New articles on q-bio


[1] 2007.01902

A computational approach to aid clinicians in selecting anti-viral drugs for COVID-19 trials

Motivation: COVID-19 has fast-paced drug re-positioning for its treatment. This work builds computational models for the same. The aim is to assist clinicians with a tool for selecting prospective antiviral treatments. Since the virus is known to mutate fast, the tool is likely to help clinicians in selecting the right set of antivirals for the mutated isolate. Results: The main contribution of this work is a manually curated database publicly shared, comprising of existing associations between viruses and their corresponding antivirals. The database gathers similarity information using the chemical structure of drugs and the genomic structure of viruses. Along with this database, we make available a set of state-of-the-art computational drug re-positioning tools based on matrix completion. The tools are first analysed on a standard set of experimental protocols for drug target interactions. The best performing ones are applied for the task of re-positioning antivirals for COVID-19. These tools select six drugs out of which four are currently under various stages of trial, namely Remdesivir (as a cure), Ribavarin (in combination with others for cure), Umifenovir (as a prophylactic and cure) and Sofosbuvir (as a cure). Another unanimous prediction is Tenofovir alafenamide, which is a novel tenofovir prodrug developed in order to improve renal safety when compared to the counterpart tenofovir disoproxil. Both are under trail, the former as a cure and the latter as a prophylactic. These results establish that the computational methods are in sync with the state-of-practice. We also demonstrate how the selected drugs change as the SARS-Cov-2 mutates over time, suggesting the importance of such a tool in drug prediction. Availability: The database along with a set of 6 matrix completion methods and prediction results are made available publicly at https://github.com/aanchalMongia/DVA


[2] 2007.01953

The $P^*$ rule in the stochastic Holt-Lawton model of apparent competition

In $1993$, Holt and Lawton introduced a stochastic model of two host species parasitized by a common parasitoid species. We introduce and analyze a generalization of these stochastic difference equations with any number of host species, stochastically varying parasitism rates, stochastically varying host intrinsic fitnesses, and stochastic immigration of parasitoids. Despite the lack of direct, host density-dependence, we show that this system is dissipative i.e. enters a compact set in finite time for all initial conditions. When there is a single host species, stochastic persistence and extinction of the host is characterized using external Lyapunpov exponents corresponding to the average per-capita growth rates of the host when rare. When a single host persists, say species $i$, a explicit expression is derived for the average density, $P_i^*$, of the parasitoid at the stationary distributions supporting both species. When there are multiple host species, we prove that the host species with the largest $P_i^*$ value stochastically persists, while the other host species are asymptotically driven to extinction. A review of the main mathematical methods used to prove the results and future challenges are given.


[3] 2007.01979

Excess deaths hidden 100 days after the quarantine in Peru by COVID-19

Objective: To make an estimate of the excess deaths caused by COVID-19 in the non-violent mortality of Peru, controlling for the effect of quarantine. Methods: Analysis of longitudinal data from the departments of Peru using official public information from the National Death Information System and the Ministry of Health of Peru. The analysis is performed between January 1, 2018 and June 23, 2020 (100 days of quarantine). The daily death rate per million inhabitants has been used. The days in which the departments were quarantined with a limit number of accumulated cases of COVID-19 were used to estimate the quarantine impact. Three limits were established for cases: less than 1, 10 and 100 cases. Result: In Peru, the daily death rate per million inhabitants decreased by -1.89 (95% CI: -2.70; -1.07) on quarantine days and without COVID-19 cases. When comparing this result with the total number of non-violent deaths, the excess deaths during the first 100 days of quarantine is 36,230. This estimate is 1.12 times the estimate with data from 2019 and 4.2 times the deaths officers by COVID-19. Conclusion: Quarantine reduced nonviolent deaths; however, they are overshadowed by the increase as a direct or indirect cause of the pandemic. Therefore, the difference between the number of current deaths and that of past years underestimates the real excess of deaths.


[4] 2007.02032

Dynamic tracking with model-based forecasting for the spread of the COVID-19 pandemic

In this paper, a susceptible-infected-removed (SIR) model has been used to track the evolution of the spread of the COVID-19 virus in four countries of interest. In particular, the epidemic model, that depends on some basic characteristics, has been applied to model the time evolution of the disease in Italy, India, South Korea and Iran. The economic, social and health consequences of the spread of the virus have been cataclysmic. Hence, it is essential that available mathematical models can be developed and used for the comparison to be made between published data sets and model predictions. The predictions estimated from the SIR model here, can be used in both the qualitative and quantitative analysis of the spread. It gives an insight into the spread of the virus that the published data alone cannot do by updating them and the model on a daily basis. For example, it is possible to detect the early onset of a spike in infections or the development of a second wave using our modeling approach. We considered data from March to June, 2020, when different communities are severely affected. We demonstrate predictions depending on the model's parameters related to the spread of COVID-19 until September 2020. By comparing the published data and model results, we conclude that in this way, it may be possible to better reflect the success or failure of the adequate measures implemented by governments and individuals to mitigate and control the current pandemic.


[5] 2007.02062

Shaping dynamics with multiple populations in low-rank recurrent networks

An emerging paradigm proposes that neural computations can be understood at the level of dynamical systems that govern low-dimensional trajectories of collective neural activity. How the connectivity structure of a network determines the emergent dynamical system however remains to be clarified. Here we consider a novel class of models, Gaussian-mixture low-rank recurrent networks, in which the rank of the connectivity matrix and the number of statistically-defined populations are independent hyper-parameters. We show that the resulting collective dynamics form a dynamical system, where the rank sets the dimensionality and the population structure shapes the dynamics. In particular, the collective dynamics can be described in terms of a simplified effective circuit of interacting latent variables. While having a single, global population strongly restricts the possible dynamics, we demonstrate that if the number of populations is large enough, a rank $R$ network can approximate any $R$-dimensional dynamical system.


[6] 2007.02169

Effective behavior of cooperative and nonidentical molecular motors

Analytical formulas for effective drift, diffusivity, run times, and run lengths are derived for an intracellular transport system consisting of a cargo attached to two cooperative but not identical molecular motors (for example, kinesin-1 and kinesin-2) which can each attach and detach from a microtubule. The dynamics of the motor and cargo in each phase are governed by stochastic differential equations, and the switching rates depend on the spatial configuration of the motor and cargo. This system is analyzed in a limit where the detached motors have faster dynamics than the cargo, which in turn has faster dynamics than the attached motors. The attachment and detachment rates are also taken to be slow relative to the spatial dynamics. Through an application of iterated stochastic averaging to this system, and the use of renewal-reward theory to stitch together the progress within each switching phase, we obtain explicit analytical expressions for the effective drift, diffusivity, and processivity of the motor-cargo system. Our approach accounts in particular for jumps in motor-cargo position that occur during attachment and detachment events, as the cargo tracking variable makes a rapid adjustment due to the averaged fast scales. The asymptotic formulas are in generally good agreement with direct stochastic simulations of the detailed model based on experimental parameters for various pairings of kinesin-1 and kinesin-2 under assisting, hindering, or no load.


[7] 2007.02185

Parameter identifiability for a profile mixture model of protein evolution

A Profile Mixture Model is a model of protein evolution, describing sequence data in which sites are assumed to follow many related substitution processes on a single evolutionary tree. The processes depend in part on different amino acid distributions, or profiles, varying over sites in aligned sequences. A fundamental question for any stochastic model, which must be answered positively to justify model-based inference, is whether the parameters are identifiable from the probability distribution they determine. Here we show that a Profile Mixture Model has identifiable parameters under circumstances in which it is likely to be used for empirical analyses. In particular, for a tree relating 9 or more taxa, both the tree topology and all numerical parameters are generically identifiable when the number of profiles is less than 74.


[8] 2007.02197

Statistical properties of color matching functions

In trichromats, color vision entails the projection of an infinite-dimensional space (the one containing all possible electromagnetic power spectra) onto the 3-dimensional space determined by the three types of cones. This drastic reduction in dimensionality gives rise to metamerism, that is, the perceptual chromatic equivalence between two different light spectra. The classes of equivalence of metamerism is revealed by color-matching experiments, in which observers equalize a monochromatic target stimulus with the superposition of three light beams of different wavelengths (the primaries) by adjusting their intensities. The linear relation between the color matching functions and the absorption probabilities of each type of cone is here used to find the collection of primaries that need to be chosen in order to obtain quasi orthogonal, or alternatively, almost-always positive, color-matching functions. Moreover, previous studies have shown that there is a certain trial-to-trial and subject-to-subject variability in the color matching functions. So far, no theoretical description has been offered to explain the trial-to-trial variability, whereas the sources of the subject-to-subject variability have been associated with individual differences in the properties of the peripheral visual system. Here we explore the role of the Poissonian nature of photon capture on the wavelength-dependence of the trial-to-trial variability in the color matching functions, as well as their correlations.


[9] 2007.02202

A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19

The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak. Most governments, enterprises, and scientific research institutions are participating in the COVID-19 struggle to curb the spread of the pandemic. As a powerful tool against COVID-19, artificial intelligence (AI) technologies are widely used in combating this pandemic. In this survey, we investigate the main scope and contributions of AI in combating COVID-19 from the aspects of disease detection and diagnosis, virology and pathogenesis, drug and vaccine development, and epidemic and transmission prediction. In addition, we summarize the available data and resources that can be used for AI-based COVID-19 research. Finally, the main challenges and potential directions of AI in fighting against COVID-19 are discussed. Currently, AI mainly focuses on medical image inspection, genomics, drug development, and transmission prediction, and thus AI still has great potential in this field. This survey presents medical and AI researchers with a comprehensive view of the existing and potential applications of AI technology in combating COVID-19 with the goal of inspiring researches to continue to maximize the advantages of AI and big data to fight COVID-19.


[10] 2007.02206

Steady State Cargo Transport Modalities of Molecular Motor Ensembles Emerge from Single Motor Behavior

Transport of intracellular cargo is often mediated by teams of molecular motors that function in a chaotic environment and varying conditions. We show that the motors have unique steady state behavior which enables transport modalities that are robust. Under reduced ATP concentrations, multi-motor configurations are preferred over single motors. Higher load force drives motors to cluster, but very high loads compel them to separate in a manner that promotes immediate cargo movement once the load reduces. These inferences, backed by analytical guarantees, provide unique insights into the coordination strategies adopted by motors.


[11] 2007.02338

Predicting potential drug targets and repurposable drugs for COVID-19 via a deep generative model for graphs

Coronavirus Disease 2019 (COVID-19) has been creating a worldwide pandemic situation. Repurposing drugs, already shown to be free of harmful side effects, for the treatment of COVID-19 patients is an important option in launching novel therapeutic strategies. Therefore, reliable molecule interaction data are a crucial basis, where drug-/protein-protein interaction networks establish invaluable, year-long carefully curated data resources. However, these resources have not yet been systematically exploited using high-performance artificial intelligence approaches. Here, we combine three networks, two of which are year-long curated, and one of which, on SARS-CoV-2-human host-virus protein interactions, was published only most recently (30th of April 2020), raising a novel network that puts drugs, human and virus proteins into mutual context. We apply Variational Graph AutoEncoders (VGAEs), representing most advanced deep learning based methodology for the analysis of data that are subject to network constraints. Reliable simulations confirm that we operate at utmost accuracy in terms of predicting missing links. We then predict hitherto unknown links between drugs and human proteins against which virus proteins preferably bind. The corresponding therapeutic agents present splendid starting points for exploring novel host-directed therapy (HDT) options.


[12] 2007.02421

Anisotropic Diffusion and Traveling Waves of Toxic Proteins in Neurodegenerative Diseases

Neurodegenerative diseases are closely associated with the amplification and invasion of toxic proteins. In particular Alzheimer's disease is characterized by the systematic progression of amyloid-$\beta$ and $\tau$-proteins in the brain. These two protein families are coupled and it is believed that their joint presence greatly enhances the resulting damage. Here, we examine a class of coupled chemical kinetics models of healthy and toxic proteins in two spatial dimensions. The anisotropic diffusion expected to take place within the brain along axonal pathways is factored in the models and produces a filamentary, predominantly one-dimensional transmission. Nevertheless, the potential of the anisotropic models towards generating interactions taking advantage of the two-dimensional landscape is showcased. Finally, a reduction of the models into a simpler family of generalized Fisher-Kolmogorov-Petrovskii-Piskunov (FKPP) type systems is examined. It is seen that the latter captures well the qualitative propagation features, although it may somewhat underestimate the concentrations of the toxic proteins.


[13] 2007.02557

Attacking COVID-19 Progression using Multi-Drug Therapy for Synergetic Target Engagement

COVID-19 is a devastating respiratory and inflammatory illness caused by a new coronavirus that is rapidly spreading throughout the human population. Over the past 6 months, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for COVID-19, has already infected over 11.6 million (25% located in United States) and killed more than 540K people around the world. As we face one of the most challenging times in our recent history, there is an urgent need to identify drug candidates that can attack SARS-CoV-2 on multiple fronts. We have therefore initiated a computational dynamics drug pipeline using molecular modeling, structure simulation, docking and machine learning models to predict the inhibitory activity of several million compounds against two essential SARS-CoV-2 viral proteins and their host protein interactors; S/Ace2, Tmprss2, Cathepsins L and K, and Mpro to prevent binding, membrane fusion and replication of the virus, respectively. All together we generated an ensemble of structural conformations that increase high quality docking outcomes to screen over >6 million compounds including all FDA-approved drugs, drugs under clinical trial (>3000) and an additional >30 million selected chemotypes from fragment libraries. Our results yielded an initial set of 350 high value compounds from both new and FDA-approved compounds that can now be tested experimentally in appropriate biological model systems. We anticipate that our results will initiate screening campaigns and accelerate the discovery of COVID-19 treatments.


[14] 2007.02569

Dismantling a dogma: the inflated significance of neutral genetic diversity in conservation genetics

The current rate of species extinction is rapidly approaching unprecedented highs and life on Earth presently faces a sixth mass extinction event driven by anthropogenic activity, climate change and ecological collapse. The field of conservation genetics aims at preserving species by using their levels of genetic diversity, usually measured as neutral genome-wide diversity, as a barometer for evaluating population health and extinction risk. A fundamental assumption is that higher levels of genetic diversity lead to an increase in fitness and long-term survival of a species. Here, we argue against the perceived importance of neutral genetic diversity for the conservation of wild populations and species. We demonstrate that no simple general relationship exists between neutral genetic diversity and the risk of species extinction. Instead, a better understanding of the properties of functional genetic diversity, demographic history, and ecological relationships, is necessary for developing and implementing effective conservation genetic strategies.


[15] 2007.02580

Forcing Seasonality of influenza-like epidemics with daily Solar resonance

Seasonality of acute viral respiratory diseases is a well-known and yet not fully understood phenomenon. Here we show that such seasonality, as well as the distribution of viral disease's epidemics with latitude on Earth, can be fully explained by the virucidal properties of UV-B and A Solar photons through a daily, minute-scale, resonant forcing mechanism. Such an induced periodicity can last, virtually unperturbed, from tens to hundreds of cycles, and even in presence of internal dynamics (host's loss of immunity) much slower than seasonal will, on a long period, generate seasonal oscillations.


[16] 2007.02712

Prospective Prediction of Future SARS-CoV-2 Infections Using Empirical Data on a National Level to Gauge Response Effectiveness

Predicting an accurate expected number of future COVID-19 cases is essential to properly evaluate the effectiveness of any treatment or preventive measure. This study aimed to identify the most appropriate mathematical model to prospectively predict the expected number of cases without any intervention. The total number of cases for the COVID-19 epidemic in 28 countries was analyzed and fitted to several simple rate models including the logistic, Gompertz, quadratic, simple square, and simple exponential growth models. The resulting model parameters were used to extrapolate predictions for more recent data. While the Gompertz growth models (mean R2 = 0.998) best fitted the current data, uncertainties in the eventual case limit made future predictions with logistic models prone to errors. Of the other models, the quadratic rate model (mean R2 = 0.992) fitted the current data best for 25 (89 %) countries as determined by R2 values. The simple square and quadratic models accurately predicted the number of future total cases 37 and 36 days in advance respectively, compared to only 15 days for the simple exponential model. The simple exponential model significantly overpredicted the total number of future cases while the quadratic and simple square models did not. These results demonstrated that accurate future predictions of the case load in a given country can be made significantly in advance without the need for complicated models of population behavior and generate a reliable assessment of the efficacy of current prescriptive measures against disease spread.


[17] 2007.02715

A universal generic description of the dynamics of the current COVID-19 pandemic

Based on the analysis of the empirical data for the number of infections in more than 20 countries we propose here a hitherto unknown universal model for the spreading of the COVID-19 pandemic that depends not on time, but on the number of infections itself. This change of the independent variable overcomes the crucial issue of analyzing very different countries worldwide within one mathematical framework with similar parameters. This was previously impossible leading to individual description for every country. Our model allows describing the pandemic including its endpoint surprisingly good and giving a figure of merit for the success of the measures to fight the pandemic.


[18] 2007.02726

Bridging the COVID-19 Data and the Epidemiological Model using Time Varying Parameter SIRD Model

This paper extends the canonical model of epidemiology, SIRD model, to allow for time varying parameters for real-time measurement of the stance of the COVID-19 pandemic. Time variation in model parameters is captured using the generalized autoregressive score modelling structure designed for the typically daily count data related to pandemic. The resulting specification permits a flexible yet parsimonious model structure with a very low computational cost. This is especially crucial at the onset of the pandemic when the data is scarce and the uncertainty is abundant. Full sample results show that countries including US, Brazil and Russia are still not able to contain the pandemic with the US having the worst performance. Furthermore, Iran and South Korea are likely to experience the second wave of the pandemic. A real-time exercise show that the proposed structure delivers timely and precise information on the current stance of the pandemic ahead of the competitors that use rolling window. This, in turn, transforms into accurate short-term predictions of the active cases. We further modify the model to allow for unreported cases. Results suggest that the effects of the presence of these cases on the estimation results diminish towards the end of sample with the increasing number of testing.


[19] 2007.02783

Phenomenological Mesoscopic Models for Seizure Activity

In this chapter we review phenomenological models of seizure like activity. We discuss dynamical mechanisms for seizure onset and offset, preictal spikes, spike and wave complexes and status epilepticus, highlighting the role played by the bifurcation structure of the model, the presence of noise and the emergence of multiple interacting time-scales. These models can be used to build large-scale patient specific brain network models serving as in-silico platforms to test clinical hypothesis and perform virtual surgeries. They suggest innovative treatment strategies, such as minimally invasive ablations or stimulations that fully exploit the network and dynamical properties of the system, or even modulation of variables and parameters to force the system in safer regions of the bifurcation diagram. We discuss insights from phenomenological models that can help to foster our understanding of the mechanisms underlying epileptic seizures.


[20] 2007.02835

GROVER: Self-supervised Message Passing Transformer on Large-scale Molecular Data

How to obtain informative representations of molecules is a crucial prerequisite in AI-driven drug design and discovery. Recent researches abstract molecules as graphs and employ Graph Neural Networks (GNNs) for task-specific and data-driven molecular representation learning. Nevertheless, two "dark clouds" impede the usage of GNNs in real scenarios: (1) insufficient labeled molecules for supervised training; (2) poor generalization capabilities to new-synthesized molecules. To address them both, we propose a novel molecular representation framework, GROVER, which stands for Graph Representation frOm self-superVised mEssage passing tRansformer. With carefully designed self-supervised tasks in node, edge and graph-level, GROVER can learn rich structural and semantic information of molecules from enormous unlabelled molecular data. Rather, to encode such complex information, GROVER integrates Message Passing Networks with the Transformer-style architecture to deliver a class of more expressive encoders of molecules. The flexibility of GROVER allows it to be trained efficiently on large-scale molecular dataset without requiring any supervision, thus being immunized to the two issues mentioned above. We pre-train GROVER with 100 million parameters on 10 million unlabelled molecules---the biggest GNN and the largest training dataset that we have ever met. We then leverage the pre-trained GROVER to downstream molecular property prediction tasks followed by task-specific fine-tuning, where we observe a huge improvement (more than 6% on average) over current state-of-the-art methods on 11 challenging benchmarks. The insights we gained are that well-designed self-supervision losses and largely-expressive pre-trained models enjoy the significant potential on performance boosting.


[21] 2007.02855

Effective epidemic model for COVID-19 using accumulated deaths

The severe acute respiratory syndrome COVID-19 has been in the center of the ongoing global health crisis in 2020. The high prevalence of mild cases facilitates sub-notification outside hospital environments and the number of those who are or have been infected remains largely unknown, leading to poor estimates of the crude mortality rate of the disease. Here we use a simple model to describe the number of accumulated deaths caused by COVID-19. The close connection between the proposed model and an approximate solution of the SIR model provides a system of equations whose solutions are robust estimates of epidemiological parameters. We find that the crude mortality varies between $10^{-4}$ and $10^{-3}$ depending on the severity of the outbreak which is lower than previous estimates obtained from laboratory confirmed patients. We also estimate quantities of practical interest such as the basic reproduction number and the expected number of deaths in the asymptotic limit with and without social distancing measures and lockdowns, which allow us to measure the efficiency of these interventions.


[22] 2007.01927

Equilibrium mechanisms of self-limiting assembly

Self assembly is a ubiquitous process in synthetic and biological systems, broadly defined as the spontaneous self-organization of multiple subunits (e.g. macromolecules, particles) into ordered multi-unit structures. The vast majority of equilibrium assembly processes give rise to two ``states'': one consisting of dispersed disassociated subunits, and the other, a bulk-condensed state of unlimited size. This review focuses on the more specialized class of {\it self-limiting assembly}, which describes equilibrium assembly processes resulting in finite-size structures. These systems pose a generic and basic question, how do thermodynamic processes involving non-covalent interactions between identical subunits ``measure'' and select the size of assembled structures? In this review, we begin with an introduction to the basic statistical mechanical framework for assembly thermodynamics, and use this to highlight the key physical ingredients that ensure equilibrium assembly will terminate at finite dimensions. Then, examples of self-limiting assembly systems will be introduced and classified within this framework based on two broad categories: {\it self-closing assemblies} and {\it open-boundary assemblies}. These will include well-known cases -- micellization of amphiphiles and shell/tubule formation of tapered subunits -- as well as less widely known classes of assemblies, such as short-range attractive/long-range repulsive systems and geometrically-frustrated assemblies. For each of these self-limiting mechanisms, we describe the physical mechanisms that select equilibrium assembly size, as well as potential limitations of finite-size selection. Finally, we discuss alternative mechanisms for finite-size assemblies and draw contrasts with the size-control that these can achieve relative to self-limitation in equilibrium, single-species assemblies.


[23] 2007.01935

Statistical hypothesis testing versus machine-learning binary classification: distinctions and guidelines

Making binary decisions is a common data analytical task in scientific research and industrial applications. In data sciences, there are two related but distinct strategies: hypothesis testing and binary classification. In practice, how to choose between these two strategies can be unclear and rather confusing. Here we summarize key distinctions between these two strategies in three aspects and list five practical guidelines for data analysts to choose the appropriate strategy for specific analysis needs. We demonstrate the use of those guidelines in a cancer driver gene prediction example.


[24] 2007.01975

Interpretation of Disease Evidence for Medical Images Using Adversarial Deformation Fields

The high complexity of deep learning models is associated with the difficulty of explaining what evidence they recognize as correlating with specific disease labels. This information is critical for building trust in models and finding their biases. Until now, automated deep learning visualization solutions have identified regions of images used by classifiers, but these solutions are too coarse, too noisy, or have a limited representation of the way images can change. We propose a novel method for formulating and presenting spatial explanations of disease evidence, called deformation field interpretation with generative adversarial networks (DeFI-GAN). An adversarially trained generator produces deformation fields that modify images of diseased patients to resemble images of healthy patients. We validate the method studying chronic obstructive pulmonary disease (COPD) evidence in chest x-rays (CXRs) and Alzheimer's disease (AD) evidence in brain MRIs. When extracting disease evidence in longitudinal data, we show compelling results against a baseline producing difference maps. DeFI-GAN also highlights disease biomarkers not found by previous methods and potential biases that may help in investigations of the dataset and of the adopted learning methods.


[25] 2007.02047

Relationship between manifold smoothness and adversarial vulnerability in deep learning with local errors

Artificial neural networks can achieve impressive performances, and even outperform humans in some specific tasks. Nevertheless, unlike biological brains, the artificial neural networks suffer from tiny perturbations in sensory input, under various kinds of adversarial attacks. It is therefore necessary to study the origin of the adversarial vulnerability. Here, we establish a fundamental relationship between geometry of hidden representations (manifold perspective) and the generalization capability of the deep networks. For this purpose, we choose a deep neural network trained by local errors, and then analyze emergent properties of trained networks through the manifold dimensionality, manifold smoothness, and the generalization capability. To explore effects of adversarial examples, we consider independent Gaussian noise attacks and fast-gradient-sign-method (FGSM) attacks. Our study reveals that a high generalization accuracy requires a relatively fast power-law decay of the eigen-spectrum of hidden representations. Under Gaussian attacks, the relationship between generalization accuracy and power-law exponent is monotonic, while a non-monotonic behavior is observed for FGSM attacks. Our empirical study provides a route towards a final mechanistic interpretation of adversarial vulnerability under adversarial attacks.


[26] 2007.02198

Scalable Bayesian Functional Connectivity Inference for Multi-Electrode Array Recordings

Multi-electrode arrays (MEAs) can record extracellular action potentials (also known as 'spikes') from hundreds or thousands of neurons simultaneously. Inference of a functional network from a spike train is a fundamental and formidable computational task in neuroscience. With the advancement of MEA technology, it has become increasingly crucial to develop statistical tools for analyzing multiple neuronal activity as a network. In this paper, we propose a scalable Bayesian framework for inference of functional networks from MEA data. Our framework makes use of the hierarchical structure of networks of neurons. We split the large scale recordings into smaller local networks for network inference, which not only eases the computational burden from Bayesian sampling but also provides useful insights on regional connections in organoids and brains. We speed up the expensive Bayesian sampling process by using parallel computing. Experiments on both synthetic datasets and large-scale real-world MEA recordings show the effectiveness and efficiency of the scalable Bayesian framework. Inference of networks from controlled experiments exposing neural cultures to cadmium presents distinguishable results and further confirms the utility of our framework.


[27] 2007.02283

Superposition of waves for modeling COVID-19 epidemic in the world and in the countries with the maximum number of infected people in the first half of 2020

On the base of logic discrete equations system mathematical modeling of COVID-19 epidemic spread was carried out in the world and in the countries with the largest number of infected people such as the USA, Brasil, Russia and India in the first half of 2020. It was shown that for the countries with strong restrictive measures the spread of COVID-19 fit on a single wave with small capacity as for a number of countries with violation of restrictive measures the spread of the epidemic fit on a waves superposition. For countries with large population mixing, the spread of the epidemic today also fits into a single wave, but with a huge capacity value (for Brazil - 80 million people, for India - 40 million people). We estimated that the epidemic spread in the world today fits into 5 waves. The first two waves are caused by the epidemic spread in China (the first - in Wuhan), the third - by the epidemic spread in European countries, the fourth mainly by the epidemic spread in Russia and the USA, the fifth wave is mainly caused by the epidemic spread in Latin America and South Asia. It was the fifth wave that led to the spread of the coronavirus epidemic COVID-19 entering a new phase, with an increase in the number of infected more than 100 thousand inhabitants. For all the studied countries and the world, for each of the superposition waves, the wave capacities and growth indicators were calculated. The local peaks of the waves and their ending times are determined. It was the fifth wave that led to the fact that COVID-19 spread is entering a new phase, with the increase in the number of infected people being more than 100 thousand inhabitants. For all the countries being examined and for the whole world, for each of the superposition waves we calculated the waves capacities and index of infected people growth.


[28] 2007.02511

Less is More: Wiring-Economical Modular Networks Support Self-Sustained Firing-Economical Neural Avalanches for Efficient Processing

Brain network is remarkably cost-efficient while the fundamental physical mechanisms underlying its economical optimization in network structure and dynamics are not clear. Here we study intricate cost-efficient interplay between structure and dynamics in biologically plausible spatial modular neuronal network models. We find that critical avalanche states from excitation-inhibition balance, under modular network topology with less wiring cost, can also achieve less costs in firing, but with strongly enhanced response sensitivity to stimuli. We derived mean-field equations that govern the macroscopic network dynamics through a novel approximate theory. The mechanism of low firing cost and stronger response in the form of critical avalanche is explained as a proximity to a Hopf bifurcation of the modules when increasing their connection density. Our work reveals the generic mechanism underlying the cost-efficient modular organization and critical dynamics widely observed in neural systems, providing insights to brain-inspired efficient computational designs.


[29] 2007.02695

Two-Stage Adaptive Pooling with RT-qPCR for COVID-19 Screening

We propose two-stage adaptive pooling schemes, 2-STAP and 2-STAMP, for detecting COVID-19 using real-time reverse transcription quantitative polymerase chain reaction (RT-qPCR) test kits. Similar to the Tapestry scheme of Ghosh et al., the proposed schemes leverage soft information from the RT-qPCR process about the total viral load in the pool. This is in contrast to conventional group testing schemes where the measurements are Boolean. The proposed schemes provide higher testing throughput than the popularly used Dorfman's scheme. They also provide higher testing throughput, sensitivity and specificity than the state-of-the-art non-adaptive Tapestry scheme. The number of pipetting operations is lower than state-of-the-art non-adaptive pooling schemes, and is higher than that for the Dorfman's scheme. The proposed schemes can work with substantially smaller group sizes than non-adaptive schemes and are simple to describe. Monte-Carlo simulations using the statistical model in the work of Ghosh et al. (Tapestry) show that 10 infected people in a population of size 961 can be identified with 70.86 tests on the average with a sensitivity of 99.50% and specificity of 99.62. This is 13.5x, 4.24x, and 1.3x the testing throughput of individual testing, Dorfman's testing, and the Tapestry scheme, respectively.


[30] 2007.02774

The role of time scale in the spreading of asymmetrically interacting diseases

Diseases and other contagion phenomena in nature and society can interact asymmetrically, such that one can benefit from the other, which in turn impairs the first, in analogy with predator-prey systems. Here, we consider two models for interacting disease-like dynamics with asymmetric interactions and different associated time scales. Using rate equations for homogeneously mixed populations, we show that the stationary prevalences and phase diagrams of each model behave differently with respect to variations of the relative time scales. We also characterize in detail the regime where transient oscillations are observed, a pattern that is inherent to asymmetrical interactions but often ignored in the literature. Our results contribute to a better understanding of disease dynamics in particular, and interacting processes in general, and could provide interesting insights for real-world applications, most notably, the interplay between the dynamics of fact-checked and fake news.


[31] 2007.02830

Predicting the properties of metachronal waves from single-cilium characteristics

On surfaces with many motile cilia, beats of the individual cilia coordinate to form metachronal waves. We present a theoretical framework that connects the dynamics of individual cilia to the collective dynamics of a ciliary carpet via systematic coarse-graining. We uncover the criteria that control the selection of frequency and wavevector of stable metchacronal waves and examine how they depend on the geometric and dynamical characteristics of single cilia, as well as the geometric properties of the array. Our results can contribute to understanding how the collective properties of ciliary arrays can be controlled, which can have significant biological, medical, and engineering implications.