruses strongly compete with each other in order to become persistent in the state of stable LI. We also present an example for a CRN with stable LI that only consists of persistent viral variants.