pwc-archive/papers-with-abstracts huggingface dataset:
RiboDiffusion: Tertiary Structure-based RNA Inverse Folding with Generative Diffusion Models
Authors: Han Huang, Ziqian Lin, Dongchen He, Liang Hong, Yu Li
Date: 2024-04-17 00:00:00
Tasks: Graph Neural Network

RNA design shows growing applications in synthetic biology and therapeutics, driven by the crucial role of RNA in various biological processes. A fundamental challenge is to find functional RNA sequences that satisfy given structural constraints, known as the inverse folding problem. Computational approaches have emerged to address this problem based on secondary structures. However, designing RNA sequences directly from 3D structures is still challenging, due to the scarcity of data, the non-unique structure-sequence mapping, and the flexibility of RNA conformation. In this study, we propose RiboDiffusion, a generative diffusion model for RNA inverse folding that can learn the conditional distribution of RNA sequences given 3D backbone structures. Our model consists of a graph neural network-based structure module and a Transformer-based sequence module, which iteratively transforms random sequences into desired sequences. By tuning the sampling weight, our model allows for a trade-off between sequence recovery and diversity to explore more candidates. We split test sets based on RNA clustering with different cut-offs for sequence or structure similarity. Our model outperforms baselines in sequence recovery, with an average relative improvement of $11\%$ for sequence similarity splits and $16\%$ for structure similarity splits. Moreover, RiboDiffusion performs consistently well across various RNA length categories and RNA types. We also apply in-silico folding to validate whether the generated sequences can fold into the given 3D RNA backbones. Our method could be a powerful tool for RNA design that explores the vast sequence space and finds novel solutions to 3D structural constraints.
Data-driven modeling and prediction of microglial cell dynamics in the ischemic penumbra
Authors: Sara Amato, Andrea Arnold
Date: 2024-04-16 00:00:00
Tasks: Uncertainty Quantification

Neuroinflammation immediately follows the onset of ischemic stroke. During this process, microglial cells are activated in and recruited to the tissue surrounding the irreversibly injured infarct core, referred to as the penumbra. Microglial cells can be activated into two distinct phenotypes; however, the dynamics between the detrimental M1 phenotype and beneficial M2 phenotype are not fully understood. Using phenotype-specific cell count data obtained from experimental studies on middle cerebral artery occlusion-induced stroke in mice, we employ sparsity-promoting system identification techniques combined with Bayesian statistical methods for uncertainty quantification to generate continuous and discrete-time predictive models of the M1 and M2 microglial cell dynamics. The resulting data-driven models include constant and linear terms but do not include nonlinear interactions between the cells. Results emphasize an initial M2 dominance followed by a takeover of M1 cells, capture potential long-term dynamics of microglial cells, and suggest a persistent inflammatory response.
Modelling adhesion in stochastic and mean-field models of cell migration
Authors: Shahzeb Raja Noureen, Richard L. Mort, Christian A. Yates
Date: 2024-04-15 00:00:00

Adhesion between cells plays an important role in many biological processes such as tissue morphogenesis and homeostasis, wound healing and cancer cell metastasis. From a mathematical perspective, adhesion between multiple cell types has been previously analysed using discrete and continuum models including the Cellular Potts models and partial differential equations (PDEs). While these models can represent certain biological situations well, Cellular Potts models can be computationally expensive and continuum models only capture the macroscopic behaviour of a population of cells, ignoring stochasticity and the discrete nature of cell dynamics. Cellular automaton models allow us to address these problems and can be used for a wide variety of biological systems. In this paper, we consider a cellular automaton approach and develop an on-lattice agent-based model (ABM) for cell migration and adhesion in a population composed of two cell types. By deriving and comparing the corresponding PDEs to the ABM, we demonstrate that cell aggregation and cell sorting are not possible in the PDE model. Therefore, we propose a set of stochastic mean equations (SMEs) which better capture the behaviour of the ABM in one and two dimensions.
A Systematic Overview of Single-Cell Transcriptomics Databases, their Use cases, and Limitations
Authors: Mahnoor N. Gondal, Saad Ur Rehman Shah, Arul M. Chinnaiyan, Marcin Cieslik
Date: 2024-04-15 00:00:00

Rapid advancements in high-throughput single-cell RNA-seq (scRNA-seq) technologies and experimental protocols have led to the generation of vast amounts of genomic data that populates several online databases and repositories. Here, we systematically examined large-scale scRNA-seq databases, categorizing them based on their scope and purpose such as general, tissue-specific databases, disease-specific databases, cancer-focused databases, and cell type-focused databases. Next, we discuss the technical and methodological challenges associated with curating large-scale scRNA-seq databases, along with current computational solutions. We argue that understanding scRNA-seq databases, including their limitations and assumptions, is crucial for effectively utilizing this data to make robust discoveries and identify novel biological insights. Furthermore, we propose that bridging the gap between computational and wet lab scientists through user-friendly web-based platforms is needed for democratizing access to single-cell data. These platforms would facilitate interdisciplinary research, enabling researchers from various disciplines to collaborate effectively. This review underscores the importance of leveraging computational approaches to unravel the complexities of single-cell data and offers a promising direction for future research in the field.
Functional Brain-to-Brain Transformation with No Shared Data
Authors: Navve Wasserman, Roman Beliy, Roy Urbach, Michal Irani
Date: 2024-04-17 00:00:00
Tasks: Anatomy

Combining Functional MRI (fMRI) data across different subjects and datasets is crucial for many neuroscience tasks. Relying solely on shared anatomy for brain-to-brain mapping is inadequate. Existing functional transformation methods thus depend on shared stimuli across subjects and fMRI datasets, which are often unavailable. In this paper, we propose an approach for computing functional brain-to-brain transformations without any shared data, a feat not previously achieved in functional transformations. This presents exciting research prospects for merging and enriching diverse datasets, even when they involve distinct stimuli that were collected using different fMRI machines of varying resolutions (e.g., 3-Tesla and 7-Tesla). Our approach combines brain-to-brain transformation with image-to-fMRI encoders, thus enabling to learn functional transformations on visual stimuli to which subjects were never exposed. Furthermore, we demonstrate the applicability of our method for improving image-to-fMRI encoding of subjects scanned on older low-resolution 3T fMRI datasets, by using a new high-resolution 7T fMRI dataset (scanned on different subjects and different stimuli).
A computational account of the development and evolution of psychotic symptoms
Authors: Albert Powers, Philip Angelos, Alexandria Bond, Emily Farina, Carolyn Fredericks, Jay Gandhi, Maximillian Greenwald, Gabriela Hernandez-Busot, Gabriel Hosein, Megan Kelley, Catalina Mourgues, William Palmer, Julia Rodriguez-Sanchez, Rashina Seabury, Silmilly Toribio, Raina Vin, Jeremy Weleff, David Benrimoh
Date: 2024-04-16 00:00:00
Tasks: Hallucination

The mechanisms of psychotic symptoms like hallucinations and delusions are often investigated in fully-formed illness, well after symptoms emerge. These investigations have yielded key insights, but are not well-positioned to reveal the dynamic forces underlying symptom formation itself. Understanding symptom development over time would allow us to identify steps in the pathophysiological process leading to psychosis, shifting the focus of psychiatric intervention from symptom alleviation to prevention. We propose a model for understanding the emergence of psychotic symptoms within the context of an adaptive, developing neural system. We will make the case for a pathophysiological process that begins with cortical hyperexcitability and bottom-up noise transmission, which engenders inappropriate belief formation via aberrant prediction error signaling. We will argue that this bottom-up noise drives learning about the (im)precision of new incoming sensory information because of diminished signal-to-noise ratio, causing an adaptive relative over-reliance on prior beliefs. This over-reliance on priors predisposes to hallucinations and covaries with hallucination severity. An over-reliance on priors may also lead to increased conviction in the beliefs generated by bottom-up noise and drive movement toward conversion to psychosis. We will identify predictions of our model at each stage, examine evidence to support or refute those predictions, and propose experiments that could falsify or help select between alternative elements of the overall model. Nesting computational abnormalities within longitudinal development allows us to account for hidden dynamics among the mechanisms driving symptom formation and to view established symptomatology as a point of equilibrium among competing biological forces.
Information encoding and decoding in in-vitro neural networks on micro electrode arrays through stimulation timing
Authors: Trym A. E. Lindell, Ola H. Ramstad, Ionna Sandvig, Axel Sandvig, Stefano Nichele
Date: 2024-04-16 00:00:00
Tasks: Decoder

A primary challenge in utilizing in-vitro biological neural networks for computations is finding good encoding and decoding schemes for inputting and decoding data to and from the networks. Furthermore, identifying the optimal parameter settings for a given combination of encoding and decoding schemes adds additional complexity to this challenge. In this study we explore stimulation timing as an encoding method, i.e. we encode information as the delay between stimulation pulses and identify the bounds and acuity of stimulation timings which produce linearly separable spike responses. We also examine the optimal readout parameters for a linear decoder in the form of epoch length, time bin size and epoch offset. Our results suggest that stimulation timings between 36 and 436ms may be optimal for encoding and that different combinations of readout parameters may be optimal at different parts of the evoked spike response.
Alpha rhythm slowing in temporal epilepsy across Scalp EEG and MEG
Authors: Vytene Janiukstyte, Csaba Kozma, Thomas W. Owen, Umair J Chaudhury, Beate Diehl, Louis Lemieux, John S Duncan, Fergus Rugg-Gunn, Jane de Tisi, Yujiang Wang, Peter N. Taylor
Date: 2024-04-16 00:00:00
Tasks: EEG, Rhythm

EEG slowing is reported in various neurological disorders including Alzheimer's, Parkinson's and Epilepsy. Here, we investigate alpha rhythm slowing in individuals with refractory temporal lobe epilepsy (TLE), compared to healthy controls, using scalp electroencephalography (EEG) and magnetoencephalography (MEG). We retrospectively analysed data from 17,(46) healthy controls and 22,(24) individuals with TLE who underwent scalp EEG and (MEG) recordings as part of presurgical evaluation. Resting-state, eyes-closed recordings were source reconstructed using the standardized low-resolution brain electrographic tomography (sLORETA) method. We extracted low (slow) 6-9 Hz and high (fast) 10-11 Hz alpha relative band power and calculated the alpha power ratio by dividing low (slow) alpha by high (fast) alpha. This ratio was computed for all brain regions in all individuals. Alpha oscillations were slower in individuals with TLE than controls (p<0.05). This effect was present in both the ipsilateral and contralateral hemispheres, and across widespread brain regions. Alpha slowing in TLE was found in both EEG and MEG recordings. We interpret greater low (slow)-alpha as greater deviation from health.
Guidelines for releasing a variant effect predictor
Authors: Benjamin J. Livesey, Mihaly Badonyi, Mafalda Dias, Jonathan Frazer, Sushant Kumar, Kresten Lindorff-Larsen, David M. McCandlish, Rose Orenbuch, Courtney A. Shearer, Lara Muffley, Julia Foreman, Andrew M. Glazer, Ben Lehner, Debora S. Marks, Frederick P. Roth, Alan F. Rubin, Lea M. Starita, Joseph A. Marsh
Date: 2024-04-16 00:00:00

Computational methods for assessing the likely impacts of mutations, known as variant effect predictors (VEPs), are widely used in the assessment and interpretation of human genetic variation, as well as in other applications like protein engineering. Many different VEPs have been released to date, and there is tremendous variability in their underlying algorithms and outputs, and in the ways in which the methodologies and predictions are shared. This leads to considerable challenges for end users in knowing which VEPs to use and how to use them. Here, to address these issues, we provide guidelines and recommendations for the release of novel VEPs. Emphasising open-source availability, transparent methodologies, clear variant effect score interpretations, standardised scales, accessible predictions, and rigorous training data disclosure, we aim to improve the usability and interpretability of VEPs, and promote their integration into analysis and evaluation pipelines. We also provide a large, categorised list of currently available VEPs, aiming to facilitate the discovery and encourage the usage of novel methods within the scientific community.
Biological computations: limitations of attractor-based formalisms and the need for transients
Authors: Daniel Koch, Akhilesh Nandan, Gayathri Ramesan, Aneta Koseska
Date: 2024-04-16 00:00:00

Living systems, from single cells to higher vertebrates, receive a continuous stream of non-stationary inputs that they sense, e.g., via cell surface receptors or sensory organs. Integrating these time-varying, multi-sensory, and often noisy information with memory using complex molecular or neuronal networks, they generate a variety of responses beyond simple stimulus-response association, including avoidance behavior, life-long-learning or social interactions. In a broad sense, these processes can be understood as a type of biological computation. Taking as a basis generic features of biological computations, such as real-time responsiveness or robustness and flexibility of the computation, we highlight the limitations of the current attractor-based framework for understanding computations in biological systems. We argue that frameworks based on transient dynamics away from attractors are better suited for the description of computations performed by neuronal and signaling networks. In particular, we discuss how quasi-stable transient dynamics from ghost states that emerge at criticality have a promising potential for developing an integrated framework of computations, that can help us understand how living system actively process information and learn from their continuously changing environment.
Convergence of reputations under indirect reciprocity
Authors: Bryce Morsky, Joshua B. Plotkin, Erol Akçay
Date: 2024-04-15 00:00:00

Previous research has shown how indirect reciprocity can promote cooperation through evolutionary game theoretic models. Most work in this field assumes a separation of time-scales: individuals' reputations equilibrate at a fast time scale for given frequencies of strategies while the strategies change slowly according to the replicator dynamics. Much of the previous research has focused on the behaviour and stability of equilibria for the replicator dynamics. Here we focus on the underlying reputational dynamics that occur on a fast time scale. We describe reputational dynamics as systems of differential equations and conduct stability analyses on their equilibria. We prove that reputations converge to a unique equilibrium for each of the five standard norms whether assessments are public or private. These results confirm a crucial but previously unconfirmed assumption underlying the theory of indirect reciprocity for the most studied set of norms.
FrackyFrac: A Standalone UniFrac Calculator
Authors: Amit Lavon, Smadar Shilo, Ayya Keshet, Eran Segal
Date: 2024-04-17 00:00:00

UniFrac is a family of distance metrics over microbial abundances, that take taxonomic relatedness into account. Current tools and libraries for calculating UniFrac have specific requirements regarding the user's technical expertise, operating system, and pre-installed software, which might exclude potential users. FrackyFrac is a native command-line tool that can run on any platform and has no requirements. It can also generate the phylogenetic trees required for the calculation. We show that FrackyFrac's performance is on par with currently existing implementations. FrackyFrac can make UniFrac accessible to researchers who may otherwise skip it due to the effort involved, and it can simplify analysis pipelines for those who already use it.
The theoretical basis of reservoir pressure in arteries
Authors: Kim H. Parker, Alun D. Hughes
Date: 2024-04-16 00:00:00

The separation of measured arterial pressure into a reservoir pressure and an excess pressure was introduced nearly 20 years ago as an heuristic hypothesis. We demonstrate that a two-time asymptotic analysis of the 1-D conservation equations in each artery coupled with the separation of the smaller arteries into inviscid and resistance arteries, based on their resistance coefficients, results, for the first time, in a formal derivation of the reservoir pressure. The key to the two-time analysis is the existence of a fast time associated with the propagation of waves through the arteries and a slow time associated with the convective velocity of the blood. The ratio between these two time scales is given by the Mach number; the ratio of a characteristic convective velocity to a characteristic wave speed. If the Mach number is small, a formal asymptotic analysis can be carried out which is accurate to the order of the square of the Mach number. The slow-time conservation equations involve a resistance coefficient that models the effect of viscosity on the convective velocity. On the basis of this resistance coefficient, we separate the arteries into the larger inviscid arteries where the coefficient is negligible and the smaller resistance arteries where it it is not negligible. The slow time pressure in the inviscid arteries is shown to be spatially uniform but varying in time. We define this pressure as the reservoir pressure. Dynamic analysis using mass conservation in the inviscid arteries shows that the reservoir pressure accounts for the storage of potential energy by the distension of the elastic inviscid arteries during early systole and its release during late systole and diastole. This analysis thus provides a formal derivation of the reservoir pressure and its physical meaning.
Recommender Systems in Financial Trading: Using machine-based conviction analysis in an explainable AI investment framework
Authors: Alicia Vidler
Date: 2024-04-17 00:00:00
Tasks: Decision Making, Recommendation Systems

Traditionally, assets are selected for inclusion in a portfolio (long or short) by human analysts. Teams of human portfolio managers (PMs) seek to weigh and balance these securities using optimisation methods and other portfolio construction processes. Often, human PMs consider human analyst recommendations against the backdrop of the analyst's recommendation track record and the applicability of the analyst to the recommendation they provide. Many firms regularly ask analysts to provide a "conviction" level on their recommendations. In the eyes of PMs, understanding a human analyst's track record has typically come down to basic spread sheet tabulation or, at best, a "virtual portfolio" paper trading book to keep track of results of recommendations. Analysts' conviction around their recommendations and their "paper trading" track record are two crucial workflow components between analysts and portfolio construction. Many human PMs may not even appreciate that they factor these data points into their decision-making logic. This chapter explores how Artificial Intelligence (AI) can be used to replicate these two steps and bridge the gap between AI data analytics and AI-based portfolio construction methods. This field of AI is referred to as Recommender Systems (RS). This chapter will further explore what metadata that RS systems functionally supply to downstream systems and their features.
Fine Tuning LLM for Enterprise: Practical Guidelines and Recommendations
Authors: Mathav Raj J, Kushala VM, Harikrishna Warrier, Yogesh Gupta
Date: 2024-03-23 00:00:00
Tasks: GPU, RAG, Retrieval, Retrieval-augmented Generation

There is a compelling necessity from enterprises for fine tuning LLMs (Large Language Models) o get them trained on proprietary domain knowledge. The challenge is to imbibe the LLMs with domain specific knowledge using the most optimial resource and cost and in the best possible time. Many enterprises rely on RAG (Retrieval Augmented Generation) which does not need LLMs to be ine-tuned but they are limited by the quality of vector databases and their retrieval capabilities rather than the intrinsic capabilities of the LLMs themselves. In our current work we focus on fine tuning LLaMA, an open source LLM using proprietary documents and code from an enterprise repository and use the fine tuned models to evaluate the quality of responses. As part of this work, we aim to guide beginners on how to start with fine tuning an LLM for documentation and code by making educated guesses on size of GPU required and options that are available for formatting the data. We also propose pre processing recipes for both documentation and code to prepare dataset in different formats. The proposed methods of data preparation for document datasets are forming paragraph chunks, forming question and answer pairs and forming keyword and paragraph chunk pairs. For code dataset we propose forming summary and function pairs. Further, we qualitatively evaluate the results of the models for domain specific queries. Finally, we also propose practical guidelines and recommendations for fine tuning LLMs.
Cognitive-Motor Integration in Assessing Bimanual Motor Skills
Authors: Erim Yanik, Xavier Intes, Suvranu De
Date: 2024-04-16 00:00:00
Tasks: Decision Making

Accurate assessment of bimanual motor skills is essential across various professions, yet, traditional methods often rely on subjective assessments or focus solely on motor actions, overlooking the integral role of cognitive processes. This study introduces a novel approach by leveraging deep neural networks (DNNs) to analyze and integrate both cognitive decision-making and motor execution. We tested this methodology by assessing laparoscopic surgery skills within the Fundamentals of Laparoscopic Surgery program, which is a prerequisite for general surgery certification. Utilizing video capture of motor actions and non-invasive functional near-infrared spectroscopy (fNIRS) for measuring neural activations, our approach precisely classifies subjects by expertise level and predicts FLS behavioral performance scores, significantly surpassing traditional single-modality assessments.