Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userTesting metabolic scaling theory using intraspecific allometries in Antarctic microarthropods
206
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Quantitative scaling relationships among body mass, temperature and metabolic rate of organisms are still controversial, while resolution may be further complicated through the use of different and possibly inappropriate approaches to statistical analysis. We propose the application of a modelling strategy based on Akaikes information criteria and non-linear model fitting (nlm). Accordingly, we collated and modelled available data at intraspecific level on the individual standard metabolic rate of Antarctic microarthropods as a function of body mass (M), temperature (T), species identity (S) and high rank taxa to which species belong (G) and tested predictions from Metabolic Scaling Theory. We also performed allometric analysis based on logarithmic transformations (lm). Conclusions from lm and nlm approaches were different. Best-supported models from lm incorporated T, M and S. The estimates of the allometric scaling exponent b linking body mass and metabolic rate indicated no interspecific difference and resulted in a value of 0.696 +/- 0.105 (mean +/- 95% CI). In contrast, the four best-supported nlm models suggested that both the scaling exponent and activation energy significantly vary across the high rank taxa to which species belong, with mean values of b ranging from about 0.6 to 0.8. We therefore reached two conclusions: 1) published analyses of arthropod metabolism based on logarithmic data may be biased by data transformation; 2) non-linear models applied to Antarctic microarthropod metabolic rate suggest that intraspecific scaling of standard metabolic rate in Antarctic microarthropods is highly variable and can be characterised by scaling exponents that greatly vary within taxa, which may have biased previous interspecific comparisons that neglected intraspecific variability.
rate research
Read More
PyRoss is an open-source Python library that offers an integrated platform for inference, prediction and optimisation of NPIs in age- and contact-structured epidemiological compartment models. This report outlines the rationale and functionality of the PyRoss library, with various illustrations and examples focusing on well-mixed, age-structured populations. The PyRoss library supports arbitrary structured models formulated stochastically (as master equations) or deterministically (as ODEs) and allows mid-run transitioning from one to the other. By supporting additional compartmental subdivision ad libitum, PyRoss can emulate time-since-infection models and allows medical stages such as hospitalization or quarantine to be modelled and forecast. The PyRoss library enables fitting to epidemiological data, as available, using Bayesian parameter inference, so that competing models can be weighed by their evidence. PyRoss allows fully Bayesian forecasts of the impact of idealized NPIs by convolving uncertainties arising from epidemiological data, model choice, parameters, and intrinsic stochasticity. Algorithms to optimize time-dependent NPI scenarios against user-defined cost functions are included. PyRosss current age-structured compartment framework for well-mixed populations will in future reports be extended to include compartments structured by location, occupation, use of travel networks and other attributes relevant to assessing disease spread and the impact of NPIs. We argue that such compartment models, by allowing social data of arbitrary granularity to be combined with Bayesian parameter estimation for poorly-known disease variables, could enable more powerful and robust prediction than other approaches to detailed epidemic modelling. We invite others to use the PyRoss library for research to address todays COVID-19 crisis, and to plan for future pandemics.
We report a droplet microfluidic method to target and sort individual cells directly from complex microbiome samples, and to prepare these cells for bulk whole genome sequencing without cultivation. We characterize this approach by recovering bacteria spiked into human stool samples at a ratio as low as 1:250 and by successfully enriching endogenous Bacteroides vulgatus to the level required for de-novo assembly of high-quality genomes. While microbiome strains are increasingly demanded for biomedical applications, the vast majority of species and strains are uncultivated and without reference genomes. We address this shortcoming by encapsulating complex microbiome samples directly into microfluidic droplets and amplify a target-specific genomic fragment using a custom molecular TaqMan probe. We separate those positive droplets by droplet sorting, selectively enriching single target strain cells. Finally, we present a protocol to purify the genomic DNA while specifically removing amplicons and cell debris for high-quality genome sequencing.
A system-level framework of complex microbe-microbe and host-microbe chemical cross-talk would help elucidate the role of our gut microbiota in health and disease. Here we report a literature-curated interspecies network of the human gut microbiota, called NJS16. This is an extensive data resource composed of ~570 microbial species and 3 human cell types metabolically interacting through >4,400 small-molecule transport and macromolecule degradation events. Based on the contents of our network, we develop a mathematical approach to elucidate representative microbial and metabolic features of the gut microbial community in a given population, such as a disease cohort. Applying this strategy to microbiome data from type 2 diabetes patients reveals a context-specific infrastructure of the gut microbial ecosystem, core microbial entities with large metabolic influence, and frequently-produced metabolic compounds that might indicate relevant community metabolic processes. Our network presents a foundation towards integrative investigations of community-scale microbial activities within the human gut.
The analysis of eight molecular datasets involving human and teleost examples along with morphological samples from several groups of Neotropical electric fish (Order: Gymnotiformes) were used in this thesis to test the dynamics of both intraspecific variation and interspecific diversity. In terms of investigating molecular interspecific diversity among humans, two experimental exercises were performed. A cladistic exchange experiment tested for the extent of discontinuity and interbreeding between H. sapiens and neanderthal populations. As part of the same question, another experimental exercise tested the amount of molecular variance resulting from simulations which treated neanderthals as being either a local population of modern humans or as a distinct subspecies. Finally, comparisons of hominid populations over time with fish species helped to define what constitutes taxonomically relevant differences between morphological populations as expressed among both trait size ranges and through growth patterns that begin during ontogeny. Compared to the subdivision found within selected teleost species, H. sapiens molecular data exhibited little variation and discontinuity between geographical regions. Results of the two experimental exercises concluded that neanderthals exhibit taxonomic distance from modern H. sapiens. However, this distance was not so great as to exclude the possibility of interbreeding between the two subspecific groups. Finally, a series of characters were analyzed among species of Neotropical electric fish. These analyses were compared with hominid examples to determine what constituted taxonomically relevant differences between populations as expressed among specific morphometric traits that develop during the juvenile phase.
A number of epidemics, including the SARS-CoV-1 epidemic of 2002-2004, have been known to exhibit superspreading, in which a small fraction of infected individuals is responsible for the majority of new infections. The existence of superspreading implies a fat-tailed distribution of infectiousness (new secondary infections caused per day) among different individuals. Here, we present a simple method to estimate the variation in infectiousness by examining the variation in early-time growth rates of new cases among different subpopulations. We use this method to estimate the mean and variance in the infectiousness, $beta$, for SARS-CoV-2 transmission during the early stages of the pandemic within the United States. We find that $sigma_beta/mu_beta gtrsim 3.2$, where $mu_beta$ is the mean infectiousness and $sigma_beta$ its standard deviation, which implies pervasive superspreading. This result allows us to estimate that in the early stages of the pandemic in the USA, over 81% of new cases were a result of the top 10% of most infectious individuals.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
