pwc-archive/papers-with-abstracts huggingface dataset:
Protein atom traps as seeing by neutron scattering
Authors: Georgii Koniukov
Date: 2024-03-25 00:00:00

The expression for the higher temperature dependence of the mean squared displacement in proteins is obtained. The quantum multi-well model explains the dynamic transitions of the proteins and minimizes the amount of parameters to a single one leading to the few-state harmonic system at low-temperatures, and, thus, justifying the possibility of using proteins as atom traps to control qubits at a bit higher temperatures.
On Recovering Higher-order Interactions from Protein Language Models
Authors: Darin Tsui, Amirali Aghazadeh
Date: 2024-03-15 00:00:00
Tasks: Language Modelling, Protein Language Model

Protein language models leverage evolutionary information to perform state-of-the-art 3D structure and zero-shot variant prediction. Yet, extracting and explaining all the mutational interactions that govern model predictions remains difficult as it requires querying the entire amino acid space for $n$ sites using $20^n$ sequences, which is computationally expensive even for moderate values of $n$ (e.g., $n\sim10$). Although approaches to lower the sample complexity exist, they often limit the interpretability of the model to just single and pairwise interactions. Recently, computationally scalable algorithms relying on the assumption of sparsity in the Fourier domain have emerged to learn interactions from experimental data. However, extracting interactions from language models poses unique challenges: it's unclear if sparsity is always present or if it is the only metric needed to assess the utility of Fourier algorithms. Herein, we develop a framework to do a systematic Fourier analysis of the protein language model ESM2 applied on three proteins-green fluorescent protein (GFP), tumor protein P53 (TP53), and G domain B1 (GB1)-across various sites for 228 experiments. We demonstrate that ESM2 is dominated by three regions in the sparsity-ruggedness plane, two of which are better suited for sparse Fourier transforms. Validations on two sample proteins demonstrate recovery of all interactions with $R^2=0.72$ in the more sparse region and $R^2=0.66$ in the more dense region, using only 7 million out of $20^{10}\sim10^{13}$ ESM2 samples, reducing the computational time by a staggering factor of 15,000. All codes and data are available on our GitHub repository https://github.com/amirgroup-codes/InteractionRecovery.
The Mathematics of Chlorophyll Photoexcitation
Authors: Archit Chaturvedi
Date: 2024-03-07 00:00:00

Photosynthesis is a fundamental process for plants to produce energy and survive. It is a well known fact that the light reactions in photosynthesis are a significant part of the overall process, and are carried out by chlorophyll molecules. This paper outlines the quantum chemical and mathematical description concerning electronic state transitions that take place in chlorophyll molecules during photosynthesis. In particular, the electronic transition of chlorophylls is assumed as a diatomic rovibrational 0 --> 0 transition, and is mathematically described accordingly. Then, the correlation of the Franck-Condon Principle in accordance to the photoexcitation of chlorophylls is delineated. Finally, existing research concerning the concepts laid out in this paper is discussed, and future implications of quantum chemical theory concerning chlorophyll photoexcitation is discussed.
PPFlow: Target-aware Peptide Design with Torsional Flow Matching
Authors: Haitao Lin, Odin Zhang, Huifeng Zhao, Dejun Jiang, Lirong Wu, Zicheng Liu, Yufei Huang, Stan Z. Li
Date: 2024-03-05 00:00:00
Tasks: Drug Design, Drug Discovery

Therapeutic peptides have proven to have great pharmaceutical value and potential in recent decades. However, methods of AI-assisted peptide drug discovery are not fully explored. To fill the gap, we propose a target-aware peptide design method called \textsc{PPFlow}, based on conditional flow matching on torus manifolds, to model the internal geometries of torsion angles for the peptide structure design. Besides, we establish a protein-peptide binding dataset named PPBench2024 to fill the void of massive data for the task of structure-based peptide drug design and to allow the training of deep learning methods. Extensive experiments show that PPFlow reaches state-of-the-art performance in tasks of peptide drug generation and optimization in comparison with baseline models, and can be generalized to other tasks including docking and side-chain packing.
Cellular dynamics of host-parasitoid interactions: Insights from the encapsulation process in a partially resistant host
Authors: Samuel Gornard, Florence Mougel, Isabelle Germon, Véronique Borday-Birraux, Pascaline Venon, Salimata Drabo, Laure Marie-Paule Kaiser-Arnauld
Date: 2024-05-13 00:00:00

Cotesia typhae is an eastern African endoparasitoid braconid wasp that targets the larval stage of the lepidopteran stem borer, Sesamia nonagrioides, a maize crop pest in Europe. The French host population is partially resistant to the Makindu strain of the wasp, allowing its development in only 40% of the cases. Resistant larvae can encapsulate the parasitoid and survive the infection. This interaction provides a very interesting frame for investigating the impact of parasitism on host cellular resistance. We characterized the parasitoid ovolarval development in a permissive host and studied the encapsulation process in a resistant host by dissection and histological sectioning compared to that of inert chromatography beads. We measured the total hemocyte count in parasitized and bead-injected larvae over time to monitor the magnitude of the immune reaction. Our results show that parasitism of resistant hosts delayed encapsulation but did not affect immune abilities towards inert beads. Moreover, while bead injection increased total hemocyte count, it remained constant in resistant and permissive larvae. We conclude that while Cotesia spp virulence factors are known to impair the host immune system, our results suggest that passive evasion could also occur.
Characterizing virulence differences in a parasitoid wasp through comparative transcriptomic and proteomic
Authors: Samuel Gornard, Pascaline Venon, Florian Lasfont, Thierry Balliau, Laure Marie-Paule Kaiser-Arnauld, Florence Mougel
Date: 2024-05-13 00:00:00

Background: Two strains of the endoparasitoid Cotesia typhae present a differential parasitism success on the host, Sesamia nonagrioides. One is virulent on both permissive and resistant host populations, and the other only on the permissive host. This interaction provides a very interesting frame for studying virulence factors. Here, we used a combination of comparative transcriptomic and proteomic analyses to unravel the molecular basis underlying virulence differences between the strains.Results: First, we report that virulence genes are mostly expressed during the nymphal stage of the parasitoid. Especially, proviral genes are broadly up-regulated at this stage, while their expression is only expected in the host. Parasitoid gene expression in the host increases with time, indicating the production of more virulence factors. Secondly, comparison between strains reveals differences in venom composition, with 12 proteins showing differential abundance. Proviral expression in the host displays a strong temporal variability, along with differential patterns between strains. Notably, a subset of proviral genes including protein-tyrosine phosphatases is specifically over-expressed in the resistant host parasitized by the less virulent strain, 24 hours after parasitism. This result particularly hints at host modulation of proviral expression.Conclusions: This study sheds light on the temporal expression of virulence factors of Cotesia typhae, both in the host and in the parasitoid. It also identifies potential molecular candidates driving differences in parasitism success between two strains. Together, those findings provide a path for further exploration of virulence mechanisms in parasitoid wasps, and offer insights into host-parasitoid coevolution.
CANA v1.0.0 and schematodes: efficient quantification of symmetry in Boolean automata
Authors: Austin M. Marcus, Jordan Rozum, Herbert Sizek, Luis M. Rocha
Date: 2024-05-12 00:00:00

The biomolecular networks underpinning cell function exhibit canalization, or the buffering of fluctuations required to function in a noisy environment. One understudied putative mechanism for canalization is the functional equivalence of a biomolecular entity's regulators (e.g., among the transcription factors for a gene). In these discrete dynamical systems, activation and inhibition of biomolecular entities (e.g., transcription of genes) are modeled as the activity of coupled 2-state automata, and thus the equivalence of regulators can be studied using the theory of symmetry in discrete functions. To this end, we present a new exact algorithm for finding maximal symmetry groups among the inputs to discrete functions. We implement this algorithm in Rust as a Python package, schematodes. We include schematodes in the new CANA v1.0.0 release, an open source Python library for analyzing canalization in Boolean networks, which we also present here. We compare our exact method implemented in schematodes to the previously published inexact method used in earlier releases of CANA and find that schematodes significantly outperforms the prior method both in speed and accuracy. We also apply CANA v1.0.0 to study the symmetry properties of regulatory function from an ensemble of experimentally-supported Boolean networks from the Cell Collective. Using CANA v1.0.0, we find that the distribution of a previously reported symmetry parameter, $k_s/k$, is statistically significantly different in the Cell Collective than in random automata with the same in-degree and activation bias (Kolmogorov-Smirnov test, $p<0.001$). In particular, its spread is much wider than in our null model (IQR 0.31 vs IQR 0.20 with equal medians), demonstrating that the Cell Collective is enriched in functions with extreme symmetry or asymmetry.
Three Dimensional Spatial Cognition: Bees and Bats
Authors: Robert Worden
Date: 2024-05-15 00:00:00

The paper describes a program which computes the best possible Bayesian model of 3D space from vision (in bees) or echo location (in bats), at Marrs [1982] Level 2. The model exploits the strong Bayesian prior probability that most other things do not move, as the animal moves. 3D locations of things are computed from successive sightings or echoes, computing structure from the animals motion (SFM). The program can be downloaded and run. It also computes a tracking approximate model, which is more tractable for animal brains than the full Bayesian computation. The tracking model is nearly as good as the full Bayesian model, but only if spatial memory storage errors are small. Neural storage of spatial positions gives too high error levels, and is too slow. Alternatively, a 3D model of space could be stored in a wave excitation, as a Fourier transform of real space. This could give high memory capacity and precision, with low spatial distortion, fast response, and simpler computation. Evidence is summarized from related papers that a wave excitation holds spatial memory in the mammalian thalamus, and in the central body of the insect brain.
The Requirement for Cognition, in an Equation
Authors: Robert Worden
Date: 2024-05-14 00:00:00

A model of the evolution of cognition is used to derive a Requirement Equation (RE), which defines what computations the fittest possible brain must make, or must choose actions as if it had made those computations. The terms in the RE depend on factors outside an animals brain, which can be modelled without making assumptions about how the brain works, from knowledge of the animals habitat and biology. In simple domains where the choices of actions have small information content, it may not be necessary to build internal models of reality; short cut computations may be just as good at choosing actions. In complex domains such as 3D spatial cognition, which underpins many complex choices of action, the RE implies that brains build Bayesian internal models of the animals surroundings; and that the models are constrained to be true to external reality.
Self-supervised contrastive learning unveils cortical folding pattern linked to prematurity
Authors: Julien Laval, Aymeric Gaudin, Vincent Frouin, Jessica Dubois, Andrea Gondova, Jean-François Mangin, Joël Chavas, Denis Rivière
Date: 2024-05-14 00:00:00
Tasks: Contrastive Learning, STS

Brain folding patterns have been reported to carry clinically relevant information. The brain folds mainly during the last trimester of pregnancy, and the process might be durably disturbed by preterm birth. Yet little is known about preterm-specific patterns. In this work, we train a self-supervised model (SimCLR) on the UKBioBank cohort (21070 adults) to represent the right superior temporal sulcus (STS) region and apply it to sulci images of 374 babies from the dHCP database, containing preterms and full-terms, and acquired at 40 weeks post-menstrual age. We find a lower variability in the preterm embeddings, supported by the identification of a knob pattern, missing in the extremely preterm population.
Cerebralization of mathematical quantities and physical features in neural science: a critical evaluation
Authors: Laurent Goffart
Date: 2024-05-14 00:00:00

At the turn of the 20th century, Henri Poincar{\'e} explained that geometry is a convention and that the properties of space and time are the properties of our measuring instruments. Intriguingly, numerous contemporary authors argue that space, time and even number are ''encoded'' within the brain, as a consequence of evolution, adaptation and natural selection. In the neuroscientific study of movement generation, the activity of neurons would ''encode'' kinematic parameters: when they emit action potentials, neurons would ''speak'' a language carrying notions of classical mechanics. In this article, we shall explain that the movement of a body segment is the ultimate product of a measurement, a filtered numerical outcome of multiple processes taking place in parallel in the central nervous system and converging on the groups of neurons responsible for muscle contractions. The fact that notions of classical mechanics efficiently describe movements does not imply their implementation in the inner workings of the brain. Their relevance to the question how the brain activity enables one to produce accurate movements is questioned within the framework of the neurophysiology of orienting gaze movements toward a visual target.
Topological Embedding of Human Brain Networks with Applications to Dynamics of Temporal Lobe Epilepsy
Authors: Moo K. Chung, Ji Bi Che, Veena A. Nair, Camille Garcia Ramos, Jedidiah Ray Mathis, Vivek Prabhakaran, Elizabeth Meyerand, Bruce P. Hermann, Jeffrey R. Binder, Aaron F. Struck
Date: 2024-05-13 00:00:00
Tasks: Topological Data Analysis

We introduce a novel, data-driven topological data analysis (TDA) approach for embedding brain networks into a lower-dimensional space in quantifying the dynamics of temporal lobe epilepsy (TLE) obtained from resting-state functional magnetic resonance imaging (rs-fMRI). This embedding facilitates the orthogonal projection of 0D and 1D topological features, allowing for the visualization and modeling of the dynamics of functional human brain networks in a resting state. We then quantify the topological disparities between networks to determine the coordinates for embedding. This framework enables us to conduct a coherent statistical inference within the embedded space. Our results indicate that brain network topology in TLE patients exhibits increased rigidity in 0D topology but more rapid flections compared to that of normal controls in 1D topology.
Rooting of thornless blackberry cuttings as induced by the extract of white willow (Salix alba L.) shoots collected in different times
Authors: Kocher Omer Salih, Aram Akram Mohammed, Ibrahim Maaroof Noori
Date: 2024-05-14 00:00:00

The aqueous extract of Salix spp contains many compounds which may act as root-promoting agents in cuttings. S. alba is a deciduous tree containing variable phytochemicals which are variable throughout the year. So, in this study, one- and two-year-old shoots of S. alba were collected on the 15th of each month in the year 2022, extracted in 2% ethanol at 9 g.L-1, and placed in a water bath at 35 {\deg}C, then they applied to thornless blackberry cuttings for 1.5 hr. The results explained that the highest rooting percentage (66.67%) was obtained in the cuttings soaked in the extract of willow shoots collected on 15th of January. They were not significantly different from control cuttings, but they were different from the cuttings soaked in the extract of willow shoots collected on 15th of August and October (33.33%). The majority of other shoot and root traits were high in the cuttings soaked in the extract of willow shoots collected on 15th of December. The willow shoots collected on 15th of January contained the lowest total phenols (51.4 {\mu}g.mL-1) and total flavonoids (29.07 {\mu}g.mL-1). Moreover, the highest total phenols (57 {\mu}g.mL-1) and IAA (365.17 {\mu}g.mL-1) were recorded in the willow shoots collected on 15th of March, however each total flavonoids (44.96 {\mu}g.mL-1) and salicylic acid (492.61 {\mu}g.mL-1) were the highest in the willow shoots collected on 15th of April. Generally, based on rooting percentage, it is advisable to collect willow shoots on 15th of January and February for extraction and application to the thornless blackberry cuttings.
Leveraging graphical model techniques to study evolution on phylogenetic networks
Authors: Benjamin Teo, Paul Bastide, Cécile Ané
Date: 2024-05-15 00:00:00

The evolution of molecular and phenotypic traits is commonly modelled using Markov processes along a phylogeny. This phylogeny can be a tree, or a network if it includes reticulations, representing events such as hybridization or admixture. Computing the likelihood of data observed at the leaves is costly as the size and complexity of the phylogeny grows. Efficient algorithms exist for trees, but cannot be applied to networks. We show that a vast array of models for trait evolution along phylogenetic networks can be reformulated as graphical models, for which efficient belief propagation algorithms exist. We provide a brief review of belief propagation on general graphical models, then focus on linear Gaussian models for continuous traits. We show how belief propagation techniques can be applied for exact or approximate (but more scalable) likelihood and gradient calculations, and prove novel results for efficient parameter inference of some models. We highlight the possible fruitful interactions between graphical models and phylogenetic methods. For example, approximate likelihood approaches have the potential to greatly reduce computational costs for phylogenies with reticulations.
The Genomic Landscape of Oceania
Authors: Consuelo D. Quinto-Cortés, Carmina Barberena Jonas, Sofía Vieyra-Sánchez, Stephen Oppenheimer, Ram González-Buenfil, Kathryn Auckland, Kathryn Robson, Tom Parks, J. Víctor Moreno-Mayar, Javier Blanco-Portillo, Julian R. Homburger, Genevieve L. Wojcik, Alissa L. Severson, Jonathan S. Friedlaender, Francoise Friedlaender, Angela Allen, Stephen Allen, Mark Stoneking, Adrian V. S. Hill, George Aho, George Koki, William Pomat, Carlos D. Bustamante, Maude Phipps, Alexander J. Mentzer, Andrés Moreno-Estrada, Alexander G. Ioannidis
Date: 2024-05-15 00:00:00

Encompassing regions that were amongst the first inhabited by humans following the out-of-Africa expansion, hosting populations with the highest levels of archaic hominid introgression, and including Pacific islands that are the most isolated inhabited locations on the planet, Oceania has a rich, but understudied, human genomic landscape. Here we describe the first region-wide analysis of genome-wide data from population groups spanning Oceania and its surroundings, from island and peninsular southeast Asia to Papua New Guinea, east across the Pacific through Melanesia, Micronesia, and Polynesia, and west across the Indian Ocean to related island populations in the Andamans and Madagascar. In total we generate and analyze genome-wide data from 981 individuals from 92 different populations, 58 separate islands, and 30 countries, representing the most expansive study of Pacific genetics to date. In each sample we disentangle the Papuan and more recent Austronesian ancestries, which have admixed in various proportions across this region, using ancestry-specific analyses, and characterize the distinct patterns of settlement, migration, and archaic introgression separately in these two ancestries. We also focus on the patterns of clinically relevant genetic variation across Oceania--a landscape rippled with strong founder effects and island-specific genetic drift in allele frequencies--providing an atlas for the development of precision genetic health strategies in this understudied region of the world.
Exact Expressions for the Log-likelihood's Hessian in Multivariate Continuous-Time Continuous-Trait Gaussian Evolution along a Phylogeny
Authors: Woodrow Hao Chi Kiang
Date: 2024-05-12 00:00:00

We presents the closed form formulae for the likelihood Hessian matrix of a family of multivariate continuous-trait Gaussian Markov trait evolution model along a given phylogeny, in which the trait vector's mean is an affine function of that of its ancestor and the variance is not dependent of the trait. Accompanied with this work is an R package called 'glinvci', publicly available on The Comprehensive R Archive Network (CRAN), that can compute Hessian-based approximate confidence regions for these models while at the same time allowing users to have missing data, lost traits, and multiple evolutionary regimes.
Impact of Hypoglycemia on Glucose Variability Over Time for Individuals with Open Source Automated Insulin Delivery Systems
Authors: Arsalan Shahid, Dana M. Lewis
Date: 2024-05-14 00:00:00
Tasks: TAR

Background: This study investigates glucose conditions preceding and following various hypoglycemia levels in individuals with type 1 diabetes using open-source automated insulin delivery (AID) systems. It also seeks to evaluate relationships between hypoglycemia and subsequent glycemic variability. Methods: Analysis of continuous glucose monitor (CGM) data from 122 individuals with type 1 diabetes using open-source AID from the OpenAPS Data Commons was conducted. The study comprehensively analyzed the effects of hypoglycemia on glycemic variability, covering time periods before and after hypoglycemia. Results: Glucose variability normalization post-hypoglycemia can take up to 48 hours, with severe hypoglycemia (40-50 mg/dL) linked to prolonged normalization. A cyclical pattern was observed where hypoglycemia predisposes individuals to further hypoglycemia, even with AID system use. A rise in glucose levels often precedes hypoglycemia, followed by an elevated mean time above range (TAR) post-severe hypoglycemia, indicating a 'rebound' effect. Different hypoglycemia categorization approaches did not show significant differences in glycemic variability outcomes. The level of hypoglycemia does influence the pattern of subsequent glucose fluctuations. Conclusion: Hypoglycemia, especially at lower levels, significantly impact subsequent glycemic variability, even with use of AID systems. This should be studied further with a broader set of commercial AID systems to understand if these patterns are true of all types of AID systems. If these patterns occur in all types of AID systems, it underscores potential opportunities for enhancements in AID algorithms and highlights the importance of educating healthcare providers and people with diabetes about post-hypoglycemia glucose variability.