Biology subjects

Berliner, A. J.

Publications and source records attributed to Berliner, A. J..

3 recordsLinked to original sources

bioRxiv2022

Radioactive Contamination in Feral Dogs in the Chernobyl Exclusion Zone: Population Body-Burden Survey and Implications for Human Radiation Exposure

This report describes a two-year effort to survey the internal 137Cs and external {beta}-emitter contamination present in the feral dog population near the Chernobyl nuclear power plant (ChNPP) site, and to quantify the potential for human radiation exposure from this contamination. This work was performed as an integral part of the radiation safety and control procedures of an animal welfare oriented trap-neuter-release (TNR) program. The measurement program employed handheld {beta}-sensitive probes, and a simple whole-body counter to measure internal 137Cs burden during post-surgical observation and recovery. External {beta} contamination surveys performed during intake showed that 21/288 animals had significant, removable external contamination. Measurements with the whole-body counter indicated internal 137Cs body burdens ranging from undetectable (minimum detection level [~]100 Bq/kg in 2017, [~] 30 Bq/kg in 2018) to approximately 30,000 Bq/kg. A total of 33 animals had 137Cs body-burdens above 1 kBq/kg. We observe that internal contamination levels are positively correlated with capture locations within ChNPP boundaries. The large variation in the 137Cs concentration in these animals is not well-understood, could be due to prey selection, access to human food scraps, or extended residence in highly contaminated areas. These internally-contaminated animals are unlikely to pose an exposure hazard despite their large body-burdens due to their limited exposure to humans. However, the small minority of animals with external contamination may pose a contamination hazard to workers, tourists, and others interacting with the dogs, as evidenced by total quantity of removable activity and incidents of transfer to materials used in animal capture.

zoology↗

bioRxiv2022

Data-Driven Flow-Map Models for Data-Efficient Discovery of Dynamics and Fast Uncertainty Quantification of Biological and Biochemical Systems

Computational models are increasingly used to investigate and predict the complex dynamics of biological and biochemical systems. Nevertheless, governing equations of a biochemical system may not be (fully) known, which would necessitate learning the system dynamics directly from, often limited and noisy, observed data. On the other hand, when expensive models are available, systematic and efficient quantification of the effects of model uncertainties on quantities of interest can be an arduous task. This paper leverages the notion of flow-map (de)compositions to present a framework that can address both of these challenges via learning data-driven models useful for capturing the dynamical behavior of biochemical systems. Data-driven flow-map models seek to directly learn the integration operators of the governing differential equations in a black-box manner, irrespective of structure of the underlying equations. As such, they can serve as a flexible approach for deriving fast-toevaluate surrogates for expensive computational models of system dynamics, or, alternatively, for reconstructing the long-term system dynamics via experimental observations. We present a data-efficient approach to data-driven flow-map modeling based on polynomial chaos Kriging. The approach is demonstrated for discovery of the dynamics of various benchmark systems and a co-culture bioreactor subject to external forcing, as well as for uncertainty quantification of a microbial electrosynthesis reactor. Such data-driven models and analyses of dynamical systems can be paramount in the design and optimization of bioprocesses and integrated biomanufacturing systems.

systems biology↗

bioRxiv2021

Evaluating the cost of pharmaceutical purification for a long-duration space exploration medical foundry

There are medical treatment vulnerabilities in longer-duration space missions present in the current International Space Station crew health care system with risks, arising from spaceflight-accelerated pharmaceutical degradation and resupply lag times. Bioregenerative life support systems may be a way to close this risk gap by leveraging in situ resource utilization (ISRU) to perform pharmaceutical synthesis and purification. Recent literature has begun to consider biological ISRU using microbes and plants as the basis for pharmaceutical life support technologies. However, there has not yet been a rigorous analysis of the processing and quality systems required to implement biologically-produced pharmaceuticals for human medical treatment. In this work, we use the equivalent system mass (ESM) metric to evaluate pharmaceutical purification processing strategies for longer-duration space exploration missions. Monoclonal antibodies, representing a diverse therapeutic platform capable of treating multiple space-relevant disease states, were selected as the target products for this analysis. We investigate the ESM resource costs (mass, volume, power, cooling, and crew time) of an affinity-based capture step for monoclonal antibody purification as a test case within a manned Mars mission architecture. We compare six technologies (three biotic capture methods and three abiotic capture methods), optimize scheduling to minimize ESM for each technology, and perform scenario analysis to consider a range of input stream compositions and pharmaceutical demand. We also compare the base case ESM to scenarios of alternative mission configuration, equipment models, and technology reusability. Throughout the analyses, we identify key areas for development of pharmaceutical life support technology and improvement of the ESM framework for assessment of bioregenerative life support technologies.

bioengineering↗