pwc-archive/papers-with-abstracts huggingface dataset:
No arbitrage and the existence of ACLMMs in general diffusion models
Authors: David Criens, Mikhail Urusov
Date: 2024-10-13 00:00:00

In a seminal paper, F. Delbaen and W. Schachermayer proved that the classical NA ("no arbitrage") condition implies the existence of an "absolutely continuous local martingale measure" (ACLMM). It is known that in general the existence of an ACLMM alone is not sufficient for NA. In this paper we investigate how close these notions are for single asset general diffusion market models. We show that NA is equivalent to the existence of an ACLMM plus a mild regularity condition on the scale function and the absence of reflecting boundaries. For infinite time horizon scenarios, the regularity assumption and the requirement on the boundaries can be dropped, showing equivalence between NA and the existence of an ACLMM. By means of counterexamples, we show that our characterization of NA for finite time horizons is sharp in the sense that neither the regularity condition on the scale function nor the absence of reflecting boundaries can be dropped.
Joint identifiability of ancestral sequence, phylogeny and mutation rates under the TKF91 model
Authors: Alex Xue, Brandon Legried, Wai-Tong Louis Fan
Date: 2024-10-12 00:00:00

We consider the problem of identifying jointly the ancestral sequence, the phylogeny and the parameters in models of DNA sequence evolution with insertion and deletion (indel). Under the classical TKF91 model of sequence evolution, we obtained explicit formulas for the root sequence, the pairwise distances of leaf sequences, as well as the scaled rates of indel and substitution in terms of the distribution of the leaf sequences of an arbitrary phylogeny. These explicit formulas not only strengthen existing invertibility results and work for phylogeny that are not necessarily ultrametric, but also lead to new estimators with less assumption compared with the existing literature. Our simulation study demonstrates that these estimators are statistically consistent as the number of independent samples tends to infinity.
Colonization times in Moran process on graphs
Authors: Lenka Kopfová, Josef Tkadlec
Date: 2024-10-12 00:00:00

Moran Birth-death process is a standard stochastic process that is used to model natural selection in spatially structured populations. A newly occurring mutation that invades a population of residents can either fixate on the whole population or it can go extinct due to random drift. The duration of the process depends not only on the total population size $n$, but also on the spatial structure of the population. In this work, we consider the Moran process with a single type of individuals who invade and colonize an otherwise empty environment. Mathematically, this corresponds to the setting where the residents have zero reproduction rate, thus they never reproduce. We present two main contributions. First, in contrast to the Moran process in which residents do reproduce, we show that the colonization time is always at most a polynomial function of the population size $n$. Namely, we show that colonization always takes at most $\frac12n^3-\frac12n^2$ expected steps, and for each $n$, we exactly identify the unique slowest spatial structure where it takes exactly that many steps. Moreover, we establish a stronger bound of roughly $n^{2.5}$ steps for spatial structures that contain only two-way connections and an even stronger bound of roughly $n^2$ steps for lattice-like spatial structures. Second, we discuss various complications that one faces when attempting to measure fixation times and colonization times in spatially structured populations, and we propose to measure the real duration of the process, rather than counting the steps of the classic Moran process.
Two waves of adaptation: speciation induced by dormancy in a model with changing environment
Authors: Fernando Cordero, Adrián González Casanova, Jason Schweinsberg
Date: 2024-10-11 00:00:00

We consider a population model in which the season alternates between winter and summer, and individuals can acquire mutations either that are advantageous in the summer and disadvantageous in the winter, or vice versa. Also, we assume that individuals in the population can either be active or dormant, and that individuals can move between these two states. Dormant individuals do not reproduce but do not experience selective pressures. We show that, under certain conditions, over time we see two waves of adaptation. Some individuals repeatedly acquire mutations that are beneficial in the summer, while others repeatedly acquire mutations that are beneficial in the winter. Individuals can survive the season during which they are less fit by entering a dormant state. This result demonstrates that, for populations in fluctuating environments, dormancy has the potential to induce speciation.
A Structural Text-Based Scaling Model for Analyzing Political Discourse
Authors: Jan Vávra, Bernd Hans-Konrad Prostmaier, Bettina Grün, Paul Hofmarcher
Date: 2024-10-14 00:00:00
Tasks: Position

Scaling political actors based on their individual characteristics and behavior helps profiling and grouping them as well as understanding changes in the political landscape. In this paper we introduce the Structural Text-Based Scaling (STBS) model to infer ideological positions of speakers for latent topics from text data. We expand the usual Poisson factorization specification for topic modeling of text data and use flexible shrinkage priors to induce sparsity and enhance interpretability. We also incorporate speaker-specific covariates to assess their association with ideological positions. Applying STBS to U.S. Senate speeches from Congress session 114, we identify immigration and gun violence as the most polarizing topics between the two major parties in Congress. Additionally, we find that, in discussions about abortion, the gender of the speaker significantly influences their position, with female speakers focusing more on women's health. We also see that a speaker's region of origin influences their ideological position more than their religious affiliation.
Systems-Structure-Based Drug Design
Authors: Vincent D. Zaballa, Elliot E. Hui
Date: 2024-10-14 00:00:00
Tasks: Drug Design, Drug Discovery

Recent advances in generative deep learning have transformed small molecule design, but most methods lack biological systems context, focusing narrowly on specific protein pockets. We introduce a non-differentiable diffusion guidance method that integrates systems biology models, enhancing small molecule generation with pathway context. Using the Bone Morphogenetic Protein (BMP) pathway, we generate small molecules specific to one protein over competing proteins. This method enhances the precision and efficiency of small molecule drug discovery by incorporating systems biology insights into generative models.
The Power Stroke Mechanism of Muscle Contraction at the Confluence of Biology and Physics
Authors: Josh Baker
Date: 2024-09-24 00:00:00
Tasks: Philosophy

While disproven by S. Carnot 200 years ago, corpuscularianism (the philosophy that the states of individual molecules determine the state of a system of those molecules) is still widely used to describe biological function. Nowhere is this more evident than in proposed power stroke mechanisms of muscle contraction. Muscle's power stroke occurs when muscle shortens against a mechanical load, performing work over time. While many mechanisms have been proposed, the actual power stroke mechanism of muscle contraction remains contested. Here I consider four distinctly different proposed mechanisms and assess their history and scientific merit. I show that over the past 65 years, molecular biologists have ignored Carnot for their corpuscular mechanic convictions in proposing a series of molecular power stroke mechanisms that regress from 19th century thermodynamics to an obsolete 17th century philosophy to a structural determinism invoked today that predates science. An isothermal power stroke in a thermodynamic muscle work loop, analogous to the power stoke in a Carnot cycle, rectifies this historic scientific blunder, defining the confluence of the bottom-up mechanics and chemistry of myosin motors and the top-down mechanics and chemistry of muscle. Specifically, created by thermal scaling, the increase in entropy with muscle's thermodynamic power stroke chemically exists within muscle and defines the irreversible energetics and kinetics of muscle contraction (the arrow of time).
Zimin patterns in genomes
Authors: Nikol Chantzi, Ioannis Mouratidis, Ilias Georgakopoulos-Soares
Date: 2024-10-16 00:00:00

Zimin words are words that have the same prefix and suffix. They are unavoidable patterns, with all sufficiently large strings encompassing them. Here, we examine for the first time the presence of k-mers not containing any Zimin patterns, defined hereafter as Zimin avoidmers, in the human genome. We report that in the reference human genome all k-mers above 104 base-pairs contain Zimin words. We find that Zimin avoidmers are most enriched in coding and Human Satellite 1 regions in the human genome. Zimin avoidmers display a depletion of germline insertions and deletions relative to surrounding genomic areas. We also apply our methodology in the genomes of another eight model organisms from all three domains of life, finding large differences in their Zimin avoidmer frequencies and their genomic localization preferences. We observe that Zimin avoidmers exhibit the highest genomic density in prokaryotic organisms, with E. coli showing particularly high levels, while the lowest density is found in eukaryotic organisms, with D. rerio having the lowest. Among the studied genomes the longest k-mer length at which Zimin avoidmers are observed is that of S. cerevisiae at k-mer length of 115 base-pairs. We conclude that Zimin avoidmers display inhomogeneous distributions in organismal genomes, have intricate properties including lower insertion and deletion rates, and disappear faster than the theoretical expected k-mer length, across the organismal genomes studied.
Fine-tuning the ESM2 protein language model to understand the functional impact of missense variants
Authors: Ali Saadat, Jacques Fellay
Date: 2024-10-14 00:00:00
Tasks: Language Modeling, Language Modelling, Protein Language Model

Elucidating the functional effect of missense variants is of crucial importance, yet challenging. To understand the impact of such variants, we fine-tuned the ESM2 protein language model to classify 20 protein features at amino acid resolution. We used the resulting models to: 1) identify protein features that are enriched in either pathogenic or benign missense variants, 2) compare the characteristics of proteins with reference or alternate alleles to understand how missense variants affect protein functionality. We show that our model can be used to reclassify some variants of unknown significance. We also demonstrate the usage of our models for understanding the potential effect of variants on protein features.
Multilayer network approaches to omics data integration in Digital Twins for cancer research
Authors: Hugo Chenel, Malvina Marku, Tim James, Andrei Zinovyev, Vera Pancaldi
Date: 2024-10-08 00:00:00
Tasks: Data Integration

This review examines current and potential applications of DTs in healthcare, focusing on the integration of multi-omics data using multilayer network approaches in cancer research. We discuss methodologies, tools and platforms commonly used for this integration, while highlighting case studies, challenges, and research gaps. Finally, we advocate the need for incorporating diverse data types to produce more effective DTs for personalization of cancer treatments and in silico clinical trials.
Modeling Platelet P2Y$_1$/$_{12}$ Pathway to Integrin Activation
Authors: Keshav B. Patel, Wolfgang Bergmeier, Aaron L. Fogelson
Date: 2024-10-16 00:00:00
Tasks: parameter estimation

Through experimental studies, many details of the pathway of integrin $\alpha_{\rm IIb}\beta_3$ activation by ADP during the platelet aggregation process have been mapped out. ADP binds to two separate G protein coupled receptors on platelet surfaces, leading to alterations in the regulation of the small GTPase RAP1. We seek to (1) gain insights into the relative contributions of both pathways to RAP1-mediated integrin activation and to (2) predict wildtype and mutated cell behavior in response to a continuous range of external agonist concentrations. To this end, we develop a dynamical systems model detailing the action of each protein in the two pathways up to the regulation of RAP1. We perform a parameter estimation using flow cytometry data to determine a number of unknown rate constants. We then validate with already published data; in particular, the model confirmed the effect of impaired P2Y$_1$ receptor desensitization or reduced RASA3 expression on RAP1 activation. We then predict the effect of protein expression levels on integrin activation and show that components of the P2Y$_{12}$ pathway are critical to the regulation of integrin. This model aids in our understanding of interindividual variability in platelet response to ADP and therapeutic P2Y$_{12}$ inhibition. It also provides a more detailed view of platelet activation in the ongoing mathematical study of platelet aggregation.
Optimal network sizes for most robust Turing patterns
Authors: Hazlam S. Ahmad Shaberi, Aibek Kappassov, Antonio Matas-Gil, Robert G. Endres
Date: 2024-10-15 00:00:00

Many cellular patterns exhibit a reaction-diffusion component, suggesting that Turing instability may contribute to pattern formation. However, biological gene-regulatory pathways are more complex than simple Turing activator-inhibitor models and generally do not require fine-tuning of parameters as dictated by the Turing conditions. To address these issues, we employ random matrix theory to analyze the Jacobian matrices of larger networks with robust statistical properties. Our analysis reveals that Turing patterns are more likely to occur by chance than previously thought and that the most robust Turing networks have an optimal size, surprisingly consisting only of a handful of molecular species, thus significantly increasing their identifiability in biological systems. This optimal size emerges from a tradeoff between the highest stability in small networks and the greatest instability with diffusion in large networks. Furthermore, we find that with multiple immobile nodes, differential diffusion ceases to be important for Turing patterns. Our findings may inform future synthetic biology approaches and provide insights into bridging the gap to complex developmental pathways.
Transcriptome and Redox Proteome Reveal Temporal Scales of Carbon Metabolism Regulation in Model Cyanobacteria Under Light Disturbance
Authors: Connah G. M. Johnson, Zachary Johnson, Liam S. Mackey, Xiaolu Li, Natalie C. Sadler, Tong Zhang, Wei-Jun Qian, Pavlo Bohutskyi, Song Feng, Margaret S. Cheung
Date: 2024-10-12 00:00:00
Tasks: Physics-informed machine learning

We develop a systems approach based on an energy-landscape concept to differentiate interactions involving redox activities and conformational changes of proteins and nucleic acids interactions in multi-layered protein-DNA regulatory networks under light disturbance. Our approach is a data-driven modeling workflow using a physics-informed machine learning algorithm to train a non-linear mathematical model for interpreting gene expression dynamics and to lead discovery for protein regulators using redox proteome analysis. We distinguish light-responsive elements within central carbon metabolism pathways from independent variables like circadian time using the publicly available transcriptome datasets of Synechococcus elongatus over diel cycles responding to light perturbations. Our approach provides interpretable de novo models for elucidating events of reactions in complex regulatory pathways in response to stressful disturbance from the environment. We discovered protein regulators in response to light disturbance in the proteome analysis involving shifts in protein abundance as well as cysteine redox states under constant illumination and after two hours of darkness. We discovered significant shifts in cysteine redox states in regulatory proteins such as transcription sigma factors and metabolic enzymes in the oxidative pentose phosphate pathway and the Calvin-Benson cycle, while the changes in their protein abundance were minimal. These results indicate that regulatory dynamics in reductant generation link photo-induced electron transport pathways and redox metabolic pathways with circadian rhythms through fast redox-induced conformational changes or slow expression regulations across networks.
Comparing Methodological Variations in Seizure Onset Localisation Algorithms using intracranial EEG
Authors: Sarah J. Gascoigne, Manel Vila-Vidal, Nathan Evans, Christopher Thornton, Heather Woodhouse, Billy Smith, Anderson Brito Da Silva, Rhys H. Thomas, Kevin Wilson, Peter N. Taylor, Adria Tauste Campo, Yujiang Wang
Date: 2024-10-17 00:00:00
Tasks: EEG

During clinical treatment for epilepsy, the area of the brain thought to be responsible for pathological activity is identified. This identification is typically performed through visual assessment of EEG recordings; however, this is time consuming and prone to subjective inconsistency. Automated onset localisation algorithms provide objective identification of the onset location by highlighting changes in signal features associated with seizure onset. In this work we investigate how methodological differences in such algorithms can result in different onset locations being identified. We analysed ictal intracranial EEG (icEEG) recordings in 16 subjects (100 seizures) with drug-resistant epilepsy from the SWEZ-ETHZ public database. We identified a series of key methodological differences that must be considered when designing or selecting an onset localisation algorithm. These differences were demonstrated using three distinct algorithms that capture different, but complementary, seizure onset features: Imprint, Epileptogenicity Index, and Low Entropy Map. We assessed methodological differences (or Decision Points), and their impact on the identified onset locations. Our independent application of all three algorithms to the same ictal icEEG dataset revealed low agreement between them: 27-60% of onset channels showed minimal or no overlap. Therefore, we investigated the effect of three key differences: (i) how to define a baseline, (ii) whether low-frequency components are considered, and finally (iii) whether electrodecrement is considered. Changes at each Decision Point were found to substantially influence resultant onset channels (r>0.3). Our results demonstrate how seemingly small methodological changes can result in large differences in onset locations. We propose that key Decision Points must be considered when using or designing an onset localisation algorithm.
Attractor-based models for sequences and pattern generation in neural circuits
Authors: Juliana Londono Alvarez
Date: 2024-10-14 00:00:00

Neural circuits in the brain perform a variety of essential functions, including input classification, pattern completion, and the generation of rhythms and oscillations that support processes such as breathing and locomotion. There is also substantial evidence that the brain encodes memories and processes information via sequences of neural activity. In this dissertation, we are focused on the general problem of how neural circuits encode rhythmic activity, as in central pattern generators (CPGs), as well as the encoding of sequences. Traditionally, rhythmic activity and CPGs have been modeled using coupled oscillators. Here we take a different approach, and present models for several different neural functions using threshold-linear networks. Our approach aims to unify attractor-based models (e.g., Hopfield networks) which encode static and dynamic patterns as attractors of the network. In the first half of this dissertation, we present several attractor-based models. These include: a network that can count the number of external inputs it receives; two models for locomotion, one encoding five different quadruped gaits and another encoding the orientation system of a swimming mollusk; and, finally, a model that connects the fixed point sequences with locomotion attractors to obtain a network that steps through a sequence of dynamic attractors. In the second half of the thesis, we present new theoretical results, some of which have already been published. There, we established conditions on network architectures to produce sequential attractors. Here we also include several new theorems relating the fixed points of composite networks to those of their component subnetworks, as well as a new architecture for layering networks which produces "fusion" attractors by minimizing interference between the attractors of individual layers.
Body Maps for Feeling of Creativity in Musical Chord Progression
Authors: Tatsuya Daikoku, Masaki Tanaka
Date: 2024-10-11 00:00:00

This study investigates how musical stimuli evoke feelings of creativity through predictive mechanisms, while also examining the role of interoceptive bodily sensations, particularly those linked to the heart and stomach regions. Furthermore, we investigate the relationship between interoceptive sensibility and the intensity of the feeling of creativity. By employing body-mapping assessments and emotional evaluations on 353 participants exposed to various chord progressions, we revealed the critical role of heart sensations in eliciting feelings of creativity. Our findings indicate that musical chord progressions characterized by high uncertainty and surprise generated heightened feelings of creativity alongside increased arousal. Notably, heart sensations correlated positively with the feeling of creativity, suggesting the crucial role of interoceptive bodily sensations in the experience of creativity. In contrast, the feelings of beauty and valence were more strongly influenced by predictable, low-uncertainty progressions, suggesting that the feelings of creativity may operate through a somewhat different cognitive mechanism than that of beauty and valence. This study highlights the complex interplay between bodily sensations and feelings of creativity based on predictive processing. By framing creativity within the framework of interoception, our research contributes to a deeper understanding of the emotional and embodied processes that shape the experience of creativity. Future research should explore these relationships to enrich our comprehension of creativity in both artistic endeavors and everyday life.
Neuropsychology of AI: Relationship Between Activation Proximity and Categorical Proximity Within Neural Categories of Synthetic Cognition
Authors: Michael Pichat, Enola Campoli, William Pogrund, Jourdan Wilson, Michael Veillet-Guillem, Anton Melkozerov, Paloma Pichat, Armanouche Gasparian, Samuel Demarchi, Judicael Poumay
Date: 2024-10-08 00:00:00

Neuropsychology of artificial intelligence focuses on synthetic neural cog nition as a new type of study object within cognitive psychology. With the goal of making artificial neural networks of language models more explainable, this approach involves transposing concepts from cognitive psychology to the interpretive construction of artificial neural cognition. The human cognitive concept involved here is categorization, serving as a heuristic for thinking about the process of segmentation and construction of reality carried out by the neural vectors of synthetic cognition.