pwc-archive/papers-with-abstracts huggingface dataset:
RF Chain-Free mmWave Transmission: Modeling and Experimental Verification
Authors: M. Yaser Yağan, Ibrahim Hökelek, Ali E. Pusane, Ali Görçin
Date: 2024-05-03 00:00:00

The utilization of millimeter wave frequency bands is expected to become prevalent in the following communication systems. However, generating and transmitting communication signals over these frequencies is not as straightforward as in sub-6 GHz frequencies due to complex transceiver structures. As an alternative to conventional transmitter architectures, this paper investigates the implementation of time-modulated arrays to effectively modulate and transmit high-quality communication signals at millimeter wave frequencies. By exploiting the array structures and analog beamformers, which are the fundamental components of millimeter wave transmitters, secure and low-cost transmission can be achieved. Though, harmonics of theoretically infinite bandwidth arise as a fundamental problem in this approach. Thus, this paper presents a frequency analysis tool for the time-modulated arrays with hardware impairments and shows how controlling the sampling period can reduce the harmonics. Furthermore, the derived results are experimentally verified at 25 GHz with two important remarks. First, the phase error of received signals can be reduced by 32% using the proposed architecture. Second, the harmonics can be significantly suppressed by the correct choice of sampling period for the given hardware.
Enhancing NLoS RIS-Aided Localization with Optimization and Machine Learning
Authors: Rafael A. Aguiar, Nuno Paulino, Luís M. Pessoa
Date: 2024-05-03 00:00:00
Tasks: Indoor Localization

This paper introduces two machine learning optimization algorithms to significantly enhance position estimation in Reconfigurable Intelligent Surface (RIS) aided localization for mobile user equipment in Non-Line-of-Sight conditions. Leveraging the strengths of these algorithms, we present two methods capable of achieving extremely high accuracy, reaching sub-centimeter or even sub-millimeter levels at 3.5 GHz. The simulation results highlight the potential of these approaches, showing significant improvements in indoor mobile localization. The demonstrated precision and reliability of the proposed methods offer new opportunities for practical applications in real-world scenarios, particularly in Non-Line-of-Sight indoor localization. By evaluating four optimization techniques, we determine that a combination of a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) results in localization errors under 30 cm in 90 % of the cases, and under 5 mm for close to 85 % of cases when considering a simulated room of 10 m by 10 m where two of the walls are equipped with RIS tiles.
The Integrated Sensing and Communication Revolution for 6G: Vision, Techniques, and Applications
Authors: Nuria González-Prelcic, Musa Furkan Keskin, Ossi Kaltiokallio, Mikko Valkama, Davide Dardari, Xiao Shen, Yuan Shen, Murat Bayraktar, Henk Wymeersch
Date: 2024-05-03 00:00:00
Tasks: Integrated sensing and communication, ISAC

Future wireless networks will integrate sensing, learning and communication to provide new services beyond communication and to become more resilient. Sensors at the network infrastructure, sensors on the user equipment, and the sensing capability of the communication signal itself provide a new source of data that connects the physical and radio frequency environments. A wireless network that harnesses all these sensing data can not only enable additional sensing services, but also become more resilient to channel-dependent effects like blockage and better support adaptation in dynamic environments as networks reconfigure. In this paper, we provide a vision for integrated sensing and communication (ISAC) networks and an overview of how signal processing, optimization and machine learning techniques can be leveraged to make them a reality in the context of 6G. We also include some examples of the performance of several of these strategies when evaluated using a simulation framework based on a combination of ray tracing measurements and mathematical models that mix the digital and physical worlds.
Multi-Layer Network Formation through HAPS Base Station and Transmissive RIS-Equipped UAV
Authors: Faical Khennoufa, Khelil Abdellatif, Halim Yanikomeroglu, Metin Ozturk, Taissir Elganimi, Ferdi Kara, Khaled Rabie
Date: 2024-05-02 00:00:00

In order to bolster future wireless networks, there has been a great deal of interest in non-terrestrial networks, especially aerial platforms including high-altitude platform stations (HAPS) and uncrewed aerial vehicles (UAVs). These platforms can integrate advanced technologies such as reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA). In this regard, this paper proposes a multi-layer network architecture consisting of HAPS and UAV, where the former acts as a HAPS super macro base station (HAPS-SMBS), while the latter serves as a relay node for the ground Internet of Things (IoT) devices. The UAV is equipped with active transmissive RIS, which is a novel technology with promising benefits. We also utilize multiple-input single-output (MISO) technology, i.e., multiple antennas at the HAPS-SMBS and a single antenna at the IoT devices. Additionally, we consider NOMA as the multiple access technology as well as the existence of hardware impairments as a practical limitation. We compare the proposed system model with various scenarios, all involving the HAPS-SMBS and RIS-equipped UAV relay combination, but with different types of RIS, antenna configurations, and access technologies. Sum rate and energy efficiency are used as performance metrics, and the findings demonstrate that, in comparison to all benchmarks, the proposed system yields significant performance gains. Moreover, hardware impairment limits the system performance at high transmit power levels.
Counting Subnetworks Under Gene Duplication in Genetic Regulatory Networks
Authors: Ashley Scruse, Jonathan Arnold, Robert Robinson
Date: 2024-05-06 00:00:00

Gene duplication is a fundamental evolutionary mechanism that contributes to biological complexity and diversity (Fortna et al., 2004). Traditionally, research has focused on the duplication of gene sequences (Zhang, 1914). However, evidence suggests that the duplication of regulatory elements may also play a significant role in the evolution of genomic functions (Teichmann and Babu, 2004; Hallin and Landry, 2019). In this work, the evolution of regulatory relationships belonging to gene-specific-substructures in a GRN are modeled. In the model, a network grows from an initial configuration by repeatedly choosing a random gene to duplicate. The likelihood that the regulatory relationships associated with the selected gene are retained through duplication is determined by a vector of probabilities. Occurrences of gene-family-specific substructures are counted under the gene duplication model. In this thesis, gene-family-specific substructures are referred to as subnetwork motifs. These subnetwork motifs are motivated by network motifs which are patterns of interconnections that recur more often in a specialized network than in a random network (Milo et al., 2002). Subnetwork motifs differ from network motifs in the way that subnetwork motifs are instances of gene-family-specific substructures while network motifs are isomorphic substructures. These subnetwork motifs are counted under Full and Partial Duplication, which differ in the way in which regulation relationships are inherited. Full duplication occurs when all regulatory links are inherited at each duplication step, and Partial Duplication occurs when regulation inheritance varies at each duplication step. Moments for the number of occurrences of subnetwork motifs are determined in each model. The results presented offer a method for discovering subnetwork motifs that are significant in a GRN under gene duplication.
Modelling the Stochastic Importation Dynamics and Establishment of Novel Pathogenic Strains using a General Branching Processes Framework
Authors: Jacob Curran-Sebastian, Frederik Mølkjær Andersen, Samir Bhatt
Date: 2024-05-03 00:00:00

The importation and subsequent establishment of novel pathogenic strains in a population is subject to a large degree of uncertainty due to the stochastic nature of the disease dynamics. Mathematical models need to take this stochasticity in the early phase of an outbreak in order to adequately capture the uncertainty in disease forecasts. We propose a general branching process model of disease spread that includes host-level heterogeneity, and that can be straightforwardly tailored to capture the salient aspects of a particular disease outbreak. We combine this with a model of case importation that occurs via an independent marked Poisson process. We use this framework to investigate the impact of different control strategies, particularly on the time to establishment of an invading, exogenous strain, using parameters taken from the literature for COVID-19 as an example. We also demonstrate how to combine our model with a deterministic approximation, such that longer term projections can be generated that still incorporate the uncertainty from the early growth phase of the epidemic. Our approach produces meaningful short- and medium-term projections of the course of a disease outbreak when model parameters are still uncertain and when stochasticity still has a large effect on the population dynamics.
AntiFold: Improved antibody structure-based design using inverse folding
Authors: Magnus Haraldson Høie, Alissa Hummer, Tobias H. Olsen, Broncio Aguilar-Sanjuan, Morten Nielsen, Charlotte M. Deane
Date: 2024-05-06 00:00:00
Tasks: Language Modeling, Language Modelling, Protein Language Model

The design and optimization of antibodies requires an intricate balance across multiple properties. Protein inverse folding models, capable of generating diverse sequences folding into the same structure, are promising tools for maintaining structural integrity during antibody design. Here, we present AntiFold, an antibody-specific inverse folding model, fine-tuned from ESM-IF1 on solved and predicted antibody structures. AntiFold outperforms existing inverse folding tools on sequence recovery across complementarity-determining regions, with designed sequences showing high structural similarity to their solved counterpart. It additionally achieves stronger correlations when predicting antibody-antigen binding affinity in a zero-shot manner, while performance is augmented further when including antigen information. AntiFold assigns low probabilities to mutations that disrupt antigen binding, synergizing with protein language model residue probabilities, and demonstrates promise for guiding antibody optimization while retaining structure-related properties. AntiFold is freely available under the BSD 3-Clause as a web server at https://opig.stats.ox.ac.uk/webapps/antifold/ and and pip installable package at https://github.com/oxpig/AntiFold
A Decade in a Systematic Review: The Evolution and Impact of Cell Painting
Authors: Srijit Seal, Maria-Anna Trapotsi, Ola Spjuth, Shantanu Singh, Jordi Carreras-Puigvert, Nigel Greene, Andreas Bender, Anne E. Carpenter
Date: 2024-05-04 00:00:00

High-content image-based assays have fueled significant discoveries in the life sciences in the past decade (2013-2023), including novel insights into disease etiology, mechanism of action, new therapeutics, and toxicology predictions. Here, we systematically review the substantial methodological advancements and applications of Cell Painting. Advancements include improvements in the Cell Painting protocol, assay adaptations for different types of perturbations and applications, and improved methodologies for feature extraction, quality control, and batch effect correction. Moreover, machine learning methods recently surpassed classical approaches in their ability to extract biologically useful information from Cell Painting images. Cell Painting data have been used alone or in combination with other -omics data to decipher the mechanism of action of a compound, its toxicity profile, and many other biological effects. Overall, key methodological advances have expanded the ability of Cell Painting to capture cellular responses to various perturbations. Future advances will likely lie in advancing computational and experimental techniques, developing new publicly available datasets, and integrating them with other high-content data types.
Stress-induced Eukaryotic Translational Regulatory Mechanisms
Authors: Dilawar Ahmad Mir, Zhengxin Ma, Jordan Horrocks, Aric N Rogers
Date: 2024-05-02 00:00:00
Tasks: Translation

The eukaryotic protein synthesis process entails intricate stages governed by diverse mechanisms to tightly regulate translation. Translational regulation during stress is pivotal for maintaining cellular homeostasis, ensuring the accurate expression of essential proteins crucial for survival. This selective translational control mechanism is integral to cellular adaptation and resilience under adverse conditions. This review manuscript explores various mechanisms involved in selective translational regulation, focusing on mRNA-specific and global regulatory processes. Key aspects of translational control include translation initiation, which is often a rate-limiting step, and involves the formation of the eIF4F complex and recruitment of mRNA to ribosomes. Regulation of translation initiation factors, such as eIF4E, eIF4E2, and eIF2, through phosphorylation and interactions with binding proteins, modulates translation efficiency under stress conditions. This review also highlights the control of translation initiation through factors like the eIF4F complex and the ternary complex and also underscores the importance of eIF2{\alpha} phosphorylation in stress granule formation and cellular stress responses. Additionally, the impact of amino acid deprivation, mTOR signaling, and ribosome biogenesis on translation regulation and cellular adaptation to stress is also discussed. Understanding the intricate mechanisms of translational regulation during stress provides insights into cellular adaptation mechanisms and potential therapeutic targets for various diseases, offering valuable avenues for addressing conditions associated with dysregulated protein synthesis.
Firing rate model for brain rhythms controlled by astrocytes
Authors: Sergey V. Stasenko, Sergey M. Olenin, Eugene A. Grines, Tatiana A. Levanova
Date: 2024-05-06 00:00:00

We propose a new mean-field model of brain rhythms governed by astrocytes. This theoretical framework describes how astrocytes can regulate neuronal activity and contribute to the generation of brain rhythms. The model describes at the population level the interactions between two large groups of excitatory and inhibitory neurons. The excitatory population is governed by astrocytes via a so-called tripartite synapse. This approach allows us to describe how the interactions between different groups of neurons and astrocytes can give rise to various patterns of synchronized activity and transitions between them. Using methods of nonlinear analysis we show that astrocytic modulation can lead to a change in the period and amplitude of oscillations in the populations of neurons.
A causal inference approach of monosynapses from spike trains
Authors: Zach Saccomano, Sam Mckenzie, Horacio Rotstein, Asohan Amarasingham
Date: 2024-05-05 00:00:00
Tasks: Causal Inference, Functional Connectivity

Neuroscientists have worked on the problem of estimating synaptic properties, such as connectivity and strength, from simultaneously recorded spike trains since the 1960s. Recent years have seen renewed interest in the problem, coinciding with rapid advances in the technology of high-density neural recordings and optogenetics, which can be used to calibrate causal hypotheses about functional connectivity. Here, a rigorous causal inference framework for pairwise excitatory and inhibitory monosynaptic effects between spike trains is developed. Causal interactions are identified by separating spike interactions in pairwise spike trains by their timescales. Fast algorithms for computing accurate estimates of associated quantities are also developed. Through the lens of this framework, the link between biophysical parameters and statistical definitions of causality between spike trains is examined across a spectrum of dynamical systems simulations. In an idealized setting, we demonstrate a correspondence between the synaptic causal metric developed here and the probabilities of causation developed by Tian and Pearl. Since the probabilities of causation are derived under distinct assumptions and include data from experimental randomization, this opens up the possibility of testing the synaptic inference framework's assumptions with juxtacellular or optogenetic stimulation. We simulate such an experiment with a biophysically detailed channelrhodopsin model and show that randomization is not achieved; strong confounding persists even with strong stimulations. A principal goal is to ask how carefully articulated causal assumptions might better inform the design of neural stimulation experiments and, in turn, support experimental tests of those assumptions.
Simulation-based Inference of Developmental EEG Maturation with the Spectral Graph Model
Authors: Danilo Bernardo, Xihe Xie, Parul Verma, Jonathan Kim, Virginia Liu, Adam L. Numis, Ye Wu, Hannah C. Glass, Pew-Thian Yap, Srikantan S. Nagarajan, Ashish Raj
Date: 2024-05-03 00:00:00
Tasks: EEG

The spectral content of macroscopic neural activity evolves throughout development, yet how this maturation relates to underlying brain network formation and dynamics remains unknown. Here, we assess the developmental maturation of electroencephalogram spectra via Bayesian model inversion of the spectral graph model, a parsimonious whole-brain model of spatiospectral neural activity derived from linearized neural field models coupled by the structural connectome. Simulation-based inference was used to estimate age-varying spectral graph model parameter posterior distributions from electroencephalogram spectra spanning the developmental period. This model-fitting approach accurately captures observed developmental electroencephalogram spectral maturation via a neurobiologically consistent progression of key neural parameters: long-range coupling, axonal conduction speed, and excitatory:inhibitory balance. These results suggest that the spectral maturation of macroscopic neural activity observed during typical development is supported by age-dependent functional adaptations in localized neural dynamics and their long-range coupling across the macroscopic structural network.
Early-stage detection of cognitive impairment by hybrid quantum-classical algorithm using resting-state functional MRI time-series
Authors: Junggu Choi, Tak Hur, Daniel K. Park, Na-Young Shin, Seung-Koo Lee, Hakbae Lee, Sanghoon Han
Date: 2024-03-16 00:00:00
Tasks: Functional Connectivity, Hippocampus, Quantum Machine Learning, Time Series

Following the recent development of quantum machine learning techniques, the literature has reported several quantum machine learning algorithms for disease detection. This study explores the application of a hybrid quantum-classical algorithm for classifying region-of-interest time-series data obtained from resting-state functional magnetic resonance imaging in patients with early-stage cognitive impairment based on the importance of cognitive decline for dementia or aging. Classical one-dimensional convolutional layers are used together with quantum convolutional neural networks in our hybrid algorithm. In the classical simulation, the proposed hybrid algorithms showed higher balanced accuracies than classical convolutional neural networks under the similar training conditions. Moreover, a total of nine brain regions (left precentral gyrus, right superior temporal gyrus, left rolandic operculum, right rolandic operculum, left parahippocampus, right hippocampus, left medial frontal gyrus, right cerebellum crus, and cerebellar vermis) among 116 brain regions were found to be relatively effective brain regions for the classification based on the model performances. The associations of the selected nine regions with cognitive decline, as found in previous studies, were additionally validated through seed-based functional connectivity analysis. We confirmed both the improvement of model performance with the quantum convolutional neural network and neuroscientific validities of brain regions from our hybrid quantum-classical model.
Development and validation of a short form of the medication literacy scale for Chinese College Students
Authors: Chen Zhenzhen, Ren Jiabao, Duan Tingyu, Chen Ke, Hou Ruyi, Li Yimiao, Zeng Leixiao, Meng Xiaoxuan, Wu Yibo, Liu Yu
Date: 2024-05-05 00:00:00
Tasks: Form

Medication literacy is integral to health literacy, pivotal for medication safety and adherence. It denotes an individual's capacity to discern, comprehend, and convey medication-related information. Existing scales, however, are time-consuming and predominantly cater to patients and community dwellers, necessitating a more succinct instrument. This study presents the development of a brief Medication Literacy Scale (MLS-14) utilizing classical test theory (CTT) and item response theory (IRT), targeting a college student demographic. The MLS-14's abbreviated version, a 6-item scale (MLS-SF), was distilled through CTT and IRT methodologies, engaging 2431 Chinese college students to scrutinize its psychometric properties. The MLS-SF demonstrated a Cronbach's {\alpha} of 0.765, with three extracted factors via exploratory factor analysis, accounting for 66% of the cumulative variance. All items exhibited factor loadings above 0.5. The scale's three-factor structure was substantiated through confirmatory factor analysis with satisfactory fit indices (chi2/df=5.11, RMSEA=0.063, GFI=0.990, AGFI=0.966, NFI=0.984, IFI=0.987, CFI=0.987). IRT modeling confirmed reasonable discrimination and location parameters for all items, free of differential item functioning (DIF) by gender. Except for items 4 and 10, the remaining items were informative at medium theta levels, indicating their utility in assessing medication literacy efficiently. The developed 6-item Medication Literacy Short Form (MLS-SF) proves to be a reliable and valid instrument for the expedited evaluation of college students' medication literacy, offering a valuable addition to the arsenal of health literacy assessment tools.