Novel Biomarkers, Including tcdB PCR Cycle Threshold, for Predicting Recurrent Clostridioides difficile Infection.

Traditional clinical models for predicting recurrent Clostridioides difficile infection do not perform well, likely owing to the complex host-pathogen interactions involved. Accurate risk stratification using novel biomarkers could help prevent recurrence by improving underutilization of effective therapies (i.e., fecal transplant, fidaxomicin, bezlotoxumab). We used a biorepository of 257 hospitalized patients with 24 features collected at diagnosis, including 17 plasma cytokines, total/neutralizing anti-toxin B IgG, stool toxins, and PCR cycle threshold (CT) (a proxy for stool organism burden). The best set of predictors for recurrent infection was selected by Bayesian model averaging for inclusion in a final Bayesian logistic regression model. We then used a large PCR-only data set to confirm the finding that PCR CT predicts recurrence-free survival using Cox proportional hazards regression. The top model-averaged features were (probabilities of >0.05, greatest to least): interleukin 6 (IL-6), PCR CT, endothelial growth factor, IL-8, eotaxin, IL-10, hepatocyte growth factor, and IL-4. The accuracy of the final model was 0.88. Among 1,660 cases with PCR-only data, cycle threshold was significantly associated with recurrence-free survival (hazard ratio, 0.95; P < 0.005). Certain biomarkers associated with C. difficile infection severity were especially important for predicting recurrence; PCR CT and markers of type 2 immunity (endothelial growth factor [EGF], eotaxin) emerged as positive predictors of recurrence, while type 17 immune markers (IL-6, IL-8) were negative predictors. In addition to novel serum biomarkers (particularly, IL-6, EGF, and IL-8), the readily available PCR CT may be critical to augment underperforming clinical models for C. difficile recurrence.

The fundamental theorem of natural selection with mutations.

The mutation-selection process is the most fundamental mechanism of evolution. In 1935, R. A. Fisher proved his fundamental theorem of natural selection, providing a model in which the rate of change of mean fitness is equal to the genetic variance of a species. Fisher did not include mutations in his model, but believed that mutations would provide a continual supply of variance resulting in perpetual increase in mean fitness, thus providing a foundation for neo-Darwinian theory. In this paper we re-examine Fisher's Theorem, showing that because it disregards mutations, and because it is invalid beyond one instant in time, it has limited biological relevance. We build a differential equations model from Fisher's first principles with mutations added, and prove a revised theorem showing the rate of change in mean fitness is equal to genetic variance plus a mutational effects term. We refer to our revised theorem as the fundamental theorem of natural selection with mutations. Our expanded theorem, and our associated analyses (analytic computation, numerical simulation, and visualization), provide a clearer understanding of the mutation-selection process, and allow application of biologically realistic parameters such as mutational effects. The expanded theorem has biological implications significantly different from what Fisher had envisioned.

Climate and human mortality in Virginia, 2005-2020.

Using an extensive database of every resident death in Virginia from 2005 to 2020, climate-mortality relationships are examined for 12 climatically homogeneous regions within the Commonwealth. Each region is represented by a first-order weather station from which archived temperature and humidity data are used to generate a variety of biometeorologically relevant indices. Using these indices and other variables (such as air quality and heat and cold waves), daily mortality and climate relationships are modeled for each region over a 21-day lag period utilizing generalized additive models and distributed lag non-linear models. Optimal models are identified for each region, and a consensus model was also run based on maximum temperature to facilitate inter-regional comparisons. The relative risk of mortality varies markedly as a function of climate between regions, with U-shaped, J-shaped, and inverse linear relationships evident. Cold mortality exceeds heat mortality across most of Virginia (typical relative risks are 1.10 for cold and 1.03 for heat), with cold risks strongest at lags 3 to 10. Low temperatures (or low humidity) are protective at lags 0-2 days except in the colder, western parts of state. Heat mortality occurs at short lags (0-2 days) for three-fourths of the stations, but the spatial pattern is random. Mortality displacement is evident for most regions for several days following the heat-related spike. Although the use of region-specific models is justified, the simple consensus model based on a consistent set of predictors provides similar results.

Spatial effects and turing instabilities in the invasive species model.

This paper demonstrates that a recently proposed dynamical model for the ecology of Easter Island admits periodic and chaotic attractors, not previously reported. Such behavior may more realistically depict the population dynamics of general ecosystems and illustrates the power of simple models to produce the kind of complex behavior that is ubiquitous in such systems.

Rat instigated human population collapse on easter island.

In this paper we develop an invasive species differential equations model for the population collapse on Easter Island. This model, motivated by recent archaeological results of T. Hunt, allows us to examine the role of rats in the collapse. In Hunt's theory, the decline of resources was accelerated by Polynesian rats and not merely the result of the overuse by the island's human population. Hunt uses archaeological data which suggests a different timeline for the settlement and the long term population dynamics of Easter Island. Our goal is to estimate the plausibility of Hunt's hypothesis.

Dynamics of a discrete population model for extinction and sustainability in ancient civilizations.

We analyze a discrete version of a recently developed ratio dependent population-resource model. This model has been used to study the decline of the human and resource populations on Easter Island and the chaotic dynamics of moose and wolf populations in Canada. The dynamical system exhibits a rich behavior of fractal basins of attraction and a Neimark-Sacker bifurcation route to chaos. The model consists of a coupled pair of logistic equations, with the carrying capacity for the predators proportional to the number of prey.