10 research outputs found
Anticipating critical transitions in multi-dimensional systems driven by time- and state-dependent noise
The anticipation of bifurcation-induced transitions in dynamical systems has
gained relevance in various fields of the natural, social, and economic
sciences. When approaching a co-dimension 1 bifurcation, the feedbacks that
stabilise the initial state weaken and eventually vanish; a process referred to
as critical slowing down (CSD). This motivates the use of variance and lag-1
autocorrelation as indicators of CSD. Both indicators rely on linearising the
system's restoring rate. Additionally, the use of variance is limited to time-
and state-independent driving noise, strongly constraining the generality of
CSD. Here, we propose a data-driven approach based on deriving a Langevin
equation to detect local stability changes and anticipate bifurcation-induced
transitions in systems with generally time- and state-dependent noise. Our
approach substantially generalizes the conditions underlying existing early
warning indicators, which we showcase in different examples. Changes in
deterministic dynamics can be clearly discriminated from changes in the driving
noise. This reduces the risk of false and missed alarms of conventional CSD
indicators significantly in settings with time-dependent or multiplicative
noise. In multi-dimensional systems, our method can greatly advance the
understanding of the coupling between system components and can avoid risks of
missing CSD due to dimension reduction, which existing approaches suffer from
Measuring tropical rainforest resilience under non-Gaussian disturbances
The Amazon rainforest is considered one of the Earth's tipping elements and
may lose stability under ongoing climate change. Recently a decrease in
tropical rainforest resilience has been identified globally from remotely
sensed vegetation data. However, the underlying theory assumes a Gaussian
distribution of forest disturbances, which is different from most observed
forest stressors such as fires, deforestation, or windthrow. Those stressors
often occur in power-law-like distributions and can be approximated by
-stable L\'evy noise. Here, we show that classical critical slowing
down indicators to measure changes in forest resilience are robust under such
power-law disturbances. To assess the robustness of critical slowing down
indicators, we simulate pulse-like perturbations in an adapted and conceptual
model of a tropical rainforest. We find few missed early warnings and few false
alarms are achievable simultaneously if the following steps are carried out
carefully: First, the model must be known to resolve the timescales of the
perturbation. Second, perturbations need to be filtered according to their
absolute temporal autocorrelation. Third, critical slowing down has to be
assessed using the non-parametric Kendall- slope. These prerequisites
allow for an increase in the sensitivity of early warning signals. Hence, our
findings imply improved reliability of the interpretation of empirically
estimated rainforest resilience through critical slowing down indicators
Early onset lung cancer, cigarette smoking and the SNP309 of the murine double minute-2 (MDM2) gene
The polymorphism SNP309 (rs2279744) in the promoter region of the MDM2 gene has been shown to alter protein expression and may play a role in the susceptibility to lung cancer. The MDM2 protein is a key inhibitor of p53 and several mechanisms of MDM2/p53 interactions are presently known: modulating DNA-repair, cell-cycle control, cell growth and apoptosis
Do genetic factors protect for early onset lung cancer? A case control study before the age of 50 years
<p>Abstract</p> <p>Background</p> <p>Early onset lung cancer shows some familial aggregation, pointing to a genetic predisposition. This study was set up to investigate the role of candidate genes in the susceptibility to lung cancer patients younger than 51 years at diagnosis.</p> <p>Methods</p> <p>246 patients with a primary, histologically or cytologically confirmed neoplasm, recruited from 2000 to 2003 in major lung clinics across Germany, were matched to 223 unrelated healthy controls. 11 single nucleotide polymorphisms of genes with reported associations to lung cancer have been genotyped.</p> <p>Results</p> <p>Genetic associations or gene-smoking interactions was found for <it>GPX1(Pro200Leu) </it>and <it>EPHX1(His113Tyr)</it>. Carriers of the Leu-allele of <it>GPX1(Pro200Leu) </it>showed a significant risk reduction of OR = 0.6 (95% CI: 0.4–0.8, p = 0.002) in general and of OR = 0.3 (95% CI:0.1–0.8, p = 0.012) within heavy smokers. We could also find a risk decreasing genetic effect for His-carriers of <it>EPHX1(His113Tyr) </it>for moderate smokers (OR = 0.2, 95% CI:0.1–0.7, p = 0.012). Considered both variants together, a monotone decrease of the OR was found for smokers (OR of 0.20; 95% CI: 0.07–0.60) for each protective allele.</p> <p>Conclusion</p> <p>Smoking is the most important risk factor for young lung cancer patients. However, this study provides some support for the T-Allel of <it>GPX1(Pro200Leu) </it>and the C-Allele of <it>EPHX1(His113Tyr) </it>to play a protective role in early onset lung cancer susceptibility.</p