pwc-archive/papers-with-abstracts huggingface dataset:
A 3D Non-Stationary Channel Model for 6G Wireless Systems Employing Intelligent Reflecting Surfaces with Practical Phase Shifts
Authors: Yingzhuo Sun, Cheng-Xiang Wang, Jie Huang, Jun Wang
Date: 2021-04-25 00:00:00
Tasks: 3D geometry

In this paper, a three-dimensional (3D) geometry based stochastic model (GBSM) for a massive multiple-input multiple-output (MIMO) communication system employing practical discrete intelligent reflecting surface (IRS) is proposed. The proposed channel model supports the scenario where both transceivers and environments move. The evolution of clusters in the space domain and the practical discrete phase shifts are considered in the channel model. The steering vector is set at the base station for the cooperation with IRS. Through studying statistical properties, the non-stationary properties are verified. We find that IRS plays a role in separating the whole channel and make the absolute value of time autocorrelation function (ACF) larger than the situation without employing IRS. Time ACF of the case using discrete phase shifts is also compared with the continuous case.
Exploiting Spatial Correlation for Pilot Reuse in Single-Cell mMTC
Authors: Lucas Ribeiro, Markus Leinonen, Hanan Al-Tous, Olav Tirkkonen, Markku Juntti
Date: 2021-04-24 00:00:00

As a key enabler for massive machine-type communications (mMTC), spatial multiplexing relies on massive multiple-input multiple-output (mMIMO) technology to serve the massive number of user equipments (UEs). To exploit spatial multiplexing, accurate channel estimation through pilot signals is needed. In mMTC systems, it is impractical to allocate a unique orthogonal pilot sequence to each UE as it would require too long pilot sequences, degrading the spectral efficiency. This work addresses the design of channel features from correlated fading channels to assist the pilot assignment in multi-sector mMTC systems under pilot reuse of orthogonal sequences. In order to reduce pilot collisions and to enable pilot reuse, we propose to extract features from the channel covariance matrices that reflect the level of orthogonality between the UEs channels. Two features are investigated: covariance matrix distance (CMD) feature and CMD-aided channel charting (CC) feature. In terms of symbol error rate and achievable rate, the CC-based feature shows superior performance than the CMD-based feature and baseline pilot assignment algorithms.
Distinguishing Level-2 Phylogenetic Networks Using Phylogenetic Invariants
Authors: Muhammad Ardiyansyah
Date: 2021-04-26 00:00:00

In phylogenetics, it is important for the phylogenetic network model parameters to be identifiable so that the evolutionary histories of a group of species can be consistently inferred. However, as the complexity of the phylogenetic network models grows, the identifiability of network models becomes increasingly difficult to analyze. As an attempt to analyze the identifiability of network models, we check whether two networks are distinguishable. In this paper, we specifically study the distinguishability of phylogenetic network models associated with level-2 networks. Using an algebraic approach, namely using discrete Fourier transformation, we present some results on the distinguishability of some level-2 networks, which generalize earlier work on the distinguishability of level-1 networks. In particular, we study simple and semisimple level-2 networks. Simple and semisimple level-2 networks can be thought as generalizations of level-1 sunlet and cycle networks, respectively. Moreover, we also compare the varieties associated with semisimple level-2 and cycle networks.
Analytical singular value decomposition for a class of stoichiometry matrices
Authors: Jacqueline M. Wentz, David M. Bortz
Date: 2021-04-28 00:00:00

We present the analytical singular value decomposition of the stoichiometry matrix for a spatially discrete reaction-diffusion system on a one dimensional domain. The domain has two subregions which share a single common boundary. Each of the subregions is further partitioned into a finite number of compartments. Chemical reactions can occur within a compartment, whereas diffusion is represented as movement between adjacent compartments. Inspired by biology, we study both 1) the case where the reactions on each side of the boundary are different and only certain species diffuse across the boundary as well as 2) the case with spatially homogenous reactions and diffusion. We write the stoichiometry matrix for these two classes of systems using a Kronecker product formulation. For the first scenario, we apply linear perturbation theory to derive an approximate singular value decomposition in the limit as diffusion becomes much faster than reactions. For the second scenario, we derive an exact analytical singular value decomposition for all relative diffusion and reaction time scales. By writing the stoichiometry matrix using Kronecker products, we show that the singular vectors and values can also be written concisely using Kronecker products. Ultimately, we find that the singular value decomposition of the reaction-diffusion stoichiometry matrix depends on the singular value decompositions of smaller matrices. These smaller matrices represent modified versions of the reaction-only stoichiometry matrices and the analytically known diffusion-only stoichiometry matrix. Our results provide a mathematical framework that can be used to study complex biochemical systems with metabolic compartments. MATLAB code for calculating the SVD equations is available at \url{www.github.com/MathBioCU/ReacDiffStoicSVD}.
Chemomechanical simulation of microtubule dynamics with explicit lateral bond dynamics
Authors: Matthias Schmidt, Jan Kierfeld
Date: 2021-04-29 00:00:00

We introduce and parameterize a chemomechanical model of microtubule dynamics on the dimer level, which is based on the allosteric tubulin model and includes attachment, detachment and hydrolysis of tubulin dimers as well as stretching of lateral bonds, bending at longitudinal junctions, and the possibility of lateral bond rupture and formation. The model is computationally efficient such that we reach sufficiently long simulation times to observe repeated catastrophe and rescue events at realistic tubulin concentrations and hydrolysis rates, which allows us to deduce catastrophe and rescue rates. The chemomechanical model also allows us to gain insight into microscopic features of the GTP-tubulin cap structure and microscopic structural features triggering microtubule catastrophes and rescues. Dilution simulations show qualitative agreement with experiments. We also explore the consequences of a possible feedback of mechanical forces onto the hydrolysis process and the GTP-tubulin cap structure.
Using the structural kinome to systematize kinase drug discovery
Authors: Zheng Zhao, Philip E. Bourne
Date: 2021-04-27 00:00:00
Tasks: Drug Design, Drug Discovery

Kinase-targeted drug design is challenging. It requires designing inhibitors that can bind to specific kinases when all kinase catalytic domains share a common folding scaffold that binds ATP. Thus, obtaining the desired selectivity, given the whole human kinome, is a fundamental task during early-stage drug discovery. This begins with deciphering the kinase-ligand characteristics, analyzing the structure-activity relationships, and prioritizing the desired drug molecules across the whole kinome. Currently, there are more than 300 kinases with released PDB structures, which provides a substantial structural basis to gain these necessary insights. Here, we review in silico structure-based methods - notably, a function-site interaction fingerprint approach used in exploring the complete human kinome. In silico methods can be explored synergistically with multiple cell-based or protein-based assay platforms such as KINOMEscan. We conclude with new drug discovery opportunities associated with kinase signaling networks and using machine/deep learning techniques broadly referred to as structural biomedical data science.
Unlocking capacities of viral genomics for the COVID-19 pandemic response
Authors: Sergey Knyazev, Karishma Chhugani, Varuni Sarwal, Ram Ayyala, Harman Singh, Smruthi Karthikeyan, Dhrithi Deshpande, Zoia Comarova, Angela Lu, Yuri Porozov, Aiping Wu, Malak Abedalthagafi, Shivashankar Nagaraj, Adam Smith, Pavel Skums, Jason Ladner, Tommy Tsan-Yuk Lam, Nicholas Wu, Alex Zelikovsky, Rob Knight, Keith Crandall, Serghei Mangul
Date: 2021-04-28 00:00:00

More than any other infectious disease epidemic, the COVID-19 pandemic has been characterized by the generation of large volumes of viral genomic data at an incredible pace due to recent advances in high-throughput sequencing technologies, the rapid global spread of SARS-CoV-2, and its persistent threat to public health. However, distinguishing the most epidemiologically relevant information encoded in these vast amounts of data requires substantial effort across the research and public health communities. Studies of SARS-CoV-2 genomes have been critical in tracking the spread of variants and understanding its epidemic dynamics, and may prove crucial for controlling future epidemics and alleviating significant public health burdens. Together, genomic data and bioinformatics methods enable broad-scale investigations of the spread of SARS-CoV-2 at the local, national, and global scales and allow researchers the ability to efficiently track the emergence of novel variants, reconstruct epidemic dynamics, and provide important insights into drug and vaccine development and disease control. Here, we discuss the tremendous opportunities that genomics offers to unlock the effective use of SARS-CoV-2 genomic data for efficient public health surveillance and guiding timely responses to COVID-19.
Integration of Unpaired Single-cell Chromatin Accessibility and Gene Expression Data via Adversarial Learning
Authors: Yang Xu, Andrew Jeremiah Strick
Date: 2021-04-26 00:00:00
Tasks: Data Integration, Domain Adaptation

Deep learning has empowered analysis for single-cell sequencing data in many ways and has generated deep understanding about a range of complex cellular systems. As the booming single-cell sequencing technologies brings the surge of high dimensional data that come from different sources and represent cellular systems with different features, there is an equivalent rise and challenge of integrating single-cell sequence across modalities. Here, we present a novel adversarial approach to integrate single-cell chromatin accessibility and gene expression data in a semi-supervised manner. We demonstrate that our method substantially improves data integration from a simple adversarial domain adaption approach, and it also outperforms two state-of-the-art (SOTA) methods.
A Birds-eye (Re)View of Acid-suppression Drugs, COVID-19, and the Highly Variable Literature
Authors: Cameron Mura, Saskia Preissner, Robert Preissner, Philip E. Bourne
Date: 2021-04-24 00:00:00
Tasks: Experimental Design

We consider the recent surge of information on the potential benefits of acid-suppression drugs in the context of COVID-19, with an eye on the variability (and confusion) across the reported findings--at least as regards the popular antacid famotidine. The inconsistencies reflect contradictory conclusions from independent clinical-based studies that took roughly similar approaches, in terms of experimental design (retrospective, cohort-based, etc.) and statistical analyses (propensity-score matching and stratification, etc.). The confusion has significant ramifications in choosing therapeutic interventions: e.g., do potential benefits of famotidine indicate its use in a particular COVID-19 case? Beyond this pressing therapeutic issue, conflicting information on famotidine must be resolved before its integration in ontological and knowledge graph-based frameworks, which in turn are useful in drug repurposing efforts. To begin systematically structuring the rapidly accumulating information, in the hopes of clarifying and reconciling the discrepancies, we consider the contradictory information along three proposed 'axes': (1) a context-of-disease axis, (2) a degree-of-[therapeutic]-benefit axis, and (3) a mechanism-of-action axis. We suspect that incongruencies in how these axes have been (implicitly) treated in past studies has led to the contradictory indications for famotidine and COVID-19. We also trace the evolution of information on acid-suppression agents as regards the transmission, severity, and mortality of COVID-19, given the many literature reports that have accumulated. By grouping the studies conceptually and thematically, we identify three eras in the progression of our understanding of famotidine and COVID-19. Harmonizing these findings is a key goal for both clinical standards-of-care (COVID and beyond) as well as ontological and knowledge graph-based approaches.
Making decisions in the dark basement of the brain: A look back at the GPR model of action selection and the basal ganglia
Authors: Mark D. Humphries, Kevin Gurney
Date: 2021-04-29 00:00:00
Tasks: GPR

How does your brain decide what you will do next? Over the past few decades compelling evidence has emerged that the basal ganglia, a collection of nuclei in the fore- and mid-brain of all vertebrates, are vital to action selection. Gurney, Prescott, and Redgrave published an influential computational account of this idea in Biological Cybernetics in 2001. Here we take a look back at this pair of papers, outlining the "GPR" model contained therein, the context of that model's development, and the influence it has had over the past twenty years. Tracing its lineage into models and theories still emerging now, we are encouraged that the GPR model is that rare thing, a computational model of a brain circuit whose advances were directly built on by others.
Falsification and consciousness
Authors: Johannes Kleiner, Erik Hoel
Date: 2020-04-07 00:00:00
Tasks: Prediction

The search for a scientific theory of consciousness should result in theories that are falsifiable. However, here we show that falsification is especially problematic for theories of consciousness. We formally describe the standard experimental setup for testing these theories. Based on a theory's application to some physical system, such as the brain, testing requires comparing a theory's predicted experience (given some internal observables of the system like brain imaging data) with an inferred experience (using report or behavior). If there is a mismatch between inference and prediction, a theory is falsified. We show that if inference and prediction are independent, it follows that any minimally informative theory of consciousness is automatically falsified. This is deeply problematic since the field's reliance on report or behavior to infer conscious experiences implies such independence, so this fragility affects many contemporary theories of consciousness. Furthermore, we show that if inference and prediction are strictly dependent, it follows that a theory is unfalsifiable. This affects theories which claim consciousness to be determined by report or behavior. Finally, we explore possible ways out of this dilemma.
Conductance-based dendrites perform Bayes-optimal cue integration
Authors: Jakob Jordan, João Sacramento, Willem A. M. Wybo, Mihai A. Petrovici, Walter Senn
Date: 2021-04-27 00:00:00

A fundamental function of cortical circuits is the integration of information from different sources to form a reliable basis for behavior. While animals behave as if they optimally integrate information according to Bayesian probability theory, the implementation of the required computations in the biological substrate remains unclear. We propose a novel, Bayesian view on the dynamics of conductance-based neurons and synapses which suggests that they are naturally equipped to optimally perform information integration. In our approach apical dendrites represent prior expectations over somatic potentials, while basal dendrites represent likelihoods of somatic potentials. These are parametrized by local quantities, the effective reversal potentials and membrane conductances. We formally demonstrate that under these assumptions the somatic compartment naturally computes the corresponding posterior. We derive a gradient-based plasticity rule, allowing neurons to learn desired target distributions and weight synaptic inputs by their relative reliabilities. Our theory explains various experimental findings on the system and single-cell level related to multi-sensory integration, which we illustrate with simulations. Furthermore, we make experimentally testable predictions on Bayesian dendritic integration and synaptic plasticity.
Completing the puzzle: why studies in non-human primates are needed to better understand the effects of non-invasive brain stimulation
Authors: Sebastian J Lehmann, Brian D Corneil
Date: 2021-04-24 00:00:00

Brain stimulation is a core method in neuroscience. Numerous non-invasive brain stimulation (NIBS) techniques are currently in use in basic and clinical research, and recent advances promise the ability to non-invasively access deep brain structures. While encouraging, there is a surprising gap in our understanding of precisely how NIBS perturbs neural activity throughout an interconnected network, and how such perturbed neural activity ultimately links to behaviour. In this review, we will consider why non-human primate (NHP) models of NIBS are ideally situated to address this gap in knowledge, and will consider why the oculomotor network that moves our line of sight offers a particularly valuable platform in which to empirically test hypothesis regarding NIBS-induced changes in brain and behaviour. NHP models of NIBS will enable investigation of the complex, dynamic effects of brain stimulation across multiple hierarchically interconnected brain areas, networks, and effectors. By establishing such links between brain and behavioural output, work in NHPs can help optimize experimental and therapeutic approaches, improve NIBS efficacy, and reduce side-effects of NIBS.
Mechanistic modeling of African swine fever: A systematic review
Authors: Brandon H Hayes, Mathieu Andraud, Luis G Salazar, Nicolas Rose, Timothee Vergne
Date: 2020-11-19 00:00:00
Tasks: Articles, Decision Making

The spread of African swine fever (ASF) poses a grave threat to the global swine industry. Understanding transmission dynamics, such as through mechanistic modeling, is critical for designing effective control strategies. Articles were examined across multiple epidemiological and model characteristics. Model filiation was determined through creation of a neighbor-joined tree using phylogenetic software. Of 34 four articles qualifying for inclusion, four main modelling objectives were identified: estimating transmission parameters (11 studies), assessing determinants of transmission (7), examining consequences of hypothetical outbreaks (5), and assessing alternative control strategies (11). Estimated transmission parameters varied widely as did parameter assumptions between models. Uncertainties on epidemiological and ecological parameters were usually accounted for to assess the impact on the modeled infection trajectory. Almost all models are host specific, being developed for either domestic pigs or wild boar despite the fact that spillover events between domestic pigs and wild boar are evidenced to play an important role in ASF outbreaks. The development of models incorporating such transmission routes is crucial. All compared control strategies were defined a priori, and future models should be built to identify optimal contributions across many control methods. Further, control strategies were examined in competition, opposed to how they would be synergistically implemented. While comparing strategies is beneficial for identifying a rank-order efficacy of control methods, this structure does not necessarily determine the most effective combination of all available strategies. In order for ASFV models to effectively support decision-making in controlling ASFV globally, these modelling limitations need to be addressed.
Multiscale modelling of oxygenic photogranules
Authors: A. Tenore, M. R. Mattei, L. Frunzo
Date: 2021-04-25 00:00:00

This work presents a mathematical model which describes both the genesis and growth of oxygenic photogranules (OPGs) and the related treatment process. The photogranule has been modelled as a free boundary domain with radial symmetry, which evolves over time as a result of microbial growth, attachment and detachment processes. A system of hyperbolic and parabolic PDEs has been considered to model the advective transport and growth of sessile biomass and the diffusive transport and conversion of soluble substrates. The reactor has been modelled as a sequencing batch reactor (SBR) through a system of first order IDEs. Phototrophic biomass has been considered for the first time in granular biofilms, and cyanobacteria and microalgae are taken into account separately to model their differences in growth rate and light harvesting and utilization. To describe the key role of cyanobacteria in the photogranules formation process, the attachment velocity of all suspended microbial species has been modelled as a function of the cyanobacteria concentration in suspended form. The model takes into account the main biological aspects and processes involved in OPGs based systems: heterotrophic and photoautotrophic activities of cyanobacteria and microalgae, metabolic activity of heterotrophic and nitrifying bacteria, microbial decay, EPS secrection, diffusion and conversion of soluble substrates (inorganic and organic carbon, ammonia, nitrate and oxygen), symbiotic and competitive interactions between the different microbial species, day-night cycle, light diffusion and attenuation across the granular biofilm and photoinhibion phenomena. The model has been integrated numerically, investigating the evolution and microbial composition of photogranules and the treatment efficiency of the OPGs-based system. The results show the consistency of the model and confirm the effectiveness of the OPGs technology.
Universal scaling laws rule explosive growth inhuman cancers
Authors: Víctor M. Pérez-García, Gabriel F. Calvo, Jesús J. Bosque, Odelaisy León-Triana, Juan Jiménez, Julián Pérez-Beteta, Juan Belmonte-Beitia, Manuel Valiente, Lucía Zhu, Pedro García-Gómez, Pilar Sánchez-Gómez, Esther Hernández-San Miguel, Rafael Hortigüela, Youness Azimzade, David Molina-García, Álvaro Martínez, Ángel Acosta Rojas, Ana Ortiz de Mendivil, Francois Vallette, Philippe Schucht, Michael Murek, María Pérez-Cano, David Albillo, Antonio F. Honguero Martínez, Germán A. Jiménez Londoño, Estanislao Arana, Ana M. García Vicente
Date: 2021-04-22 00:00:00

Most physical and other natural systems are complex entities composed of a large number of interacting individual elements. It is a surprising fact that they often obey the so-called scaling laws relating an observable quantity with a measure of the size of the system. Here we describe the discovery of universal superlinear metabolic scaling laws in human cancers. This dependence underpins increasing tumour aggressiveness, due to evolutionary dynamics, which leads to an explosive growth as the disease progresses. We validated this dynamic using longitudinal volumetric data of different histologies from large cohorts of cancer patients. To explain our observations we put forward increasingly complex biologically-inspired mathematical models that captured the key processes governing tumor growth. Our models predicted that the emergence of superlinear allometric scaling laws is an inherently three-dimensional phenomenon. Moreover, the scaling laws thereby identified allowed us to define a set of metabolic metrics with prognostic value, thus providing added clinical utility to the base findings.