Bacterial evolution and the Bak-Sneppen model

Recently, Lenski et al \cite{Elena,Lenski,Travisano} have carried out several experiments on bacterial evolution. Their findings support the theory of punctuated equilibrium in biological evolution. They have further quantified the relative contributions of adaptation, chance and history to bacterial evolution. In this Brief Report, we show that a modified M-trait Bak-Sneppen model can explain many of the experimental results in a qualitative manner.Comment: 11 pages, 4 figure

Percolation-like phase transition in a non-equilibrium steady state

We study the Gierer-Meinhardt model of reaction-diffusion on a site-disordered square lattice. Let p be the site occupation probability of the square lattice. For p greater than a critical value pc, the steady state consists of stripe-like patterns with long-range connectivity. For p < pc, the connectivity is lost. The value of pc is found to be much greater than that of the site percolation threshold for the square lattice. In the vicinity of pc, the cluster-related quantities exhibit power-law scaling behaviour. The method of finite-size scaling is used to determine the values of the fractal dimension df , the ratio, γ/ν, of the average cluster size exponent γ and the correlation length exponent ν and also ν itself. The values appear to indicate that the disordered GM model belongs to the universality class of ordinary percolation

Punctuated equilibrium in an evolving bacterial population

Recently, Lenski et al have carried out an experiment on bacterial evolution. Their findings support the theory of punctuated equilibrium in biological evolution. We show that the M=2 Bak-Sneppen model can explain some of the experimental results in a qualitative manner.Comment: LaTeX, Five PS figures, Accepted for publication in Physica A, 1st August, 199

Effect of randomness and anisotropy on Turing patterns in reaction-diffusion systems

We study the effect of randomness and anisotropy on Turing patterns in reaction-diffusion systems. For this purpose, the Gierer-Meinhardt model of pattern formation is considered. The cases we study are: (i)randomness in the underlying lattice structure, (ii)the case in which there is a probablity p that at a lattice site both reaction and diffusion occur, otherwise there is only diffusion and lastly, the effect of (iii) anisotropic and (iv) random diffusion coefficients on the formation of Turing patterns. The general conclusion is that the Turing mechanism of pattern formation is fairly robust in the presence of randomness and anisotropy.Comment: 11 pages LaTeX, 14 postscript figures, accepted in Phys. Rev.

Development and Evaluation of Active Case Detection Methods to Support Visceral Leishmaniasis Elimination in India.

As India moves toward the elimination of visceral leishmaniasis (VL) as a public health problem, comprehensive timely case detection has become increasingly important, in order to reduce the period of infectivity and control outbreaks. During the 2000s, localized research studies suggested that a large percentage of VL cases were never reported in government data. However, assessments conducted from 2013 to 2015 indicated that 85% or more of confirmed cases were eventually captured and reported in surveillance data, albeit with significant delays before diagnosis. Based on methods developed during these assessments, the CARE India team evolved new strategies for active case detection (ACD), applicable at large scale while being sufficiently effective in reducing time to diagnosis. Active case searches are triggered by the report of a confirmed VL case, and comprise two major search mechanisms: 1) case identification based on the index case's knowledge of other known VL cases and searches in nearby houses (snowballing); and 2) sustained contact over time with a range of private providers, both formal and informal. Simultaneously, house-to-house searches were conducted in 142 villages of 47 blocks during this period. We analyzed data from 5030 VL patients reported in Bihar from January 2018 through July 2019. Of these 3033 were detected passively and 1997 via ACD (15 (0.8%) via house-to-house and 1982 (99.2%) by light touch ACD methods). We constructed multinomial logistic regression models comparing time intervals to diagnosis (30-59, 60-89 and ≥90 days with =90 days compared to the referent of <30 days for ACD vs PCD were 0.88, 0.56 and 0.42 respectively. These ACD strategies not only reduce time to diagnosis, and thus risk of transmission, but also ensure that there is a double check on the proportion of cases actually getting captured. Such a process can supplement passive case detection efforts that must go on, possibly perpetually, even after elimination as a public health problem is achieved

Cellular automata in the light of COVID-19

Currently, the world has been facing the brunt of a pandemic due to a disease called COVID-19 for the last 2 years. To study the spread of such infectious diseases it is important to not only understand their temporal evolution but also the spatial evolution. In this work, the spread of this disease has been studied with a cellular automata (CA) model to find the temporal and the spatial behavior of it. Here, we have proposed a neighborhood criteria which will help us to measure the social confinement at the time of the disease spread. The two main parameters of our model are (i) disease transmission probability (q) which helps us to measure the infectivity of a disease and (ii) exponent (n) which helps us to measure the degree of the social confinement. Here, we have studied various spatial growths of the disease by simulating this CA model. Finally we have tried to fit our model with the COVID-19 data of India for various waves and have attempted to match our model predictions with regards to each wave to see how the different parameters vary with respect to infectivity and restrictions in social interaction

Studying ECG signals using nonlinear oscillators and Genetic Algorithm

Cardiovascular diseases are the leading cause of death and disability in the world and thus their detection is extremely important as early as possible so that it can be prognosed and managed appropriately. Hence, electrophysiological models dealing with cardiac conduction are critically important in the field of interdisciplinary sciences. The primary aim of this paper is to reproduce a normal sinus rhythm ECG waveform which will act as the baseline for fitting and then fit any clinical ECG waveform that does not deviate much from normal sinus rhythm. To reproduce the ECG, we modeled the pacemaker complex using three coupled van der Pol (VDP) oscillators with appropriate delays to generate the action potentials. These action potentials are responsible for the excitation of the non-pacemaker cells of the atria and ventricles whose electrical activity gets recorded as the ECG signal. The ECG signal is composed of a periodic set of individual waves corresponding to atrial and ventricular contraction and relaxation. These waves are modeled with the help of four FitzHugh-Nagumo (FHN) equations with impulses corresponding to the action potentials generated by the pacemaker cells. After the successful reproduction of a normal sinus rhythm ECG, we have developed a framework where we have used genetic algorithm (GA) to fit a given clinical ECG data with parameters belonging to the above mentioned system of delay differential equations (DDEs). The GA framework has enabled us to fit ECG data representing different cardiac conditions reasonably well. We aim to use this work to get a better understanding of the cardiac conduction system and cardiovascular diseases which will help humanity in the future.Comment: 24 pages, 20 figure