Showing papers in "IEEE Transactions on Molecular, Biological, and Multi-Scale Communications in 2021"

Molecular Communications in Viral Infections Research: Modeling, Experimental Data, and Future Directions

Abstract: Hundreds of millions of people worldwide are affected by viral infections each year, and yet, several of them neither have vaccines nor effective treatment during and post-infection. This challenge has been highlighted by the COVID-19 pandemic, showing how viruses can quickly spread and impact society as a whole. Novel interdisciplinary techniques must emerge to provide forward-looking strategies to combat viral infections, as well as possible future pandemics. In the past decade, an interdisciplinary area involving bioengineering, nanotechnology and information and communication technology (ICT) has been developed, known as Molecular Communications. This new emerging area uses elements of classical communication systems to molecular signalling and communication found inside and outside biological systems, characterizing the signalling processes between cells and viruses. In this paper, we provide an extensive and detailed discussion on how molecular communications can be integrated into the viral infectious diseases research, and how possible treatment and vaccines can be developed considering molecules as information carriers. We provide a literature review on molecular communications models for viral infection (intra-body and extra-body), a deep analysis on their effects on immune response, how experimental can be used by the molecular communications community, as well as open issues and future directions.

Machine Learning in Nano-Scale Biomedical Engineering

Abstract: Machine learning (ML) empowers biomedical systems with the capability to optimize their performance through modeling of the available data extremely well, without using strong assumptions about the modeled system. Especially in nano-scale biosystems, where the generated data sets are too vast and complex to mentally parse without computational assist, ML is instrumental in analyzing and extracting new insights, accelerating material and structure discoveries and designing experience as well as supporting nano-scale communications and networks. However, despite these efforts, the use of ML in nano-scale biomedical engineering remains still under-explored in certain areas and research challenges are still open in fields such as structure and material design and simulations, communications and signal processing, and bio-medicine applications. In this article, we review the existing research regarding the use of ML in nano-scale biomedical engineering. In more detail, we first identify and discuss the main challenges that can be formulated as ML problems. These challenges are classified in three main categories: structure and material design and simulation, communications and signal processing and biomedicine applications. Next, we discuss the state of the art ML methodologies that are used to countermeasure the aforementioned challenges. For each of the presented methodologies, special emphasis is given to its principles, applications and limitations. Finally, we conclude the article with insightful discussions, that reveal research gaps and highlight possible future research directions.

A Molecular Communication Perspective on Airborne Pathogen Transmission and Reception via Droplets Generated by Coughing and Sneezing

Abstract: Infectious diseases spread via pathogens such as viruses and bacteria. Airborne pathogen transmission via droplets is an important mode for infectious diseases. In this paper, the spreading mechanism of infectious diseases by airborne pathogen transmission between two humans is modeled with a molecular communication perspective. An end-to-end system model which considers the pathogen-laden cough/sneeze droplets as the input and the infection state of the human as the output is proposed. This model uses the gravity, initial velocity and buoyancy for the propagation of droplets and a receiver model which considers the central part of the human face as the reception interface is proposed. Furthermore, the probability of infection for an uninfected human is derived by modeling the number of propagating droplets as a random process. The numerical results reveal that exposure time affects the probability of infection. In addition, the social distance for a horizontal cough should be at least 1.7 m and the safe coughing angle of a coughing human to infect less people should be less than −25°.

Secrecy Optimization for Diffusion-Based Molecular Timing Channels

Abstract: Security in the context of molecular communication systems is an important design aspect that has not attracted much attention till date. This paper analyzes the information-theoretic secrecy of diffusive molecular timing channels when the distance of the eavesdropper is assumed to be random and uniformly distributed. Using an existing upper bound on the timing channel capacity, we calculate the optimal secrecy rate and optimal transmission rate for Bob which would help in achieving an improved secrecy throughput performance. Based on this optimal rate, we calculate the maximum achievable throughput. We then use this formulation to minimize the generalized secrecy outage probability (GSOP) by simultaneously maximizing the average fractional equivocation and minimizing the average information leakage rate. The numerical results show that while choosing the system parameters, there is always a trade-off between different performance metrics like GSOP, average fractional equivocation, and average information leakage rate. The proposed secrecy optimization provides a robust understanding of the physical layer secrecy at the molecular level, enabling the design of secure molecular communication systems.

Duality Between Coronavirus Transmission and Air-Based Macroscopic Molecular Communication

Abstract: This contribution exploits the duality between a viral infection process and macroscopic air-based molecular communication. Airborne aerosol and droplet transmission through human respiratory processes is modeled as an instance of a multiuser molecular communication scenario employing respiratory-event-driven molecular variable-concentration shift keying. Modeling is aided by experiments that are motivated by a macroscopic air-based molecular communication testbed. In artificially induced coughs, a saturated aqueous solution containing a fluorescent dye mixed with saliva is released by an adult test person. The emitted particles are made visible by means of optical detection exploiting the fluorescent dye. The number of particles recorded is significantly higher in test series without mouth and nose protection than in those with a well-fitting medical mask. A simulation tool for macroscopic molecular communication processes is extended and used for estimating the transmission of infectious aerosols in different environments. Towards this goal, parameters obtained through self experiments are taken. The work is inspired by the recent outbreak of the coronavirus pandemic.

Investigation of Multiple Fluorescent Dyes in Macroscopic Air-Based Molecular Communication

Abstract: An air-based macroscopic molecular communication testbed exploiting fluorescence properties of water-based solutions of Uranine and Rhodamine 6G is presented in this work. The testbed comprises of an industrial sprayer as its transmitter, a 2 m long tube as transmission channel, and a high-speed camera-based detector. The transmission distances considered cover a range over several tens of centimeters to meters. Concerning modulation schemes, molecular shift keying and molecular concentration shift keying are implemented and compared with on-off keying serving as a benchmark. It is shown that the former two can be used to decrease the bit error rate and/or to increase the bit rate. Furthermore, a platform is set for implementing a multiuser scenario, where the same channel is made accessible to multiple users by simultaneous use of different dyes for data transmission with small co-channel interference.

A Novel Multi-stage Residual Feature Fusion Network for Detection of COVID-19 in Chest X-ray Images

Abstract: To suppress the spread of COVID-19, accurate diagnosis at an early stage is crucial, chest screening with radiography imaging plays an important role in addition to the real-time reverse transcriptase polymerase chain reaction (RT-PCR) swab test. Due to the limited data, existing models suffer from incapable feature extraction and poor network convergence and optimization. Accordingly, a multi-stage residual network, MSRCovXNet, is proposed for effective detection of COVID-19 from chest x-ray (CXR) images. As a shallow yet effective classifier with the ResNet-18 as the feature extractor, MSRCovXNet is optimized by fusing two proposed feature enhancement modules (FEM), i.e. low-level and high-level feature maps (LLFMs and HLFMs), which contain respectively more local information and rich semantic information, respectively. For effective fusion of these two features, a single-stage FEM (MSFEM) and a multi-stage FEM (MSFEM) are proposed to enhance the semantic feature representation of the LLFMs and the local feature representation of the HLFMs, respectively. Without ensembling other deep learning models, our MSRCovXNet has a precision of 98.9% and a recall of 94% in detection of COVID-19, which outperforms several state-of-the-art models. When evaluated on the COVIDGR dataset, an average accuracy of 82.2% is achieved, leading other methods by at least 1.2%.

Molecular Communication Theoretical Modeling and Analysis of SARS-CoV2 Transmission in Human Respiratory System

Abstract: Severe Acute Respiratory Syndrome-CoronaVirus 2 (SARS-CoV2) caused the ongoing pandemic. This pandemic devastated the world by killing more than a million people, as of October 2020. It is imperative to understand the transmission dynamics of SARS-CoV2 so that novel and interdisciplinary prevention, diagnostic, and therapeutic techniques could be developed. In this work, we model and analyze the transmission of SARS-CoV2 through the human respiratory tract from a molecular communication perspective. We consider that virus diffusion occurs in the mucus layer so that the shape of the tract does not have a significant effect on the transmission. Hence, this model reduces the inherent complexity of the human respiratory system. We further provide the impulse response of SARS-CoV2-ACE2 receptor binding event to determine the proportion of the virus population reaching different regions of the respiratory tract. Our findings confirm the results in the experimental literature on higher mucus flow rate causing virus migration to the lower respiratory tract. These results are especially important to understand the effect of SARS-CoV2 on the different human populations at different ages who have different mucus flow rates and ACE2 receptor concentrations in the different regions of the respiratory tract.

Bounds on the Constrained Capacity for the Diffusive Poisson Molecular Channel With Memory

Abstract: This letter focuses on the analysis of a diffusion-based molecular communication (MC) system where the received signal is approximated as a Poisson random variable. Concentration shift keying (CSK) is used as the modulation technique for encoding information in the system. In particular, this work aims to study the performance of the MC system in terms of reliable information exchange for the channel with finite-state memory, which introduces intersymbol interference (ISI). The main objective is the derivation of analytical expressions for the upper and the lower bound of the constrained channel capacity for a range of values of the modulated symbols, i.e., for a number of different sets of amplitude levels of CSK modulation, and for various levels of ISI. In addition, the numerical evaluation of the derived expressions is presented. Results allow discussing the relationship between ISI level and achievable channel capacity. Moreover, numerical outcomes highlight how the estimation of the bounds is not affected by the presence of ISI in the case of binary CSK modulation, as the bounds remain quite tight.

A Droplet-Based Signal Reconstruction Approach to Channel Modeling in Molecular Communication

Abstract: In this article, a novel droplet-based signal reconstruction (SR) approach to channel modeling, which considers liquid droplets as information carriers instead of molecules in the molecular communication (MC) channel, is proposed for practical sprayer-based macroscale MC systems. These practical MC systems are significant, since they can be used in order to investigate airborne pathogen transmission with biological sensors due to the similar mechanisms of sneezing/coughing and sprayer. Our proposed approach takes a two-phase flow which is generated by the interaction of droplets in liquid phase with air molecules in gas phase into account. Two-phase flow is combined with the SR of the receiver (RX) to propose a channel model. The SR part of the model quantifies how the accuracy of the sensed molecular signal in its reception volume depends on the sensitivity response of the RX and the adhesion/detachment process of droplets. The proposed channel model is validated by employing experimental data.

On Mutual Information Analysis of Infectious Disease Transmission via Particle Propagation

Abstract: Besides mimicking bio-chemical and multi-scale communication mechanisms, molecular communication forms a theoretical framework for virus infection processes. Towards this goal, aerosol and droplet transmission has recently been modeled as a multiuser scenario. In this letter, the “infection performance” is evaluated by means of a mutual information analysis, and by an even simpler probabilistic performance measure which is closely related to absorbed viruses. The so-called infection rate depends on the distribution of the channel input events as well as on the transition probabilities between channel input and output events. The infection rate is investigated analytically for five basic discrete memoryless channel models. Numerical results for the transition probabilities are obtained by Monte Carlo simulations for pathogen-laden particle transmission in four typical indoor environments: two-person office, corridor, classroom, and bus. Particle transfer contributed significantly to infectious diseases like SARS-CoV-2 and influenza.

VIVID: In Vivo End-to-End Molecular Communication Model for COVID-19

Abstract: As an alternative to ongoing efforts for vaccine development, scientists are exploring novel approaches to provide innovative therapeutics, such as nanoparticle- and stem cell-based treatments. Thus, understanding the transmission and propagation dynamics of coronavirus inside the respiratory system has attracted researchers’ attention. In this work, we model the transmission and propagation of coronavirus inside the respiratory tract, starting from the nasal area to alveoli using molecular communication theory. We performed experiments using COMSOL, a finite-element multiphysics simulation software, and Python-based simulations to analyze the end-to-end communication model in terms of path loss, delay, and gain. The analytical results show the correlation between the channel characteristics and pathophysiological properties of coronavirus. For the initial 50% of the maximum production rate of virus particles, the path loss increases more than 16 times than the remaining 50%. The delayed response of the immune system and increase in the absorption of virus particles inside the respiratory tract delay the arrival of virus particles at the alveoli. Furthermore, the results reveal that the virus load is more in case of asthmatic patients as compared to the normal subjects.

Transfer Function Models for Cylindrical MC Channels with Diffusion and Laminar Flow

Abstract: The analysis and design of advection-diffusion based molecular communication (MC) systems in cylindrical environments is of particular interest for applications such as micro-fluidics and targeted drug delivery in blood vessels. Therefore, the accurate modeling of the corresponding MC channel is of high importance. The propagation of particles in these systems is caused by a combination of diffusion and flow with a parabolic velocity profile, i.e., laminar flow. The propagation characteristics of the particles can be categorized into three different regimes: The flow dominant regime where the influence of diffusion on the particle transport is negligible, the dispersive regime where diffusion has a much stronger impact than flow, and the mixed regime where both effects are important. For the limiting regimes, i.e., the flow dominant and dispersive regimes, well-known solutions and approximations for particle transport exist. For the mixed regime, approximations, numerical techniques, and particle based simulations are employed. However, the few analytical models that are applicable in all three regimes impose significant constraints on the possible transmitter locations and particle release profiles. In this paper, we develop a more general analytical model for the advection-diffusion problem in cylindrical environments, which is applicable in all three regimes and accounts for general particle release models. The proposed model exhibits a higher accuracy than existing models and is based on a transfer function approach, where the main challenge is the incorporation of laminar flow. The properties of the proposed model are analyzed by numerical evaluation for different scenarios including the uniform and point release of particles. We provide a comparison with particle based simulations and existing analytical models from the literature to demonstrate the validity of the proposed analytical model.

Embedded Codes for Reassembling Non-Overlapping Random DNA Fragments

Abstract: We consider the problem of recovering the stored data from randomly broken Deoxyribonucleic Acid (DNA) fragments. More precisely, we assume a channel that breaks the DNA into a random number of non-overlapping fragments where the length of each fragment comes from a memoryless Geometric process. We devise an embedded code structure that enables reassembly of the DNA and analyze its error probability. We observe that the error decreases with DNA length for various breaking probabilities of the Geometric process. Our code is built upon Varshamov-Tenengolts (VT) codes, and enables a much more efficient recovery strategy compared to brute-force search among all permutations of the DNA fragments.

Molecular Type Spread Molecular Shift Keying for Multiple-Access Diffusive Molecular Communications

Abstract: In nano-networking, many nano-machines need to share common communication media, in order to achieve information exchange and data fusion. Multiple-access is an important technique for multiple nano-machines to send information to one access point (AP) or fusion center. Built on the Molecular Shift Keying (MoSK) modulation, this article proposes a novel Molecular Type Spread MoSK (MTS-MoSK) scheme for achieving multiple-access transmission in diffusion-based molecular communications (DMC). Correspondingly, two detection schemes are introduced and investigated, which are the Maximum Selection assisted Majority Voter Detection (MS-MVD) and Equal-Gain Combining Detection (EGCD). The error performance of the MTS-MoSK DMC systems with respectively the two detection schemes is analyzed, when both Multiple-Access Interference (MAI) and Inter-Symbol Interference (ISI) are taken into account. Furthermore, the performance of MTS-MoSK DMC systems is investigated and compared with the aid of analytical results as well as Monte-Carlo and particle-based simulations. Our studies and performance results show that MTS-MoSK constitutes a promising candidate for implementing multiple-access DMC, and MS-MVD has the potential to outperform EGCD in DMC.

A Molecular Communications System for the Detection of Inflammatory Levels Related to COVID-19 Disease

Abstract: A recent and extensive research activity highlighted the process behind the attack and spread of COVID-19 in the human body. What emerged is that the SARS-CoV-2 virus makes use of both the ACE2 receptor, expressed by pneumocytes in the ephitelial alveolar lining, and by the endothelium to spread the disease and to replicate itself. Since the endothelium is an extended tissue lying in the circulatory system, this may lead to a large state of diffuse endothelial inflammation with serious clinical consequences. This situation may be further compromised by the immune system, that may generate pro-inflammatory cytokines (IL-6) as a consequence of the infection. In this paper we propose and analyze a molecular communication system, designed for the detection of excessive IL-6 level, that allows monitoring its evolution in the blood vessels. The proposed analysis was performed by using the BiNS2 simulator, which is suitable for the numerical analysis of flow-based molecular communications in blood vessels, as well as Markov models of the endothelium.

Viral Aerosol Concentration Characterization and Detection in Bounded Environments

Abstract: Viral spread has been intermittently threatening human life over time. Characterizing the viral concentration and modelling the viral transmission are, therefore, considered major milestones for enhancing viral detection capabilities. This paper addresses the problem of viral aerosol detection from the exhaled breath in a bounded environment, e.g., a bounded room. The paper models the exhaled breath as a cloud which is emitted through the room, and analyzes the temporal-spatial virus-laden aerosol concentration by accounting for partial absorption and reflection at each side of the room. The paper first derives a closed form expression of the temporal-spatial virus-laden aerosol concentration. It then considers the deployment of a receiver composed of an air sampler and a bio-sensor to detect the viral existence of a specific virus. We, therefore, assess the detection capabilities of the proposed system via evaluating the viral miss-detection probability as a function of the sampling volume and the detection time-instance at the receiver side. Our numerical simulations verify the validity of the analytical results, and illustrate the ability of the proposed system to detect viruses in indoor environments. The results further characterize the impacts of several system parameters on the miss-detection probability.

Equilibrium Signaling in Spatially Inhomogeneous Diffusion and External Forces

Abstract: Complex fluid media where molecules are susceptible to forces due, for example, to external magnetic fields, complicates the design of molecular communication systems. In particular, the equations governing the motion of each molecule in time do not typically admit tractable solutions, which makes receiver design challenging for standard communication schemes; e.g., based on concentration shift keying. In this letter, a new communication scheme is proposed, which leads to simple expressions for receiver statistics, even when spatially inhomogeneous diffusion and external forces are present. The proposed scheme exploits the equilibrium statistics of the system, which arise in a wide range of scenarios. This approach is illustrated in a bounded system with inhomogeneous diffusion and external forces determined by a quadratic potential.

Larger Connection Radius Increases Hub Astrocyte Number in a 3-D Neuron–Astrocyte Network Model

Abstract: Astrocytes - a prominent glial cell type in the brain - form networks that tightly interact with the brain’s neuronal circuits. Thus, it is essential to study the modes of such interaction if we aim to understand how neural circuits process information. Thereby, calcium elevations, the primary signal in astrocytes, propagate to the adjacent neighboring cells and directly regulate neuronal communication. It is mostly unknown how the astrocyte network topology influences neuronal activity. Here, we used a computational model to simulate planar and 3D neuron-astrocyte networks with varying topologies. We investigated the number of active nodes, the shortest path, and the mean degree. Furthermore, we applied a graph coloring analysis that highlights the network organization between different network structures. With the increase of the maximum distance between two connected astrocytes, the information flow is more centralized to the most connected cells. Our results suggest that activity-dependent plasticity and the topology of brain areas might alter the amount of astrocyte controlled synapses.

Kolmogorov turbulence and information dissipation in molecular communication

Abstract: Waterborne chemical plumes are studied as a paradigm for representing a means for molecular communication in a macro-scale system. Results from the theory of fluid turbulence are applied and interpreted in the context of molecular communication to characterize an information cascade, the information dissipation rate and the critical length scale below which information modulated onto the plume can no longer be decoded. The results show that the information dissipation decreases with increasing Reynolds number and that there exists a theoretical potential for encoding smaller information structures at higher Reynolds numbers.

A Quantized Representation of Intertemporal Choice in the Brain

Abstract: Value (Kahneman and Tversky, 1979), (Tversky Kahneman, 1992) is typically modeled using a continuous representation (i.e., a Real number). A discrete representation of value has recently been postulated (Woodford, 2020). A quantized representation of probability in the brain was also posited and supported by experimental data (Tee and Taylor, 2019). Value and probability are inter-related via Prospect Theory (Kahneman and Tversky, 1979), (Tversky Kahneman, 1992). In this article, we hypothesize that intertemporal choices may also be quantized. For example, people may treat (or discount) 16 days indifferently to 17 days. To test this, we analyzed an intertemporal task by using 2 novel models: quantized hyperbolic discounting, and quantized exponential discounting. Our work here is a re-examination of the behavioral data previously collected for an fMRI study (Cox and Kable, 2014). Both quantized hyperbolic and quantized exponential models were compared using AIC and BIC tests. We found that 13/20 participants were best fit to the quantized exponential model, while the remaining 7/20 were best fit to the quantized hyperbolic model. Overall, 15/20 participants were best fit to models with a 5-bit precision (i.e., $2^{5}= 32$ steps). In conclusion, regardless of hyperbolic or exponential, quantized versions of these models are better fit to the experimental data than their continuous forms. We finally outline some potential applications of our findings.

Intercellular Communication as a Series of Narrow Escape Problems

Abstract: Molecular communication is key for multicellular organisms. In plants, the exchange of nutrients and signals between cells is facilitated by tunnels called plasmodesmata. Such transport processes in complex geometries can be simulated using particle-based approaches, these, however, are computationally expensive. Here, we evaluate the narrow escape problem as a framework for describing intercellular transport. We introduce a volumetric adjustment factor for estimating escape times from non-spherical geometries. We validate this approximation against full 3D stochastic simulations and provide results for a range of cell sizes and diffusivities. We discuss how this approach can be extended using recent results on multiple trap problems to account for different plasmodesmata distributions with varying apertures.

TDMA-MTMR-Based Molecular Communication With Ligand-Binding Reception

Abstract: Multiple access-based molecular communication (MC) is an important way to implement MC nanonetworks for the Internet of Bio-Nano Things. In this letter, we propose a framework for time division multiple access (TDMA)-based MC by considering multiple-type transmission multiple-type reception (MTMR) for a system with multiple transmitter nanomachines and one receiver nanomachine (RN) which is non-ideal and has a switching delay. The reception process is based on ligand-binding reception. We study the drug release management approaches by minimizing the error probability. Besides, we propose a metric to find signal-to-interference ratio based on the channel memory. Analytical results, which are verified by the simulation, show that the TDMA-MTMR MC outperforms the state-of-the-art TDMA framework.

Modeling the Role of Inter-Cellular Communication in Modulating Photosynthesis in Plants

Abstract: In this paper we show how inter-cellular molecular communication may change the overall levels of photosynthesis in plants. Individual plant cells respond to external stimuli, such as illumination levels, to regulate their photosynthetic output. Here, we present a mathematical model which shows that by sharing information internally using molecular communication, plants may increase overall photosynthate production. Numerical results show that higher mutual information between cells corresponds to an increase in overall photosynthesis by as much as 25 per cent. This suggests that molecular communication plays a vital role in maximising the photosynthesis in plants and therefore suggests new routes to influence plant development in agriculture and elsewhere.

A Concentration-Time Hybrid Modulation Scheme for Molecular Communications

Abstract: Significant inter-symbol interference (ISI) challenges the achievement of reliable, high data-rate molecular communication via diffusion. In this paper, a hybrid modulation based on pulse position and concentration is proposed to mitigate ISI. By exploiting the time dimension, molecular concentration and position modulation (MCPM) increases the achievable data rate over conventional concentration and position-based modulations. In addition, unlike multi-molecule schemes, this hybrid scheme employs a single-molecule type and so simplifies transceiver implementations. In the paper, the optimal sequence detector of the proposed modulation is provided as well as a reduced complexity detector (two-stage, position-concentration detector, TPCD). A tractable cost function based on the TPCD detector is proposed and employed to optimize the design of the hybrid modulation scheme. In addition, the approximate probability of error for the MCPM-TPCD system is derived and is shown to be tight with respect to simulated performance. Numerically, MCPM shows improved performance over standard concentration and pulse position-based schemes in the low transmission power and high bit-rate regime. Furthermore, MCPM offers increased robustness against synchronization errors.

Using Vector Fields for Efficient Simulation of Macroscopic Molecular Communication

Abstract: Molecular communication has been identified as a communication concept complementing radio communication in some areas and being the unique solution in others. This particularly includes communication between nano machines but, more recently, also macroscopic application domains such as in fluid systems in a chemical factory. We are interested in simulating such macroscopic molecular communication both accurately as well as on a large scale. In this work, we make use of the concept of vector fields for efficient simulation of particle movements in a fluid environment. Such vector fields can be pre-computed so that the simulation of the communication itself is very fast. We discuss both the concepts and the methodological approach to outline the advantages of this idea and validate the system compared to lab measurements. Going beyond previous work, we also integrated both on-off keying (OOK) and pulse position modulation (PPM) to demonstrate the feasibility of the simulation concept even for more complex signal processing tasks.