pwc-archive/papers-with-abstracts huggingface dataset:
Human Protein Protein Interaction Networks: A Topological Comparison Review
Authors: Rodrigo Henrique Ramos, Cynthia de Oliveira Lage Ferreira, Adenilso Simao
Date: 2023-10-03 00:00:00

Protein-Protein Interaction Networks aim to model the interactome, providing a powerful tool for understanding the complex relationships governing cellular processes. These networks have numerous applications, including functional enrichment, discovering cancer driver genes, identifying drug targets, and more. Various databases make protein-protein networks available for many species, including Homo sapiens. This work topologically compares four Homo sapiens networks using a coarse-to-fine approach, comparing global characteristics, sub-network topology, specific nodes centrality, and interaction significance. Results show that the four human protein networks share many common protein-encoding genes and some global measures, but significantly differ in the interactions and neighbourhood. Small sub-networks from cancer pathways performed better than the whole networks, indicating an improved topological consistency in functional pathways. The centrality analysis shows that the same genes play different roles in different networks. We discuss how studies and analyses that rely on protein-protein networks for humans should consider their similarities and distinctions.
Modeling Temporal Lobe Epilepsy during Music Large-Scale Form Perception using the Impulse Pattern Formulation (IPF) Brain Mode
Authors: Rolf Bader
Date: 2023-10-05 00:00:00
Tasks: EEG, Form, Rhythm

Musical large-scale form is investigated using an Electronic Dance Music (EDM) piece fed into a Finite-Difference Time Domain (FDTD) physical model of the cochlear which again inputs into an Impulse-Pattern Formulation (IPF) brain model. In previous studies, experimental EEG data showed an enhanced correlation between brain synchronization and the musical piece's amplitude and fractal correlation dimension in good agreement with a FitzHugh-Nagumo oscillator model\cite{Sawicki2022}. Still, this model cannot display temporal developments of large-scale forms. The IPF Brain model also shows a high correlation between cochlear input and brain synchronization at the gamma band range around 50 Hz, but also a strong negative correlation for low frequencies, associated with musical rhythm, during time frames of low cochlear input amplitude. Such high synchronization corresponds to temporal lobe epilepsy, often associated with creativity or spirituality. Therefore, the IPF Brain model suggests those conscious states to happen in times of low external input at low frequencies where isochronous musical rhythms are present.
Brain development dictates energy constraints on neural architecture search: cross-disciplinary insights on optimization strategies
Authors: Martin G. Frasch
Date: 2023-10-03 00:00:00
Tasks: Neural Architecture Search

Present day artificial neural architecture search (NAS) strategies are essentially prediction-error-optimized. That holds true for AI functions in general. From the developmental neuroscience perspective, I present evidence for the central role of metabolically, rather than prediction-error-optimized neural architecture search (NAS). Supporting evidence is drawn from the latest insights into the glial-neural organization of the human brain and the dynamic coordination theory which provides a mathematical foundation for the functional expression of this optimization strategy. This is relevant to devising novel NAS strategies in AI, especially in AGI. Additional implications arise for causal reasoning from deep neural nets. Together, the insights from developmental neuroscience offer a new perspective on NAS and the foundational assumptions in AI modeling.
A computational model for synaptic message transmission
Authors: Lizhi Xin, Kevin Xin, Houwen Xin
Date: 2023-10-02 00:00:00
Tasks: model

A computational model incorporating insights from quantum theory is proposed to describe and explain synaptic message transmission. We propose that together, neurotransmitters and their corresponding receptors, function as a physical "quantum decision tree" to "decide" whether to excite or inhibit the synapse. When a neurotransmitter binds to its corresponding receptor, it is the equivalent of randomly choosing different "strategies"; a "strategy" has two actions to take: excite or inhibit the synapse with a certain probability. The genetic programming can be applied for learning the observed data sequence to simulate the synaptic message transmission.
Is controlling a brain-computer interface just a matter of presence of mind? The limits of cognitive-motor dissociation
Authors: Perrine Seguin, Emmanuel Maby, Fabien Perrin, Alessandro Farnè, Jérémie Mattout
Date: 2023-09-30 00:00:00
Tasks: Brain Computer Interface

Brain-computer interfaces (BCI) are presented as a solution for people with global paralysis, also known as locked-in syndrome (LIS). The targeted population includes the most severe patients, with no residual eye movements, who cannot use any communication device (Complete LIS). However, BCI reliability is low precisely in these cases, technical pitfalls being considered responsible so far. Here, we propose to consider also that global paralysis could have an impact on cognitive functions that are crucial for being able to control a BCI. We review a bundle of arguments about the role of motor structures in cognition. Especially, we uncover that these patients without oculomotor activity often have injuries in more 'cognitive' structures such as the frontal eye field or the midbrain, exposing them to cognitive deficits further than canonical LIS population. We develop a hypothesis about the putative role of the motor system in (covert) attention, a capacity which is a prerequisite for most BCI paradigms and which should therefore be both better assessed in patients and considered.
Why and How Did the COVID Pandemic End Abruptly?
Authors: J. C. Phillips
Date: 2023-10-03 00:00:00

Phase transition theory, implemented quantitatively by thermodynamic scaling, has explained the evolution of Coronavirus extremely high contagiousness caused by a few key mutations from CoV2003 to CoV2019 identified among hundreds, as well as the later 2021 evolution to Omicron caused by 30 mutations. It also showed that the 2022 strain BA.5 with five mutations began a new path. Here we show that the early 2023 strains BKK with one stiffening mutation confirm that path, and the single flexing mutation of a later 2023 variant EG.5 strengthens it further. The few mutations of the new path have greatly reduced pandemic deaths, for mechanical reasons proposed here.
Unraveling Post-COVID-19 Immune Dysregulation Using Machine Learning-based Immunophenotyping
Authors: Maitham G. Yousif, Ghizal fatima, Hector J. Castro, Fadhil G. Al-Amran, Salman Rawaf
Date: 2023-09-28 00:00:00

The COVID-19 pandemic has left a significant mark on global healthcare, with many individuals experiencing lingering symptoms long after recovering from the acute phase of the disease, a condition often referred to as "long COVID." This study delves into the intricate realm of immune dysregulation that ensues in 509 post-COVID-19 patients across multiple Iraqi regions during the years 2022 and 2023. Utilizing advanced machine learning techniques for immunophenotyping, this research aims to shed light on the diverse immune dysregulation patterns present in long COVID patients. By analyzing a comprehensive dataset encompassing clinical, immunological, and demographic information, the study provides valuable insights into the complex interplay of immune responses following COVID-19 infection. The findings reveal that long COVID is associated with a spectrum of immune dysregulation phenomena, including persistent inflammation, altered cytokine profiles, and abnormal immune cell subsets. These insights highlight the need for personalized interventions and tailored treatment strategies for individuals suffering from long COVID-19. This research represents a significant step forward in our understanding of the post-COVID-19 immune landscape and opens new avenues for targeted therapies and clinical management of long COVID patients. As the world grapples with the long-term implications of the pandemic, these findings offer hope for improving the quality of life for those affected by this enigmatic condition.
Early Detection of Post-COVID-19 Fatigue Syndrome Using Deep Learning Models
Authors: Fadhil G. Al-Amran, Salman Rawaf, Maitham G. Yousif
Date: 2023-09-26 00:00:00
Tasks: Deep Learning

The research titled "Early Detection of Post-COVID-19 Fatigue Syndrome using Deep Learning Models" addresses a pressing concern arising from the COVID-19 pandemic. Post-COVID-19 Fatigue Syndrome (PCFS) has become a significant health issue affecting individuals who have recovered from COVID-19 infection. This study harnesses a robust dataset comprising 940 patients from diverse age groups, whose medical records were collected from various hospitals in Iraq over the years 2022, 2022, and 2023. The primary objective of this research is to develop and evaluate deep learning models for the early detection of PCFS. Leveraging the power of deep learning, these models are trained on a comprehensive set of clinical and demographic features extracted from the dataset. The goal is to enable timely identification of PCFS symptoms in post-COVID-19 patients, which can lead to more effective interventions and improved patient outcomes. The study's findings underscore the potential of deep learning in healthcare, particularly in the context of COVID-19 recovery. Early detection of PCFS can aid healthcare professionals in providing timely care and support to affected individuals, potentially reducing the long-term impact of this syndrome on their quality of life. This research contributes to the growing body of knowledge surrounding COVID-19-related health complications and highlights the importance of leveraging advanced machine learning techniques for early diagnosis and intervention. Keywords: Early Detection, Post-COVID-19 Fatigue Syndrome, Deep Learning Models, Healthcare, COVID-19 Recovery, Medical Data Analysis, Machine Learning, Health Interventions.
Improving alkaline fen functioning and Liparis loeselii (L.) Rich., 1817 preservation: towards a better water level management
Authors: Adrien Berquer, Antoine Gazaix, Laura Czerniak, Valentin Dromard, Guillaume Meire, Gaëtan Rivière
Date: 2023-10-02 00:00:00
Tasks: Management

Alkaline fens are known to be wetlands that host large quantities of carbon, but also a huge biodiversity. However, anthropic pressures are degrading the peatlands with drainage, and conversion to agriculture or urbanization. These pressures induce externalities like a large release of greenhouse gas (GHG) by peat mineralization, and a loss of biodiversity, since they host numerous protected or endangered species. One of them is the Liparis loeselii, a small orchid facing declines in Europe and for which conservation measures are taken. Nevertheless, if recent studies inferred some factors shaping its population dynamics, they are still not clearly understood, particularly in fen contexts. This study aims at disentangle the processes shaping a continental population of L. loeselii in the Somme valley, among factors related to the hydrology of the site and the elevation of the individuals. We used Bayesian generalized linear models to infer the seasonal water level effects on the presence of L. loeselii. Moreover, we showed that the elevation of L. loeselii population is around the mean water levels of winter, and that the occurrence can be promoted by water level, notably in summer. Conversely, the highest water levels reported seemed detrimental to the L. loeselii population, suggesting a probably negative effect of flooding acting on local dispersal. Finally, this study provides insights to take restoration measures of alkaline fens, like a better hydrological functioning and an optimal water management.
Bonds and Bytes: the Odyssey of Structural Biology
Authors: Samuel Hoff, Maximilian Zinke, Nadia Izadi-Pruneyre, Massimiliano Bonomi
Date: 2023-10-03 00:00:00

Characterizing structural and dynamic properties of proteins and large macromolecular assemblies is crucial to understand the molecular mechanisms underlying biological functions. In the field of Structural Biology, no single method comprehensively reveals the behavior of biological systems across various spatio-temporal scales. Instead, we have a versatile toolkit of techniques, each contributing a piece to the overall puzzle. Integrative Structural Biology combines different techniques to create accurate and precise multi-scale models that expand our understanding of complex biological systems. This review outlines recent advancements in computational and experimental methods in Structural Biology, with special focus on recent Artificial Intelligence techniques, emphasizes integrative approaches that combine different types of data for precise spatio-temporal modeling, and provides an outlook into future directions of this field.
Crocodile perception of distress in hominid baby cries
Authors: Julie Thévenet, Léo Papet, Gérard Coureaud, Nicolas Boyer, Florence Levréro, Nicolas Grimault, Nicolas Mathevon
Date: 2023-10-02 00:00:00

It is generally argued that distress vocalizations, a common modality for alerting conspecifics across a wide range of terrestrial vertebrates, share acoustic features that allow heterospecific communication. Yet studies suggest that the acoustic traits used to decode distress may vary between species, leading to decoding errors. Here we found through playback experiments that Nile crocodiles are attracted to infant hominid cries (bonobo, chimpanzee and human), and that the intensity of crocodile response depends critically on a set of specific acoustic features (mainly deterministic chaos, harmonicity and spectral prominences). Our results suggest that crocodiles are sensitive to the degree of distress encoded in the vocalizations of phylogenetically very distant vertebrates. A comparison of these results with those obtained with human subjects confronted with the same stimuli further indicates that crocodiles and humans use different acoustic criteria to assess the distress encoded in infant cries. Interestingly, the acoustic features driving crocodile reaction are likely to be more reliable markers of distress than those used by humans. These results highlight that the acoustic features encoding information in vertebrate sound signals are not necessarily identical across species.
Deep Learning in Computational Biology: Advancements, Challenges, and Future Outlook
Authors: Suresh Kumar, Dhanyashri Guruparan, Pavithren Aaron, Philemon Telajan, Kavinesh Mahadevan, Dinesh Davagandhi, Ong Xin Yue
Date: 2023-10-02 00:00:00
Tasks: Deep Learning, Drug Discovery, Prediction, Protein Structure Prediction

Deep learning has become a powerful tool in computational biology, revolutionising the analysis and interpretation of biological data over time. In our article review, we delve into various aspects of deep learning in computational biology. Specifically, we examine its history, advantages, and challenges. Our focus is on two primary applications: DNA sequence classification and prediction, as well as protein structure prediction from sequence data. Additionally, we provide insights into the outlook for this field. To fully harness the potential of deep learning in computational biology, it is crucial to address the challenges that come with it. These challenges include the requirement for large, labelled datasets and the interpretability of deep learning models. The use of deep learning in the analysis of DNA sequences has brought about a significant transformation in the detection of genomic variants and the analysis of gene expression. This has greatly contributed to the advancement of personalised medicine and drug discovery. Convolutional neural networks (CNNs) have been shown to be highly accurate in predicting genetic variations and gene expression levels. Deep learning techniques are used for analysing epigenetic data, including DNA methylation and histone modifications. This provides valuable insights into metabolic conditions and gene regulation. The field of protein structure prediction has been significantly impacted by deep learning, which has enabled accurate determination of the three-dimensional shape of proteins and prediction of their interactions. The future of deep learning in computational biology looks promising. With the development of advanced deep learning models and interpretation techniques, there is potential to overcome current challenges and further our understanding of biological systems.
Automation and miniaturization of Golden Gate DNA assembly reactions using acoustic dispensers
Authors: Tania S. Koebel, Daniel Schindler
Date: 2023-09-30 00:00:00

Golden Gate cloning has become one of the most popular DNA assembly techniques. Its modular and hierarchical structure allows the construction of complex DNA fragments. Over time, Golden Gate cloning allows for the creation of a repository of reusable parts, reducing the cost of frequent sequence validation. However, as the number of reactions and fragments increases, so does the cost of consumables and the potential for human error. Typically, Golden Gate reactions are performed in volumes of 10 to 25 microlitre. Recent technological advances have led to the development of liquid handling robots that use sound to transfer liquids in the nL range from a source plate to a target plate. These acoustic dispensers have become particularly popular in the field of synthetic biology. The use of this technology allows miniaturization and parallelization of molecular reactions in a tip-free manner, making it sustainable by reducing plastic waste and reagent usage. Here, we provide a step-by-step protocol for performing and parallelizing Golden Gate cloning reactions in 1 microlitre total volume.
A semi-Lagrangian $ε$-monotone Fourier method for continuous withdrawal GMWBs under jump-diffusion with stochastic interest rate
Authors: Yaowen Lu, Duy-Minh Dang
Date: 2023-10-01 00:00:00

We develop an efficient pricing approach for guaranteed minimum withdrawal benefits (GMWBs) with continuous withdrawals under a realistic modeling setting with jump-diffusions and stochastic interest rate. Utilizing an impulse stochastic control framework, we formulate the no-arbitrage GMWB pricing problem as a time-dependent Hamilton-Jacobi-Bellman (HJB) Quasi-Variational Inequality (QVI) having three spatial dimensions with cross derivative terms. Through a novel numerical approach built upon a combination of a semi-Lagrangian method and the Green's function of an associated linear partial integro-differential equation, we develop an $\epsilon$-monotone Fourier pricing method, where $\epsilon > 0$ is a monotonicity tolerance. Together with a provable strong comparison result for the HJB-QVI, we mathematically demonstrate convergence of the proposed scheme to the viscosity solution of the HJB-QVI as $\epsilon \to 0$. We present a comprehensive study of the impact of simultaneously considering jumps in the sub-account process and stochastic interest rate on the no-arbitrage prices and fair insurance fees of GMWBs, as well as on the holder's optimal withdrawal behaviors.
A systematic review of early warning systems in finance
Authors: Ali Namaki, Reza Eyvazloo, Shahin Ramtinnia
Date: 2023-09-30 00:00:00
Tasks: Articles, Sentiment Analysis

Early warning systems (EWSs) are critical for forecasting and preventing economic and financial crises. EWSs are designed to provide early warning signs of financial troubles, allowing policymakers and market participants to intervene before a crisis expands. The 2008 financial crisis highlighted the importance of detecting financial distress early and taking preventive measures to mitigate its effects. In this bibliometric review, we look at the research and literature on EWSs in finance. Our methodology included a comprehensive examination of academic databases and a stringent selection procedure, which resulted in the final selection of 616 articles published between 1976 and 2023. Our findings show that more than 90\% of the papers were published after 2006, indicating the growing importance of EWSs in financial research. According to our findings, recent research has shifted toward machine learning techniques, and EWSs are constantly evolving. We discovered that research in this area could be divided into four categories: bankruptcy prediction, banking crisis, currency crisis and emerging markets, and machine learning forecasting. Each cluster offers distinct insights into the approaches and methodologies used for EWSs. To improve predictive accuracy, our review emphasizes the importance of incorporating both macroeconomic and microeconomic data into EWS models. To improve their predictive performance, we recommend more research into incorporating alternative data sources into EWS models, such as social media data, news sentiment analysis, and network analysis.
Modeling the yield curve of Burundian bond market by parametric models
Authors: Rédempteur Ntawiratsa, David Niyukuri, Irène Irakoze, Menus Nkurunziza
Date: 2023-09-30 00:00:00
Tasks: Decision Making, Management

The term structure of interest rates (yield curve) is a critical facet of financial analytics, impacting various investment and risk management decisions. It is used by the central bank to conduct and monitor its monetary policy. That instrument reflects the anticipation of inflation and the risk by investors. The rates reported on yield curve are the cornerstone of valuation of all assets. To provide such tool for Burundi financial market, we collected the auction reports of treasury securities from the website of the Central Bank of Burundi. Then, we computed the zero-coupon rates, and estimated actuarial rates of return by applying the Nelson-Siegel and Svensson models. This paper conducts a rigorous comparative analysis of these two prominent parametric yield curve models and finds that the Nelson-Siegel model is the optimal choice for modeling the Burundian yield curve. The findings contribute to the body of knowledge on yield curve modeling, enhancing its precision and applicability in financial markets. Furthermore, this research holds implications for investment strategies, risk management, second market pricing, financial decision-making, and the forthcoming establishment of the Burundian stock market.
Robust Long-Term Growth Rate of Expected Utility for Leveraged ETFs
Authors: Tim Leung, Hyungbin Park, Heejun Yeo
Date: 2023-10-03 00:00:00

This paper analyzes the robust long-term growth rate of expected utility and expected return from holding a leveraged exchange-traded fund (LETF). When the Markovian model parameters in the reference asset are uncertain, the robust long-term growth rate is derived by analyzing the worst-case parameters among an uncertainty set. We compute the growth rate and describe the optimal leverage ratio maximizing the robust long-term growth rate. To achieve this, the worst-case parameters are analyzed by the comparison principle, and the growth rate of the worst-case is computed using the martingale extraction method. The robust long-term growth rates are obtained explicitly under a number of models for the reference asset, including the geometric Brownian motion (GBM), Cox--Ingersoll--Ross (CIR), 3/2, and Heston and 3/2 stochastic volatility models. Additionally, we demonstrate the impact of stochastic interest rates, such as the Vasicek and inverse GARCH short rate models. This paper is an extended work of \citet{Leung2017}.