pwc-archive/papers-with-abstracts huggingface dataset:
Towards Unified Molecule-Enhanced Pathology Image Representation Learning via Integrating Spatial Transcriptomics
Authors: Minghao Han, Dingkang Yang, Jiabei Cheng, Xukun Zhang, Linhao Qu, Zizhi Chen, Lihua Zhang
Date: 2024-12-01 00:00:00
Tasks: Data Integration, Representation Learning, whole slide images

Recent advancements in multimodal pre-training models have significantly advanced computational pathology. However, current approaches predominantly rely on visual-language models, which may impose limitations from a molecular perspective and lead to performance bottlenecks. Here, we introduce a Unified Molecule-enhanced Pathology Image REpresentationn Learning framework (UMPIRE). UMPIRE aims to leverage complementary information from gene expression profiles to guide the multimodal pre-training, enhancing the molecular awareness of pathology image representation learning. We demonstrate that this molecular perspective provides a robust, task-agnostic training signal for learning pathology image embeddings. Due to the scarcity of paired data, approximately 4 million entries of spatial transcriptomics gene expression were collected to train the gene encoder. By leveraging powerful pre-trained encoders, UMPIRE aligns the encoders across over 697K pathology image-gene expression pairs. The performance of UMPIRE is demonstrated across various molecular-related downstream tasks, including gene expression prediction, spot classification, and mutation state prediction in whole slide images. Our findings highlight the effectiveness of multimodal data integration and open new avenues for exploring computational pathology enhanced by molecular perspectives. The code and pre-trained weights are available at https://github.com/Hanminghao/UMPIRE.
LLaMA-Gene: A General-purpose Gene Task Large Language Model Based on Instruction Fine-tuning
Authors: Wang Liang
Date: 2024-11-30 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Building a general-purpose task model similar to ChatGPT has been an important research direction for gene large language models. Instruction fine-tuning is a key component in building ChatGPT, but existing instructions are primarily based on natural language. Natural language and gene sequences have significant differences in tokenization and encoding. Therefore, constructing a multilingual model that can handle both natural language and gene sequences is crucial for solving this problem.In this paper, we expand the capabilities of the LLaMA large language model to include gene language. This involves expanding the vocabulary using the Byte Pair Encoding (BPE) method, specifically tailored for DNA and protein sequences, and conducting further pre-training on these sequences. We then convert various downstream gene task data into a unified format for instruction fine-tuning and further fine-tune the model on this data.Our study demonstrates that a mixed model of gene and natural language, fine-tuned with instructions, achieves results comparable to the current state-of-the-art (SOTA) in tasks such as gene classification and gene sequence interaction. This provides a promising direction for building a unified large language model for gene tasks.
Multi-timescale synaptic plasticity on analog neuromorphic hardware
Authors: Amani Atoui, Jakob Kaiser, Sebastian Billaudelle, Philipp Spilger, Eric Müller, Jannik Luboeinski, Christian Tetzlaff, Johannes Schemmel
Date: 2024-12-03 00:00:00

As numerical simulations grow in complexity, their demands on computing time and energy increase. Hardware accelerators offer significant efficiency gains in many computationally intensive scientific fields, but their use in computational neuroscience remains limited. Neuromorphic substrates, such as the BrainScaleS architectures, offer significant advantages, especially for studying complex plasticity rules that require extended simulation runtimes. This work presents the implementation of a calcium-based plasticity rule that integrates calcium dynamics based on the synaptic tagging-and-capture hypothesis on the BrainScaleS-2 system. The implementation of the plasticity rule for a single synapse involves incorporating the calcium dynamics and the plasticity rule equations. The calcium dynamics are mapped to the analog circuits of BrainScaleS-2, while the plasticity rule equations are numerically solved on its embedded digital processors. The main hardware constraints include the speed of the processors and the use of integer arithmetic. By adjusting the timestep of the numerical solver and introducing stochastic rounding, we demonstrate that BrainScaleS-2 accurately emulates a single synapse following a calcium-based plasticity rule across four stimulation protocols and validate our implementation against a software reference model.
Hierarchical feature extraction on functional brain networks for autism spectrum disorder identification with resting-state fMRI data
Authors: Yiqian Luo, Qiurong Chen, Fali Li, Liang Yi, Peng Xu, Yangsong Zhang
Date: 2024-12-03 00:00:00
Tasks: Diagnostic

Autism Spectrum Disorder (ASD) is a pervasive developmental disorder of the central nervous system, primarily manifesting in childhood. It is characterized by atypical and repetitive behaviors. Currently, diagnostic methods mainly rely on questionnaire surveys and behavioral observations, which are prone to misdiagnosis due to their subjective nature. With advancements in medical imaging, MR imaging-based diagnostics have emerged as a more objective alternative. In this paper, we propose a Hierarchical Neural Network model for ASD identification, termed ASD-HNet, which hierarchically extracts features from functional brain networks based on resting-state functional magnetic resonance imaging (rs-fMRI) data. This hierarchical approach enhances the extraction of brain representations, improving diagnostic accuracy and aiding in the identification of brain regions associated with ASD. Specifically, features are extracted at three levels: (1) the local region of interest (ROI) scale, (2) the community scale, and (3) the global representation scale. At the ROI scale, graph convolution is employed to transfer features between ROIs. At the community scale, functional gradients are introduced, and a K-Means clustering algorithm is applied to group ROIs with similar functional gradients into communities. Features from ROIs within the same community are then extracted to characterize the communities. At the global representation scale, we extract global features from the whole community-scale brain networks to represent the entire brain. We validate the effectiveness of our method using the publicly available Autism Brain Imaging Data Exchange I (ABIDE-I) dataset. Experimental results demonstrate that ASD-HNet outperforms existing methods. The code is available at https://github.com/LYQbyte/ASD-HNet.
The Thermodynamic Model to Study the Slow Afterhyperpolarization in a Single Neuron at Different ATP Levels
Authors: Jianwei Li, Simeng Yu, Mingye Guo, Xuewen Shen, Qi Ouyang, Fangting Li
Date: 2024-12-02 00:00:00

The neuron consumes energy from ATP hydrolysis to maintain a far-from-equilibrium steady state inside the cell, thus all physiological functions inside the cell are modulated by thermodynamics. The neurons that manage information encoding, transferring, and processing with high energy consumption, displaying a phenomenon called slow afterhyperpolarization after burst firing, whose properties are affected by the energy conditions. Here we constructed a thermodynamical model to quantitatively describe the sAHP process generated by $Na^+-K^+$ ATPases(NKA) and the Calcium-activated potassium(K(Ca)) channels. The model simulates how the amplitude of sAHP is effected by the intracellular ATP concentration and ATP hydrolysis free energy $\Delta$ G. The results show a trade-off between NKA and the K(Ca)'s modulation on the sAHP's energy dependence, and also predict an alteration of sAHP's behavior under insufficient ATP supply if the proportion of NKA and K(Ca)'s expression quantities is changed. The research provides insights in understanding the maintenance of neural homeostasis and support furthur researches on metabolism-related and neurodegenerative diseases.
New Graphs at the braingraph.org Website for Studying the Aging Brain Circuitry
Authors: Balint Varga, Vince Grolmusz
Date: 2024-12-02 00:00:00
Tasks: Diffusion  MRI

Human braingraphs or connectomes are widely studied in the last decade to understand the structural and functional properties of our brain. In the last several years our research group has computed and deposited thousands of human braingraphs to the braingraph.org site, by applying public structural (diffusion) MRI data from young and healthy subjects. Here we describe a recent addition to the {\tt braingraph.org} site, which contains connectomes from healthy and demented subjects between 42 and 95 years of age, based on the public release of the OASIS-3 dataset. The diffusion MRI data was processed with the Connectome Mapper Toolkit v.3.1. We believe that the new addition to the braingraph.org site will become a useful resource for enlightening the aging circuitry of the human brain in healthy and diseased subjects, including those with Alzheimer's disease in several stages.
Eye dominance and testing order effects in the circularly-oriented macular pigment optical density measurements that rely on the perception of structured light-based stimuli
Authors: Mukhit Kulmaganbetov, Taranjit Singh, Dmitry Pushin, Pinki Chahal, David Cory, Davis Garrad, Connor Kapahi, Melanie Mungalsingh, Iman Salehi, Andrew Silva, Ben Thompson, Zhangting Wang, Dusan Sarenac
Date: 2024-11-17 00:00:00
Tasks: Sensitivity

Psychophysical discrimination of structured light (SL) stimuli may be useful in screening for various macular disorders, including degenerative macular diseases. The circularly-oriented macular pigment optical density (coMPOD), calculated from the discrimination performance of SL-induced entoptic phenomena, may reveal a novel functional biomarker of macular health. In this study, we investigated the potential influence of eye dominance and testing order effects on SL-based stimulus perception, factors that potentially influence the sensitivity of screening tests based on SL technology. A total of 28 participants (aged 18-38 years) were selected for the study after undergoing a comprehensive eye examination. A psychophysical task was performed where various SL-based entoptic images with multiple azimuthal fringes rotating with a specific temporal frequency were projected onto the participants' retinas. By occluding the central areas of entoptic images, we measured the retinal eccentricity ($R$) of the perceivable area of the stimuli. The slope of the coMPOD profile ($a$-value) was calculated for each participant using a spatiotemporal sensitivity model that takes into account the perceptual threshold measurements of structured light stimuli with varying spatial densities and temporal frequencies. The Pearson correlation coefficient between eye dominance and testing order effects was $r=0.8$ ($p<0.01$). The Bland-Altman plots for both factors indicated zero bias. The results indicate repeatable measurements for both eyes, implying minimal impact from eye dominance and testing order on SL-based stimulus perception. The results provide a foundation for future studies exploring the clinical utility of SL tools in eye health.
BEOL Electro-Biological Interface for 1024-Channel TFT Neurostimulator with Cultured DRG Neurons
Authors: Haobin Zhou, Bowen Liu, Taoming Guo, Hanbin Ma, Chen Jiang
Date: 2024-11-16 00:00:00

The demand for high-quality neurostimulation, driven by the development of brain-computer interfaces, has outpaced the capabilities of passive microelectrode-arrays, which are limited by channel-count and biocompatibility. This work proposes a back-end-of-line (BEOL) process for 1024-channel stimulator with bioelectrodes and waterproof encapsulation to stimulate dorsal root ganglion neurons. We introduce an active-matrix neurostimulator based on n-type low-temperature poly-silicon thin-film transistor, adding PEDOT:PSS and SU-8 as bioelectrodes and encapsulation. This enables precise stimulation of DRG neurons, addressing key challenges in neurostimulation systems.
The Copernican Argument for Alien Consciousness; The Mimicry Argument Against Robot Consciousness
Authors: Eric Schwitzgebel, Jeremy Pober
Date: 2024-11-12 00:00:00

On broadly Copernican grounds, we are entitled to default assume that apparently behaviorally sophisticated extraterrestrial entities ("aliens") would be conscious. Otherwise, we humans would be inexplicably, implausibly lucky to have consciousness, while similarly behaviorally sophisticated entities elsewhere would be mere shells, devoid of consciousness. However, this Copernican default assumption is canceled in the case of behaviorally sophisticated entities designed to mimic superficial features associated with consciousness in humans ("consciousness mimics"), and in particular a broad class of current, near-future, and hypothetical robots. These considerations, which we formulate, respectively, as the Copernican and Mimicry Arguments, jointly defeat an otherwise potentially attractive parity principle, according to which we should apply the same types of behavioral or cognitive tests to aliens and robots, attributing or denying consciousness similarly to the extent they perform similarly. Instead of grounding speculations about alien and robot consciousness in metaphysical or scientific theories about the physical or functional bases of consciousness, our approach appeals directly to the epistemic principles of Copernican mediocrity and inference to the best explanation. This permits us to justify certain default assumptions about consciousness while remaining to a substantial extent neutral about specific metaphysical and scientific theories.
Dynamic Indicators of Adherence and Retention in Digital Health Studies: Insights from the Brighten Study
Authors: Dylan Hamitouche, Youcef Barkat, Deven Parekh, Eva Hammer, David Benrimoh
Date: 2024-12-01 00:00:00

Background: Effective use of mobile health technologies requires high participant adherence and retention. However, remote digital health studies often face high attrition and low adherence, potentially introducing bias and limiting generalizability. Objective: This study aims to identify longitudinal indicators of participant retention and adherence to develop strategies for improving data collection in digital health studies and understanding how cohorts are shaped by participant withdrawal and non-adherence. Methods: We conducted analyses on the Brighten study, a smartphone-based randomized controlled trial evaluating apps for depression treatment. Participants were asked to complete seven digital questionnaires regularly. Outcomes included adherence (questionnaire completion), engagement (post-baseline participation), and retention (continued participation over time). We analyzed relationships between these outcomes, static factors (e.g., demographics, average questionnaire scores) and dynamic factors (e.g., questionnaire score changes over time). Results: Of 2,201 participants, 1,093 completed at least one non-baseline questionnaire (median completion rate: 37.6%). Adherence was higher among participants with lower average depression severity (P<.001) and those perceiving improvement (P=.001). Demographic factors significantly influenced adherence and engagement. Participants with greater baseline depressive symptoms were more likely to withdraw before completing non-baseline questionnaires (t=-2.53, P=.01). However, symptom improvement was linked to better adherence (U=127,084; P<.001) and retention (HR=0.78, P=.002). Conclusion: Clinical trajectories and perceived improvements in depressive symptoms are key indicators of engagement, adherence, and retention. These findings may enhance data interpretation and inform strategies to boost retention and adherence in future trials.
How reproducible are data-driven subtypes of Alzheimer's disease atrophy?
Authors: Emma Prevot, Cameron Shand, Neil Oxtoby, for Alzheimer's Disease Neuroimaging Initiative
Date: 2024-11-29 00:00:00

Alzheimer's disease (AD) exhibits substantial clinical and biological heterogeneity, complicating efforts in treatment and intervention development. While new computational methods offer insights into AD progression, the reproducibility of these subtypes across datasets remains understudied, particularly concerning the robustness of subtype definitions when validated on diverse databases. This study evaluates the consistency of AD progression subtypes identified by the Subtype and Stage Inference (SuStaIn) algorithm using T1-weighted MRI data across 5,444 subjects from ANMerge, OASIS, and ADNI datasets, forming four independent cohorts. Each cohort was analyzed under two conditions: one using the full cohort, including cognitively normal controls, and another excluding controls to test subtype robustness. Results confirm the three primary atrophy subtypes identified in earlier studies: Typical, Cortical, and Subcortical, as well as the emergence of rare and atypical AD variants such as posterior cortical atrophy (PCA). Notably, each subtype displayed varying robustness to the inclusion of controls, with certain subtypes, like Subcortical, more influenced by cohort composition. This investigation underscores SuStaIn's reliability for defining stable AD subtypes and suggests its utility in clinical stratification for trials and diagnosis. However, our findings also highlight the need for improved dataset diversity, particularly in terms of ethnic representation, to enhance generalizability and support broader clinical application.
The Underlying Dynamics of Life and Its Evolution: A Prigogine-Inspired Informational Dissipative System
Authors: Salvatore Chirumbolo, Antonio Vella
Date: 2024-12-03 00:00:00

Life is fundamentally a scientific enigma. The interplay between chaos, entropy dynamics, and Prigogine's dissipative systems offers profound insights into the emergence, stabilization, and eventual collapse of far-from-equilibrium systems. This study proposes that, alongside thermodynamic dissipative systems as highlighted by Ilya Prigogine, informational dissipative systems actively contribute to granting inanimate matter properties characteristic of living systems, such as autopoiesis. By examining cyclic entropy flows between water topology (Shannon space) and molecular systems (Boltzmann space), the work emphasizes the pivotal role of disquisotropic entropy, an informational entropy reservoir arising from imperfections in molecular structures. The analysis demonstrates that chaos functions as a stabilizing force, enhancing resilience, adaptability, and longevity by delaying thermodynamic equilibrium. This research connects foundational thermodynamic principles with the emergent behavior of chaotic systems, paving the way for a deeper understanding of complexity in natural and technological contexts. By exploring the relationship between chaos, entropy, and dissipative dynamics, the study advances a paradigm where disorder becomes a mechanism to sustain order, a hallmark of life and complex systems.
Unsupervised learning-based calibration scheme for Rough Bergomi model
Authors: Changqing Teng, Guanglian Li
Date: 2024-12-03 00:00:00
Tasks: Math

Current deep learning-based calibration schemes for rough volatility models are based on the supervised learning framework, which can be costly due to a large amount of training data being generated. In this work, we propose a novel unsupervised learning-based scheme for the rough Bergomi (rBergomi) model which does not require accessing training data. The main idea is to use the backward stochastic differential equation (BSDE) derived in [Bayer, Qiu and Yao, {SIAM J. Financial Math.}, 2022] and simultaneously learn the BSDE solutions with the model parameters. We establish that the mean squares error between the option prices under the learned model parameters and the historical data is bounded by the loss function. Moreover, the loss can be made arbitrarily small under suitable conditions on the fitting ability of the rBergomi model to the market and the universal approximation capability of neural networks. Numerical experiments for both simulated and historical data confirm the efficiency of scheme.
On the relative performance of some parametric and nonparametric estimators of option prices
Authors: Carlo Marinelli, Stefano d'Addona
Date: 2024-11-28 00:00:00
Tasks: Model Selection

We examine the empirical performance of some parametric and nonparametric estimators of prices of options with a fixed time to maturity, focusing on variance-gamma and Heston models on one side, and on expansions in Hermite functions on the other side. The latter class of estimators can be seen as perturbations of the classical Black-Scholes model. The comparison between parametric and Hermite-based models having the same "degrees of freedom" is emphasized. The main criterion is the out-of-sample relative pricing error on a dataset of historical option prices on the S&P500 index. Prior to the main empirical study, the approximation of variance-gamma and Heston densities by series of Hermite functions is studied, providing explicit expressions for the coefficients of the expansion in the former case, and integral expressions involving the explicit characteristic function in the latter case. Moreover, these approximations are investigated numerically on a few test cases, indicating that expansions in Hermite functions with few terms achieve competitive accuracy in the estimation of Heston densities and the pricing of (European) options, but they perform less effectively with variance-gamma densities. On the other hand, the main large-scale empirical study show that parsimonious Hermite estimators can even outperform the Heston model in terms of pricing errors. These results underscore the trade-offs inherent in model selection and calibration, and their empirical fit in practical applications.
Deep Learning-Based Electricity Price Forecast for Virtual Bidding in Wholesale Electricity Market
Authors: Xuesong Wang, Sharaf K. Magableh, Oraib Dawaghreh, Caisheng Wang, Jiaxuan Gong, Zhongyang Zhao, Michael H. Liao
Date: 2024-11-25 00:00:00

Virtual bidding plays an important role in two-settlement electric power markets, as it can reduce discrepancies between day-ahead and real-time markets. Renewable energy penetration increases volatility in electricity prices, making accurate forecasting critical for virtual bidders, reducing uncertainty and maximizing profits. This study presents a Transformer-based deep learning model to forecast the price spread between real-time and day-ahead electricity prices in the ERCOT (Electric Reliability Council of Texas) market. The proposed model leverages various time-series features, including load forecasts, solar and wind generation forecasts, and temporal attributes. The model is trained under realistic constraints and validated using a walk-forward approach by updating the model every week. Based on the price spread prediction results, several trading strategies are proposed and the most effective strategy for maximizing cumulative profit under realistic market conditions is identified through backtesting. The results show that the strategy of trading only at the peak hour with a precision score of over 50% produces nearly consistent profit over the test period. The proposed method underscores the importance of an accurate electricity price forecasting model and introduces a new method of evaluating the price forecast model from a virtual bidder's perspective, providing valuable insights for future research.