4 results on '"Srikiran Chandrasekaran"'
Search Results
2. A dose response model for Staphylococcus aureus
- Author
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Srikiran Chandrasekaran and Sunny C. Jiang
- Subjects
0301 basic medicine ,Staphylococcus aureus ,Response model ,Computer science ,Science ,030106 microbiology ,medicine.disease_cause ,Article ,03 medical and health sciences ,Hormesis ,Microbial risk ,medicine ,Humans ,Computational models ,Independence (probability theory) ,Computational model ,Multidisciplinary ,Stochastic process ,Soft Tissue Infections ,Bacterial Infections ,Models, Theoretical ,Computational biology and bioinformatics ,030104 developmental biology ,Host-Pathogen Interactions ,Medicine ,Infectious diseases ,Staphylococcal Skin Infections ,Biological system - Abstract
Dose-response models (DRMs) are used to predict the probability of microbial infection when a person is exposed to a given number of pathogens. In this study, we propose a new DRM for Staphylococcus aureus (SA), which causes skin and soft-tissue infections. The current approach to SA dose-response is only partially mechanistic and assumes that individual bacteria do not interact with each other. Our proposed two-compartment (2C) model assumes that bacteria that have not adjusted to the host environment decay. After adjusting to the host, they exhibit logistic/cooperative growth, eventually causing disease. The transition between the adjusted and un-adjusted states is a stochastic process, which the 2C DRM explicitly models to predict response probabilities. By fitting the 2C model to SA pathogenesis data, we show that cooperation between individual SA bacteria is sufficient (and, within the scope of the 2C model, necessary) to characterize the dose-response. This is a departure from the classical single-hit theory of dose-response, where complete independence is assumed between individual pathogens. From a quantitative microbial risk assessment standpoint, the mechanistic basis of the 2C DRM enables transparent modeling of dose-response of antibiotic-resistant SA that has not been possible before. It also enables the modeling of scenarios having multiple/non-instantaneous exposures, with minimal assumptions.
- Published
- 2021
3. Introducing and benchmarking the accuracy of cayenne: a Python package for stochastic simulations
- Author
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Srikiran Chandrasekaran and Dileep Kishore
- Subjects
Cayenne ,Mathematical model ,Computer science ,Stochastic simulation ,Benchmarking ,Python (programming language) ,computer ,Implementation ,computer.programming_language ,Computational science ,Gillespie algorithm - Abstract
Biological systems are intrinsically noisy and this noise may determine the qualitative outcome of the system. In the absence of analytical solutions to mathematical models incorporating noise, stochastic simulation algorithms are useful to explore the possible trajectories of these systems. Algorithms used for such stochastic simulations include the Gillespie algorithm and its approximations. In this study we introduce cayenne, an easy to use Python package containing accurate and fast implementations of the Gillespie algorithm (direct method), the tau-leaping algorithm and a tau-adaptive algorithm. We compare the accuracy of cayenne with other stochastic simulation libraries (BioSimulator.jl, GillespieSSA and Tellurium) and find that cayenne offers the best trade-off between accuracy and speed. Additionally, we highlight the importance of performing accuracy tests for stochastic simulation libraries, and hope that it becomes standard practice when developing the same.The cayenne package can be found at https://github.com/Heuro-labs/cayenne while the bench-marks can be found at https://github.com/Heuro-labs/cayenne-benchmarks
- Published
- 2020
4. Assessing the water quality impacts of two Category-5 hurricanes on St. Thomas, Virgin Islands
- Author
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Sunny C. Jiang, Srikiran Chandrasekaran, Yingcong Fang, Christina A. Kellogg, and Muyue Han
- Subjects
Environmental Engineering ,Legionella ,Rain ,0208 environmental biotechnology ,Indicator bacteria ,02 engineering and technology ,010501 environmental sciences ,01 natural sciences ,Article ,Enterococcus faecalis ,Feces ,United States Virgin Islands ,Disaster area ,Next generation sequencing ,Virgin Islands ,Water Quality ,Humans ,Rain cistern ,14. Life underwater ,Waste Management and Disposal ,0105 earth and related environmental sciences ,Water Science and Technology ,Civil and Structural Engineering ,Islands ,biology ,Cyclonic Storms ,Cistern ,Ecological Modeling ,Disaster management ,biology.organism_classification ,Pollution ,6. Clean water ,020801 environmental engineering ,Fishery ,Geography ,Microbial population biology ,13. Climate action ,Water quality ,Water Microbiology ,Surface runoff - Abstract
Managing waterborne and water-related diseases is one of the most critical factors in the aftermath of hurricane-induced natural disasters. The goal of the study was to identify water-quality impairments in order to set the priorities for post-hurricane relief and to guide future decisions on disaster preparation and relief administration. Field investigations were carried out on St. Thomas, U.S. Virgin Islands as soon as the disaster area became accessible after the back-to-back hurricane strikes by Irma and Maria in 2017. Water samples were collected from individual household rain cisterns, the coastal ocean, and street-surface runoffs for microbial concentration. The microbial community structure and the occurrence of potential human pathogens were investigated in samples using next generation sequencing. Loop mediated isothermal amplification was employed to detect fecal indicator bacteria, Enterococcus faecalis. The results showed both fecal indicator bacteria and Legionella genetic markers were prevalent but were low in concentration in the water samples. Among the 22 cistern samples, 86% were positive for Legionella and 82% for Escherichia-Shigella. Enterococcus faecalis was detected in over 68% of the rain cisterns and in 60% of the coastal waters (n = 20). Microbial community composition in coastal water samples was significantly different from cistern water and runoff water. Although identification at bacterial genus level is not direct evidence of human pathogens, our results suggest cistern water quality needs more organized attention for protection of human health, and that preparation and prevention measures should be taken before natural disasters strike., Graphical abstract Image 1, Highlights • Rain cistern, coastal ocean and surface runoff waters were sampled post hurricanes. • Microbial community composition was dramatically different in each type of water. • Fecal indicator bacteria and Legionella were prevalent in all water samples. • The concentrations of Legionella and fecal indicator bacteria were generally low.
- Published
- 2020
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