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In disciplines from biology to climate science, a routine task is to compute a correlation between a pair of time series and determine whether the correlation is statistically significant (i.e., unlikely under the null hypothesis that the time series are independent). This problem is challenging because time series typically exhibit autocorrelation and thus cannot be properly analyzed with the standard iid-oriented statistical tests. Although there are well-known parametric tests for time series, these are designed for linear correlation statistics and thus not suitable for the increasingly popular nonlinear correlation statistics. There are also nonparametric tests that can be used with any correlation statistic, but for these, the conditions that guarantee correct false positive rates are either restrictive or unclear. Here, we describe the truncated time-shift (TTS) test, a nonparametric procedure to test for dependence between 2 time series. We prove that this test correctly controls the false positive rate as long as one of the time series is stationary, a minimally restrictive requirement among current tests. The TTS test is versatile because it can be used with any correlation statistic. Using synthetic data, we demonstrate that this test performs correctly even while other tests suffer high false positive rates. In simulation examples, simple guidelines for parameter choices allow high statistical power to be achieved with sufficient data. We apply the test to datasets from climatology, animal behavior, and microbiome science, verifying previously discovered dependence relationships and detecting additional relationships. Testing the significance of a correlation between time series without substantial assumptions is a longstanding challenge. This study presents the truncated time-shift (TTS) test, a conservative significance test that is valid as long as one time series is statistically stationary. [ABSTRACT FROM AUTHOR]

The importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth's soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. Here, we argue that addressing this challenge requires close coordination of experimental data collection and method development with mathematical model building. We discuss specific examples where model-experiment integration has already resulted in important insights into MC function and structure. We also highlight key research questions that still demand better integration of experiments and models. We argue that such integration is needed to achieve significant progress in our understanding of MC dynamics and function, and we make specific practical suggestions as to how this could be achieved.

Assimilation of sulfur is vital to all organisms. In S. cerevisiae, inorganic sulfate is first reduced to sulfide, which is then affixed to an organic carbon backbone by the Met17 enzyme. The resulting homocysteine can then be converted to all other essential organosulfurs such as methionine, cysteine, and glutathione. This pathway has been known for nearly half a century, and met17 mutants have long been classified as organosulfur auxotrophs, which are unable to grow on sulfate as their sole sulfur source. Surprisingly, we found that met17Δ could grow on sulfate, albeit only at sufficiently high cell densities. We show that the accumulation of hydrogen sulfide gas underpins this density-dependent growth of met17Δ on sulfate and that the locus YLL058W (HSU1) enables met17Δ cells to assimilate hydrogen sulfide. Hsu1 protein is induced during sulfur starvation and under exposure to high sulfide concentrations in wild-type cells, and the gene has a pleiotropic role in sulfur assimilation. In a mathematical model, the low efficiency of sulfide assimilation in met17Δ can explain the observed density-dependent growth of met17Δ on sulfate. Thus, having uncovered and explained the paradoxical growth of a commonly used "auxotroph," our findings may impact the design of future studies in yeast genetics, metabolism, and volatile-mediated microbial interactions. Assimilation of sulfur is vital to all organisms. This study shows that a novel sulfide assimilation pathway involving HSU1 allows the commonly used budding yeast MET17 auxotroph to overcome its metabolic defect, but only at high cell densities when hydrogen sulfide gas can accumulate. [ABSTRACT FROM AUTHOR]

In many organisms, polo kinases appear to play multiple roles during M-phase progression. To provide new insights into the function of budding yeast polo kinase Cdc5p, we generated novel temperature-sensitive cdc5 mutants by mutagenizing the C-terminal domain. Here we show that, at a semipermissive temperature, the cdc5-3 mutant exhibited a synergistic bud elongation and growth defect with loss of HSL1, a component important for normal [G.sub.2]/M transition. Loss of SWE1, which phosphorylates and inactivates the budding yeast Cdk1 homolog Cdc28p, suppressed the cdc5-3 hsl1[DELTA] defect, suggesting that Cdc5p functions at a point upstream of Swe1p. In addition, the cdc5-4 and cdc5-7 mutants exhibited chained cell morphologies with shared cytoplasms between the connected cell bodies, indicating a cytokinetic defect. Close examination of these mutants revealed delayed septin assembly at the incipient bud site and loosely organized septin rings at the mother-bud neck. Components in the mitotic exit network (MEN) play important roles in normal cytokinesis. However, loss of BFA1 or BUB2, negative regulators of the MEN, failed to remedy the cytokinetic defect of these mutants, indicating that Cdc5p promotes cytokinesis independently of Bfa1p and Bub2p. Thus, Cdc5p contributes to the activation of the Swe1p-dependent Cdc28p/C1b pathway, normal septin function, and cytokinesis.