pwc-archive/papers-with-abstracts huggingface dataset:
A theory of passive market impact
Authors: Youssef Ouazzani Chahdi, Mathieu Rosenbaum, Grégoire Szymanski
Date: 2024-12-10 00:00:00

While the market impact of aggressive orders has been extensively studied, the impact of passive orders, those executed through limit orders, remains less understood. The goal of this paper is to investigate passive market impact by developing a microstructure model connecting liquidity dynamics and price moves. A key innovation of our approach is to replace the traditional assumption of constant information content for each trade by a function that depends on the available volume in the limit order book. Within this framework, we explore scaling limits and analyze the market impact of passive metaorders. Additionally, we derive useful approximations for the shape of market impact curves, leading to closed-form formulas that can be easily applied in practice.
Deep learning assisted SERS detection of prolines and hydroxylated prolines using nitrilotriacetic acid functionalized gold nanopillars
Authors: Yuan Zhang, Kuo Zhan, Peilin Xin, YingQi Zhao, Shubo Wang, Aliaksandr Hubarevich, Xuejin Zhang, JianAn Huang
Date: 2024-12-11 00:00:00

Proline (Pro) is one kind of proteinogenic amino acid and an important signaling molecule in the process of metabolism. Hydroxyproline (Hyp) is a product on Pro oxygen sensing post-translational modification (PTM), which is efficiently modulated tumor cells for angiogenesis. Distinguishing between Pro and Hyp is crucial for diagnosing connective tissue disorders, as elevated levels of Hyp can indicate abnormal collagen metabolism, often associated with diseases like osteogenesis imperfecta or fibrosis. However, there is a very small difference between molecular structures of Pro and Hyp, which is a big challenge for current detection technologies to distinguish them. For surface-enhanced Raman scattering (SERS) sensors, the similar molecule structure leads to similar Raman spectra that are difficult to distinguish. Furthermore, another problem is the weak affinity between amino acids sample and SERS-active substrates by physical adsorption. The selecting capturing of Pro and Hyp in the mixture of amino acids is not easy to achieve. In this work, we designed a new method for Pro and Hyp specifical detection and recognition by using gold nanopillars as the SERS substrate and combing nitrilotriacetic acid (NTA) with nickel (Ni) to form NTA-Ni structure as a specifical affinity agent. One side of NTA-Ni was attached to gold nanopillars through thiol binding. Another side captured the amino acids using reversible binding by receptor-ligand interaction between Ni and amino acids. Because of the different binding time with NTA-Ni and amino acids, the sensor can recognize Pro and Hyp from amino acids mixture. Then we used automatic peak assignment program for data analysis and machine learning model to distinguish between Pro and Hyp. The label-free SERS detection of amino acids PTM using gold nanopillars provides a potential method to further biomolecule detection and specifical capture.
Protein Evolution as a Complex System
Authors: Barnabas Gall, Sacha B. Pulsford, Dana Matthews, Matthew A. Spence, Joe A. Kaczmarski, John Z. Chen, Mahakaran Sandhu, Eric Stone, James Nichols, Colin J. Jackson
Date: 2024-12-09 00:00:00

Protein evolution underpins life, and understanding its behavior as a system is of great importance. However, our current models of protein evolution are arguably too simplistic to allow quantitative interpretation and prediction of evolutionary trajectories. Viewing protein evolution as a complex system has the potential to advance our understanding and ability to model protein evolution. In this perspective, we discuss aspects of protein evolution that are typical of complex systems, from nonlinear dynamics, sensitivity to initial conditions, self-organization, and the emergence of order from chaos and disorder. We discuss how the growth in sequence and structural data, insights from laboratory evolution and new machine learning tools can advance the study of protein evolution and that by treating protein evolution as a complex adaptive system, we may gain new insights into the fundamental principles driving biological innovation and adaptation and apply this to protein engineering and design.
An RNA condensate model for the origin of life
Authors: Jacob L. Fine, Alan M. Moses
Date: 2024-12-06 00:00:00

The RNA World hypothesis predicts that self-replicating RNAs evolved before DNA genomes and coded proteins. Despite widespread support for the RNA World, self-replicating RNAs have yet to be identified in a natural context, leaving a key 'missing link' for this explanation of the origin of life. Inspired by recent work showing that condensates of charged polymers are capable of catalyzing chemical reactions, we consider a catalytic RNA condensate as a candidate for the self-replicating RNA. Specifically, we propose that short, low-complexity RNA polymers formed catalytic condensates capable of templated RNA polymerization. Because the condensate properties depend on the RNA sequences, RNAs that formed condensates with improved polymerization and demixing capacity would be amplified, leading to a 'condensate chain reaction' and evolution by natural selection. Many of the needed properties of this self-replicating RNA condensate have been realized experimentally in recent studies and our predictions could be tested with current experimental and theoretical tools. Our theory addresses central problems in the origins of life: (i) the origin of compartmentalization, (ii) the error threshold for the accuracy of templated replication, (iii) the free energy cost of maintaining an information-rich population of replicating RNA polymers. Furthermore, we note that the extant nucleolus appears to satisfy many of the requirements of an evolutionary relic for the model we propose. More generally, we suggest that future work on the origin of life would benefit from condensate-centric biophysical models of RNA evolution.
HiCat: A Semi-Supervised Approach for Cell Type Annotation
Authors: Chang Bi, Kailun Bai, Xing Li, Xuekui Zhang
Date: 2024-11-25 00:00:00
Tasks: Dimensionality Reduction, Feature Engineering

We introduce HiCat (Hybrid Cell Annotation using Transformative embeddings), a novel semi-supervised pipeline for annotating cell types from single-cell RNA sequencing data. HiCat fuses the strengths of supervised learning for known cell types with unsupervised learning to identify novel types. This hybrid approach incorporates both reference and query genomic data for feature engineering, enhancing the embedding learning process, increasing the effective sample size for unsupervised techniques, and improving the transferability of the supervised model trained on reference data when applied to query datasets. The pipeline follows six key steps: (1) removing batch effects using Harmony to generate a 50-dimensional principal component embedding; (2) applying UMAP for dimensionality reduction to two dimensions to capture crucial data patterns; (3) conducting unsupervised clustering of cells with DBSCAN, yielding a one-dimensional cluster membership vector; (4) merging the multi-resolution results of the previous steps into a 53-dimensional feature space that encompasses both reference and query data; (5) training a CatBoost model on the reference dataset to predict cell types in the query dataset; and (6) resolving inconsistencies between the supervised predictions and unsupervised cluster labels. When benchmarked on 10 publicly available genomic datasets, HiCat surpasses other methods, particularly in differentiating and identifying multiple new cell types. Its capacity to accurately classify novel cell types showcases its robustness and adaptability within intricate biological datasets.
Multi-modal Representation Learning Enables Accurate Protein Function Prediction in Low-Data Setting
Authors: Serbülent Ünsal, Sinem Özdemir, Bünyamin Kasap, M. Erşan Kalaycı, Kemal Turhan, Tunca Doğan, Aybar C. Acar
Date: 2024-11-22 00:00:00
Tasks: Protein Function Prediction, Representation Learning, Transfer Learning

In this study, we propose HOPER (HOlistic ProtEin Representation), a novel multimodal learning framework designed to enhance protein function prediction (PFP) in low-data settings. The challenge of predicting protein functions is compounded by the limited availability of labeled data. Traditional machine learning models already struggle in such cases, and while deep learning models excel with abundant data, they also face difficulties when data is scarce. HOPER addresses this issue by integrating three distinct modalities - protein sequences, biomedical text, and protein-protein interaction (PPI) networks - to create a comprehensive protein representation. The model utilizes autoencoders to generate holistic embeddings, which are then employed for PFP tasks using transfer learning. HOPER outperforms existing methods on a benchmark dataset across all Gene Ontology categories, i.e., molecular function, biological process, and cellular component. Additionally, we demonstrate its practical utility by identifying new immune-escape proteins in lung adenocarcinoma, offering insights into potential therapeutic targets. Our results highlight the effectiveness of multimodal representation learning for overcoming data limitations in biological research, potentially enabling more accurate and scalable protein function prediction. HOPER source code and datasets are available at https://github.com/kansil/HOPER
A mathematical model for smooth muscle cell phenotype switching in atherosclerotic plaque
Authors: Joseph P. Ndenda, Michael G. Watson, Ashish Misra, Mary R. Myerscough
Date: 2024-12-09 00:00:00

Smooth muscle cells (SMCs) play a fundamental role in the development of atherosclerotic plaques. SMCs may ingest lipids in a similar way to monocyte-derived macrophages (MDMs) in the plaque. This can stimulate SMCs to undergo a phenotypic switch towards a macrophage-like phenotype. We formulate an ordinary differential equation (ODE) model for the populations of SMCs, MDMs and smooth muscle cell-derived macrophages (SDMs) and the internalised lipid load in each population. We use this model to explore the effect on plaque fate of SMC phenotype switching. We find that when SMCs switch to a macrophage-like phenotype, the total lipid contained in cells in the plaque increases. Additionally, removal of SMCs from the plaque via phenotype switching reduces the fibrous plaque cap, increases the lipid in the necrotic core, and increases plaque inflammation. This makes the plaque more vulnerable to rupture, which can lead to heart attacks and strokes. When SDMs are highly proliferative and resistant to cell death, the plaque grows rapidly and becomes highly pathological. The model suggests that plaque dynamics, driven by the switch of SMCs to a macrophage-like phenotype, may drive the development of unstable, vulnerable and pathological plaques.
DNA Fragments in Crude Oil Reveals Earth's Hidden History
Authors: Wan-Qian Zhao, Zhan-Yong Guo, Yu-Qi Guo, Mei-Jun Li, Gang-Qiang Cao, Zeng-Yuan Tian, Ran Chai, Li-You Qiu, Jin-Hua Zeng, Xin-Ge Zhang, Tian-Cang Qin, Jin-Yu Yang, Ming-Jie Chen, Mei-Rong Song, Fei Liang, Jun-Hui Geng, Chun-Yan Zhou, Shu-Jie Zhang, Li-Juan Zhao
Date: 2024-12-09 00:00:00

This groundbreaking research extracted DNA from petroleum using nanoparticle affinity bead technology, yielding 3,159,020 petroleum DNA (pDNA) sequences, primarily environmental DNA. While most original in situ DNA (oriDNA) was lost, ancient DNA (aDNA) from petroleum offers an important source of ecological and evolutionary information, surpassing traditional fossils. This study reveals that oil, mainly sourced from algae and lower aquatic plants, now serves as a new type of fossil, providing detailed insights into Earth's hidden history, including unclassified species and ancient events, revolutionizing petroleum geology and paleontology.
Ancient DNA from 120-Million-Year-Old Lycoptera Fossils Reveals Evolutionary Insights
Authors: Wan-Qian Zhao, Zhan-Yong Guo, Zeng-Yuan Tian, Tong-Fu Su, Gang-Qiang Cao, Zi-Xin Qi, Tian-Cang Qin, Wei Zhou, Jin-Yu Yang, Ming-Jie Chen, Xin-Ge Zhang, Chun-Yan Zhou, Chuan-Jia Zhu, Meng-Fei Tang, Di wu, Mei-Rong Song, Yu-Qi Guo, Li-You Qiu, Fei Liang, Mei-Jun Li, Jun-Hui Geng, Li-Juan Zhao, Shu-Jie Zhang
Date: 2024-12-09 00:00:00

High quality ancient DNA (aDNA) is essential for molecular paleontology. Due to DNA degradation and contamination by environmental DNA (eDNA), current research is limited to fossils less than 1 million years old. The study successfully extracted DNA from Lycoptera davidi fossils from the Early Cretaceous period, dating 120 million years ago. Using high-throughput sequencing, 1,258,901 DNA sequences were obtained. We established a rigorous protocol known as the mega screen method. Using this method, we identified 243 original in situ DNA (oriDNA) sequences, likely from the Lycoptera genome. These sequences have an average length of over 100 base pairs and show no signs of deamination. Additionally, 10 transposase coding sequences were discovered, shedding light on a unique self-renewal mechanism in the genome. This study provides valuable DNA data for understanding ancient fish evolution and advances paleontological research.
Emerging Challenges in Molecular Paleontology: Misapplication of Environmental DNA Fragments and Misconception of Deamination as a Key Criterion for In Situ DNA Identification
Authors: Wan-Qian Zhao, Shu-Jie Zhang, Zhan-Yong Guo, Zeng-Yuan Tian, Gang-Qiang Cao, Mei-Jun Li, Li-You Qiu, Jin-Yu Yang, Yong-Kai Wang, Shu-Hui Zhang, Zhi-Fang Zheng, Min-Zhi Wu
Date: 2024-12-09 00:00:00

This article critically examines the methodologies applied in ancient DNA (aDNA) research, particularly those developed by Dr. P\"a\"abo's team, which have significantly influenced the field. The focus is on the challenges of distinguishing original in situ DNA (oriDNA) from environmental DNA (eDNA) contamination in fossil samples. Recent analyses indicate that even with rigorous extraction and sequencing protocols, a considerable amount of eDNA remains present, often misinterpreted as oriDNA. This misidentification risks the accuracy of species ascription and evolutionary interpretations derived from fossil analyses. The paper explores fossil preservation's physical and chemical dynamics, which allow eDNA from similar and disparate species to infiltrate bone matrices. We propose enhancements to methodological frameworks, such as broader BLAST database usage and stringent E-value criteria, to improve species-specific aDNA identification. Additionally, the article critiques the reliance on deamination patterns as a definitive marker for aDNA, suggesting a reevaluation of this criterion due to its inconsistency and the potential for misleading sequencing results. Ultimately, our findings advocate for a more cautious and refined approach to aDNA research, ensuring more reliable and verifiable scientific outcomes
Hypothesizing the Transmission of Smells: Six Methods of Chemical Travel
Authors: Usman Ahmad
Date: 2024-12-09 00:00:00

In this paper, we propose a hypothesis regarding the travel and movement of chemicals between locations. We introduce six distinct methods to explain this process. The chemicals referred to in this article are those that we detect by the nose and can travel through the air. These are generally known as odorants or volatile compounds. Method 1 involves decomposing chemicals and transmitting their formulas digitally. Method 2 embeds chemicals in water, freezes it into ice, and transports it to the receiver, where the chemicals are extracted upon melting. Method 3 transfers chemicals via water logistics, with the receiver extracting them from the water. Method 4 uses airtight pipelines to transport chemicals in water, which are then extracted by the receiver. Method 5 projects chemical odors into the air, where the receiver captures them using suction. Method 6 draws from optical fiber technology to transmit chemical odors through a cable, ensuring the chemical composition remains intact. Each method ensures the receiver experiences the same scent as the sender.
Network Dynamics of Emotional Processing: A Structural Balance Theory Approach
Authors: Sepehr Gourabi, Parinaz Khosravani, Shahrzad Nosrat, Roya Mohammadi, Masoud Lotfalipour
Date: 2024-12-12 00:00:00

Understanding emotional processing in the human brain requires examining the complex interactions between different brain regions. While previous studies have identified specific regions involved in emotion processing, a holistic network approach may provide deeper insights. We use Structural Balance Theory to investigate the stability and triadic structures of signed brain networks during resting state and emotional processing, specifically in response to fear-related stimuli. We hypothesized that imbalanced triadic interactions would be more prevalent during emotional processing, especially in response to fear-related stimuli, potentially reflecting the brain's adaptation to emotional challenges. By analyzing fMRI data from 138 healthy, right-handed participants, we found that emotional processing was marked by an increase in positive connections and a decrease in negative connections compared to the resting state. Our findings clearly show that balanced triads significantly decreased while imbalanced triads increased, indicating a shift toward instability in the brain's functional network during emotional processing. Additionally, the number of influential hubs was significantly lower during fear processing than in neutral conditions, suggesting a more centralized network and higher levels of network energy. These findings reveal the brain's remarkable adaptive capacity during emotional processing, demonstrating how network stability dynamically shifts through changes in balanced and imbalanced triads, hub tendencies, and energy dynamics. Our research illuminates a complex mechanism by which the brain flexibly reconfigures its functional network in response to emotional stimuli with potential implications for understanding emotional resilience and neurological disorders.
Gradient Diffusion: Enhancing Existing Neural Models with Homeostatic Control and Tuning
Authors: Lennart P. L. Landsmeer, Mario Negrello, Said Hamdioui, Christos Strydis
Date: 2024-12-10 00:00:00

Realistic brain modeling requires precise estimation of numerous unobserved parameters, a task hindered by complex nonlinearities and the inaccessibility of the brain's full dynamical state. Current multicompartmental-model simulations predominantly rely on gradient-free optimization methods, which suffer from the ``curse of dimensionality'' and are incompatible with online tuning crucial for emulating biological homeostasis. Gradient-based methods offer superior scalability and facilitate online adaptation but are currently inaccessible within existing brain simulators due to the significant resource investment and incompatibility with established simulators. This work introduces a novel methodology for computing parameter gradients for any existing model-and-simulator combination, enabling both offline and online tuning, including the implementation of homeostatic-control mechanisms. Our approach seamlessly integrates traditional simulations with gradient-based optimization, facilitating scalable, robust and adaptive brain simulation without the need for developing new simulators.
Different factors determining Motor Execution and Motor Imagery performance in a serial reaction time task with intrinsic variability
Authors: Patricia Silva de Camargo, Paulo Roberto Cabral-Passos, André Frazão Helene
Date: 2024-12-03 00:00:00
Tasks: Motor Imagery

Motor imagery corresponds to the mental practice of simulating visual and kinesthetic aspects of a given motor task. This practice shares a similar neural substrate and correlated temporal scale with motor execution. Besides that, it can lead to performance improvements in the actual execution of the imagined task. Therefore it is important to understand functional differences and equivalences between motor imagery and motor execution. To tackle that we employed a finger-tapping serial reaction time task in two groups of participants, a Motor Execution (n=10) and a Motor imagery (n=10). The sequence of stimuli defining the task had 750 items composed of three distinct auditory stimuli. Also, this sequence had some intrinsic variability making some of the next items unpredictable. Each auditory stimulus was mapped to a single right hand finger in the Motor Imagery group. The Motor imagery group indicated the end of the imagination with a single response using the left hand. The results show improvement in performance of the Motor Imagery group throughout the task and that the duration of the motor imagery, indirectly measured by reaction times, are influenced by distinct factors than those of Motor Execution.
Swin fMRI Transformer Predicts Early Neurodevelopmental Outcomes from Neonatal fMRI
Authors: Patrick Styll, Dowon Kim, Jiook Cha
Date: 2024-11-25 00:00:00
Tasks: Dimensionality Reduction

Brain development in the first few months of human life is a critical phase characterized by rapid structural growth and functional organization. Accurately predicting developmental outcomes during this time is crucial for identifying delays and enabling timely interventions. This study introduces the SwiFT (Swin 4D fMRI Transformer) model, designed to predict Bayley-III composite scores using neonatal fMRI from the Developing Human Connectome Project (dHCP). To enhance predictive accuracy, we apply dimensionality reduction via group independent component analysis (ICA) and pretrain SwiFT on large adult fMRI datasets to address the challenges of limited neonatal data. Our analysis shows that SwiFT significantly outperforms baseline models in predicting cognitive, motor, and language outcomes, leveraging both single-label and multi-label prediction strategies. The model's attention-based architecture processes spatiotemporal data end-to-end, delivering superior predictive performance. Additionally, we use Integrated Gradients with Smoothgrad sQuare (IG-SQ) to interpret predictions, identifying neural spatial representations linked to early cognitive and behavioral development. These findings underscore the potential of Transformer models to advance neurodevelopmental research and clinical practice.
Explicit modeling of density dependence in spatial capture-recapture models
Authors: Qing Zhao, Yunyi Shen
Date: 2024-12-12 00:00:00

Density dependence occurs at the individual level but is often evaluated at the population level, leading to difficulties or even controversies in detecting such a process. Bayesian individual-based models such as spatial capture-recapture (SCR) models provide opportunities to study density dependence at the individual level, but such an approach remains to be developed and evaluated. In this study, we developed a SCR model that links habitat use to apparent survival and recruitment through density dependent processes at the individual level. Using simulations, we found that the model can properly inform habitat use, but tends to underestimate the effect of density dependence on apparent survival and recruitment. The reason for such underestimations is likely due to the fact that SCR models have difficulties in identifying the locations of unobserved individuals while assuming they are uniformly distributed. How to accurately estimate the locations of unobserved individuals, and thus density dependence, remains a challenging topic in spatial statistics and statistical ecology.