pwc-archive/papers-with-abstracts huggingface dataset:
Identification and characterization of dominant microflora isolated from selected ripened cheese varieties produced in Uganda
Authors: Andrew Mwebesa Muhame, Ediriisa Mugampoza, Paul Alex Wacoo, Stellah Byakika
Date: 2024-10-03 00:00:00

In this study, the predominant lactic acid bacteria (LAB) isolates were obtained from Gouda, Jack, Cheddar, and Parmesan cheeses produced in Uganda. The isolates were identified through Gram staining, catalase and oxidase tests, and 16S rDNA sequencing. Approximately 90% of the isolates were cocci (n=192), including Streptococcus, Enterococcus, and Lactococcus. The remaining 10% were identified as rod-shaped bacteria, primarily belonging to the Lactobacillus species (n=23). BLAST analysis revealed that Pediococcus pentosaceus dominated in all cheese samples (23.7%, of the total 114 isolates). This was followed by uncultured bacterium (15.8%), uncultured Pediococcus species (13.2%), Lacticaseibacillus rhamnosus (8.8%) among others
AgentBasedModeling.jl: a tool for stochastic simulation of structured population dynamics
Authors: Paul Piho, Philipp Thomas
Date: 2024-09-28 00:00:00

Agent-based models capture heterogeneity among individuals in a population and are widely used in studies of multi-cellular systems, disease, epidemics and demography to name a few. However, existing frameworks consider discrete time-step simulation or assume that agents' states only change as a result of discrete events. In this note, we present AgentBasedModeling$.$jl, a Julia package for simulating stochastic agent-based population models in continuous time. The tool allows to easily specify and simulate agents evolving through generic continuous-time jump-diffusions and interacting via continuous-rate processes. AgentBasedModeling$.$jl provides a powerful methodology for studying the effects of stochasticity on structured population dynamics.
Efficiency-fluctuation trade-offs in biomolecular assembly processes
Authors: Brayden Kell, Andreas Hilfinger
Date: 2024-09-26 00:00:00

Stochastic fluctuations of molecular abundances are a ubiquitous feature of cellular processes and lead to significant cell-to-cell variability. Recent theoretical work established lower bounds for stochastic fluctuations in cells for broad classes of cellular processes by analyzing the dynamics of reaction motifs that are embedded within a larger network with arbitrary interactions and dynamics. For example, a class of generalized assembly processes in which two co-regulated subunits irreversibly form a complex was shown to exhibit an unavoidable trade-off between assembly efficiency and subunit fluctuations: Regardless of rate constants and details of feedback control, subunit fluctuations were shown to diverge as the assembly efficiency approaches 100%. In contrast, other work has reported how efficient assembly processes work as stochastic noise filters or can achieve robust adaptation through integral control. While all of these results are technically correct their seemingly contradictory conclusions raise the question of how broadly applicable the previously reported efficiency-fluctuation trade-off is. Here, we show that a much broader class of assembly processes than previously considered is subject to an efficiency-fluctuation trade-off which diverges in the high efficiency regime. We find the proposed noise filtering property of efficient assembly processes corresponds to a singular limit of this class of systems. Additionally, we show that combining feedback control with distinct subunit synthesis rates is a necessary condition to overcome the generalized efficiency-fluctuation trade-off. Through numerical examples, we show that biomolecular integral controllers are one of several realizations of such control. How small a change to joint subunit control is sufficient to avoid diverging fluctuations in the high-efficiency limit remains an open question.
A generalizable framework for unlocking missing reactions in genome-scale metabolic networks using deep learning
Authors: Xiaoyi Liu, Hongpeng Yang, Chengwei Ai, Ruihan Dong, Yijie Ding, Qianqian Yuan, Jijun Tang, Fei Guo
Date: 2024-09-20 00:00:00
Tasks: Hyperedge Prediction

Incomplete knowledge of metabolic processes hinders the accuracy of GEnome-scale Metabolic models (GEMs), which in turn impedes advancements in systems biology and metabolic engineering. Existing gap-filling methods typically rely on phenotypic data to minimize the disparity between computational predictions and experimental results. However, there is still a lack of an automatic and precise gap-filling method for initial state GEMs before experimental data and annotated genomes become available. In this study, we introduce CLOSEgaps, a deep learning-driven tool that addresses the gap-filling issue by modeling it as a hyperedge prediction problem within GEMs. Specifically, CLOSEgaps maps metabolic networks as hypergraphs and learns their hyper-topology features to identify missing reactions and gaps by leveraging hypothetical reactions. This innovative approach allows for the characterization and curation of both known and hypothetical reactions within metabolic networks. Extensive results demonstrate that CLOSEgaps accurately gap-filling over 96% of artificially introduced gaps for various GEMs. Furthermore, CLOSEgaps enhances phenotypic predictions for 24 GEMs and also finds a notable improvement in producing four crucial metabolites (Lactate, Ethanol, Propionate, and Succinate) in two organisms. As a broadly applicable solution for any GEM, CLOSEgaps represents a promising model to automate the gap-filling process and uncover missing connections between reactions and observed metabolic phenotypes.
On the Minimal Theory of Consciousness Implicit in Active Inference
Authors: Christopher J. Whyte, Andrew W. Corcoran, Jonathan Robinson, Ryan Smith, Rosalyn J. Moran, Thomas Parr, Karl J. Friston, Anil K. Seth, Jakob Hohwy
Date: 2024-10-09 00:00:00
Tasks: Bayesian Inference

The multifaceted nature of subjective experience poses a challenge to the study of consciousness. Traditional neuroscientific approaches often concentrate on isolated facets, such as perceptual awareness or the global state of consciousness and construct a theory around the relevant empirical paradigms and findings. Theories of consciousness are, therefore, often difficult to compare; indeed, there might be little overlap in the phenomena such theories aim to explain. Here, we take a different approach: starting with active inference, a first principles framework for modelling behaviour as (approximate) Bayesian inference, and building up to a minimal theory of consciousness, which emerges from the shared features of computational models derived under active inference. We review a body of work applying active inference models to the study of consciousness and argue that there is implicit in all these models a small set of theoretical commitments that point to a minimal (and testable) theory of consciousness.
Mechanisms and Controversies of tACS (Transcranial Alternating Current Stimulation)
Authors: Julien Modolo, Mengsen Zhang, Joan Duprez, Flavio Frohlich
Date: 2024-10-08 00:00:00

The use of small amplitude electric currents delivered through the scalp, termed transcranial current stimulation (tCS, tDCS / tACS when using DC / AC currents) holds considerable promise for developing safe and effective treatments for central nervous system disorders. Initially welcomed with skepticism due to significant gaps of knowledge in terms of neurophysiology and biophysical mechanisms, tCS is maturing as a technology while its mechanisms of action are gradually being elucidated. However, there remain open questions about the mechanisms of action that warrant clarification to bring tCS to its full potential. In this review focused on tACS, we make an attempt at providing an overview of the converging experimental evidence, from results obtained in various species, regarding the mechanisms of action. We also highlight the remaining points of uncertainty regarding potential confounds, and propose possible experimentally testable solutions to address those issues. Finally, we outline how a continued focus on deepening our understanding of tACS mechanisms might provide significant insights into fundamental, long-standing questions in neuroscience.
Seizure freedom after surgical resection of diffusion-weighted MRI abnormalities
Authors: Jonathan Horsley, Gerard Hall, Callum Simpson, Csaba Kozma, Rhys Thomas, Yujiang Wang, Jane de Tisi, Anna Miserocchi, Andrew McEvoy, Sjoerd Vos, Gavin Winston, John Duncan, Peter Taylor
Date: 2024-10-04 00:00:00

Importance: Many individuals with drug-resistant epilepsy continue to have seizures after resective surgery. Accurate identification of focal brain abnormalities is essential for successful neurosurgical intervention. Current clinical approaches to identify structural abnormalities for surgical targeting in epilepsy do not use diffusion-weighted MRI (dMRI), despite evidence that dMRI abnormalities are present in epilepsy and may relate to the epileptogenic zone. Objective: To investigate whether surgical resection of diffusion abnormalities relates to post-operative seizure freedom. Design: This retrospective case-control study was conducted between 2009 and 2022. Data were acquired at the National Hospital for Neurology and Neurosurgery, UK. Study participants included 200 individuals with drug-resistant focal epilepsy, who underwent resective surgery, and 97 healthy controls used as a normative baseline. Main Outcomes: Spatial overlap between diffusion abnormality clusters and surgical resection masks, and relation to post-surgical outcome. Results: Surgical resections overlapping with the largest abnormal cluster significantly correlated with sustained seizure freedom at 12 months (83% vs 55%; p<0.0001) and over five years (p<0.0001). Notably, resecting only a small proportion of the largest cluster was associated with better seizure outcomes than cases with no resection of this cluster (p=0.008). Furthermore, sparing the largest cluster but resecting other large clusters still improved seizure freedom rates compared to no overlap (p=0.03). Conclusions: Our results suggest that abnormal clusters, identified using dMRI, are integral to the epileptogenic network, and even a partial removal of such an abnormal cluster is sufficient to achieve seizure freedom. The study highlights the potential of incorporating dMRI into pre-surgical planning to improve outcomes in focal epilepsy.
Cognitive maps and schizophrenia
Authors: Matthew M Nour, Yunzhe Liu, Mohamady El-Gaby, Robert A McCutcheon, Raymond J Dolan
Date: 2024-10-04 00:00:00
Tasks: counterfactual, Representation Learning

Structured internal representations (cognitive maps) shape cognition, from imagining the future and counterfactual past, to transferring knowledge to new settings. Our understanding of how such representations are formed and maintained in biological and artificial neural networks has grown enormously. The cognitive mapping hypothesis of schizophrenia extends this enquiry to psychiatry, proposing that diverse symptoms - from delusions to conceptual disorganisation - stem from abnormalities in how the brain forms structured representations. These abnormalities may arise from a confluence of neurophysiological perturbations (excitation-inhibition imbalance, resulting in attractor instability and impaired representational capacity), and/or environmental factors such as early life psychosocial stressors (which impinge on representation learning). This proposal thus links knowledge of neural circuit abnormalities, environmental risk factors, and symptoms.
Learning grid cells by predictive coding
Authors: Mufeng Tang, Helen Barron, Rafal Bogacz
Date: 2024-10-01 00:00:00

Grid cells in the medial entorhinal cortex (MEC) of the mammalian brain exhibit a strikingly regular hexagonal firing field over space. These cells are learned after birth and are thought to support spatial navigation but also more abstract computations. Although various computational models, including those based on artificial neural networks, have been proposed to explain the formation of grid cells, the process through which the MEC circuit ${\it learns}$ to develop grid cells remains unclear. In this study, we argue that predictive coding, a biologically plausible plasticity rule known to learn visual representations, can also train neural networks to develop hexagonal grid representations from spatial inputs. We demonstrate that grid cells emerge robustly through predictive coding in both static and dynamic environments, and we develop an understanding of this grid cell learning capability by analytically comparing predictive coding with existing models. Our work therefore offers a novel and biologically plausible perspective on the learning mechanisms underlying grid cells. Moreover, it extends the predictive coding theory to the hippocampal formation, suggesting a unified learning algorithm for diverse cortical representations.
Bayesian Mechanics of Synaptic Learning under the Free Energy Principle
Authors: Chang Sub Kim
Date: 2024-10-03 00:00:00

The brain is a biological system comprising nerve cells and orchestrates its embodied agent's perception, behavior, and learning in the dynamic environment. The free energy principle (FEP) advocated by Karl Friston explicates the local, recurrent, and self-supervised neurodynamics of the brain's higher-order functions. In this paper, we continue to finesse the FEP through the physics-guided formulation; specifically, we apply our theory to synaptic learning by considering it an inference problem under the FEP and derive the governing equations, called Bayesian mechanics. Our study uncovers how the brain infers weight change and postsynaptic activity, conditioned on the presynaptic input, by deploying the generative models of the likelihood and prior belief. Consequently, we exemplify the synaptic plasticity in the brain with a simple model: we illustrate that the brain organizes an optimal trajectory in neural phase space during synaptic learning in continuous time, which variationally minimizes synaptic surprisal.
Cumulative, Adaptive, Open-ended Change through Self-Other Reorganization: Reply to comment on 'An evolutionary process without variation and selection'
Authors: Liane Gabora, Mike Steel
Date: 2024-10-03 00:00:00

Self-Other Reorganization (SOR) is a theory of how interacting entities or individuals, each of which can be described as an autocatalytic network, collectively exhibit cumulative, adaptive, open-ended change, or evolution. Zachar et al.'s critique of SOR stems from misunderstandings; it does not weaken the arguments in (Gabora & Steel, 2021). The formal framework of Reflexively Autocatalytic and foodset-derived sets (RAFs) enables us to model the process whereby, through their interactions, a set of elements become a 'collective self.' SOR shows how the RAF setting provides a means of encompassing abiogenesis and cultural evolution under the same explanatory framework and provides a plausible explanation for the origins of both evolutionary processes. Although SOR allows for detrimental stimuli (and products), there is (naturally) limited opportunity for elements that do not contribute to or reinforce a RAF to become part of it. Replication and cumulative, adaptive change in RAFs is well-established in the literature. Contrary to Zachar et al., SOR is not a pure percolation model (such as SIR); it encompasses not only learning (modeled as assimilation of foodset elements) but also creative restructuring (modeled as generation of foodset-derived elements), as well as the emergence of new structures made possible by new foodset- and foodset-derived elements. Cultural SOR is robust to degradation, and imperfect replication. Zachar et al.'s simulation contains no RAFs, and does not model SOR.
A Mathematical Perspective on Neurophenomenology
Authors: Lancelot Da Costa, Lars Sandved-Smith, Karl Friston, Maxwell J. D. Ramstead, Anil K. Seth
Date: 2024-09-30 00:00:00

In the context of consciousness studies, a key challenge is how to rigorously conceptualise first-person phenomenological descriptions of lived experience and their relation to third-person empirical measurements of the activity or dynamics of the brain and body. Since the 1990s, there has been a coordinated effort to explicitly combine first-person phenomenological methods, generating qualitative data, with neuroscientific techniques used to describe and quantify brain activity under the banner of "neurophenomenology". Here, we take on this challenge and develop an approach to neurophenomenology from a mathematical perspective. We harness recent advances in theoretical neuroscience and the physics of cognitive systems to mathematically conceptualise first-person experience and its correspondence with neural and behavioural dynamics. Throughout, we make the operating assumption that the content of first-person experience can be formalised as (or related to) a belief (i.e. a probability distribution) that encodes an organism's best guesses about the state of its external and internal world (e.g. body or brain) as well as its uncertainty. We mathematically characterise phenomenology, bringing to light a tool-set to quantify individual phenomenological differences and develop several hypotheses including on the metabolic cost of phenomenology and on the subjective experience of time. We conceptualise the form of the generative passages between first- and third-person descriptions, and the mathematical apparatus that mutually constrains them, as well as future research directions. In summary, we formalise and characterise first-person subjective experience and its correspondence with third-person empirical measurements of brain and body, offering hypotheses for quantifying various aspects of phenomenology to be tested in future work.
Sex Differences in Hierarchical and Modular Organization of Functional Brain Networks: Insights from Hierarchical Entropy and Modularity Analysis
Authors: Wenyu Chen, Ling Zhan, Tao Jia
Date: 2024-09-24 00:00:00

Existing studies have demonstrated significant sex differences in the neural mechanisms of daily life and neuropsychiatric disorders. The hierarchical organization of the functional brain network is a critical feature for assessing these neural mechanisms. But sex differences on the hierarchical organization is not fully investigated. Here, we explore whether hierarchical structure of brain network differs between females and males. At the group level, we measure the hierarchical entropy and the maximum modularity of each individual, and identify a significant negative correlation between the complexity of hierarchy and modularity in brain networks. On average, female brain networks have stronger connectivity within the module, whereas male brain networks demonstrate more complex hierarchy. At the consensus level, we use co-classification matrix to investigate the detailed differences in the hierarchical organization between sexes and observe that females and males exhibit different interaction patterns of brain regions in the dorsal attention network (DAN) and visual network (VIN). Our finding suggests that females and males employ different network topology to achieve brain functions. In addition, the negative correlation between hierarchy and modularity implies a need to balance the complexity in hierarchical organization of the brain network, which shed light on future studies of brain functions.
n:m Phase-Locking of Heterogeneous and Strongly Coupled Oscillators
Authors: Youngmin Park
Date: 2024-09-22 00:00:00

We introduce a scalar reduction method beyond weak perturbations for forced or coupled systems to determine the existence and stability of $n{:}m$ phase-locked states affected by heterogeneity. We consider various biologically relevant oscillators including the complex Ginzburg-Landau oscillator, a thalamic neuron oscillator, and a model of circadian rhythms. The scalar reduction successfully captures the emergence and disappearance of phase-locked states as a function of coupling strength and heterogeneity. We find that even small amounts of heterogeneity (often orders of magnitude smaller than the coupling strength) can significantly alter phase-locked states. The proposed method is a straightforward means to both reduce and analyze potentially high dimensional systems of oscillators that exist closer to biologically-realistic settings.
Diagnosis and Pathogenic Analysis of Autism Spectrum Disorder Using Fused Brain Connection Graph
Authors: Lu Wei, Yi Huang, Guosheng Yin, Fode Zhang, Manxue Zhang, Bin Liu
Date: 2024-09-22 00:00:00
Tasks: Diagnostic, Graph Classification

We propose a model for diagnosing Autism spectrum disorder (ASD) using multimodal magnetic resonance imaging (MRI) data. Our approach integrates brain connectivity data from diffusion tensor imaging (DTI) and functional MRI (fMRI), employing graph neural networks (GNNs) for fused graph classification. To improve diagnostic accuracy, we introduce a loss function that maximizes inter-class and minimizes intra-class margins. We also analyze network node centrality, calculating degree, subgraph, and eigenvector centralities on a bimodal fused brain graph to identify pathological regions linked to ASD. Two non-parametric tests assess the statistical significance of these centralities between ASD patients and healthy controls. Our results reveal consistency between the tests, yet the identified regions differ significantly across centralities, suggesting distinct physiological interpretations. These findings enhance our understanding of ASD's neurobiological basis and offer new directions for clinical diagnosis.
The content and structure of dreams are coupled to affect
Authors: Luke Leckie, Anya K. Bershad, Jes Heppler, Mason McClay, Sofiia Rappe, Jacob G. Foster
Date: 2024-09-22 00:00:00

Dreams offer a unique window into the cognitive and affective dynamics of the sleeping and the waking mind. Recent quantitative linguistic approaches have shown promise in obtaining corpus-level measures of dream sentiment and topic occurrence. However, it is currently unclear how the affective content of individual dreams relates to their semantic content and structure. Here, we combine word embedding, topic modeling, and network analysis to investigate this relationship. By applying Discourse Atom Topic Modeling (DATM) to the DreamBank corpus of >18K dream reports, we represent the latent themes arising within dreams as a sparse dictionary of topics and identify the affective associations of those topics. We show that variation in dream affect (valence and arousal) is associated with changes in topical content. By representing each dream report as a network of topics, we demonstrate that the affective content of dreams is also coupled to semantic structure. Specifically, positively valenced dreams exhibit more coherent, structured, and linear narratives, whilst negatively valenced dreams have more narrative loops and dominant topics. Additionally, topic networks of high arousal dreams are structurally dominated by few high arousal topics and incoherent topical connections, whereas low arousal dreams contain more loops. These findings suggest that affective processes are associated with both the content and structure of dreams. Our approach showcases the potential of integrating natural language processing and network analysis with psychology to elucidate the interplay of affect, cognition and narrative in dreams. This methodology has broad applications for the study of narrated experience and psychiatric symptomatology.